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System, Version 8.2, Technical User Guide

1. Depression A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication Diabetes A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication Hyperlipidemia A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication Hypertension A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication Ischemic Heart Disease Technical User Guide A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication The Johns Hopkins ACG System Version 8 2 Installing and Using ACG Software BTH ICD and Rx Indication Low Back Pain A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication Unscaled Total Cost Resource Index ACG Predictive Model ACG PM Predicted Resource Index PRI for Total Cost the estimated total costs including pharmacy costs for this patient for the year follow
2. Mean Rx PRI Average or Rescaled Pharmacy Cost Resource Index for patients in this stratification High Risk Percent of patients with Probability High Total Cost gt 0 4 in this stratification HOSDOM D Percent of patients with Hospital Dominant Count gt 1 in this stratification Frail D Percent of patients with indications of Frailty in this stratification Psychosocial Percent of patients with indications of Psychosocial conditions in this stratification Discretionary D Percent of patients with indications of discretionary diagnoses in this stratification Age Sex Relative Risk The age sex adjusted relative risk for all patients in this stratification Observed to Expected D Technical User Guide Observed to Expected ratio calculated as actual cost ACG adjusted expected cost Useful only for sub group analysis Scores lt 1 0 consuming less than expected gt 1 0 consuming more than expected The Johns Hopkins ACG System Version 8 2 5 38 Installing and Using ACG Software Simple Profile Analysis The Simple Profile Analysis compares actual costs to expected costs to present a simplified profile The report layout and description of the calculation of each data field is as follows Table 15 Simple Profile Analysis Report Layout Column Definition Name Patient Count The number of patients within the current stratification Total Actual
3. 4 Tip If you have a previous version of the ACG System installed and you wish to retain it be sure to install the new version of the software into a separate folder directory The application will present installation status and the current step Technical User Guide The Johns Hopkins ACG System Version 8 2 5 8 Installing and Using ACG Software Figure 7 Installation Status YE Johns Hopkins ACG 8 2 Ea Installing Johns Hopkins ACG 8 2 Introduction Install Folder hortcut Folder Cancel The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 9 Figure 8 Install Complete YE Johns Hopkins ACG 8 2 aia Install Complete Introduction Eongratulations Johns Hopkins ACG 8 2 has been successfully installed to Choose Install Folder C Program Files Johns Hopkins ACG 8 2 Choose Shortcut Folder Pre Installation Summary Press Done to quit the installer Installing Install Complete Previous Technical User Guide The Johns Hopkins ACG System Version 8 2 5 10 Installing and Using ACG Software ACG License File Upon the first initiation of the software you must accept a standard licensing agreement and then you are asked to install a license file Each license file is specific to your contract period licensing terms Licenses control access to the model types Diagnosis or Pharmacy regional code sets i e ATC and risk assessment variables i
4. csssccssssssssssccsssees 4 9 Table 2 Classification of Miler arnt ices cscersdecdaasecachaarelatneiecontennents 4 10 Identifying Special Populations with Augmented Data Inputs 4 10 Pregnancy TAI reor E E E E 4 10 Delivery Sta eee ee ane n a a OR 4 11 Low Birth Weight less than 2500 grams cc sc scssesscesscsasecescssssnneedersass 4 11 Constructing Resource Consumption Measures sssescscsssseessecesocessocseo 4 12 Summarizing Total or Ambulatory Charges iniessccocastescscdicscnseriossatanss 4 12 Ambulatory Encounters sniene oa A a Ei 4 12 Risk Assessment Variables scccsssesosssscacesssssssencssectscenssecessavescasssnsasonsssasssnansees 4 13 Summary REVICW sasuscisncinnenunnnnnuanintinnwancnunnenunnunnu 4 14 Technical User Guide The Johns Hopkins ACG System Version 8 2 4 11 Basic Data Requirements This page was left blank intentionally The Johns Hopkins ACG System Version 8 2 Technical User Guide Basic Data Requirements 4 1 Overview This chapter provides an overview of the general data requirements for the ACG System Software and its subsequent applications The chapter is intended for the analysts and programmers who will be planning and performing ACG based analyses The ACG System Software is designed to operate using data typically retained in machine readable health insurance claims or encounter data files In addition member enrollment files detailing age gender and other demographics for
5. PMPM Per member per month PMPY Per member per year Note Although 12 months were used here other extended periods could also be used to calculate per member per period weights The Johns Hopkins ACG System Version 8 2 Technical User Guide Making Effective Use of Risk Scores 7 13 Section B of Table 4 shows the results using a PMPY calculation While there is a slight overpayment associated with shorter term enrollees e g one month enrollees are overpaid by 8 6 percent on average the extent of the deviation between actual and expected costs is markedly lower for each subgroup i e each row as a result of using the PMPY orientation The sum of the absolute error of each enrollment cohort reflected in section B of the table is less than 700 000 while the comparable figure is 8 3 million reflected in section A R squared R is a measure of the extent to which expected values explain variation in actual costs The R for the population as a whole using a PMPM calculation is 338 shown in the row labeled Total in Section A of the table and this measure decreases with shorter term enrollment particularly for those with less than five months of enrollment The R is higher using a PMPY calculation 395 in section B of the table and remains largely stable regardless of the length of time a patient has been enrolled The modest tendency of the PMPY approach to overpay or inflate expected costs associated with very short
6. Resource Index following the observation period expressed as a relative weight Probability High Total Cost The probability that this patient will have high total costs in the year following the observation period Hospital Dominant Count The number of ADGs this patient has that indicate hospital dominant diagnoses Chronic Condition Count The number of EDCs this patient has that indicate chronic condition diagnoses Frailty Flag A flag indicating that this patient appears to be clinically frail A flag indicating if this patient has this medical condition and how it was Arthritis indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication A flag indicating if this patient has this medical condition and how it was Asthma indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication Congestive Heart Failure A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication Technical User Guide The Johns Hopkins ACG System Version 8 2 5 40 Installing and Using ACG Software A flag indicating if this patient has this medical condition and how it was Chronic Renal Failure indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication A flag indicating if this patient has this medical condition a
7. The Johns Hopkins ACG System JOHNS HOPKINS Technical User Guide Version 8 2 December 2008 JOHNS HOPKINS A BLOOMBERG EBV SCHOOL PUBLIC HEALTH This page is intentionally left blank Important Warranty Limitation and Copyright Notices Copyright 2008 The Johns Hopkins University All rights reserved This document is produced by the Health Services Research amp Development Center at The Johns Hopkins University Bloomberg School of Public Health The terms The Johns Hopkins ACG System ACG System ACG ADG Adjusted Clinical Groups Ambulatory Care Groups Aggregated Diagnostic Groups Ambulatory Diagnostic Groups Johns Hopkins Expanded Diagnosis Clusters EDCs ACG Predictive Model Rx Defined Morbidity Groups Rx MGs ACG PM Dx PM Rx PM and DxRx PM are trademarks of The Johns Hopkins University All materials in this document are copyrighted by The Johns Hopkins University It is an infringement of copyright law to develop any derivative product based on the grouping algorithm or other information presented in this document This document is provided as an information resource on measuring population morbidity for those with expertise in risk adjustment models The documentation should be used for informational purposes only Information contained herein does not constitute recommendation for or advice about medical treatment or business practices No permissi
8. 2 N U oo N 1OIDIOlA 26 53 8 56 8 SNS TRI n a o asthmaticus asthmaticus ALL06 Disorders of the Immune system CARO4 Congenital heart disease CAROS5 Congestive heart failure CAR06 Cardiac valve disorders CARO7 Cardiomyopathy CARO8 O 8 0 o 15 6 25 5 9 23 6 5 9 1 oO Ww an 6 31 2 43 44 22 2 30 1 23 9 ASJ 5 4 N 1 6 13 5 5 4 4 4 1 9 1 1 54 Bi aes E 2 oly a gt ft 23 25 The Johns Hopkins ACG System Version 8 2 Technical User Guide Selecting the Right Tool 3 19 RUB 1 RUB 5 Very RUB 2 RUB 3 RUB 4 Very EDC Description Low Low Average High High CAR09 i 3 7 l 17 0 CAR09 Cardiac arrhythmia 00 CAR10 Generalized atherosclerosis CAR11 Disorders of lipoid metabolism infarction 5 4 PAY 9 2 E 44 2 You can develop your own reports and the EDCs that define the rows in Tables 5 and 6 could be replaced by episodes of illness categories that an organization may obtain from other sources ACG based RUBs are equally effective in explaining variations in resource use within episodes of care Table 10 Estimated Concurrent Resource Use by RUB by MEDC Samples RUB 1 Very RUB 2 RUB 3 EDC Description Low Low Average ADMO ADM02 0 6 2 3 0 6 2 3 0 5 2 0 2 1 ALL04 Asthma w o status asthmaticus ALLO5 Asthma with status asthmaticus ALL06 Disorders of the immune system CAR04 Congenital heart
9. 33 2 4 0 5 1 3 6 4 2 6 6 22 0 0 8 7 4 4 0 0 5 0 6 2 0 0 0 0 4 0 0 0 8 0 0 4 4 10 2 8 1 3 0 7 4 2 5 15 3 14 1 11 6 4 8 1 3 46 7 0 3 2 6 1 4 14 4 27 4 12 3 10 6 3 3 5 0 17 2 14 9 7 7 37 1 28 8 2 8 1 4 5 3 0 0 2 4 0 1 5 2 0 0 5 0 13 7 14 3 5 5 20 3 20 1 24 4 32 3 28 9 10 4 3 8 29 5 4 0 0 3 1 7 N Signs Symptoms Major 14 8 3 See and Reassure 1 8 B5 L0 22 1 4 Table 1 illustrates how ADGs the building blocks of the ACG System can quickly demonstrate differences in types of morbidity categories across sub groupings within your organization In this example the case mix profile of Group 2 tends to be more complex than that of Group 1 with the prevalence of the chronic medical and psychosocial ADGs being especially high LoS T a 8l 2 Ea The Johns Hopkins ACG System Version 8 2 Technical User Guide Selecting the Right Tool 3 7 An advantage of ADGs is that they can quickly identify clinically meaningful morbidity trends that may be obscured at the disease specific or relative morbidity index levels Another approach to describing a population s health or contrasting morbidity between population sub groupings would be to compare ACG categorical cell distributions Here one is typically looking for different prevalence rates or frequencies within certain ACG cells e g preg
10. condition identified by both diagnosis and NDC code RX condition identified by NDC code ICD condition identified by diagnosis code A flag indicating the presence of the condition NP condition not present BTH condition identified by both diagnosis and NDC code RX condition identified by NDC code ICD condition identified by diagnosis code A flag indicating the presence of the condition NP condition not present BTH condition identified by both diagnosis and NDC code RX condition identified by NDC code ICD condition identified by diagnosis code The Johns Hopkins ACG System Version 8 2 5 88 Installing and Using ACG Software A flag indicating the presence of the condition NP condition not present Hyperlipidemia BTH condition identified by both diagnosis and NDC code RX condition identified by NDC code ICD condition identified by diagnosis code A flag indicating the presence of the condition NP condition not present Hypertension BTH condition identified by both diagnosis and NDC code RX condition identified by NDC code ICD condition identified by diagnosis code A flag indicating the presence of the condition NP condition not present BTH condition identified by both diagnosis and NDC code RX condition identified by NDC code ICD condition identified by diagnosis code Ischemic Heart Disease A flag indicating the
11. e g malnutrition dementia incontinence difficulty in walking Frailty Flag A count of ADGs containing trigger diagnoses Hospital Dominant Count indicating a high probability typically greater than 50 percent of future admission The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software Column Name Chronic Condition Count A count of EDCs containing trigger diagnoses indicating a chronic condition with significant expected duration and resource requirements A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication Arthritis A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication Congestive Heart Failure A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication Chronic Renal Failure A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication
12. 4 5 Q Quality of care assessment 3 16 R Reference manual topics 1 3 getting started 1 3 Release notes 2 1 Report options 5 66 Reports produced by the analyze menu 6 5 Resource bands 7 16 utilization 5 18 7 17 Technical User Guide Resource utilization band distribution analysis 5 19 Resource utilization bands RUBs 5 18 7 17 Review of reports produced automatically by the software summary statistics tab 6 2 Risk assessment variables 4 13 frailty marker 4 13 hospital dominant condition marker 4 13 predictive modeling coefficients 4 13 reference concurrent weights 4 13 reference prevalence rates 4 13 release notes 2 3 resource utilization bands 4 13 RUB distribution example 6 7 Rule out diagnosis 4 5 suspected and provisional diagnoses 4 5 Rx MG RUB distribution analysis 5 26 S Sample size 8 5 final considerations 8 5 Selecting relevant diagnosis for input into the software 4 7 ACG 4 7 basic data requirements 4 7 Selecting the right tool 3 1 ACG predictive modeling 3 4 additional information 3 37 adjusted clinical groups ACGs 3 3 aggregated diagnostic groups ADGs 3 2 evaluating productivity and distributing workload 3 15 expanded diagnosis clusters EDCs 3 3 health status monitoring 3 12 managing pharmacy risk 3 30 medication therapy management program MTMP candidate selection 3 30 provider performance assessment 3 13 quality of care asssessment 3 16 Simple pr
13. A review of this table should help with determining holes or missing ACGs indicative of missing incomplete or improperly processed data For example users should check the relationship between deliveries and newborns as well as excess patient counts associated with ACGs 5110 and 5200 Proper ACG assignment depends in large part on defining the underlying population appropriately In some specialized cases the study population will be defined in such a way that all ACG categories are not utilized For example ACG Software runs that are limited to adults should not have persons assigned to ACGs that reflect pediatric patients More generally however anomalies in the distribution of ACGs may suggest either 1 problems with the definition of the denominator population or 2 holes in the claims used to identify patients diagnosis codes e g claims for carved out benefits not being submitted to the plan Using the ACG distribution displayed in this report you can assess some of the following potential distribution errors e ACGs 0100 0200 0300 1700 series 1900 2200 2900 3300 3800 5070 and the 5300 series incorporate the age of the patient Are there an appropriate number of infants in ACGs 5310 5332 If these ACGs have an insufficient number of patients or a larger than expected number of patients the analyst should review the way age was coded on the input data set A similar review can be performed for other age catego
14. For example all women receiving a blood test for pregnancy will likely be classified as pregnant if the assignment is based on this lab service claim Therefore when identifying ICD codes to input to the ACG grouper selecting diagnoses from all service claims within a specified time frame excluding lab and x ray is the recommended approach Table 1 provides a listing of the typical place of service codes and procedure code ranges to exclude Technical User Guide The Johns Hopkins ACG System Version 8 2 4 8 Basic Data Requirements Table 1 Typical Place of Service Codes to Exclude and Procedure Code Ranges to Exclude Typical Place of Service Codes to Exclude 12 private residence home 31 skilled nursing facility 32 nursing home 33 custodial care 34 hospice 41 ambulance land 42 ambulance other 65 renal dialysis 81 independent lab 99 unknown 00 non CMS code for pharmacy Procedure code ranges to exclude 36415 36416 drawing blood 70000 76999 x ray and ultrasound 78000 78999 imaging 80000 87999 lab tests 88000 88099 autopsy 88104 88299 cytopathology 88300 88399 surgical pathology 92551 92569 hearing tests 93000 93350 ECG and ultrasound 99000 99001 specimen handling G0001 drawing blood HC
15. Table 4 Comparison of Actual and ACG Expected Costs Months of Member Enrollment PMPM versus PMPY Weight Calculation Approaches Senet en ee ee ney cer unr nE Re BCD rt entre nS 7 12 Table 5 Effect of Enrollment Period on Selected ACG UI WG St Sasa ices sedacnisedonesahagaviad sau ens dwtininbedamaiatianeiaaniuataiedanausaeas 7 14 Addressing the Impact of Age on the Calculation of ACG Weights 7 15 Concurrent versus Prospective Calculations sc csassiccensisssssnesececessorsaseaaanoce 7 15 Local Calibration of ACG Predictive Modeling Scores ccceeeee 7 16 Resource Bands ssscciccscsiscsecscctscincccscstasstesecucccassancssaneanceuscousssaciebcccensanccneaseacs 7 16 Resource Utilization Bands RUBS J ssscccsascescevssavscccaccesceesessacssatatacstaaves 7 17 Table 6 Relative Concurrent PMPY Weights and RUB Categories 7 18 Technical User Guide The Johns Hopkins ACG System Version 8 2 This page was left blank intentionally The Johns Hopkins ACG System Version 8 2 Technical User Guide Making Effective Use of Risk Scores 7 1 Introduction While there are separate chapters that discuss the conceptual and clinical underpinnings of the risk assessment variables produced by the ACG System please refer to the Reference Manual for explanation of the ADG ACG EDC and Rx MG typologies the purpose of this chapter is to provide an overview of the risk scores or weights produced by the software In this chapter
16. The objective of this manual is to provide basic instructions on how to create and use data from which conclusions and decisions can be made Technical User Guide Navigation Locating information in the technical user guide is facilitated by the following search methods e Master Table of Contents The master table of contents contains the chapter names and principal headings for each chapter e Chapter Table of Contents Each chapter has a table of contents which lists the principal headings and subheadings and figures and tables e Index Each chapter is indexed and organized alphabetically Technical User Guide The Johns Hopkins ACG System Version 8 2 1 2 Getting Started Technical User Guide Topics The Technical User Guide contains chapters on the following subjects Chapter 1 Getting Started Provides a general overview of the physical organization of the manual as well as content Chapter 2 Release Notes Intended for all users this chapter quickly summarizes the major enhancements included in Version 8 2 Chapter 3 Selecting the Right Tool Intended for all users this chapter provides a brief overview of the ACG toolkit and illustrates how the components might be combined for comparing population health or morbidity used to demonstrate variability of cost within disease category and for profiling disease case management predictive modeling and or payment application Chapter 4 Basic Data Requirements Int
17. The type of Rx code in the rx_code column rx_code_type This column can contain a N for NDC code Text N or an A for an ATC code 4 Tip NDC codes and ATC codes are licensed individually You must have a license to Rx PM with the appropriate code type in order for the application to recognize pharmacy codes Technical User Guide The Johns Hopkins ACG System Version 8 2 5 78 Installing and Using ACG Software Custom File Formats ACGs for Windows is designed to handle custom file formats You can add delete and rename fields in the patient file Patient ID age and sex are required fields Once you have added custom fields these can then be used in the analyses for filters and groups Use the following steps to create a custom patient file format 1 Select File Select New 2 3 From the New File window click the radial button for Create Custom Patient File 4 Click Next Figure 40 Create Custom File Format x Johns Hopkins ACG System 8 2 E x Choose the type of file you wish to create New ACG File Create ACG File From Imported Data Create ACG File From Sample Data New Data File Format Create Custom Patient File Format 5 Click Finish 6 To rename a column double click on the existing name and insert new name 7 To delete a column click on the column name and then click the delete z button or select Edit Delete The Johns Hopkins ACG System Version 8 2 Technic
18. a localization enhancements b technical enhancements and c documentation enhancements Details on each change to the software are presented in the following sections Files created under Version 8 1 of the software may be opened in Version 8 2 When opening a file created with Version 8 1 the user will be prompted to upgrade the file A copy of the original file will be saved with the file extension acgd saved old version Note If files created in Version 8 1 are upgraded to Version 8 2 then some summary statistics calculated at the time of file creation and new to Version 8 2 will be left blank Localization Enhancements Version 8 2 of the Johns Hopkins ACG Software supports diagnoses based on ICD 9 CM and ICD 10 WHO coding standards For pharmacy data the software supports National Drug Codes NDC and Anatomical Therapeutic Chemical ATC classification systems for prescription drugs Other references in the system are based on either a U S Elderly population or U S Non elderly population as sourced from a national cross section of managed care plans provided by PharMetrics Inc a unit of IMS Watertown MA As the diversity of ACG users continues to grow globally and across new and unique product types we have received many requests to calibrate the system to unique coding systems and data sources The following enhancements represent technical changes that will provide for future flexibility in delivering new content If yo
19. a summary statistic provided on the Summary Statistics and selected during the predictive model selection phase of data input currently has two defaults either US elderly or US non elderly The underlying weights or predictive modeling scores used in any given report are a function of either the default selected at the time of data input see Figure 21 below OR it is controlled via the Report Options menu shown above in Figure 20 Figure 21 Select the Risk Assessment Variables Choose the data sources for your new ACG data file Patient Data Patient Data File C acgdata My_patient_file csv Skip First Row i e column headers in data file Use Tab Delimited File Format O Use Comma Delimited File Format Use Custom File Format Diagnosis Data Diagnosis Data File C acgdata My_diagnosis_file csv Skip First Row i e column headers in data file Use Tab Delimited File Format Use Comma Delimited File Format Pharmacy Data Pharmacy Data File C acgdata My_pharmacy_file csv Skip First Row i e column headers in data file Use Tab Delimited File Format Use Comma Delimited File Format Model Options Risk Assessment Variables US Elderly US Non Elderly LI Prior Costs All Models Back lt Next gt Cancel Technical User Guide The Johns Hopkins ACG System Version 8 2 5 24 Installing an
20. and drag a copy of the ACG icon to make a shortcut to the software on your desktop The Johns Hopkins ACGs subfolder in the Start Menu also contains links to the Technical User Guide and Reference Manual two important pieces of reference material intended to assist you in your implementation of Release 8 2 For almost all reports available in the software results for a Commercial and Medicare reference data set for the under age 65 working age population as well as the over age 65 Medicare eligible population are available electronically as an Excel template which may be accessed via the pull down menu of the Johns Hopkins ACG 8 2 start menu Users are encouraged to produce their own reports and use this reference comparison data as a benchmark Technical User Guide The Johns Hopkins ACG System Version 8 2 5 16 Installing and Using ACG Software The ACGs for Windows application includes an uninstall utility It is recommended that this uninstall utility be used to remove the ACGs for Windows application to ensure that all aspects of the installation are removed This can be accessed by using Windows Control Panel Add Remove Programs ACG for Windows Desktop ACGs for Windows provides a range of functions available through its desktop as shown in Figure 17 Figure 17 AGGs for Windows Taskbar 1 Johns Hopkins ACG System 8 1 File Edit View Analyze Tools Help Ska M x g I F D ACGs for Windows has a standard taskbar with
21. column headers in data file Use Tab Delimited File Format Use Comma Delimited File Format Model Options Risk Assessment Variables lUS Non Elderly X Prior Costs Us Elderly US Non Elderly alcUlate all Yall p All Models on of technical support Back lt Next gt Cancel Release Notes The Johns Hopkins ACG System Version 8 2 Release Notes This Risk Assessment Variables used to process the data through the ACG System is stored with the ACG data file and recorded in the Summary Statistics tab reference Figure 3 Figure 3 Summary Statistics Tab x Johns Hopkins ACG System 8 2 File Edit View Analyze Tools Help Mx ene lt S aca vata Fie 2senel ace Summary Statistics Patient Sample Local Weights Age Gender Dist Probability Dist Build Options Description value Minutes To load data Total cost model selected DxRx PM total cost gt total cost Pharmacy cost model selected DxRx PM rx cost gt rx cost Date loaded il 10 28 Created with ACG version Created with ACG mapping version 8 1 a Quarter 2008 Release Created with 4CG mapping release date 2008 07 07 The Johns Hopkins ACG System Version 8 2 Release Notes Release Notes 2 5 The Risk Assessment Variables are also stored with the Build Options for the ACG data file reference Figure 4 Figure 4 Build Options Tab x Johns Hopkins ACG System 8 2 File Edit View Analyze Tools Help x k
22. d514AAAAAACCJBSM 9 E888 d514AAAAAACCLIIN 9 E888 d514AAAAAACCMMYB 9 E929 d514AAAAAACCPJOV 9 E888 d514AAAAAACCWVJO 9 E888 d514AAAAAACCWVPW 9 E888 The Johns Hopkins ACG System Version 8 2 Technical User Guide Assessing the ACG Grouper s Output 6 11 Common Input File Problems Some common problems with the input file that can lead to high mismatches are as follows e Codes that have been padded out to five digits using zeros will not be assigned to an ADG unless the five digit code is in the mapping If all codes have been padded on the right with zeroes mismatch rates will be high and patients may not be assigned the correct risk assessment variables e Ifthe same code is rejected repeatedly for multiple members this may be a home grown plan specific code You can usually recode these to a valid ICD code Before assigning risk assessment variables all common homegrown codes should be reviewed and re assigned in this manner Please contact your ACG support contact for assistance with this process if needed If decimal points are included in the input diagnosis codes are they appropriately placed Decimals will be stripped from diagnoses that include them by the ACG Software before assignments are made Codes that include decimals can have a maximum of three characters to the left of the decimal and two characters to the right of the decimal If a non conventional location of the decimal point seems to be posing a problem remo
23. e These figures reflect a retrospective concurrent analysis Addressing the Impact of Age on the Calculation of ACG Weights Age is incorporated as a control variable in the sorting algorithm that determines final ACG assignment At the same time there are some ACGs that include both pediatric and adult populations because splitting on age was not consistently found to contribute to variation explained within those categories Despite this pediatric populations those younger than 18 tend to generate fewer costs than adult populations within broadly defined commercial populations Where ACG based applications are stratified by pediatric versus adult populations risk adjusted resource weights derived from the population as a whole may over or under represent expected values associated with these groups For example in profiling primary care providers weights derived from a broadly defined population may over represent expected values for physicians whose practice is limited to pediatric cases Those providers will on average tend to look more efficient than providers for the health plan as a whole One common way to address this issue is to calculate ACG weights separately for pediatric and adult cohorts within a health plan For example two weights could be calculated for ACG0500 Likely to Recur without Allergies One ACG weight would be based on the resource used by adults who were assigned to ACG0500 The second ACG weight would
24. key consideration is time frame is the analysis retrospective or concurrent in nature involving a comparison of morbidity across or between population subgroupings or is the application prospective or predictive in nature Each of these issues will be discussed in more detail subsequently Technical User Guide The Johns Hopkins ACG System Version 8 2 8 2 Final Considerations Time Frames and Basic Population Perspectives For profiling the population s health characteristics i e diagnoses used to adjust the profiles typically come from the same time period as the resource use being profiled Thus the process is designated retrospective or concurrent For example to understand the differences in per person pharmacy use across two provider panels in a given year you would assign risk assessment variables using diagnosis codes derived from patient physician contacts during that same year In contrast the most common approach for risk adjusting capitation payments is to prospectively set rates in the following years for a cohort of enrollees based on the diagnosis codes documented in data derived from the prior year s For administrative reasons there is usually a lag period often of about three months duration between the risk assessment period and the target payment period Additionally some patients may be enrolled during the first period but not the second and vice versa Others may be enrolled during the entire period but u
25. whether the Avg Pred Resource Use displayed is Total Cost or Pharmacy Cost Technical User Guide The Johns Hopkins ACG System Version 8 2 5 34 Installing and Using ACG Software Figure 23 Selecting Report Options for Cost Predictions by Select Conditions Analysis Report Options Filters Options Groups Set these options to control how your report is calculated Options control how your analysis is calculated See the help for more information regarding how each option impacts a given report Concurrent Weight Options Weight Type Predictive Model Options Model Type Total Cost Tot Prevalence Com Pharm y Cost Prevalence Type C ox J canc The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 35 Cost Predictions by Rx MGs Analysis The Cost Predictions by Rx MGs describes risks and predicts expenditures in the subsequent time period by Rx MGs and using only pharmacy data This analysis allows the user to stratify a particular population by predicted risk This can be helpful in sizing programs or understanding the resource expectations for specific risk groups The average predicted resource use columns have the option reflect total cost or pharmacy cost The report layout is as follows Table 13 Cost Predictions by Rx MGs Analysis Report Layout Rx MGs and ALL CASES all patients even those without any of the listed Rx Rx Morbidity Groups
26. 19 Report Options x Johns Hopkins ACG System 8 2 File Edit Yiew Analyze Tools Help SoM R iv fall 825ample acgd B SMR By EDC Standardized Morbidity Ratio By EDC using Refe Company Product Benefit Plan Report Options Option Selection ACG Data File C acqdata 825ample acgd Filter line_of_business equals Commercial Column Groups Company Company Product Product Benefit Plan Benefit Plan Prevalence Type Reference Resource Utilization Band RUB Distribution Analysis ACGs were designed to represent clinically logical categories for persons expected to require similar levels of healthcare resources However enrollees with similar predicted or expected overall utilization may be assigned different ACGs because they have different epidemiological patterns of morbidity For example a pregnant woman with significant morbidity an individual with a serious psychological condition or someone with two chronic medical conditions may all be expected to use approximately the same level of resources even though they each fall into different ACG categories In many instances users may find it useful to collapse the full set of ACGs into fewer categories particularly where resource use similarity and not clinical cogency is a desired objective Often a fewer number of combined categories will be easier to handle from an administrative perspective ACGs can be combined into what we term Resource
27. 3 18 3 3 41 8 1 12 N 2 2 2 2 4 an 66 86 Re MIENEN ENES eee ales BIDIN M jN N 1 01 2 j oe oS 6 3 5 5 5 9 82 7 5 6 5 7 5 5 3 9 5 2 a ee call Swi oy wjojlNnjiNj e w j N AIAJ Win LO N NA NM o O NIOJ A oO N A olo oluo S SIN z5 I I I I 2 2 AJU Go Bln 25 181 5 ek 2 2 a ee ee S SSR S Oo uju H o oo 0 S ae in P vE m 5 aje 1 19 4 41 11 12 106 Pe 0 The Johns Hopkins ACG System Version 8 2 All Enrollees users Avg 1 024 660 696 6 13 0 12 Months Avg 1 04 Cases 87 092 1 197 2 779 4 658 226 245 Cases 74 082 2 22 3 74 16 59 4 19 1 1 779 5 244 254 M A 6 782 22 9 4 9 117 3 955 1 384 2 2 57 2 30 68 1 50 0 2 582 752 1 618 ice DI WlLAIA O NJU 5 536 605 1 351 286 1 550 1 180 290 203 2 947 74 41 2 55 1 19 25 1 47 1 10 26 18 2 82 oO O 349 268 230 156 694 1 465 169 120 Ja 4 3 6 m 2 3 2 2 1 5 2 O O Nn Wl OoO RIA OloO o R1 o Technical User Guide Making Effective Use of Risk Scores 7 15 All Enrollees 1 3 Months 4 6 Months 7 9 Months 10 12 Months users Notes e Average mean costs include total 1996 paid claims truncated at 35 000 for users in a large commercial HMO population
28. 37 48 0 10 5 1 042 0 744 1 212 0 881 11 1 184 1 58 6 10 8 40 6 27 2 140 12 4 149 8 1 120 0 880 1 233 0 867 1 598 0 893 1 136 0 950 0 756 1 275 Ww lon nr S gt aJelS N oe N O Nn INIIAI SOINI SI olw A OA Ww oOlNs Bl Re o o lao w AJA Nn Nn 99 3 A o0 Oo P oo io a XQ n na The Johns Hopkins ACG System Version 8 2 Technical User Guide Selecting the Right Tool 3 9 A similar prevalence analysis is available based upon the Rx Morbidity Groups This analysis presents prevalence of treated conditions within a subpopulation of interest after taking into account the age and sex mix of the group relative to either the underlying population or a national comparison group The user can determine the population to be used for comparison by using the report options when the analysis is run This analysis identifies prevalence of very specific patient populations such as insulin dependent diabetics medicated hypertension patients or patients on anti depressants The benefit in using prescriptions to define conditions is that certain conditions are under coded by diagnosis This is particularly true for depression for example where Rx MGs possibly provide a truer prevalence identified by the use of anti depressants Technical User Guide The Johns Hopkins ACG System Version 8 2 3 10 Selecting the Right Tool Table 4 Observed to Expected Standardized Morbidity Ratio SMR by Rx Morbidity
29. 5 iv Installing and Using ACG Software Export Data Fil S sssri sss E 5 68 Figure 38 Export Data ao tc Seer ee ne eer Bere nny Rett EPR s te tee pest ePreecrrrerr ed 5 69 Figure 39 Select ING sarcastica i 5 70 Use Your Own Data sssiscccssvscscssviesisnassocssnessessocsensesnsenessnossntncssesnsentsoonnaasonss 5 73 Patient File FPOrmM t sssrinin EE E 5 73 Table 15 Patient File Format isisisi aaea 5 74 Diagnosis Data File Pranab si cacscaacasces viseadouininges nie i 5 76 Table 16 Diagnosis Data File Format peicisniennon 5 76 Pharmacy Data Pile Format scssiaesiosssnsenna s 5 77 Table 17 Pharmacy Data File Pornatoi ccsjisoccssatiasscsiatascemntusds anesenias 5 77 Custom File Formats ssi jaa dacessssistaduninndinessnteisediorndonan eaaa 5 78 Fig re 40 Cr ate Custom Pile Form t sinisisi 5 78 Figure 41 Enter Custom File Fotmat sssisissssioisssisssssnssoinisis 5 79 Open acad TES isere E aE 5 80 L ad Your Owa Data Case St dy sciis autsciacdiorsonisaduatainsanlaauiesinnsan 5 80 Figure 42 Step 1 Load Your Own Data sccccsccssccsssccssescssissccsenscdissssuacess 5 80 Figure 43 Step 2 Load Your Own Datta ncsscccossssecssssasczccsssrsrecevssazaveens 5 81 Model OPUONS scacos since eiuscce ed nlanctiateantnad bandas isleuctebialseblitessbeulundatuaaciapeiaa 5 82 Figure 44 Step 3 Load Your Own Datasiicssiccsscscsccssesssvinsasssscstissascosass 5 83 Figure 45 Final Step Load Your Own Data cccccssecssceseeeeteeeeees 5 84 Additio
30. 8 installing the software 5 3 Java API 5 96 license agreement 5 11 load the sample dataset 5 51 load your own data case study 5 80 local weights tab 5 57 mapping file communication error 5 15 mapping file manager 5 14 MEDC by RUB distribution analysis 5 22 memory RAM 5 1 open acgd files 5 80 operating system 5 1 options 5 64 patient clinical profile report 5 41 patient file format 5 73 patient list analysis 5 44 patient sample tab 5 56 pharmacy data file format 5 77 population distribution by age band and morbidity analysis 5 21 predictive modeling options 5 82 pre installation summary 5 7 probability distribution tab 5 59 report options 5 66 report options for MEDC by RUB distribution analysis 5 22 resource utilization band distribution analysis 5 19 Rx MG by RUB distribution analysis 5 26 save ACG sample 5 52 select destination location 5 5 select report options for standardized morbidity ratio by EDC analysis 5 29 select the risk assessment variables 5 23 selecting report options for cost predictions by select conditions analysis 5 34 simple profile analysis 5 38 standardized morbidity ratio by EDC analysis 5 28 standardized morbidity ratio by Major Rx MG analysis 5 31 standardized morbidity ratio by MEDC analysis 5 30 standardized morbidity ratio by Rx MG analysis 5 32 step 1 load your own data 5 80 step 2 load your own data 5 81 summ
31. 8 IF S aca vata Fie Esame Summary Statistics Patient Sample Local weights Age Gender Dist Probability Dist Build Options Selection C acgdata My_patient_file csv None Ignore Use Prior Costs Use Release Notes The Johns Hopkins ACG System Version 8 2 2 6 Release Notes The change in model selection also prompted a change to the output of the All Models file export option reference Figure 5 Figure 5 All Models File Export Option x Johns Hopkins ACG System 8 2 Ble Edt __ Yew Export ACG Data ACG Data File 825 Choose the type of data to export and the file location Export Data Patients and ACG Results Pharmacy Codes Summary Statistic Patient EDC Assignments C Non Matched Diagnosis Codes Patients processed Patients processed Diagnoses processe Unique diagnoses amp Patient Rx MG Assignments Local Weights Unique unknown di A Percentage of diag Unknown diagnose Diagnosis Codes Patients with unknd Unique matched dij Export Options Unique unknown dis V Write Header Row Patients with unsup Pharmacy codes pri Unique pharmacy c Comma Separated Value commas with quotes Unique unknown p Percentage of pha Unknown pharmac Patients with unkne Unique matched ph Export File Name Unique unknown p Patients with unsup Number of EDCs as Lx J Cancel Number of MEDCs 4 Number of ADGs assigned 284872 Patient MEDC Assignments C Non
32. Calculated as Low SMR 1 96 x SQRT SMR expected count 95 Confidence The upper range of the 95 confidence interval Calculated as High SMR 1 96 x SQRT SMR expected count An indication of statistical significance Contains a minus sign when the SMR is Significance significant and less than 1 contains a plus sign when the SMR is significant and greater than 1 Age Sex Expected 1000 Technical User Guide The Johns Hopkins ACG System Version 8 2 5 32 Installing and Using ACG Software Standardized Morbidity Ratio by Rx MG Analysis The Standardized Morbidity Ratio Analysis produces a summary by Rx MG with observed expected and o e ratio This report is useful in understanding how the prevalence of certain conditions as defined by Rx MGs are more or less common than average across the subpopulation of interest The significance indicator identifies categories that are statistical different from the age sex adjusted expected value At the user s discretion the expected values can be derived from either the population mean or the national benchmark data The methodology for this analysis is explained more fully in the EDC Chapter in the Reference Manual The report layout is as follows Table 11 Standardized Morbidity Ratio by Rx MG Analysis Report Layout Column Definition Name Rx MG Cd Each Rx MG code that was assigned to at least one patient Rx MG Name The description for Rx MG Cd The numbe
33. Distribution Analysis Report Layout 5 25 Rx MG by RUB Distribution Analy 618 os cic ccicssssassissdeccovasiccascssbaddiesansedess 5 26 Table 7 Rx MG by RUB Distribution Analysis Report Layout 5 26 Standardized Morbidity Ratio by EDC Analysis ccceceeeeeeseetees 5 28 Table 8 Standardized Morbidity Ratio by EDC Analysis Report Ta as iride aE REA 5 28 Figure 22 Select Report Options for Standardized Morbidity Ratio by ay BGS 1 bi eee ee dn A 5 29 Standardized Morbidity Ratio by MEDC Analysis eeceeeeseerees 5 30 Table 9 Standardized Morbidity Ratio by MEDC Analysis Report LayoUt eenen naen a N aimee 5 30 Standardized Morbidity Ratio by Major Rx MG Analysis 0 006 5 31 Table 10 Standardized Morbidity Ratio by Major Rx MG Analysis Report Cay c 0cncepsnmicchnennaninrenmumanmmmaaimenn 5 31 Standardized Morbidity Ratio by Rx MG Analysis eceeeeeeenees 5 32 Table 11 Standardized Morbidity Ratio by Rx MG Analysis Report Layonna aona a a a 5 32 Cost Predictions by Select Conditions Analysis c ccccesseeeseeeeeeeeees 5 33 Table 12 Cost Predictions by Select Conditions Analysis Repon Skt a ee eee Ee penal EE 5 33 Figure 23 Selecting Report Options for Cost Predictions by Select Conditions ATG IV SIG ssanie ea aa SaS 5 34 Cost Predictions by Rx MGs Analysis vecicicsssssnscssssaveinseasacensoievedleasenienss 5 35 Table 13 Cost Predictions by Rx MGs Analysis Report Layou
34. Dx Rx or DxRx PM Adjusted Weights Reference Rescaled Weight Rescaled Total Cost Resource Index Reference weights that are rescaled so that the mean across the population is 1 0 The Total Cost Resource Index rescaled so that the local population mean is 1 0 Sub group analyses provide comparisons to local norms Rescaling facilitates internal comparisons of morbidity burden based on reference population between different subpopulations The Johns Hopkins ACG System Version 8 2 Technical User Guide Making Effective Use of Risk Scores Rescaled Pharmacy Cost Resource Index Local Weight Probability High Total Cost Probability High Pharmacy Cost Resource Utilization Band Technical User Guide The Pharmacy Cost Resource Index rescaled so that the overall population mean is 1 0 Sub group analyses provide comparisons to local norms A concurrent weight assigned to this patient based upon their ACG Cd using local cost data The weight for each ACG is calculated as the simple average total cost of all individuals assigned to each category Probability Scores ACG Predictive Probability Score for total cost The probability that this patient will have high total costs including pharmacy costs in the year following the observation period ACG Predictive Model Probability Score for pharmacy cost The probability that this patient will have high pharmacy costs in the year following th
35. Guide Selecting the Right Tool 3 28 uOIsUud 13ad AH erwepidiy 13ad AP eee ea JANIE J eusy IUO JANIE 1183H IANYSIUO syy y sea AMIA yuno uorrpuop HUO yunoy yueuruo q eydsoy 1509 BI0L 4351H Amqeqord xopuy 9dINOSIY 1509 810 PLISI 1509 6o L y PI Uned z z z z i z a z T B B E A T a T a a T a a A B B T a a T a a T a T T B a a a a a a a x x a Z Z Z Z Z Z Z a4 Z T Q Q Q a a a a a a efelefelefelefelefe Q Q T T eleleletelefefefefe a a x a a a a a a a A a z a a a a a a a Q Z m Z Z Z Z Z Z Z el d fele eleTefeel d fede o0 o0 co N N N N N N N o0 foe o0 o0 foe o0 o0 o0 foe ioe ajalejejalefaja a e N N eN N co co o0 N N co o0 N No Ya Ye wo Ya Re O 1920314 1950530 19551215 19621114 6547141 14 6775544 16 6777442 6351677 7111144 6541544 7113531 14 7416121 14 7142172 14 6141214 14 Technical User Guide The Johns Hopkins ACG System Version 8 2 3 29 Selecting the Right Tool uostu eruoprdy Aa efe na pele JANIE J eusy IUO JANIE J dAI SISUOD sey Ayreag 7 uorjIpuo HUO yueuruo q eydsoy B10L 4351H AyIqeqoig PI yuoHed 19611211 7144164 16 6146255 16 The Johns Hopkins ACG System Version 8 2 Technical User Guide 3 30 Selecting the Right Tool Managing Pharmacy Risk Prescription Drug Plans PDPs have unique challenges
36. ICD coding issues 4 4 Identifying special populations with augmented data inputs ACG 4 10 Input files common problems 6 11 Install a license file example 5 93 usage details 5 90 Install a mapping file usage details 5 90 Installing and using ACG software 5 1 ACG command line usage 5 89 ACG distribution analysis 5 19 ACG for Windows desktop 5 16 ACG license file 5 10 ACG output data 5 56 5 85 actuarial cost projections 5 37 additional sources of information 5 85 ADG distribution analysis 5 20 age gender distribution tab 5 58 analyze menu 5 17 5 61 analyze report options 5 62 batch mode processing 5 89 build options tab 5 60 care management list 5 39 central processing unit CPU 5 1 choose shortcut folder 5 6 choose the license file 5 12 cost predictions by Rx MGs analysis 5 35 cost predictions by select conditions analysis 5 33 create ACG File from sample data 5 51 custom file formats 5 78 diagnosis data file format 5 76 disk space 5 2 EDC by RUB distribution analysis 5 25 edit menu 5 16 exportdata files 5 68 exportreport tables 5 67 extraction status 5 4 file menu 5 16 filters 5 62 final step loading your own data 5 83 first setup screen 5 3 groups 5 63 guided setup 5 4 guidelines 5 92 help menu 5 50 install complete 5 10 install the license file 5 12 install updated mapping file 5 14 The Johns Hopkins ACG System Version 8 2 IN 4 Index installation status 5
37. Max Use Date 2008 09 01 Comments License for The ACG System Release 8 0 Technical User Guide The Johns Hopkins ACG System Version 8 2 5 14 Installing and Using ACG Software Updating the Diagnoses and Pharmacy Mapping Files The ACG application uses a mapping file to determine the use of diagnosis codes and pharmacy codes within the system The ACG System installation includes a current mapping file The mapping file will be updated from time to time to reflect new codes or groupings and reference data values When the application is first opened there will be a prompt asking if you would like to look for an updated mapping file If you confirm with a yes the software will attempt to connect to the ACG website to look for an updated mapping file If a more recent file is available you will be provided with the date of update and asked if you want to install the updated mapping file Figure 14 Install Updated Mapping File Update Available The ACG System will attempt to connect to the internet to look for updates periodically and you will be prompted to install the update You can deny any particular update and return at a later time to manually initiate the update process This process is started by selecting Manage mappings from the Tools menu Click Check for Updates to connect to the ACG website Figure 15 Mapping File Manager Mapping File Manager Current Mapping Data Version 8 1 3rd Quarter 2008 Release R
38. Output Figure 2 Population RUB Distribution x Johns Hopkins ACG System 8 2 File Edit View Analyze Tools Help aoM x ee lt F fal Commercial_v82 acgd E RUB Distribution RUB Distribution Analysis For Commercial_v82 Overall Line of Business Company Product Employer Id Benefit Plan gt Resource Utilization Band RUB Description Frequency Freq 0 No or Only Invalid Dx 788 914 24 45 1 Healthy Users 489 381 15 17 2 Low 671 890 20 82 3 Moderate 1 028 966 31 89 4 High 197 847 6 13 5 Very High 49 387 1 53 A Tip For each report an explanation of each field may be found in Chapter 5 of the Technical Users Guide or the on line help within the ACG Software Comparison to Reference or External Data For almost all reports available in the software results for a Commercial and Medicare reference data set for the under age 65 working age population as well as the over age 65 Medicare eligible population are available electronically as an Excel spreadsheet which may be accessed via the pull down menu of the Johns Hopkins ACG 8 2 start menu Users are encouraged to produce their own reports and use this reference comparison data as a benchmark Key is not does your data match the reference data exactly but rather does it make sense given the context of your particular application The Johns Hopkins ACG System Version 8 2 Technical User Guide Assessing the ACG Grouper s Output 6 9 Additiona
39. PM 1 total E total cost Risk Assessment Variables US non elderly Indicates the type of ACG predictive model Possible values include 2 3 4 Dx PM for diagnosis based predictive modeling Rx PM for pharmacy based predictive modeling or DxRx PM for diagnosis plus pharmacy based predictive modeling Indicates whether or not and the type of prior cost information included in the calibration of the predictive model Possible values include No cost for no cost information was incorporated Total cost for total cost or Rx cost for pharmacy cost Indicates what is being predicted Possible values include Total cost for total cost Rx cost for pharmacy cost Indicates the population to which the model has been calibrated Possible values include US Non elderly for less than 65 years old and US Elderly for populations 65 years or older Technical User Guide The Johns Hopkins ACG System Version 8 2 5 56 Installing and Using ACG Software Figure 27 Patient Sample Tab The Patient Sample tab is a sample of records from the ACG output file xy Johns Hopkins ACG System 8 2 File Edit view Analyze Tools Help M x 8 l m 825AMPLE acgd Summary Statistics Patient Sample Local Weights Age Gender Dist Probability Dist Build Options Patient Id Age Sex Line of Business Company Product Empl
40. Processing ccsscssssscssssscesssccssssccsssscssssses D7 OO Appendix C Jaya APL anncasaaciccnnnmmnnnnonniaonannanminmnnO 00 The Johns Hopkins ACG System Version 8 2 Technical User Guide Table of Contents iii 6 Assessing the ACG Grouper s Output sssesssesssesssecesocssoosssoeessecssocssoosssosssse Onl Tethr OCUCEOI scssscccisssccisrnisonesnin tones naa OFL ACG Compressed Data Fil sssscissssssssssessssssossssissosos sssossssessusosiessessssssssessisss OF Basic Review Process ssississsscsssessosssusssscesssssonsssnosssoansssseasiessdsssrtanctsnassscesssssees OF L Review of Reports Produced Automatically by the Software 6 2 Review of Reports Produced by the Analyze Menu ssccsssssssssseseeee 0 5 Additional Considerations wacsscnssensccessscsssessccessssnonscasssansnenssasssessonsiassnsesannseacs 619 CONCIISION isicistccasncinesreasmniainninaansmianunimnnnisauiemisnd OLE 7 Making Effective Use of Risk Scores cccssscssssscssssscsssccssssccssssccssssscees 7 DAP OCU CHO arisccccssscisssinsocaicsnin in amr a FOL Software Produced Weights and Their USe6 scsssccssssssssssscsesscsssseees D7 Concurrent ACU re VV CLINGS cs can casescenszsaoceeid sccvensiesusaeniesnasisdeceninesnernseaummn O Prospective Risk ScOreS ssiccsssssssccinscarsescessncinnmnnnnnnwunmennnnnads 170 Converting Scores to Dollars scccssssssssssssssssscsssssesessscssscscessccsssscssssseee 7 7 Cust
41. Psychosocial w o Psych Unstable 154 140 867 22 0 42 1400 Psychosocial with Psych Unstable w o Psych Stable 16 35 828 49 1 02 1500 Psychosocial with Psych Unstable w Psych Stable 8 10 583 12 0 60 1600 Preventive Administrative 775 231 791 25 0 14 1711 Pregnancy 0 1 ADGs delivered 12 87 306 89 alek 1712 Pregnancy 0 1 ADGs not delivered 20 24 437 42 0 56 1721 Pregnancy 2 3 ADGs no Major ADGs delivered 40 292 434 83 3 34 1722 Pregnancy 2 3 ADGs no Major ADGs not delivered 35 43 461 48 0 57 1731 Pregnancy 2 3 ADGs 1 Major ADGs delivered 7 54 469 83 3 56 1732 Pregnancy 2 3 ADGs 1 Major ADGs not delivered 4 11 161 82 1 28 1741 Pregnancy 4 5 ADGs no Major ADGs delivered 15 109 210 87 33 1742 Pregnancy 4 5 ADGs no Major ADGs not delivered 16 26 483 93 0 76 1751 Pregnancy 4 5 ADGs 1 Major ADGs delivered 11 93 336 82 3 88 1752 Pregnancy 4 5 ADGs 1 Major ADGs not delivered 12 75 699 46 2 88 1761 Pregnancy 6 ADGs no Major ADGs delivered 8 91 443 95 5 23 1762 Pregnancy 6 ADGs no Major ADGs not delivered 6 27 118 44 2 07 iri Pregnancy 6 ADGs 1 Major ADGs delivered 19 255 184 87 6 14 1772 Pregnancy 6 ADGs 1 Major ADGs not delivered 12 90 842 86 3 46 1800 Acute Minor and Acute Major 735 1 103 164 29 0 69 1900 Acute Minor and Likely to Recur Age 1 42 60 533 98 0 66 2000 Acute Minor and Likely to Recur Age 2 to 5 220 206 755 87 0 43 2100 Acute Minor and Likely to Recur Age gt 5 w o Allergy 805 887 205 66 0
42. R O hiatal hernia still consumes the resources associated with the differential diagnosis of these disorders Although the extent of their impact is not well understood in applications designed to predict resource consumption in the next time period the presence of rule out or suspected diagnosis codes may have an effect if they appear in large numbers or if certain providers or groups use these more than other providers or groups This impact is especially relevant if the ruled out diagnoses resolve to ADGs that the patient would not be otherwise assigned to based upon the array of his her other confirmed diagnoses For patients with multiple comorbidities the probability of this is lower than for patients who are relatively healthy While it is certainly possible for rule out diagnoses to make healthy individuals appear sicker than they really are this distortion should occur for only a small subgroup of patients To some extent the user can assess this by linking a count of ADGs assigned to a broad measure of resource consumption such as total charges and a narrow one such as office visits and then comparing the correlation between ADG counts and the two resource consumption measures Persons with many ADGs low total charges and many visits may suggest that rule out diagnoses play a role in the assignment of the ADGs When a particular health plan or physician consistently appears to have a high morbidity mix but relatively low resource use i
43. System Version 8 2 Column Name Probability High Rx Cost Installing and Using ACG Software The probability that this patient will be in the top 5 percent of pharmacy cost in the subsequent year Predicted Rx Cost Range The predicted pharmacy cost for this patient for the subsequent year A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication Arthritis A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication Congestive Heart Failure A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication Chronic Renal Failure A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication Depression A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication Diabetes A flag indicating if
44. The organizations are at financial risk yet have access to very limited data to manage that risk The ACG Rx PM and the pharmacy based morbidity groups Rx MGs provide a unique opportunity to leverage this information for comparing population health SMR reports reference Table 4 predicting resource needs Table 12 and providing useful and relevant information to care managers Table 13 Medication Therapy Management Program MTMP Candidate Selection Medicare PDPs have unique challenges in that one of the regulatory requirements of PDPs is that they implement Medication Therapy Management Programs MTMPs MTMPs are designed to improve medication adherence patient safety and quality The programs typically focus on promoting beneficiary education and counseling increasing enrollee adherence to prescription medication regimens and of detecting adverse drug events and patterns of over use and under use of prescription drugs These outreach programs should reach individuals with multiple chronic diseases such as but not limited to diabetes asthma hypertension hyperlipidemia and congestive heart failure who are taking multiple covered Part D Drugs and who are identified as likely to incur annual costs for covered Part D drugs that exceed the level specified by the Secretary of Health and Human Services Since PDPs have access only to prescription history under their program meeting this criteria can be a challenge Rx PM and the Rx Morbi
45. These condition specific designations were used only when there was a very strong correlation between drug and disease and when there were no substantive off label uses of the drug The remaining categories are more generalized groupings and indicate the general action of the drug in addition to the duration stability and or therapeutic goal For example anti diarrheals laxatives and antacids are classified within Rx MG GASx010 Gastrointestinal Hepatic Acute Minor For more information on Rx MGs please refer to the chapter in the Reference Manual entitled Predicting Future Resource Use with Pharmacy Data Adjusted Clinical Group Predictive Modeling ACG PM Predictive modeling also known as high risk case identification allows healthcare organizations to target patients who would benefit from case management a personalized interactive process to manage disease preventively before it results in costly care With the cost of healthcare rising each year predictive modeling can help align premium levels with the risk of the employer group Because the ACG System can stratify members within a disease category health plans can adjust care and resources to match the degree of care needed If for instance a health plan has a concentration of women over a certain age with diabetes the ACG system stratifies the women by risk allowing the health plan to assess higher risk women Once identified the plan may direct healthcare pers
46. a model does not apply to a data set it will be left blank The columns in this file are as follows Patient ID MODEL NAME pri MODEL NAME prir MODEL NAME prob Use Your Own Data Using the chapter Basic Data Requirements as a guide you may use your own data to create a Patient or enrollment Data File and a Diagnosis Data File according to the following specifications Patient File Format The default enrollee data file format is a tab delimited or comma delimited optionally quote enclosed text file sometimes called a tab delimited data file or CSV with the following columns in order This format is directly supported by Microsoft Excel and Microsoft Access and a variety of other tools This file contains one row per Patient ID only The only required columns in this file are patient_ID age and sex We encourage providing as many data elements as possible 4 Tip While the minimum data requirements are only patient_ID age and sex the suite of ACG Predictive Models are calibrated at your discretion see additional details below to take advantage of all available data To maximize performance of these models users should be sure to provide both pharmacy _cost and total_cost information for each member 4 Tip The ACG application will use the Windows Regional settings to format the pharmacy cost and total cost fields on input and for display If these costs fields are formatted other than a comma thousands separ
47. by inputting further relevant information about their patient populations Through the use of optional flags you may supply additional information about pregnancy status whether or not a pregnant woman has delivered and information about an infant s birth weight Pregnancy Status It is possible for analysts to provide the software with a flag indicating that a woman is pregnant The rationale for including this option is that it is not uncommon in some plans for the charges associated with a woman s pregnancy and subsequent delivery to be reimbursed as a global or fixed payment at the time of delivery In this reimbursement scenario a woman s claims history may not include a pregnancy diagnosis until she actually delivers However given the importance of this information the plan often does know that the woman is pregnant despite this lack of related ICD codes during the prenatal care period In cases where the plan wishes to supplement the standard claims data e g if a pregnancy registry is believed to be more accurate than standard claims The Johns Hopkins ACG System Version 8 2 Technical User Guide Basic Data Requirements 4 11 data the user may submit a special delivery flag that can supplement the standard ICD stream Refer to the Installing and Using ACGs Software chapter in the Technical User Guide for a discussion of how to implement this approach 4 Tip ICD 9 CM codes used to identify pregnancy 640xx 677xx
48. codes into a single rubric EDCs identify patients with specific diseases and are applicable to both pediatric and adult populations For more information on EDCs please refer to the chapter in the Reference Manual entitled Expanded Diagnosis Clusters EDCs Formerly Ambulatory Care Groups Technical User Guide The Johns Hopkins ACG System Version 8 2 3 4 Selecting the Right Tool Rx Defined Morbidity Groups Rx MGs Rx defined Morbidity Groups Rx MGs classify NDC codes into unique clinical groupings that are the building blocks of the Rx Predictive Model In addition to the generic drug active ingredient the route of administration is a key variable in determining the Rx MG Rx MGs group drugs that are similar in terms of morbidity duration stability and therapeutic goal For example drugs in the class of corticosteroids may be delivered orally topically by injection or inhaled to reduce inflammation The route of administration is a key consideration in determining whether the drug is being used to treat joint conditions such as arthritis respiratory conditions such as asthma or to treat allergic reactions There are 60 Rx MGs organized within 19 broad clinical categories Of the 60 categories approximately half represent highly differentiated groupings that indicate a clinical condition For example proton pump inhibitors are classified into the Rx MG GASx060 Gastrointestinal Hepatic Peptic Disease
49. disease CAROS Congestive heart failure RUB 5 RUB 4 Very High High 8 23 29 89 7 49 2541 743 25 40 8 23 7 20 8 07 8 23 27 06 8 17 25 14 7 87 26 28 1 74 o o NIN a 7 9 o o O n N JHRIR NA ow i N 3 9 20 2 62 2 42 2 37 2 22 2 37 2 47 N LoS gt gt s ley nN BR fon CARO7 Cardiomyopathy CARO8 CAR06 Cardiac valve disorders ojo re r oa f am CAR09 Cardiac arrhythmia CAR10 Generalized atherosclerosis CAR11 Disorders of lipoid metabolism CARI2 Acute myocardial infarction CAR13 Cardiac arrest shock 29 1 85 2 12 l D jnt h wj a joj N Technical User Guide The Johns Hopkins ACG System Version 8 2 3 20 Selecting the Right Tool High Risk Case Identification for Case Management The suite of ACG Predictive Models includes the Dx PM based on diagnosis codes the Rx PM based on drug codes and the combined DxRx PM which uses both diagnostic and medication information These represent a real advance if you want to establish or augment care management programs within your organization Existing ACG measures have many applications in this domain as well There are a great number of variants within the ACG predictive models You can select a model based on data source diagnosis pharmacy or both calibration data elderly or non elderly and prior cost total cost pharmacy cost or no prior cost In general the accu
50. e reference and calibration data If your license expires prior to receiving an update please contact your software vendor A standard Windows Wizard guides you through the installation of the new license file Figure 9 Welcome to the Johns Hopkins ACG System Setup Johns Hopkins ACG System Setup Welcome to act Johns Hopkins ACG System Welcome to the Johns Hopkins ACG System Before you can use this application you must accept the License Agreement on the Following screen If you have any questions concerning your rights and or your ability to use this application please consult your contract Cancel The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 11 Figure 10 License Agreement Johns Hopkins ACG System Setup License Agreement Please read the following License Agreement carefully End User Acknowledgement Lawful use of this software is contingent on full agreement to the terms of an executed and current license agreement JHU MAKES NO REPRESENTATION OR WARRANTIES WITH RESPECT TO THE PERFORMANCE OF THIS SOFTWARE INCLUDING WITHOUT LIMITATION ALL WARRANTIES EXPRESS OR IMPLIED OF MERCHANTABILITY DEMONSTRATION AND FITNESS FOR ANY PARTICULAR PURPOSE Any use not authorized within the license agreementis prohibited including by way of illustration and not by way of limitation making copies of the Johns Hopkins ACG System for resale or reverse engineerin
51. e 9 Ignore all information about delivery status e Other Value Patient did not deliver a baby during the observation period pcp_group name A code to control the low birth weight related grouping logic e 9 or Blank Ignore all information about low birth weight e Patient was born with a low birth weight low_birthweight e Other Value Patient was not born with Number a low birth weight Note The ACG grouping logic cannot determine low birth weight information via diagnosis codes So this is the only way to know that a patient was delivered with a low birth weight Miama coat The total pharmacy cost for this patient Number 10250 00 during the observation period The total cost pharmacy plus medical total_cost for this patient during the observation Number 125000 00 period Technical User Guide The Johns Hopkins ACG System Version 8 2 5 76 Installing and Using ACG Software Diagnosis Data File Format The default diagnosis data file format is a tab delimited or comma delimited optionally quote enclosed text file with the following columns in order This format is directly supported by Microsoft Excel and Microsoft Access and a variety of other tools This file should contain all diagnosis codes that were experienced for each patient during the observation period There can be zero 1 or more rows per Patient ID The patient_id icd_version_1 and the icd_cd_1 columns are required You can optiona
52. for commonly used outcome indicators such as re hospitalizations and even mortality Table 8 shows how outcomes can vary dramatically between groups characterized as low or high risk based upon Dx PM risk score The Johns Hopkins ACG System Version 8 2 Technical User Guide Selecting the Right Tool 3 17 Table 8 Percentage of Patients with Selected Outcomes by ACG PM Risk Group 4 0 3 5 3 0 2 5 2 0 1 5 1 0 F S N N g 0 5 0 0 gt S S gt A S N S MES 2 e 5 g g 2a a a gt o 2 28m 6 8 g e 5 2 S8 O 4 FB S eo 2e e4 2m 2 glg 8 8 oe eee E o Ble S oS ESE Taj Aja 8 85 2a S fje g ZlS o gla es 8 58 jS 9 3 3 5 aS g S 3 8 wit ole 8 fg T e o lej e gt omy eo Ta 2 Sh e lt le o8 oxe xiz Z aw aw Secondary Care Primary Care Healthy Patients Population Mean W Very High Risk Patients FROM PILOTING AND EVALUATING CASE MIX AND PREDICTIVE MODELLING MEASURES WITHIN THE BRITISH PRIMARY CARE SECTOR FEB 2007 Technical User Guide The Johns Hopkins ACG System Version 8 2 3 18 Selecting the Right Tool Care Management and Predictive Modeling Providing Information for Disease and Care Managers As discussed previously concurrent ACG RUB morbidity information can be combined with EDCs to control for morbidity differences across a given disease specific group of interest e
53. for this patient for the year following the observation period Based upon a reference database with a mean of 1 0 the predicted value is expressed as a relative weight Population or sub group analyses provide comparisons to reference populations as defined by the selected Risk Assessment Variables ACG Predictive Model PRI Score for Pharmacy Costs The estimated pharmacy costs for this patient for the year following the observation period Based upon a reference database with a mean of 1 0 the predicted value is expressed as a relative weight Population or sub group analyses provide comparisons to reference populations as defined by the selected Risk Assessment Variables Useful in drawing external comparisons between your population morbidity burden and that of the reference database Generally scores greater than 1 0 indicated the case mix or predicted risk of your population is sicker than the reference population while scores less than 1 0 indicate they are healthier 4 Tip Remember that the ACG predictive model selection is determined by a combination of user specified options e g selection of reference data as specified by the Risk Assessment Variables option and the inclusion exclusion of prior cost and available input files e g diagnostic and or pharmacy See the Summary Statistics or Build Options Tab s for clarification on which model and set of reference weights was implemented by the software eg
54. future review and or analysis The following three report tabs will be on the desktop The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 53 Summary Statistics Tab The first tab presented is Summary Statistics This information should be used to validate the number of input records data warnings and percentage of non grouped diagnosis and pharmacy codes Percentages of non grouped codes above 1 for diagnoses and above 10 for pharmacy codes warrant further investigation Technical User Guide The Johns Hopkins ACG System Version 8 2 5 54 Installing and Using ACG Software Figure 26 Summary Statistics x Johns Hopkins ACG System 8 2 aA File Edt View Analyze Tools Help Bo x t8 BV fi 82Sample acad ACG Data File 82Sample acad Summary Statistics Patient Sample Local Weights ApelGendet Dist Probability Dist Build Options Description T Value Patients processed 90054 Patients processed 65 years and older 6277 Diagnoses processed 486621 Unique diagnoses encountered Unique unknown diagnoses encountered Unique unknown diagnosis code sets encountered Patients with unsupported diagnosis code sets encountered Pharmacy codes processed Unique pharmacy codes encountered Unique unknown pharmacy codes encountered Percentage of pharmacy codes that were unknown Unknown pharmacy codes encountered Patients with unknown pharmacy codes encountered Unique matched p
55. help identify persons who potentially would be well served by special attention from the organization s care management infrastructure This high risk case identification process could be used to target a person for interventions such as a referral to a case manager special communication with the patient s physician structured disease management programs or educational outreach There are several benefits to this approach to case selection e The various clinical categories and markers from the system provide a comprehensive patient profile that can improve the productivity of the screener e A rapid assessment can be performed on the whole population not just those being referred through other programs e Predictive modeling helps to identify a unique population of members at risk By identifying members that are complex and co morbid but not necessarily currently high cost you identify a population that is more open to care management services and therefore higher case open rates are seen using ACG predictive models as a referral tool This is a productivity improvement for the care management staff as well Approximately 25 of the members correctly identified as high risk by an ACG predictive model were not previously high cost This percentage seems to hold regardless of the model Dx PM Rx PM or DxRx PM When using Rx PM this percentage holds true with as little as 1 month of data Figure 1 illustrates two pie chart
56. helps to illustrate that not all individuals taking a certain type of medication may be in need of intervention or case management rather it is individuals in the far right of the table those individuals exhibiting a specific condition AND multiple co occurring conditions who are most likely to need high levels of health care services This analysis has the option to report the estimated concurrent resource use in terms of local weights or national weights Note The percent distributions are calculated across each row stratification It is not likely but possible for a row to have a total of less than 100 because RUB 0 is not included in the output The report layout is as follows Table 7 Rx MG by RUB Distribution Analysis Report Layout Rx MG Cd Each Rx MG code that was assigned to at least one patient with a RUB gt 0 Rx MG Description The description for Rx MG Cd Total Cases The number of patients that are assigned the related Rx MG Cd The mean of the national rescaled or local concurrent weight based upon which weight type was selected in Report Options for all patients in this stratification across all RUBs The percentage of patient assignments to this stratification in this RUB is out 0 RUE ho of the total patients in this RUB Est Concurrent Resource Use RUB 1 Est The mean of the national rescaled or local concurrent weight based upon Concurrent Resource which weight type was selected in Report Options for all
57. interested in ambulatory provider productivity use the ACG System to case mix adjust profiles of provider patient contacts Users should realize the potential difficulties associated with trying to define ambulatory encounters Physician visits are relatively straightforward mechanisms for estimating face to face encounters however tabulating ancillary and surgical services into encounters is problematic This issue is a focus of much ongoing research and few workable solutions currently exist However in the context of provider profiling it is probably sufficient for analysts to estimate ambulatory encounters in exactly the same way for each group to be compared Using this approach even if the estimate of an ambulatory encounter is biased valid ACG adjusted comparisons can still be performed The notion of using compatible techniques for estimating ambulatory encounters is especially important when the comparison involves two different types of service delivery environments such as The Johns Hopkins ACG System Version 8 2 Technical User Guide Basic Data Requirements 4 13 comparing a fully capitated at risk independent practice association IPA and a staff model HMO operating under a negotiated global budget Risk Assessment Variables One way that the user can affect the output from the ACG System is with the selection of Risk Assessment Variables Risk Assessment Variables are inputs to the system provided by Johns Hopkins which cont
58. is expressed in absolute dollars in the column labeled over under 000 Section A of Table 4 illustrates a shift of expected dollars from part year enrollees to 12 month enrollees The net result of this for profiling applications is that subpopulations that include a disproportionate number of shorter term enrollees will look inefficient because the associated expected dollars calculated on a PMPM basis will tend to be lower than their actual costs Conversely a population comprised exclusively of 12 month enrollees will be overpaid and appear to be efficient because of the shift of expected dollars embedded in the PMPM calculation Technical User Guide The Johns Hopkins ACG System Version 8 2 Table 4 Comparison of Actual and ACG Expected Costs Months of Member Enrollment PMPM versus PMPY Weight Calculation Approaches Sie A Using A PMPM Calculation B Using a PMPY Calculation Months Over Under Adjusted Over Under Adjusted Enrolled Months Deviation R squared Deviation 000 R squared pS IT sef af oy pS eaf ew ozs oof of O86 Pp o OT ye ee 8l 849 432 3 943 0 380 0 2 134 0 385 957 127 0 0 OD 0 338 0 0 0 395 e Costs include total paid claims truncated at 35 000 e The population was limited to service users in a large commercial HMO population for 1996 e Total absolute error was 8 3 million using a PMPM calculation and 677 000 PMPY calculation See text for a description of these calculations
59. listing of all ADG codes assigned to this patient separated by spaces A vector of zeros and ones to indicate which ADG codes this patient was assigned A 1 in the fifth position indicates the patient was assigned ADG 5 Note ADG15 and ADG19 are no longer in use and thus should always be Zero Expanded Diagnosis Clusters All of the EDC codes assigned to this patient separated by spaces The EDC taxonomy identifies patients with specific diseases or symptoms that are treated in ambulatory and inpatient settings Major Expanded Diagnoses Clusters All of the MEDC codes assigned to this patient separated by spaces The EDC taxonomy is structured into broad clinical categories called MEDCs The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 87 Rx MG Codes Pharmacy Morbidity Group Codes all of the Rx MG codes assigned to this patient separated by spaces Major Rx MG Codes Major ADG Count Frailty Flag Hospital Dominant Count Chronic Condition Count Congestive Heart Failure Chronic Renal Failure Technical User Guide Major Pharmacy Morbidity Group Codes all of the Major Rx MG codes assigned to this patient separated by spaces The number of major ADGs assigned to this patient A major ADG is an ADG found to have a significant impact on concurrent or future resource consumption There are separate major ADGs for pediatric and adult populations
60. minimal contribution to future cost Technical User Guide The Johns Hopkins ACG System Version 8 2 5 44 Installing and Using ACG Software Patient List Analysis The patient list analysis generates all of the output of the system as a single row per patient This is very similar to the information that is presented in the patient sample but the user may apply filters prior to exporting the data Table 18 Patient List Analysis Report Layout A banded indicator of historic pharmacy costs based upon pharmacy cost percentiles Possible values include 0 0 pharmacy costs 1 1 10 percentile 2 11 25 percentile Pharmacy Cost Band 3 26 50 percentile 4 51 75 percentile 5 76 90 percentile 6 91 93 percentile 7 94 95 percentile 8 96 97 percentile 9 98 99 percentile A banded indicator of historic total costs based upon total cost percentiles Possible values include 0 0 total costs 1 1 10 percentile 2 11 25 percentile 3 26 50 percentile Total Cost Band 4 51 75 percentile 5 76 90 percentile 6 91 93 percentile 7 94 95 percentile 8 96 97 percentile 9 98 99 percentile A banded indicator of patient age Possible values Age Band include e lt 0 The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software Column Name Unknown Adjusted Clinical Groups the ACG code assigned to this patient ACGs assign persons
61. name associated with General Motors employer _group name Text employer_group_id Inc The patient s benefit plan This is typically used by a health plan to identify beni plan a benefit package or group of benefit Tos HMO Ete packages The health system that this patient is assigned to This is typically used by a health_system health plan to identify a risk sharing Text Pigaan T 3 MidWest arrangement or the hospital system in which the patient s PCP belongs A code to identify the patient s Primary P24050 Care Practitioner ear name The readable name associated with Text Dr John Doe tie pep_id MD A code to identify the group or financial pcp_group_id company for the patient s primary care Text V9604 practitioner The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 75 Column Description Example SignaMed MidWest Family Practice A readable name associated with pep_group_id A code to control the ACG pregnancy related grouping logic e 0 or Blank Determine pregnancy based upon the patient s diagnoses pregnant e Patient was pregnant during the Number observation period e Other Value Patient was not pregnant during the observation period A code to control the ACG delivery related grouping logic e 0 or Blank Determine delivery based upon the patient s diagnosis deliv r e Patient delivered a baby during the Niribei 1 observation period
62. patients in this Use stratification in this RUB The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 27 RUB 2 Dist RUB 2 Est Concurrent Resource Use RUB 3 Dist RUB 3 Est Concurrent Resource Use RUB 4 Dist RUB 4 Est Concurrent Resource Use RUB 5 Dist RUB 5 Est Concurrent Resource Use Technical User Guide The percentage of patient assignments to this stratification in this RUB is out of the total patients in this RUB The mean of the national rescaled or local concurrent weight based upon which weight type was selected in Report Options for all patients in this stratification in this RUB The percentage of patient assignments to this stratification in this RUB is out of the total patients in this RUB The mean of the national rescaled or local concurrent weight based upon which weight type was selected in Report Options for all patients in this stratification in this RUB The percentage of patient assignments to this stratification in this RUB is out of the total patients in this RUB The mean of the national rescaled or local concurrent weight based upon which weight type was selected in Report Options for all patients in this stratification in this RUB The percentage of patient assignments to this stratification in this RUB is out of the total patients in this RUB The mean of the national rescaled or local concurrent weight based
63. presence of the condition NP condition not present Low Back Pain BTH condition identified by both diagnosis and NDC code RX condition identified by NDC code ICD condition identified by diagnosis code ACG PM Predicted Resource Index PRI for Total Cost The estimated total costs including pharmacy costs for this patient for the year following the observation period Based upon a national reference database with a mean of 1 0 the predicted value is expressed as a relative weight Population or sub group analyses provide comparisons to national norms Rescaled Total Cost The Total Cost Resource Index rescaled so that the local population mean is Resource Index 1 0 Sub group analyses provide comparisons to local norms Unscaled Total Cost Resource Index ACG Predictive Probability Score for total cost The probability that this patient will have high total costs including pharmacy costs in the year following the observation period ACG Predictive Model PRI Score for Pharmacy Costs The estimated pharmacy costs for this patient for the year following the observation period Based upon a national reference database with a mean of 1 0 the predicted value is expressed as a relative weight Population or sub group analyses provide comparisons to national norms Rescaled Pharmacy The Pharmacy Cost Resource Index rescaled so that the overall population Cost Resource Index mean is 1 0 Sub group analyses provide comp
64. productivity 3 15 comparison of observed to expected visits and calculation of three profiling ratios 3 14 comparison of PMPM and PMPY average costs by months enrolled within an HMO population 7 10 cost predictions by Rx MGs analysis report layout 5 35 cost predictions by select conditions analysis 5 33 diagnosis data file format 5 76 EDC by RUB distribution analysis report layout 5 25 effect of enrollment period on selected ACG specific weights 7 14 estimated concurrent resource use by RUB by MEDC samples 3 19 estimating costs in a sample of cases 7 8 MEDC by RUB distribution analysis report layout 5 24 movers analysis tracking morbidity burden over time 3 12 number of cases and the Johns Hopkins ACG Dx PM predicted relative resource use by risk probability thresholds for selected chronic conditions 3 25 observed to expected standardized morbidity ratio SMR by major EDC MEDC 3 8 observed to expected standardized morbidity ratio SMR by Rx morbidity group 3 10 patient clinical profile report 5 41 patient file format 5 74 patient list analysis report 5 44 percentage distribution of each co morbidity level within an EDC samples 3 18 The Johns Hopkins ACG System Version 8 2 ACG PM risk group 3 17 pharmacy data file format 5 77 population distribution by age band and morbidity report layout 5 21 predictive ratios by quintile for the Johns Hopkins ACG Dx PM applied to commercial and M
65. productivity Physicians may be under pressure to reduce the duration of visits in order to increase the number of daily visits performed This can be counter productive when the physician s panel is more complex Communication with the patient about primary and secondary prevention medication adherence and treatment decisions are key to the successful management of a patient with multiple co morbid conditions Time and discussion with the patient is needed to identify a patient s psychosocial problems or a lack of support at home Additional time with a patient can also improve patient satisfaction and may even reduce utilization of laboratory tests consultations and medications Case mix adjustment is key to understanding the differences in physician productivity Table 7 Comparison of Characteristics Affecting Physician Productivity Fr Tir patients with gt 2 major ADGs patients with psycho social condition 11 5 21 7 patients with frailty condition Technical User Guide The Johns Hopkins ACG System Version 8 2 3 16 Selecting the Right Tool Quality of Care Assessment Case mix adjustment is relevant in population based assessments of provider clinical performance where there is a plausible basis for results to vary among patients with different levels of morbidity burden Many long standing performance assessment programs such as those promulgated by the National Committee on Quality Assurance and the Joint Commissio
66. regardless of the number of non matched codes encountered Technical User Guide The Johns Hopkins ACG System Version 8 2 5 84 Installing and Using ACG Software Figure 45 Final Step Load Your Own Data x Johns Hopkins ACG System 8 2 Eile Confirm the following choices then press FINISH Choices Creating New ACG Data File By Importing Patient Data From C acqdata My_patient_file csv using comma delimited Format Diagnosis Data From C acgdata My _diagnosis_file tab using tab delimited Format Pharmacy Data From C acadata My_pharmacy_file tab using tab delimited Format Using Risk Assement Variables US Non Elderly Create ACG File 82S5ample You will be given one last opportunity to confirm your file selections before the ACG assignment process begins Click Finish to begin processing files The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 85 Additional Sources of Information It is hoped that this chapter combined with the chapter entitled Basic Data Requirements and the built in and searchable help function of the ACGs for Windows software will be enough to get most users up and running at least with the mechanics of most ACG based analyses However we encourage you to use the other important chapters of this detailed Technical User Guide and Reference Manual for a complete understanding of the implementation of the ACG System
67. s output 6 1 basic review process 6 1 conclusion 6 14 introduction 6 1 Basic data requirements 4 1 analysis time frame 4 7 coding issues using National Drug Codes NDC 4 9 coding issues using the International Classification of Diseases ICD 4 4 constructing resource consumption measures 4 12 data items usually required for ACG analysis in a managed care context 4 3 delivery status 4 11 identifying special populations with augmented data inputs 4 10 low birth weight 4 11 pregnancy status 4 10 procedure code ranges to exclude 4 8 The Johns Hopkins ACG System Version 8 2 IN 2 Index rule out suspected and provisional diagnoses 4 5 selecting relevant diagnosis for input into the software 4 7 summary review 4 14 using ICD 9 and ICD 10 simultaneously 4 6 Basic review process assessing the ACG grouper s output 6 1 Batch mode processing 5 89 C Capitation and rate setting 3 31 Care management and predictive modeling providing information for disease and care managers 3 18 Care management list 5 39 Central processing unit CPU 5 1 Changes to installation release notes 2 8 Changes to the output format release notes 2 10 Code sets release notes 2 1 Coding issues International Classification of Diseases ICD 4 4 National Drug Codes NDC 4 9 Command line usage 5 89 Common input file problems 6 11 Components of the ACG toolkit 3 2 Concurrent ACG weights 7 5 Concurrent ver
68. search in the Resource Center at www acg jhsph edu for a pdf of our chapter Technical User Guide The Johns Hopkins ACG System Version 8 2 3 38 Selecting the Right Tool This page was left blank intentionally The Johns Hopkins ACG System Version 8 2 Technical User Guide Basic Data Requirements 4 1 4 Basic Data Requirements Over VIEW oanien E E vaakssppausadans vanesannease 4 1 Data Items Usually Required for ACG Analysis in a Managed Care CI ones irs acaivanasensniasicecenasasend EEEE a E OEE 4 3 Coding Issues Using the International Classification of Diseases ICD sossccscscsadaessssnccscssicddecsscncesessdeddneseeacesess tedden sp andesssoecddeasounddassnoecdee 4 4 Diagnosis Codes with Three and Four Digits 00 0 0 cccceeeeeceeeseeeteeeeeeees 4 5 Rule Out Suspected and Provisional Diagnoses c ccesceeeseeseeeeteeees 4 5 Special Note for ICD 10 USC cacicacecancecysecasusnaciasescsesgcnaccansnrgeivocauteuanwes 4 6 Using ICD 9 and ICD 10 Simultaneously cs ccacssesssces vesecacsenseeaneretarsoroeene 4 6 Selecting Relevant Diagnoses for Input to the ACG Software 0 4 7 Analysis Time Frame espe enei E i Eia 4 7 Excluding Lab and X Ray Clais sis cvonsssasedscnssiason usseuscdiceadauiensan Gasisaats 4 7 Table 1 Typical Place of Service Codes to Exclude and Procedure Code Ranges to Excl d iisescsioscastsesncaseranicssariseaissdiaiaseranisisaniaate 4 8 Coding Issues Using National Drug Codes NDC
69. term eligibility e g one to three months of enrollment reaffirms that time has some effect on the calculation of diagnosis specific expected values To examine the nature of this effect in more detail within this case study population Table 5 presents the average costs per person and the number of persons by three month enrollment windows for selected ACGs Some ACGs have relatively low mean costs given shorter term enrollment as opposed to costs for all cases during the full period a year At the same time many ACGs are quite stable regardless of time enrolled particularly for persons enrolled more than three months The highest morbidity highest cost ACGs e g ACGs 4940 5070 tend to be uncommon for those enrolled for the shortest periods but nonetheless are fairly consistent in terms of average costs per period across the enrollment windows even given the small numbers of cases for shorter periods of time Generally much of the variability in average costs probably can be attributed to the very small sample size in the shorter enrollment columns Again while enrollment time has an influence on costs associated with some ACGs the general consistency of costs across the columns in Table 5 and the relatively limited number of persons with less than 12 months enrollment tend to limit the overall plan wide effect of time on risk adjusted concurrent analyses However analyses where some sub cohorts include a disproportionate number of short t
70. the pharmacy based predictive model Rx PM Also included is a discussion of how therapeutic classes are assigned to morbidity groups as well as how these groupings get incorporated into the model Additionally the combination model the DxRx PM is presented An appendix is provided for those wishing to locally calibrate Chapter 7 Predictive Modeling Statistical Performance This chapter demonstrates the ACG predictive models statistical performance while describing the various ways in which they can be applied in health care applications Chapter 8 Provider Performance Assessment This chapter outlines the basic steps to taking a population based approach to practitioner profiling Appendix A ACG Publication List Appendix B Sample Listing of Common ICD 9 CM Diagnosis Codes Assigned to ADG Cluster Technical User Guide The Johns Hopkins ACG System Version 8 2 1 4 Getting Started e Appendix C Variables Necessary to Locally Calibrate the ACG Predictive Models e Index Customer Commitment and Contact Information As part of our ongoing commitment to furthering the international state of the art of risk adjustment methodology and supporting users of the ACG System worldwide we will continue to perform evaluation research and development We will look forward to sharing the results of this work with our user base via white papers our web site peer reviewed articles and in person presentations After you have carefully revi
71. the term weight is used to represent a relative value for resource use with respect to some population average and is generally expressed as a numeric value with a mean of 1 0 i e where the resource use is the same as that of the reference population Relative weights can be applied to mean resource use for a population to arrive at expected resource use Weights can be generated concurrently i e for the current period or prospectively Software Produced Weights and Their Uses Table 1 provides a summary of the risk weights and scores produced by the software and briefly summarizes their potential application The remainder of this chapter discusses custom or local calibration of weights Table 1 begins on the next page Technical User Guide The Johns Hopkins ACG System Version 8 2 7 2 Making Effective Use of Risk Scores Table 1 Risk Weights and Scores Unadjusted Weights Reference Unscaled Weight Unscaled Total Cost Resource Index Unscaled Pharmacy Cost Resource Index An estimate of concurrent resource use associated with a given ACG based on a reference database and expressed as a relative value Each patient is assigned a weight based on his or her ACG Separate weights for non elderly and elderly eligible populations will be applied depending on the Risk Assessment Variable selected by the user ACG PM Predicted Resource Index PRI for Total Cost The estimated total costs including pharmacy costs
72. to Recur Age 1 Acute Minor and Likely to Recur Age 2 to 5 The Johns Hopkins ACG System Version 8 2 Technical User Guide Making Effective Use of Risk Scores 7 19 Relative ACG ACG Label Weight 2100 0 31 2200 0 36 2300 0 32 2400 0 25 2500 Acute Minor and Psychosocial w o Psych Unstable 0 49 Acute Minor and Psychosocial with Psych Unstable w o Psych 2600 Stable 1 025 Acute Minor and Psychosocial with Psych Unstable and Psych 2700 Stable 2 696 2800 0 658 2900 1 334 3000 0 795 3100 Acute Minor Acute Major Likely to Recur Age 6 to 11 0 686 Acute Minor Acute Major Likely to Recur Age gt 11 w o 3200 Allergy 0 963 Acute Minor Acute Major Likely to Recur Age gt 11 with 3300 Allergy 0 914 3400 Acute Minor Likely to Recur Eye amp Dental 0 468 3500 0 819 3600 L719 3700 1 835 3800 0 590 3900 0 655 4000 0 545 4100 0 665 4210 0 810 4220 1 676 4310 0 839 4320 1 581 4330 2 949 4410 4 5 Other ADG Combinations Age gt 44 no Major ADGs 0 96 4420 4 5 Other ADG Combinations Age gt 44 1 Major ADGs 4430 3 490 4510 1 603 4520 3 618 4610 1 499 4620 3 686 6 9 Other ADG Combinations Males Age 18 to 34 no Major 4710 ADGs 1 412 6 9 Other ADG Combinations Males Age 18 to 34 1 Major 4720 ADGs 2 487 6 9 Other ADG Combinations Males Age 18 to 34 2 Major 4730 ADGs 5 959 Ta oN lon me Re ajoj it Technical User Guide The Johns Hopkins ACG System Version 8 2 Relat
73. traditional Windows like pull down menus A brief overview of the functionality of the Windows taskbar follows File Menu The File menu is for opening saving ACG data files These are files created by the ACG for Windows software and are appended with the acgd extension These files are working databases containing summary information on each member processed through the software Note It is not necessary to re run your claims data each time you open the software rather ACG assignments can be stored in the acgd file for later use The software can utilize multiple acgd files simultaneously and or filters can be applied to the core database to create multiple acgd files to facilitate multi level analyses For your convenience the last five files opened will be shown from the File menu Edit Menu The Edit menu contains useful functions such as Sort and Find 4 Tip Sorting can be accomplished in three ways 1 use the sort item under the edit a menu 2 use the lz button on the menu bar or 3 click the column heading on the ACG desktop click once for ascending and twice for descending order View Menu The View menu allows switching between ACG data files more than one data file can be open at a time as well as switching between reports within one particular data file of interest The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software Analyze Menu The Analyze menu p
74. valid models may cause substantial processing delays This is a data intensive activity producing multiple scores for each individual The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 83 After filling in all the filenames patient data file file formats location of diagnosis and pharmacy data files and specifying your predictive modeling options press Next A pop up menu provides filter options to control the selection of patients from the active data file to be included in the analysis a screen shot and discussion of this functionality was presented previously in the section Report Options After implementing any filters press Next Figure 44 Step 3 Load Your Own Data x Johns Hopkins ACG System 8 2 Select a new filename to save the ACG Data ACG File ACG File Name s25ample C Stop building after too many non matched codes encountered Max Non matches Back lt Next gt Cancel As shown in Figure 44 you must type the name and location of the files to which the ACG database will be saved If you are uncertain as to the quality or source of diagnosis or pharmacy codes you can enforce a maximum number of unmatched codes When checked if the ACG System encounters non matched codes either diagnosis or pharmacy in excess of the typed threshold the application will stop processing with an error message By default the application will process all records
75. 0519 PCP20519 2051 PCP_GRP2051 TUYXTZQQRYQRQZSYTY 15M COMMERCIAL_B COMPANY_A PPO 2051 GROUP2051 POS_B H501 20519 PCP20519 2051 PCP_GRP2051 TUYXTZQQRYQSQYYYWY 25M COMMERCIAL_B COMPANY_A PPO 2052 GROUP2052 POS_B H501 20519 PCP20519 2051 PCP_GRP2051 TUYXTZQQRYQTXZVZVZ 42F COMMERCIAL_B COMPANY_A PPO 2054 GROUP2054 POS_B H501 20519 PCP20519 2051 PCP_GRP2051 TUZZYTRYUYQQTZSYXZ 8F COMMERCIAL_D COMPANY_C PPO 2448 GROUP2448 POS_B HS02 24424 PCP24424 2442 PCP_GRP2442 TYSXTVSVVYQSVYRRYY 30M COMMERCIAL_D COMPANY_4 PPO 1413 GROUP1413 POS_B H503 14159 PCP14159 1415 PCP_GRP1415 TYSXTVVQSYQRWZRYYZ 21F COMMERCIAL_D COMPANY_4 PPO 1552 GROUP1552 POS_B H504 15582 PCP15582 1558 PCP_GRP1558 TYSXTYVQSYQSSZQYUY 27M COMMERCIAL_D COMPANY_ amp PPO 1552 GROUP1552 POS_B H504 15582 PCP15582 1558 PCP_GRP1558 TYTWRQUXSYQRZYZZQZ2 24F COMMERCIAL_D COMPANY_A PPO 2482 GROUP2482 POS_B H503 24865 PCP24865 2486 PCP_GRP2486 TYTWRQUXSYQSTZRYXY 28M COMMERCIAL_D COMPANY_A PPO 2482 GROUP2482 POS_B H503 24865 PCP24865 2486 PCP_GRP2486 TYUWVSVUWYQSUZXZTY 30M COMMERCIAL_C COMPANY_B PPO 0123 GROUPO123 POS_B HS04 01261 PCPO1261 0126 PCP_GRPO126 TYUZQSTSTYQSZZTXZY 34M COMMERCIAL_B COMPANY_4 PPO 2053 GROUP2053 POS_B HSO1 20549 PCP20549 2054 PCP_GRP2054 TYYTYSXWTYQQQZYXSY 6M COMMERCIAL_D COMPANY_B PPO 1546 GROUP 1546 POS_A HS02 15451 PCP15451 1545 PCP_GRP1545 TVWYXZTZWYQQVZSYYY 10M COMMERCIAL_D COMPANY_C PPO 1591 GROUP1591 POS_B HSO1 15969 PCP15969 1596 PCP_GRP1596 TYXTXYYZWYQSYZSYQZ 33 F COMMERCIAL_D COM
76. 10009 1 04 0 94 0 99 1 07 2 21 159 i 17 23 10010 1 06 1 19 2 24 212 el 21 21 20011 E 5 i H 14 24 20012 20013 20014 4 Technical Enhancements Changes to Installation The installation package was changed In Windows environments the installation now affirms The Johns Hopkins University as the publisher using a digital signature If your installation does not indicate The Johns Hopkins University as the publisher please contact your distributor reference Figure 8 Figure 8 The Johns Hopkins University Digital Signature Open File Security Warning Do you want to run this file Hl Name JHUACGSetup4 Win 8 2 20081014 exe Publisher The Johns Hopkins University Type Application From C Documents and Settings asalls My Documents Always ask before opening this file potentially harm your computer Only run software from publishers While files from the Internet can be useful this file type can you trust What s the risk The Johns Hopkins ACG System Version 8 2 Release Notes Release Notes 2 9 For Unix users the installation no longer includes the Java Runtime Your Unix administrator will need to install Java Runtime 1 6 or greater and have it accessible in the path for the ACG System to run correctly The benefit of separating Java from the installation allows the Unix system administrator greater control over the J
77. 2 Technical User Guide Selecting the Right Tool 3 1 Introduction Targeted for both new and current users this chapter offers a quick overview of the myriad ACG System applications and suggests how the various components of the System s toolkit can be combined to maximize their usefulness to you This section also attempts to summarize some material that is presented elsewhere in our documentation Where possible links to more detailed discussion are noted One System Many Tools Many Solutions The ACG System s suite of tools has been used to support basic and complex applications in finance administration care delivery and evaluative research for over a decade These applications have been both real time concurrent and forward looking prospective They may involve simple spreadsheet calculations or complex multi variable statistical models No other risk adjustment methodology has been used for so many purposes in so many places while at the same time showing such high levels of quantitative and qualitative success The flexibility offered by the ACG System demonstrates that we recognize that one size does not fit all This also means that a bit of custom tailoring may be needed to get the best fit within your organization The following list provides potential uses and applications of The Johns Hopkins ACG System e Performance profiling of providers and assessing provider efficiency e Rate setting capitation payment and ac
78. 3 Combining Rx and Dx Predictive Modeling Scores for Targeted Interventi i sssusa EE EI 3 24 Table 12 Number of Cases and The Johns Hopkins ACG Dx PM Predicted Relative Resource Use by Risk Probability Thresholds for Selected Chronic Conditions sessssssssssrorsiinonsrceierssses eirese 3 25 The ACG Predictive Model s Probability Score cecccecsseeeteeeseeeees 3 26 Table 13 Cate Management Listing 2cc uvsccrnsasataretieswnmciemaewces 3 27 Managing Pharmacy Wisi iss siscsicccecsicccscscacaviesniacsarsanmassotensiaoensavincnantaniens 3 30 Medication Therapy Management Program MTMP Candidate EEE E a EAE EE EEE AAE N 3 30 Capitation and Rate Setting scscccossesssasscesssessssssveseseosessoessvesseassonensesennsones 3 31 ACGs in Mu ltivatiate Models sissesrssssssssnessien nnns 3 32 Predictive Model Predicted Resource Index the PM PRI Score 3 32 Table 14 Predictive Ratios by Quintile for The Johns Hopkins ACG Dx PM Applied to Commercial and Medicare Populations 3 33 Dy Lesa 1b ee eee ne enone Rett nee eter ene pete ule reyenntr oer tardees sewer fee erpret ese ee 3 33 Table 15 Actuarial Cost Projections sisescaveoseateovsionssansesesceeeseraceniossss 3 35 Concurrent versus Prospective Applications e sseesscsescocesscessecesocesooesso 3 36 Additional Informati n osincisesssisssosssossosssesessseassvasensssessvensessensssossnessesesonessens 3 37 The Johns Hopkins ACG System Version 8
79. 50 2200 Acute Minor and Likely to Recur Age gt 5 with Alleray 170 236 315 10 0 64 b Technical User Guide The Johns Hopkins ACG System Version 8 2 5 58 Figure 29 Age Gender Distribution Tab Installing and Using ACG Software The Age Gender Distribution displays the percent distribution of members in the population by age and gender The age bands are calculated by the system and are used as input to the predictive model and as the basis for age sex adjusting the standardized morbidity ratio analyses This tab provides an opportunity to review the distribution and to ensure that the age field was input into the system correctly xs Johns Hopkins ACG System 8 2 File Edit View Analyze Tools a Mx 8 itv A 82SAMPLE acad ACG Data File 825AMPLE acgd p Summary Statistics Patient Sample Local Weights Age Gender Dist Age Band Males Male Females Female Total Total 00 04 05 11 12 17 18 34 35 44 45 54 55 64 65 69 70 74 75 79 80 84 85 All Ages 596 3 01 4 44 4 62 16 43 8 06 8 85 6 21 0 41 0 07 0 03 0 01 0 01 10 314 52 14 609 839 845 2 424 1 387 1 619 1 615 3 08 4 24 4 27 12 25 7 01 8 18 8 16 0 56 0 04 0 02 0 03 0 02 1 205 1 717 1 758 5 674 2 982 3 369 2 844 191 22 9 6 6 47 86 19 783 6 09 8 68 8 89 28 68 15 07 17 03 14 38 0 97 0 11 0 05 0 03 0 03 100 00 gt The Johns Hopkins
80. 86 1074 211 37 6 41 0 8 1 Prior Period Case Mix Moderate 1 02 Morbidity P 2 116 P 2 123 P 3 599 C 2 549 C 1 844 C 9 507 130 94 124 High 5 0 3 6 4 7 Morbidity P 11 060 P 10 035 P 11 577 C 6 539 C 2 554 C 9 947 The Johns Hopkins ACG System Version 8 2 Technical User Guide Selecting the Right Tool 3 13 Provider Performance Assessment Profiles such as those summarized below are a useful tool for evaluating performance and allocating resources for a wide range of ACG users The most common profiling activities include e Financial exchange between organizations and providers e Provider efficiency assessment e Resource planning e Access to care evaluation e Fraud waste and abuse detection e Quality of care assessment Profiling Resource Use One of the most popular uses of the ACG System Software is to set risk adjusted resource consumption norms for subgroups of patients members within an organization These norms are compared to actual resource use in order to profile provider efficiency and to develop performance reports to help suggest where over use and under use may be a problem Profiling applications are very amenable to simple actuarial cell strategies for risk adjustment Most users apply the ACG mutually exclusive cells for this purpose while others have chosen to combine ACGs and use RUBs for these applications The simpler RUB method is sometimes selected when
81. A flag for any one of 11 diagnostic clusters that represent discrete conditions consistent with frailty e g malnutrition dementia incontinence difficulty in walking A count of ADGs containing a trigger diagnoses indicating a high probability typically greater than 50 percent of future admission A count of EDCs containing trigger diagnoses indicating a chronic condition with significant expected duration and resource requirements A flag indicating the presence of the condition NP condition not present BTH condition identified by both diagnosis and NDC code RX condition identified by NDC code ICD condition identified by diagnosis code A flag indicating the presence of the condition NP condition not present BTH condition identified by both diagnosis and NDC code RX condition identified by NDC code ICD condition identified by diagnosis code A flag indicating the presence of the condition NP condition not present BTH condition identified by both diagnosis and NDC code RX condition identified by NDC code ICD condition identified by diagnosis code A flag indicating the presence of the condition NP condition not present BTH condition identified by both diagnosis and NDC code RX condition identified by NDC code ICD condition identified by diagnosis code A flag indicating the presence of the condition NP condition not present BTH
82. ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 59 Figure 30 Probability Distribution Tab The probability distribution tab shows the percent distribution of the population across 4 ranges of probability scores In a typical population a very small percentage of patients will have probability scores greater than 0 40 This distribution gives the user a sense of the percentage of patients that would be reviewed when selecting each of these high risk cutpoints xq Johns Hopkins ACG System 8 1 File Edit View Analyze Tools Help omx r 1 9 fal 81sample acad ACG Data File 81sample acqd Summary Statistics Patient Sample Local Weights Age Gender Dist Probability Dist Build Options Total 0 02 0 05 0 02 0 03 0 03 0 03 0 04 0 11 0 56 191 0 97 596 3 08 1 205 6 09 878 4 24 1 717 8 68 913 4 27 1 758 8 89 1 595 7 01 2 982 15 07 1 229 8 16 2 844 14 38 1 750 8 18 3 369 17 03 3 250 12 25 5 674 28 68 10 314 47 86 19 783 100 00 Technical User Guide The Johns Hopkins ACG System Version 8 2 Installing and Using ACG Software Figure 31 Build Options Tab The build options tab stores information about the source files filters and parameters used to build the acgd file The parameters include x Johns Hopkins ACG System 8 2 File Edit view Analyze Tools Help NaoM x g Wo fim Ver82_Demo_No_Medicare acad ACG Data File Ver82_Demo_No_Medicare acad S Su
83. Although EDCs are useful for identifying individuals with specific high impact diseases it is important to note that they do not account for burden of co morbidity as do ACGs Therefore we do not generally recommend that EDCs be used as the only means of controlling for case mix in regression analysis However there is also a potential drawback since regression may introduce some assumptions and statistical pitfalls that can be troublesome without seasoned analytical support Their inherent complexity makes them difficult to calibrate to local cost patterns and regression models are also potentially easier to game because more factors can be manipulated Finally while it is possible to introduce a wide range of variables that improve the model s explanatory power this explanatory power is often confined to the data set and time period on which the model is based The model s results may end up differing significantly from year to year depending on the inter relations of the myriad risk factors that have been included a phenomenon referred to as over fitting Predictive Model Predicted Resource Index the PM PRI Score To address some of the analytic challenges inherent in regression based approaches the ACG Predictive Model provides a ready made solution and assigns a relative value that can be readily converted to dollars Termed the Predicted Resource Index or PRI for short this output is most relevant for prospective financial applica
84. Appendix A ACG Output Data The ACG import process imports patient demographic and utilization data from the patient import file all of the diagnoses that a patient has experienced over the observation period from the diagnosis import file and adds a number of calculated data elements These data elements form the basis for all analyses provided in the ACG System You can see each of these data elements in the Patient Sample section of the ACG Data File see Table 18 Table 18 Column Definitions for the ACG Output File A banded indicator of historic pharmacy costs based upon pharmacy cost percentiles Possible values include e 0 0 pharmacy costs e 1 10 percentile e 2 11 25 percentile Pharmacy Cost e 3 26 50 percentile Band e 4 51 75 percentile e 5 76 90 percentile e 6 91 93 percentile e 7 94 95 percentile e 8 96 97 percentile e 9 98 99 percentile A banded indicator of historic total costs based upon total cost percentiles Possible values include e 0 0 pharmacy costs e 1 10 percentile e 2 11 25 percentile e 3 26 50 percentile e 4 51 75 percentile e 5 76 90 percentile e 6 91 93 percentile e 7 94 95 percentile e 8 96 97 percentile e 9 98 99 percentile Total Cost Band Technical User Guide The Johns Hopkins ACG System Version 8 2 Installing and Using ACG Software Resource Utilization Band National Unscaled Weight Nat
85. Cost Sum of total cost within the current stratification Plan Average Sum of total cost total patient count for entire plan Note this is taken from the Total Cost complete data file ignoring any report specific filters are applied A ratio expressing the actual cost to plan average cost A value greater than 1 indicates the actual cost is greater than the plan average Calculated as Total Actual Cost Patient Count Plan Average Total Cost Actual To Plan Average Expected costs based upon the ACGs experienced within the current stratification Calculated as the sum of number of patients within each ACG within the current stratification x the plan wide average cost per ACG for all ACGs Note that the plan wide average cost per ACG is taken from the complete data file ignoring any report specific filters ACG Adjusted Expected Cost A ratio expressing the expected cost to the plan average cost A value greater than 1 indicates that the expected costs were higher than the actual costs Calculated as ACG Adjusted Expected Cost Patient Count Plan Average Total Cost Expected to Plan Average A ratio expressing the actual costs to the ACG expected costs A value greater than 1 indicates that the actual costs were higher than the expected costs Calculated as Total Actual Cost ACG Adjusted Expected Cost Actual to Expected Ratio Case Mix vs National Reference Data Calculated as the mean of National U
86. DGs assigned 47355 l Sea Number of Rx MGs assigned 37946 a Percentage of patients with total cost gt 100 and no diagnoses 3 0 Export File Percentage of patients with pharmacy cost gt 100 and no pharmacy codes 0 0 j Number of patients with diagnosis information and no pharmacy codes 0 Export File Name ai Number of patients with pharmacy codes and no diagnoses 0 Number of data warnings 0 Cx Cancel Number of patients with data warnings 0 l Minutes To load data 2 Total cost model selected DxRx PM total cost gt total cost Pharmacy cost model selected DxRx PM rx cost gt rx cost Date loaded 2008 10 20 LE C ith ACG versi 8 2 v All of the underlying ACG data elements that are used throughout the ACG System are exportable through this option When the Export ACG Data options are displayed you must choose one of the following data sets to export e Patients and ACG Results By default this data file contains all of the data elements from your original patient import file with any missing default columns added as blanks and all of the ACG calculated fields The columns in this export file are the same columns in the same order as shown in the Patient Sample section of the ACG Data File see Appendix A The output file can be customized by selecting the Select Columns button on the Export ACG Data Screen Technical User Guide The Johns Hopkins ACG System Version 8 2 5 70 Installing and Using ACG Software Figu
87. Group Rx MG Approximate Observed Age Sex Standard 95 Prevalence Expected Morbidity confidence Per 1 000 Prevalence Ratio interval Rx MG Description Population per 1 000 SMR Low High 1 20 Allergy Immunology Acute Minor 7 1 15 Allergy Immunology Chronic Inflammatory 1 11 Cardiovascular Chronic Medical 1 01 Cardiovascular Congestive Heart Failure 1 093 0 96 Cardiovascular High Blood Pressure 0 99 Cardiovascular Hyperlipidemia 1 02 Cardiovascular Vascular Disorders 0 96 Ears Nose Throat Acute Minor 1 10 Endocrine Bone Disorders 1 04 Endocrine Chronic Medical 1 05 Endocrine Diabetes With Insulin 9 09 9 86 0 922 0 81 Endocrine Diabetes Without Insulin 0 94 Endocrine Thyroid Disorders 0 98 Eye Acute Minor Curative 1 15 Eye Acute Minor Palliative 1 15 Female Reproductive Hormone Regulation 1 05 Gastrointestinal Hepatic Acute Minor 19 04 1 07 Gastrointestinal Hepatic Peptic Disease 1 11 General Signs and Symptoms Nausea and Vomiting 1 27 General Signs and Symptoms Pain 143 55 1 173 1 14 General Signs and Symptoms Pain and Inflammation 1 12 Genito Urinary Acute Minor 1 16 The Johns Hopkins ACG System Version 8 2 Technical User Guide 1013 Wr ju 0S K a x 0S p Allergy Immunology Acute Minor __ Allergy Immunology Chronic Inflammatory Cardiovascular Chronic Medical _ Cardiovascular Congestive Heart Failure _ Cardiova
88. Johns Hopkins ACG 8 2 aHa Choose Shortcut Folder Introduction Where would you like to create product icons Choose Install Folder In a new Program Group Johns Hopkins ACG 8 2 Choose Shortcut Folder In an existing Program Group Johns Hopkins ACG 8 2 ation Surnrrary Installing Inthe Start Menu Install Cornplete On the Desktop In the Quick Launch Bar Other Don t create icons Create Icons for All Users cancel Je The installation wizard will confirm that there is sufficient free disk space and then present a pre installation summary for review prior to installing the application Click Install to begin the process of copying files and installing the application The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 7 Figure 6 Pre Installation Summary SE Johns Hopkins ACG 8 2 Introduction Choose Install Folder Choose Shortcut Folder Pre Installation Summary Installing Install Gornplete Sie Pre Installation Summary Please Review the Following Before Continuing Product Name Johns Hopkins ACG 6 2 Install Folder C Program Files Johns Hopkins ACG 6 2 Shortcut Folder C Documents and Settings asalls Start Menu Programs Johns Hopkins ACG 8 2 Disk Space Information for Installation Target Required 155 521 889 bytes Available 1 480 003 584 bytes Previous Jf Inca
89. MGs The number of patients that had Rx Morbidity Group within the current T otal Cases stratification Cases Prob lt 0 4 The number of Total Cases that have a probability of being high cost lt 0 4 Cases Prob 0 4 The number of Total Cases that have a probability of being high cost gt 0 4 Cases Prob gt 0 6 The number of Total Cases that have a probability of being high cost gt 0 6 Cases Prob 0 8 The number of Total Cases that have a probability of being high cost gt 0 8 Avg Pred Resource The mean of the predicted resource use for all patients within the current Use stratification Avg Pred Resource The mean of the predicted cost resource index for all patients within the current Use Prob lt 0 4 stratification that have a probability of being high cost lt 0 4 Avg Pred Resource The mean of the predicted cost resource index for all patients within the current Use Prob 0 4 stratification that have a probability of being high cost 0 4 Avg Pred Resource The mean of the predicted cost resource index for all patients within the current Use Prob 0 6 stratification that have a probability of being high cost 0 6 Avg Pred Resource The mean of the predicted cost resource index for all patients within the current Use Prob 0 8 stratification that have a probability of being high cost gt 0 8 Technical User Guide The Johns Hopkins ACG System Version 8 2 5 36 Installing and Us
90. Major ADGs delivered Pregnancy 2 3 ADGs no Major ADGs not delivered Pregnancy 2 3 ADGs 1 Major ADGs delivered Pregnancy 2 3 ADGs 1 Major ADGs not delivered Pregnancy 4 5 ADGs no Major ADGs delivered Pregnancy 4 5 ADGs no Major ADGs not delivered Pregnancy 4 5 ADGs 1 Major ADGs delivered 1752 Pregnancy 4 5 ADGs 1 Major ADGs not delivered 1761 Pregnancy 6 4DGs no Major ADGs delivered a 1762 Pregnancy 6 ADGs no Major ADGs not delivered 6 0 03 1771 Pregnancy 6 4DGs 1 Major ADGs delivered 19 0 10 1772 Pregnancy 6 ADGs 1 Major ADGs not delivered 12 0 06 1800 Acute Minor and Acute Major 735 3 72 a 1900 Acute Minor and Likely to Recur Age 1 42 0 21 v Choose the type of file to export and the file location Export Type Export All Tabs To Excel File Export Current Tab To Delimited Data File Delimited File Options Column Delimiter Column Enclosure Row Delimiter Export File Export Eile Technical User Guide The Johns Hopkins ACG System Version 8 2 5 68 Installing and Using ACG Software 4 Tip The Export All Tabs To Excel File option will not be available if the current analysis contains at least one tab that has over 65 000 rows because Microsoft Excel cannot accept data extracts that large If exporting data to a delimited data file it is necessary to specify the type of column delimiter column enclosure and row delimite
91. Matched Pharmacy Codes C Patient ADG Assignments C Data Warnings Patient Major Rx MG Assignments Model Markers G Tab Separated Value tabs without quotes Export File This data file contains all possible predictive model scores for each patient The previous format was 109 columns with 55 columns populated at one time based on the model selected The columns presented in this file now represent the columns associated with the selected Risk Assessment Variables Enhanced License Management The license file that is required for the operation of the software now considers the Code Sets and Risk Assessment Variables available to individual users in addition to the Predictive Models Dx PM Rx PM DxRx PM that are licensed Existing license files will provide continued access to currently licensed components in Version 8 2 The Johns Hopkins ACG System Version 8 2 Release Notes Release Notes 2 7 Label Changes The new Risk Assessment Variables controlling concurrent ACG weights predictive modeling scores and prevalence rates are now customer driven and may not always be based upon national data sets Therefore the Report Options and Report Columns have been changed to reflect Reference to describe the selected Risk Assessment Variables reference Figure 6 below and Figure 7 on the next page Figure 6 Report Options Tab Report Options Eilters Options Groups Set the
92. PANY_C PPO 1653 GROUP1653 POS_A HSOS 16577 PCP16577 1657 PCP_GRP1657 TVYUVUZZRYQQTYYRAY 8M COMMERCIAL_D COMPANY_B PPO 1548 GROUP1543 POS_A HSOS 15451 PCP15451 1545 PCP_GRP1545 TYZWVURZSYQRWZ VARY 21M COMMERCIAL_B COMPANY_B PPO 2062 GROUP2062 POS_B H502 20606 PCP20606 2060 PCP_GRP2060 TVZYXVXQUYQRVYXYSZ 20F COMMERCIAL_B COMPANY_C PPO 2052 GROUP2052 POS_B H502 20511 PCP20511 2051 PCP_GRP2051 TYZZUQUWRYQRSYXYSY 17M COMMERCIAL_D COMPANY_B PPO 1621 GROUP1621 POS_B H503 16275 PCP16275 1627 PCP_GRP1627 TWQOWRYRTOYQQWYZXXZ uF COMMERCIAL_D COMPANY_A PPO 1621 GROUP1621 POS_B Hs02 16243 PCP16243 1624 PCP_GRP1624 TWRYTYZYUYQSYYXZXY 33M COMMERCIAL_C COMPANY_A PPO 0123 GROUP0123 POS_B H505 01277 PCP01277 0127 PCP_GRP0127 ie 20M COMMERCIAL B COMPANY B PPO 2052 GROUP2052 POS B H504 20511 PCP20511 2051 PCP S b 4 gt Note The Patient Sample view display is limited to only the first 1 000 records though an export of the data at this point would yield the entire data set The sample is meant to help with validating data Not all of the columns available for viewing are presented above ACG Output Data In addition to most of the variables found on the input data age gender string of diagnoses the ACG Output Data contains the list of risk assessment variables assigned by the software Please see Appendix A at the end of this chapter for additional detail on the ACG Output Data The Johns Hopkins ACG System Version 8 2 Technical User Guide Ins
93. PCS E0100 E9999 durable medical equipment A Sample R O Implementation Method 1 Apply R O claims line identification criteria to identify non institutional claims that either have a POS or a CPT in one of the listed categories 2 Identify whole claims that contain only R O lines When a claim contains a mix of R O and non R O lines then retain the entire claim 3 Discard diagnoses from claims that contain 100 R O lines The Johns Hopkins ACG System Version 8 2 Technical User Guide Basic Data Requirements 4 9 Coding Issues Using National Drug Codes NDC The National Drug Code NDC is a drug product classification system First compiled and organized as part of a Medicare outpatient drug reimbursement plan it has grown and spread to numerous sectors within the health care industry among which include managed care organizations pharmaceutical manufacturers wholesalers hospitals and Medicaid Its usages span from clinical patient profile screening to inventory control and drug claims processes Recorded within a database headed by the Food and Drug Administration it is used specifically by the government for product tracking evaluations research and drug approval within the United States The code itself is comprised of three segments Two forms exist a ten and an eleven digit configuration The ten digit code referred to as a regulation NDC is used mainly by the FDA However the maj
94. Step 2 Load Your Own Data Johns Hopkins ACG System 8 2 Choose the data sources for your new ACG data file Patient Data Patient Data File My_Patient_File Skip First Row i e column headers in data file Use Tab Delimited File Format Use Comma Delimited File Format Use Custom File Format Patient Format File My_Custom_Format Diagnosis Data Diagnosis Data File My_Diagnosis_File Skip First Row i e column headers in data file Use Tab Delimited File Format Use Comma Delimited File Format Pharmacy Data Pharmacy Data File My_Pharmacy_File O Skip First Row i e column headers in data file Use Tab Delimited File Format Use Comma Delimited File Format Model Options Risk Assessment Variables US Non Elderly Prior Costs C Ignore prior cost data All Models Calculate all valid predictive models for use under the direction of technical support Back lt Next gt Cancel In the second step of importing your own data you must provide the names of your patient data file and specify the file format and the location of the custom file format if applicable provide the location of the diagnosis and pharmacy data files and specify their file formats and finally specify predictive modeling options All of the options on this screen are simple point and click windows commands Click on the rad
95. UACGSetup executable file The software uses a standard Windows Setup Wizard to install the software into the default or user defined destination location and will optionally add program shortcuts to the Start Menu Folder The software installation uses a digital signature to identify The Johns Hopkins University as the publisher of the software If your software does not identify The Johns Hopkins University contact your distributor to verify the application s authenticity Once you verify the publisher select Run to continue with the installation Figure 1 First Setup Screen Open File Security Warning Do you want to run this file Name JHUACGSetup4 Win 8 2 20081014 exe Publisher The Johns Hopkins University Type Application From C Documents and Settings asalls My Documents Always ask before opening this file potentially harm your computer Only run software from publishers Y While files from the Internet can be useful this file type can you trust What s the risk The software will begin extracting files for installation and will present a status screen during this step Technical User Guide The Johns Hopkins ACG System Version 8 2 5 4 Installing and Using ACG Software Figure 2 Extraction Status InstallAnywhere q InstallAnywhere is preparing to install Extracting C 1997 2008 Acresso Software Inc and or InstallShield Co Inc The installation wizard will then begin a guided setup for in
96. V22xx V23xx V24xx V27xx and V28xx Delivery Status Each ACG from 1710 through 1770 is split into two categories 1711 1712 through 1771 1772 based on whether or not the women within these categories have delivered during the period of analysis After extensive testing the ACG System development team at Johns Hopkins is confident the standard ICD 9 CM codes used by the software for identifying deliveries are effective with positive predictive accuracy that is the women did actually deliver averaging greater than 96 among all plans tested However for a variety of reasons diagnosis codes for delivery may not appear in a woman s claim history even though she did in fact deliver For example the delivery may have occurred in an outpatient birthing center or other non traditional venue and claims were never submitted containing any delivery codes Also if an analyst is using only ambulatory data not generally recommended the ICD 9 CM delivery codes are not available or the analyst is processing ICD 10 or ICD 9 data to assign ACGs then it is suggested that the user provide a delivered flag in the input data stream Low Birth Weight less than 2500 grams In a manner similar to the way pregnant women are subdivided by delivery status infants can at the user s discretion be subdivided into subcategories based on their birth weight However utilization of this feature is somewhat more difficult Although ICD codes allow for ide
97. _jhuacg license acal File Name Files of Type ACG License Files acql v Install Cancel Click the My Documents button to search the desktop for the appropriate file which is provided with the software installation CD 4 Tip ACG license files have the acgl extension If you are having difficulty finding this file you can use the search function of Internet Explorer to search your desktop for files with this extension or call your software vendor for additional support Occasionally this file may be e mailed to you so it may be necessary to first save the file from your e mail program to the desktop before beginning the search using the My Documents button The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 13 4 Tip Each license file is specific to the modules licensed from your software vendor The modules available are diagnosis only pharmacy only or both diagnosis and pharmacy To determine which components of the system you have access to please select About under the Help section within the tool bar Figure 13 View the Installed License Johns Hopkins ACG System About Johns Hopkins ACG System You can click on the tabs below to see information about the tool s version and the system s state Tool System License License Mode Licensed Modules dx rx User Name ACG Team Company Name Johns Hopkins University
98. a file contains one row for each Rx MG code assigned to a Patient ID The columns in the file are Patient ID Rx MG Rx MG Description e Patient Major Rx MG Assignments This data file contains one row for each Major Rx MG code assigned to a Patient ID The columns in the file are Patient ID Major Rx MG Major Rx MG Description e Diagnoses This data file contains one row for each diagnosis experienced for a Patient ID This file is basically an unduplicated version of the diagnosis import file The columns in this file are Patient ID ICD Version ICD Code e Pharmacy Codes This data file contains one row for each pharmacy code experienced for a Patient ID The file is basically an unduplicated version of the pharmacy import file The columns in this file are Patient ID Rx Fill Date Rx Code Rx Code Type e Non Matched Diagnoses This data file contains one row for each non matched unknown diagnosis code encountered for a Patient ID The columns in this file are Patient ID ICD Version ICD Code Technical User Guide The Johns Hopkins ACG System Version 8 2 5 72 Installing and Using ACG Software e Non Matched Pharmacy Codes This data file contains one row for each non matched unknown pharmacy code encountered for a Patient ID The columns in this file are Patient ID Rx Code Type Rx Code e Data Warnings This data file contains one ro
99. a files 5 68 data from an ACG data file example 5 95 data from an ACG data file usage details 5 90 report tables 5 67 F Figures ACGs for Windows taskbar 5 16 age gender distribution tab 5 58 all models file export option 2 6 analyze menu 5 17 5 61 6 6 build options tab 2 5 5 60 choose shortcut folder 5 6 choose the license file 5 12 create ACG File from sample data 5 51 create custom file format 5 78 5 79 export data files 5 69 exporting files 6 13 extraction status 5 4 filters 5 63 final step load your own data 5 84 first setup screen 5 3 groups 5 64 guided setup 5 4 install complete 5 10 install the license file 5 12 install updated mapping file 5 14 installation status 5 8 license agreement 5 11 local weights tab 5 57 mapping file communication error 5 15 mapping file manager 5 14 new file screen 2 3 2 10 options 5 65 patient sample tab 5 56 population RUB distribution 6 8 pre installation summary 5 7 Technical User Guide Index IN 3 probability distribution tab 5 59 reference option selection 2 8 report export tables 5 67 report options 5 66 report options for MEDC by RUB distribution analysis 5 22 report options tab 2 7 risk adjustment pyramid 8 1 sample warning distribution 6 9 save ACG sample 5 52 select columns 5 70 select destination location 5 5 select report options for standardized morbidity ratio by EDC analysis 5 29 selecting report optio
100. acy codes and no diag Number of data warnings 0 Number of patients with data warnings 0 Minutes To load data 2 Total cost model selected DxRx PM total cost gt total cost Pharmacy cost model selected DxRx PM rx cost gt rx cost Date loaded 2008 10 20 LJ C ith ACG versi 8 2 v e Patient EDC Assignments This data file contains one row for each EDC code assigned to a Patient ID This file is organized in a manner so that it can be easily loaded into a database like Microsoft Access or another relational database The columns in this file are Patient ID EDC Code EDC Description MEDC Code MEDC Description e Patient MEDC Assignments This data file contains one row for each MEDC code assigned to a Patient ID An MEDC code is a higher level grouping for an EDC code The MEDC code is also included in the Patient EDC Assignments file This file provides the added advantage of removing duplicate MEDC codes for each patient whereas the Patient EDC Assignment file may contain duplicates for an MEDC code for a patient The columns in the file are Patient ID The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 71 MEDC Code MEDC Description e Patient ADG Assignments This data file contains one row for each ADG code assigned to a Patient ID The columns in the file are Patient ID ADG Code ADG Description e Patient Rx MG Assignments This dat
101. al User Guide Installing and Using ACG Software 5 79 8 To adda column click on the empty column name and type your new column name Add data type and column description and press Enter Figure 41 Enter Custom File Format x Johns Hopkins ACG System 8 2 File Edit Yiew Analyze Tools Help Se Hx t g l S File Format Delimiters Column Delimiter Column Enclosure Row Delimiter CR LF Windows w Columns Column Name Data Type Column Description patient_id String Patient Id age Integer Age sex String Sex lin _of_business String Line of Business company String Company product String Product employer_group_id String Employer Id employer_group_name String Employer Name benefit_plan String Benefit Plan health_system String Health System pcp_id String PCP Id pcp_name String PCP Name pcp_group_id String PCP Group Id Pcp_group_name String PCP Group Name pregnant Integer Pregnant delivered Integer Delivered low_birthweight Integer Low Birthweight pharmacy_cost Double Pharm Cost total_cost Double Total Cost 9 Select File 10 Select Save As to save the file format Technical User Guide The Johns Hopkins ACG System Version 8 2 5 80 Installing and Using ACG Software Open acgd files Once the input text files have been processed by the system the results will be stored in a acgd format Use the Open option on the File menu to select a previously processed aced file If you attempt to
102. al User Guide for more detail The Johns Hopkins ACG System Version 8 2 Technical User Guide Selecting Relevant Diagnoses for Input to the ACG Software In the United States and elsewhere healthcare providers of all types record diagnostic codes on insurance claim forms and other types of administrative records These diagnoses are generally reasonably accurate and have proven quite useful in understanding the case mix of various populations However there is a series of coding related issues and analytic approaches that is discussed here to help the user maximize the accuracy of the ACG assignment by preprocessing the ICD stream input into the ACG grouper Analysis Time Frame The ACG System is calibrated to use one year of data with an appropriate run out period For example the data required to perform a retrospective profiling analysis on calendar year 2004 should include all diagnosis and demographic information collected between 01 01 2004 and 12 31 2004 after allowing for run out claims lag Excluding Lab and X Ray Claims Most health plans collect claims information from clinical laboratory diagnostic imaging and durable medical equipment providers that include diagnosis information These claims should not be used as input for the ACG Software The diagnoses on these claims often and perhaps even primarily represent rule out suspected or provisional codes The inclusion of such diagnoses could result in many false positives
103. anagement ois scccsissccsiscssecsastssnssiessaaidesbenaatisseginibageons 2 6 Label Change aiian saie enn En EEE 2 7 Pipe 6 Report Options TaDacrsresiei nea oi E i 2 7 Figure 7 Reference Option Selec Oth sisirin 2 8 Technical EnhantemMentS sssisssssssssssssessicssssssecsosssisssosssssssssssasscosssssssssossssososss 2 8 Changesto We ect cia er Ge aoe 2 8 Figure 8 The Johns Hopkins University Digital Signature 0 2 8 Support for Vista ss cccsscoss cans fe cinnzaesanceanceivastochanostardneeiaecsdennmniaenonameasoaees 2 9 Support for Larger ACGD Files x cosccssanisccaaveseatsaceevnsdandnixanensonaneoueacines 2 9 Application of Regional Settings os cccccetca pecan oniomseeteaneeae nese 2 9 Mismatch Ee Ao cae cei ce cc sc ceaansanca cence a eae 2 9 Figure 9 New File Sereen meee 2 10 Changes to the Output Format ic assctacsacescovsceuncevensncsesaeascorseevaceesoeavaeeveanes 2 10 Documentation Enhancements sccscsssscssssscsscsssssssscsssessssssssssssoess 2 10 Release Notes The Johns Hopkins ACG System Version 8 2 2 ii Release Notes This page is intentionally left blank The Johns Hopkins ACG System Version 8 2 Release Notes Release Notes 2 1 Overview This chapter discusses the enhancements incorporated into Version 8 2 of the Johns Hopkins ACG Software To briefly summarize Version 8 2 of the Johns Hopkins ACG Software includes a number of new enhancements which can be organized into a few broad categories
104. anagement programs The Johns Hopkins ACG System Version 8 2 Technical User Guide Selecting the Right Tool 3 25 Table 12 Number of Cases and The Johns Hopkins ACG Dx PM Predicted Relative Resource Use by Risk Probability Thresholds for Selected Chronic Conditions Numbes of Cases Predicted Relative Resource Use Probability Score Category Probability Score Category 2 1 6 8 764 307 460 184 Hyperlipidemia 31 240 1 170 529 186 1 97 7 13 9 49 15 46 61 98 5 7 940 545 147 Technical User Guide The Johns Hopkins ACG Case Mix System Version 8 2 3 26 Selecting the Right Tool The ACG Predictive Model s Probability Score The ACG predictive model probability score used in Table 12 identifies persons in your organization who would be likely to benefit from special attention To capitalize on this method you will want to develop periodic reports of members with high PM scores who also meet other organizational criteria such as e Enrolling with certain providers e Falling into certain eligibility categories e Residing in certain geographic areas e Meeting previous patterns of utilization After these other stratifiers are taken into consideration as appropriate a case finding report should list all in scope individuals arrayed from highest to lowest based upon the overall PM high risk probability score within your organization Table 13 provides an example of a case finding report In addition to running the
105. arisons to local norms Probability High ACG Predictive Model Probability Score for pharmacy cost The probability Pharmacy Cost that this patient will have high pharmacy costs in the year following the observation period Probability High Total Cost Unscaled Pharmacy Cost Resource Index The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 89 Appendix B Batch Mode Processing Windows DOS During the ACG System Windows installation process a separate executable file jhuacg exe is loaded for command line use The jhuacg exe file is initiated at the command prompt in Windows DOS and utilizes the same input files as the Windows release The command line version produces an ACG Data File with the extension acgd The adcg file is readable in the Windows version Click the File menu and select Open Type the filename or use the Windows Explorer feature to double click the acgd file of interest You can also access the processed data using command line functions explained below in the ACG Command Line Usage section UNIX The UNIX versions of the ACG application support command line use in both the installer and the runtime version The installer comes in the form of an executable for each target UNIX platform To install the software e Log inas root move to the directory that the installation is located in and run in JHUACGSetup4AIX 8 2 20060614 bin e The software will
106. ary statistics 5 54 summary statistics tab 5 53 sytem requirements 5 1 tools menu 5 50 UNIX 5 89 updating the diagnoses and pharmacy mapping files 5 14 usage details 5 90 use your own data 5 73 using the software 5 15 view menu 5 16 view results of the grouping process 5 52 view the installed license 5 13 warning distribution analysis 5 50 warning list 5 49 The Johns Hopkins ACG System Version 8 2 welcome to the Johns Hopkins ACG sysem setup 5 10 Windows DOS 5 89 Introduction assessing the ACG grouper s output 6 1 components of the ACG toolkit 3 2 final considerations 8 1 installing and using ACGs for Windows 5 1 making effective use of risk scores 7 1 selecting the right tool 3 1 the Johns Hopkins ACG system 1 1 J Java API 5 96 L Label changes release notes 2 7 Load your own data case study 5 80 Load the sample dataset 5 51 Local calibration of ACG PM scores 7 16 Local weights 6 4 Localization enhancements release notes 2 1 Low birth weight 4 11 ACG 4 11 basic data requirements 4 11 Making effective use of risk scores 7 1 addressing the impact of age on the calculation of ACG weights 7 15 adjustments for inflation 7 8 all file model 7 6 concurrent ACG 7 5 concurrent versus prospective calculations 7 15 converting scores to dollars 7 7 customizing risk scores using local cost data 7 9 how to rescale and assign dollar values 7 7 local calibration o
107. as the basis of actuarial cells is the fact that for almost a decade they have been used to facilitate the exchange of many billions of dollars within numerous private and public health plans in both the United States and Canada 4 Example For a simple case study illustrating the use of ACG actuarial cells for prospective payment see The Development of Risk Adjusted Capitation Payment System For Medicaid MCOs The Maryland Model Weiner et al Journal of Ambulatory Care Management January 1998 Technical User Guide The Johns Hopkins ACG System Version 8 2 3 32 Selecting the Right Tool ACGs in Multivariate Models Multivariate regression for risk adjustment has been used for many years by some of the more sophisticated users of the ACG System If additional risk descriptors are available beyond diagnosis age and sex this approach has the potential for improved predictive models that have both actuarial and payment applications The strength of regression based strategies is the ease with which additional risk factor information can be incorporated and thereby introduce better control for the effects of case mix If you have access to additional well validated risk factor data and if you have previous experience using regression models within your organization then you should consider using regression In regression strategies ACGs ADGs and EDCs remain valuable as distinct risk factors to be supplemented by additional data NOTE
108. assessment on every member thereby reducing the medical underwriting effort This reduction in effort in turn reduces the elapsed time needed for analysis and consequently will reduce the lag between the experience period and the rating period Rx PM can reduce this lag further This leads to greater accuracy e The ACG predictive models provide an objective reproducible method which is favored by regulators It offers greater consistency among underwriters and is more defensible to customers than manual approaches e The various clinical groupings and markers from the system provide supporting detail that can be used by sales and marketing Discordant predictions based on Rx PM and Dx PM can be used as a data quality check and prompt more targeted investigation by medical underwriters e Predictive modeling better matches premium to future costs allowing for more competitive renewals and improved customer retention The Johns Hopkins ACG System Version 8 2 Technical User Guide Selecting the Right Tool 3 35 Table 15 Actuarial Cost Projections Age Sex Mean Mean Relative Observed National Local Total Rx High Employer Cases Risk Expected CMI CMI PRI Risk HOSDOM Frail Chronic Psychosocial Discretionary 33472 08 1214 37 1317 37 65466 93 4114253 37 34565 08 65215 16 1322 37 323 16 08 74134 06 4112725 11 Technical User Guide The Joh
109. ator and period decimal separator make sure that this is reflected in your Regional Options in the Windows Control Panel Technical User Guide The Johns Hopkins ACG System Version 8 2 5 74 Installing and Using ACG Software Table 15 Patient File Format Data A unique string to identify this individual 9567213984 01 member The patient s age in years as of the end of the observation reporting period Numer 2 A single character or digit to indicate sx whether the patient is a Male or Female Text M The software will use F or 2 to identify a Female all other values indicate Male A code to indicate the category of the patient s insurance type This is typically line_of_business nat by a neal Panig identity Text COMM Commercial Medicaid Medicare Choice or some other similar category A code to indicate the financial company for this patient This is typically used by company a health plan to differentiate financial Text Generic Care 01 companies financial products or state or regional company systems A code to indicate the patient s insurance roduct type This is typically used by a Peedi health ts to m Ri HMO a HMO PPO or POS product line A code to indicate the employer or group that this patient is covered under This is f typically used by a health plan to identify Simployer Sroupaid an employer e g General Motors or e uM another logical member patient grouping e g Maryland Medicaid The readable
110. ava runtime environment and allows the ACG software greater compatibility with regard to operating system patch levels Support for Vista The new installation package now makes the ACG System compatible with Vista Vista is now a supported platform Support for Larger ACGD Files The data files created by the ACG System acgd files are stored in a compressed format Previous versions of the ACG System used a 32 bit compression tool and were limited to patient files that did not exceed 2 GB after compression approximately 16 million members The 32 bit compression tool has been replaced with a 64 bit compression tool allowing individual patient files of up to 2 TB or approximately 16 billion members This capacity is cut in half when the All Models selection is applied Application of Regional Settings The ACG System will now use Windows regional settings to determine the format of numbers for importing Previously the ACG System would format numbers using the regional settings but would fail to import numbers using a format other than a comma thousands separator or period decimal separator The regional settings are accessible from the Windows control panel Mismatch Break With Version 8 2 there are many variants of Models Code Sets and Risk Assessment Variables all of which are licensed components The New File screen reference Figure 9 on the following page allows you to optionally set an error threshold so that processing is
111. be based on similar data but restricted to those under age 18 Note Only those ACGs not automatically split by age are affected Concurrent versus Prospective Calculations In theory there is no difference in the basic methodological approach for calculating concurrent also called retrospective or prospective weights The primary difference hinges on the timeframe from which resource measures are drawn R aca mi and M as outlined in the preceding sections For concurrent analyses diagnoses used to assign ACGs come from the same period for which the resource use variable is calculated In contrast for prospective analyses resource use is calculated based on concurrent data for some future time period typically year 2 The special challenge of prospective analysis hinges on sample selection or whom to include in the population for the calculation of R aca mj and M For calculation of prospective weights the sample is typically limited to those enrolled during both time periods Last the PMPM calculation of ACG weights is the preferred method for prospective applications Technical User Guide The Johns Hopkins ACG System Version 8 2 Local Calibration of ACG Predictive Modeling Scores The prospective scores provided in the Dx PM Rx PM and DxRx PM are based upon multivariate linear regression models To develop a locally based PRI score would involve fitting a regression to local data using the variables included within the ACG predi
112. casecsidecancucuesscnacaresmnreeitecreeenmase 5 6 Figure 6 Pre Installation Summary dscndcccnnunnenenkouruinemnonan 5 7 Figur 7 Installation Status sscsesiiircciccnecicse tinente 5 8 ap ea ame bce 2 oe ee ee Oe See IEE Der enter cep Beer S 5 9 ACG EAI File fcc cree sss cetensczacwetin beat rida eae anne 5 10 Figure 9 Welcome to the Johns Hopkins ACG System Setup 5 10 Figure 10 License Far C0 occ canicavioeesaseusuncenssenactaaecaseeaseenisedammneavenne 5 11 Fig re 11 Install the License File a cacscoincesassasserieaeicsseseseasaeiaremsaavese 5 12 Figur 12 Choose the License Fle eis scadccerenncseacemanteoncuavneacemnenese 5 12 Figure 13 View th Installed License lt 2 4c asscncceveeuscacsueedoneeadaamneniets 5 13 Updating the Diagnoses and Pharmacy Mapping Files cee 5 14 Figure 14 Install Updated Mapping Pile sc isiciccsiesscinasanadecssinvateiessescannense 5 14 Figur 15 Mapping Pile Managert iio co se cacdscascansezssareadcerscarvesaasiasuntetenae 5 14 Figure 16 Mapping File Communication Error sscesccsssssesseseees 5 15 Using th Son ware siccisissscessssssacassesscasesvassesee cxsssasnnstaannsvtensunenssesdensieoneo neat 5 15 ACG for Windows Desktop scasacosssiistsnssssasosenvesensenssesesasvasicnssivasssansesticensies 5 16 Figure 17 AGGs for Windows Taskbat sccsssssscssccssrcssstsssencsees 5 16 Fle MCI asics seeciadis idasasnaciatass daca a eed aera 5 16 Edit MMU os
113. cation that were assigned to this Observed 1000 MEDC Calculated as Patient Count total Patient Count within the same stratification for all MEDCs x 1000 The number of expected observations per 1 000 after adjusting for the age sex Age Sex distribution in the current stratification Calculated as total of overall age sex Expected 1000 prevalence rate x number of patients in age sex in current stratification for all age sex combinations number of patients in the current stratification for all MEDCs x 1000 Observed to Expected Ratio Calculated as Observed 1000 Age Sex Expected 1000 95 Confidence The lower range of the 95 confidence interval Calculated as Low SMR 1 96 x SQRT SMR expected count 95 Confidence The upper range of the 95 confidence interval Calculated as High SMR 1 96 x SQRT SMR expected count An indication of statistical significance Contains a minus sign when the SMR is Significance significant and less than 1 contains a plus sign when the SMR is significant and greater than 1 The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 31 Standardized Morbidity Ratio by Major Rx MG Analysis The Standardized Morbidity Ratio Analysis produces a summary by Major Rx MG with observed expected and o e ratio This report is useful in understanding how the prevalence of certain conditions as defined by Major Rx MGs are more or l
114. centage of patient assignments to this stratification in this RUB is out of the total patients in this RUB The mean of the national rescaled or local concurrent weight based upon which weight type was selected in Report Options for all patients in this stratification in this RUB The percentage of patient assignments to this stratification in this RUB is out of the total patients in this RUB The Johns Hopkins ACG System Version 8 2 5 26 Installing and Using ACG Software RUB 4 Est The mean of the national rescaled or local concurrent weight based upon Concurrent Resource which weight type was selected in Report Options for all patients in this Use stratification in this RUB i The percentage of patient assignments to this stratification in this RUB is out 0 RUBS an of the total patients in this RUB RUB 5 Est The mean of the national rescaled or local concurrent weight based upon Concurrent Resource which weight type was selected in Report Options for all patients in this Use stratification in this RUB Rx MG by RUB Distribution Analysis The Rx MG by RUB Distribution Analysis produces a frequency distribution of Rx MG by Resource Utilization Band RUB A patient can be assigned to multiple Rx MG codes but only one RUB Just as there is variability of cost across disease category using diagnoses there is variability of cost across disease category using pharmacy data This report is useful for case managers because it
115. ctive models A listing of the predictor variables the independent variables is provided as an appendix to the chapter on predictive modeling in the Reference Manual Using these variables and local cost data an experienced analyst could develop a new set of PRI scores that are customized for the local enrollee population Custom models should be based on populations of no fewer than 100 000 individuals 4 Tip In the Export ACG Data Menu there is a Model Markers file that contains two columns a member ID and a string of Boolean 0 1 flags representing the right hand side of the regression equation Local calibration can be performed by merging this file with cost information We strongly recommend you talk to your ACG support analyst for technical support in implementing this application at least the first time The Model Marker file contains all necessary flags for the DxRx PM model Resource Bands The software incorporates both prior total cost and prior pharmacy cost bands into the ACG predictive models They are a useful adjunct to analysts wishing to stratify their populations Possible values include e 0 0 orno pharmacy costs e 1 10 percentile e 2 11 25 percentile e 3 26 50 percentile e 4 51 75 percentile e 5 76 90 percentile e 6 91 93 percentile e 7 94 95 percentile e 8 96 97 percentile e 9 98 99 percentile The Johns Hopkins ACG System Version 8 2 Technical User Guide Making Effective Use of Ri
116. ctly alike and given the potentially major impact that such a process may have on the management or financial applications within your organization it is essential that you seek and follow advice from experienced statistical or actuarial specialists before finalizing the general processes described above The Johns Hopkins ACG System Version 8 2 Technical User Guide Making Effective Use of Risk Scores 7 9 Customizing Risk Scores Using Local Cost Data Two approaches for calculating ACG weights from local data are e PMPM per member per month e PMPY per member per year or other extended period of time The calculations for these two approaches are 1 PMPM ACG R ACG Months ACG per member per month 2 PMPY ACG R ACG IN ACG per member or other extended period of time Where R aca is calculated as the sum of resource use across all members assigned to a particular ACG and Months acc is calculated as the total number of member months of eligibility for this cohort N aca is the number of individuals in this cohort Weights are calculated separately for each ACG category The primary difference between these two methodologies hinges on whether or not costs are annualized to account for part year enrollment more on this issue later in the chapter The default calculation for local calibration of ACG weights within the software is the PMPY approach Compared to the more widely used PMPM the PMPY approach represe
117. curately characterized by a single disease assignment These populations consist of individuals with multiple possibly unrelated conditions The Johns Hopkins ACG Research Team arrived at the conclusion that the clustering of morbidity is a better predictor of health services resource use than the presence of specific diseases This conclusion is the fundamental concept that differentiates ACGs from other case mix adjustment methodologies For more information on ACGs please refer to the chapter in the Reference Manual entitled Clinical Aspects of ACGs Expanded Diagnosis Clusters EDCs Each assigned ICD code maps to a single EDC ICD codes within an EDC share similar clinical characteristics and are expected to evoke similar types of diagnostic and therapeutic responses The main criterion used for the ICD to EDC assignment is diagnostic similarity Codes that refer to the same disease or condition are grouped together As broad groupings of diagnosis codes EDCs help to remove differences in coding behavior between practitioners Each EDC is classified into one of 27 broad clinical categories termed a Major EDC MEDC MEDCs may further aggregated into five MEDC types Administrative Medical Surgical Obstetric Gynecologic Psychosocial providing a concise way of summarizing all diagnosis codes 4 Example There are 56 ICD 9 CM codes that practitioners can record as a diagnosis for otitis media The EDC for otitis media combines these
118. d 19783 Patients processed 65 years and older 234 Export ACG Data Diagnoses processed 69985 Unique diagnoses encountered 4261 Choose the type of data to export and the file location Unique unknown diagnoses encountered 35 Percentage of diagnoses that were unknown 0 3 Export Data Unknown diagnoses encountered 187 Patients and ACG Results Pharmacy Codes Patients with unknown diagnoses encountered 184 i fs 3 k z Unique diagnosis code sets encountered 1 Patient EDC Assignments Non Matched Diagnosis Codes Unique unknown diagnosis code sets encountered 0 Patient MEDC Assignments Non Matched Pharmacy Codes Patients with ted di is code set tered 0 cere eee d Be ee COUR SES EERE 57013 Patient ADG Assignments Data Warnings Unique pharmacy codes encountered 5487 Patient Rx MG Assignments Local Weights Unique unknown pharmacy codes encountered 220 O Patient Major Rx MG Assignments Model Markers Percentage of pharmacy codes that were unknown i3 5 E Unknown pharmacy codes encountered 715 Diagnosis Codes All Models Patients with unknown pharmacy codes encountered 434 Unique pharmacy code sets encountered 1 Export Options Unique unknown pharmacy code sets encountered 0 V Write Header Row Patients with unsupported pharmacy code sets encountered 0 Tab Separated Value tabs without quotes Number of EDCs assigned 55221 Number of MEDCs assigned 43180 Comma Separated Value commas with quotes Number of A
119. d Using ACG Software Note The percent distributions are calculated across each row stratification It is not likely but possible for a row to have a total of less than 100 because RUB 0 is not included in the output The report layout is as follows Table 5 MEDC by RUB Distribution Analysis Report Layout Est Concurrent Resource Use RUB 1 Dist RUB 1 Est Concurrent Resource Use RUB 2 Dist RUB 2 Est Concurrent Resource Use RUB 3 Dist RUB 3 Est Concurrent Resource Use RUB 4 Dist RUB 4 Est Concurrent Resource Use RUB 5 Dist RUB 5 Est Concurrent Resource Use The Johns Hopkins ACG System Version 8 2 The mean of the national rescaled or local concurrent weight based upon which weight type was selected in Report Options for all patients in this stratification across all RUBs The percentage of patient assignments to this stratification in this RUB is out of the total patients in this RUB The mean of the national rescaled or local concurrent weight based upon which weight type was selected in Report Options for all patients in this stratification in this RUB The percentage of patient assignments to this stratification in this RUB is out of the total patients in this RUB The mean of the national rescaled or local concurrent weight based upon which weight type was selected in Report Options for all patients in this stratification in this RUB The percentage of patient assignmen
120. d to begin basic report building and profiling The Johns Hopkins ACG System Version 8 2 Technical User Guide Making Effective Use of Risk Scores 7 1 7 Making Effective Use of Risk Scores Fav tr RCE OH isis sissnsas races sens sasanastesnacascianccaovesvedusvesnetenssstecenvisvasessassudenistsunanseaen 7 1 Software Produced Weights and Their USeS cccscccssscssssscsssssceseeceess 7 1 Table l Risk Weights and SCOTES aisaiss sccnsasacdsoencsdusraltsvensaondseuasnaunGsebnanise 7 2 Concurrent ACG Welghts sisssssessissreciissssisisn saot naan 7 5 Prospective Risk SCOr6S sccsssissccsssscessensssesssassvesssessscssesseveseesovesssersesveenssenceseens 7 6 AN Model ai ae tpeeer ane Rr eee ease Reset Emre E at ye cmae oe ar ere eee or re rere 7 6 Converting Scores to Dollars se ssoossoocsooscosesssocssooccosscosssooessssosssocsosseosssss 7 7 How to Rescale and Assign Dollar Values ccccccssecsteceseceeeceeteeenseeees 7 7 Table 2 Estimating Costs in a Sample of Cases asccicscisasccusvcsacsnssncecsevsaces 7 8 Adjustments for Inflation 5 cc c lt swasccaceeeceseusssaensoavadeasvasseseamnaesteamcedenrueewnte 7 8 Customizing Risk Scores Using Local Cost Data cccsscccssscssssscsseeees 7 9 Incl ding Part Y car NEOUS ao cas cosscticxconsrssedasasomiaicesscucandseesncusteinsasesiods 7 9 Table 3 Comparison of PMPM and PMPY Average Costs by Months Enrolled Within a HMO Population eee eeeeeseeneeereeeeees 7 10
121. d with each ACG for the analysis period divided by the number of persons associated with that ACG Therefore total expected costs associated with any given individual would be independent of the time enrolled during the analysis period Based on total paid costs truncated at 35 000 to mimic stop loss reinsurance levels in this plan ACG weights were calculated using both the PMPM and PMPY alternative approaches for the population shown in Table 3 Based on each of these approaches actual costs were compared to expected ACG costs within that population Sections A and B of Table 4 present a series of measures comparing actual to expected costs for cohorts of enrollees defined in terms of the months they were enrolled during a 12 month period This table as does the previous one represents a retrospective cohort analysis of users as appropriate for a provider profiling assessment Section A of Table 4 presents the results using a PMPM calculation The column labeled deviation reflects expected costs divided by actual costs minus one For persons enrolled for one month the 85 1 figure indicates that when the actual 1996 costs of these 488 single month enrollees are compared to their ACG expected costs calculated on a PMPM basis the cohort would have been underpaid by 85 1 percent on average In contrast persons who were enrolled for the full 12 months of the year were overpaid on average by 5 3 percent The deviation column
122. de renders this number an estimate The most obvious difference between ICD 9 and ICD 10 is the format of the codes to include alphanumeric categories Some chapters and conditions are organized differently and ICD 10 has almost twice as many categories as ICD 9 Since the ICD was originally developed to code causes of death its underlying assumptions lack an appreciation for the problem oriented nature of differential diagnosis in clinical medicine particularly for conditions seen in primary care and other ambulatory care settings Many clinical problems have uncertain or at best tentative diagnoses in these settings As a result rule out diagnoses may be coded as definitive diagnoses when claim forms are submitted see the Rule Out Suspected and Provisional Diagnosis section below Furthermore the use of ICD diagnosis codes by providers is inconsistent and often confusing Nonetheless it is our belief supported by evaluation of many health plan databases that the overwhelming majority of providers strive to report codes that adequately characterize the condition of their members The JHU team and other The Johns Hopkins ACG System Version 8 2 Technical User Guide Basic Data Requirements 4 5 researchers have repeatedly assessed the integrity of diagnosis codes assigned by care providers and have found that they convey a sufficiently accurate picture of patients health status and resource requirements The next sections describe
123. dentify pharmacological subgroups when that is considered more appropriate than therapeutic or chemical subgroups On the following page Reference Table 2 for the complete classification of metformin and code structure Technical User Guide The Johns Hopkins ACG System Version 8 2 4 10 Basic Data Requirements Table 2 Classification of Metformin The complete classification of metformin illustrates the structure of the code Alimentary tract and metabolism 1st level anatomical main group Drugs used in diabetes 2nd level therapeutic subgroup A10B Blood glucose lowering drugs excl insulins 3rd level pharmacological subgroup A10BA Biguanides 4th level chemical subgroup A10BA02 Metformin 5th level chemical substance The ATC system was created to serve as a tool for drug utilization research Because the ATC system has been specifically designed to capture the therapeutic use of the main active ingredient there is much more relevant information imbedded in an ATC code for making Rx MG assignments See Reference Manual Chapter 6 for a more detailed description of the Rx MG assignment methodology Identifying Special Populations with Augmented Data Inputs As noted previously the ACG System is designed to operate on the data typically retained in machine readable health insurance claims or encounter files Recognizing the limitations of ICD diagnosis information in common usage users may augment diagnosis information
124. des the user the option to use local or national concurrent weights local or national prevalence rates or total or pharmacy predicted resource use as appropriate Examples of where and how each report might be affected have been provided previously but to summarize the analyses and corresponding options are listed below e Local or National Concurrent weights Estimated concurrent resource use in EDC by RUB Distribution MEDC by RUB Distribution and Rx MG by RUB Distribution Such comparisons allow within group comparisons to be contrasted to external or reference comparisons Local or National Prevalence Rates Age sex adjusted expected rate 1000 in Standardized Morbidity Ratio by EDC Standardized Morbidity Ratio by MEDC Standardized Morbidity Ratio by Rx MG and Standardized Morbidity Ratio by Major Rx MG The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 65 e Total or Pharmacy Model Type Predicted Resource Use in Cost Predictions by Selected Condition Cost Predictions by Rx MGs Again the interpretation is how do sub populations compare to the within group average contrasted to comparing the same sub population to an external reference Figure 35 Options Report Options Filters Options Groups Set these options to control how your report is calculated Options control how your analysis is calculated See the help For more information regarding how each option impact
125. dity groups provide an excellent means of finding the population of individuals defined in the regulations The Rx MGs identify members being treated for particular conditions while the Rx PM predicted resource index calibrated for an elderly population can be used to calculate an individual cost forecast Using these tools for the identification of candidates for MTMPs allows a PDP to screen the whole population with an objective and reproducible method The Johns Hopkins ACG System Version 8 2 Technical User Guide Selecting the Right Tool 3 31 Capitation and Rate Setting The ACG System has made it possible to accomplish risk adjustment with fairly simple and straightforward analytic strategies and the ACG actuarial cells have long been the primary actuarial method for capitation and rate setting Actuarial cells represent a fixed number of discrete categories into which individuals are placed based on their expected use of resources There are a number of advantages associated with using an actuarial cell based approach to risk adjustment for capitation and underwriting which include Simplicity Once the population has been classified into around 100 ACG cells it is possible to risk adjust the population by using a spreadsheet Some users have chosen to simplify this approach even further by collapsing the ACGs into smaller homogeneous groupings called resource utilization bands RUBs Even when grouped into RUBs studies indicate tha
126. e Summarizing Total or Ambulatory Charges Most plans retain the submitted charge allowed or eligible amount and paid amount for healthcare services in their machine readable claims files The submitted charge refers to the charge submitted on the provider s claim The allowed or eligible amount refers to the amount the plan has determined it will pay for the covered service after applying reasonable and customary charge screens or a fee schedule The paid charge is the allowed amount reduced by any applicable copayments and deductibles required by the subscriber A Tip Providing summarized total charges including pharmacy cost and or a separate summary pharmacy cost field on the patient input file will improve predictive model performance Typically it is recommended that users aggregate either the paid charge or the allowed amount for each patient as the most appropriate measure of total and or ambulatory charges Since the ACG System can be used to compare the consumption of resources across groups different copayment and deductible amounts as well as different paid charge amounts may prevent accurate comparison of different subscriber groups Therefore the allowed amount is typically used as the best measure of resource consumption when comparing groups or profiling providers In the case of capitation where the focus is in plan liability paid amounts may be appropriate Ambulatory Encounters Some users particularly those
127. e ACG Software 06 4 7 Coding Issues Using National Drug Codes NDC ssssssessccesocesocessoessceessee 4 9 Identifying Special Populations with Augmented Data Inputs 4 10 Constructing Resource Consumption Measures sccssssccssssecsssssesees 4 12 Risk Assessment Variables scccscssscsssccsscessssscsssssssssssscssesssesesssecseoes F213 S mimary ROVICW siciinwnsiinnausimimucnsiannnwnunnsianaunine ale 5 Installing and Using ACG Softwar e cccscccsssscssssscssssscssssccssssssesssscssssssees D ite 0 ee J System Requirements siscsssassssscesssesonssssoovssvsessoevsssevonsseensovssveasouvssenenessoansseesses O71 Tistaline th SoftWare cs secccstsvcsccvissstesctensentisasnarciueserimiermeenatinemnin OO Usine the Siew E secs ccceeetesinrceiseerseeneneianm aman ore ACG for Windows Desktop siviissovesscsossssssosssasssesonsocsnnssesdossnescsoesoeasssssseveveeys D7 LO Load the Sample Dataset e esoocsoossooesssoossooscosscoosssoocsooossoossosseosessseesssssssss D O L Export Report TaDpleS aiscsissstccsnanesiiaiscisiioniaiiniiniinnnnids BOF Export Data Piles shicsineniscinaiieoiinenessnaniwana mninemnnnseune oe Use Your Own Data scncswncitineniniisnnniccnnainniiocummmumennumnuinn 2 10 Additional Sources of Information scccscsssecsscesscescssssssssssesessesees D7OD Appendix A ACG Output Data ccssccssssscssssscssssscsssscsssscsssssssssserss D7 OO Appendix B Batch Mode
128. e Johns Hopkins ACG System is built to handle relatively large data volumes and processing requirements The performance of the software is very much based upon the speed and memory of your computer Operating System The following versions of Windows are supported e Windows XP Professional with Service Pack 1 or greater e Windows XP Home e Windows Vista Central Processing Unit CPU Any Intel 32 bit compatible CPU is supported A Pentium 4 at 2 0 GHz or faster is recommended Memory RAM 512 megabytes MB RAM is recommended The application will immediately utilize 64 MB upon startup and expand up to 512 MB RAM as necessary The size and complexity of the analyses spreadsheet like reports are limited by the amount of RAM on your computer If you experience Out of Memory errors while running an analysis you should close any other open applications or otherwise expand the amount of available RAM and try re running the analyses Technical User Guide The Johns Hopkins ACG System Version 8 2 5 2 Installing and Using ACG Software Disk Space The application itself consumes approximately 165 MB of hard drive space The temporary space required to build an ACG data file is approximately four to five times the size of the import data files An ACG data file can consume anywhere from five to 40 megabytes per 100 000 patients depending on the length of member ID number of diagnoses etc One to five gigabytes of free d
129. e contact your software vendor for documentation and certification The Johns Hopkins ACG System Version 8 2 Technical User Guide Assessing the ACG Grouper s Output 6 i 6 Assessing the ACG Grouper s Output WY UD EI css ies seat acter cand snsacestamsacstins Senta bdeenseteenbaecnnntentaadeabbacabnioecelssecan 6 1 ACG Compressed Data Pile scscsccscscsscessasssessevssssvscisenssssssnssastassssesevessanesassoosse 6 1 Basic Review ProCESS ssssssssssssssoscssscsssassssssocssosecssousssoosssascssssesssoscssaspsssss ssesssst 6 1 Review of Reports Produced Automatically by the Software 6 2 S mMmary Statistics TaBe nie E ARE S N 6 2 Which Predictive Model sizes cc ccan sas ncscesspndiracesncsaatecsiereenadecuatnansiceutnwin ae 6 3 Pateni Sap ee econ Ome EN nec reary Pele mnt es tere a et en re ener here tp iene Peer esr nese ert ee 6 4 Local Wee hesena a A RS 6 4 Age Gender Distribution ssssessssssessesseseesseesesresseeseserssteseseresressessees 6 4 PM Scores Distribuo N aseisiin akiai 6 5 Review of Reports Produced by the Analyze Menu sssccsssscssssscssees 6 5 Figure T Analyze Meni soca udievsinsssvasspssarcusenasadarnmiindanicareddmielaeneassecimmanmusts 6 6 Example RUB Distribution srities aai iai 6 7 Figure 2 Population RUB Distribution jicsssicsncsicvseicesssvensdsosiondusssantasctions 6 8 Comparison to Reference or External Data sic ccscsssctescsstascicsiniatassdcceessagcans 6 8 Additional Consid
130. e observation period Resource Bands Aggregations of ACGs based upon estimates of concurrent resource use providing a way of separating the population into broad co morbidity groupings as follows 0 No or Only Invalid Dx 1 Healthy Users 2 Low 3 Moderate 4 High 5 Very High 7 3 Local weights are calibrated to reflect the unique properties of your population and do not make use of national norms Probability scores can be used as the initial selection criteria for identifying members for early intervention Only a small percentage of individuals typically less than two percent have probability scores greater than 0 5 Roughly 10 percent of the population have scores greater than 0 10 RUBs provide a way of separating the population into broad co morbidity groupings Also useful when individual ACG cell counts fall below minimum thresholds The Johns Hopkins ACG System Version 8 2 Total Cost Band A banded indicator of historic total costs Strictly prior cost markers these bands based upon total cost percentiles Possible are used optionally by the ACG values include predictive models and may prove a useful adjunct to analysts wishing to 0 0 pharmacy costs stratify their populations 1 1 10 percentile 2 11 25 percentile 3 26 50 percentile 4 51 75 percentile 5 76 90 percentile 6 91 93 percentile 7 94 95 percentile 8 96 97 percenti
131. each unique patient not just the subscriber to the insurance policy are generally required Assignment of risk assessment variables can be accomplished by constructing a minimal data set composed of at least the minimum following data elements e A unique identifier for every member eligible to use services during the study period e The age or date of birth and e The gender of each member In addition the user must provide either or both of the following e All relevant ICD diagnosis codes assigned by providers for all encounters during the risk assessment time period in question and or e All codes from the pharmacy prescriptions filled for each patient during the risk assessment time period in question If ICD diagnosis information is available the software will assign all of the following e Aggregated Diagnosis Groups ADGs the 32 morbidity markers e Adjusted Clinical Groups ACGs the actuarial cells e Expanded Diagnosis Clusters EDCs disease clusters e Concurrent weights for each ACG category based on national reference data e Resource Utilization Bands ACGs collapsed into 6 categories from very low to very high resource use If pharmacy information is available the software will assign the following e Rx Morbidity Groups Rx MGs 60 morbidity markers In addition the software is a predictive modeling tool Predictions for total healthcare expenditures pharmacy expenditures and the probability of having hi
132. edicare populations 3 33 relative concurrent PMPY weights and RUB categories 7 18 resource utilization band distribution analysis report layout 5 19 risk weights and scores 7 2 RUB distribution analysis report layout 5 19 Rx MG by RUB distribution analysis report layout 5 26 sample of non matched ICD file 6 10 sample of non matched pharmacy file 6 12 simple profile analysis report layout 5 38 standardized morbidity ratio by EDC analysis report layout 5 28 5 30 standardized morbidity ratio by major Rx MG analysis report layout 5 31 standardized morbidity ratio by Rx MG analysis report layout 5 32 warning distribution analysis report layout 5 50 warning list layout 5 49 Technical enhancements release notes 2 8 Technical user guide navigation 1 1 topics 1 2 Time frames and basic population perspectives 8 2 final considerations 8 2 Tools menu 5 50 Typical place of service codes to exclude 4 8 ACG 4 8 U Underwriting 3 34 UNIX 5 89 Usage details 5 90 Use your own data 5 73 Using ICD 9 and ICD 10 simultaneously 4 6 V View menu 5 16 View results of the grouping process 5 52 W Warning distribution analysis 5 50 Technical User Guide
133. elease Date Jul 2008 Check for Updates Install File The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 15 If the ACG System fails to connect to the ACG website on three consecutive tries you will receive a message letting you know that it was unable to connect If you are unable to connect to the internet for updates you can receive a mapping file directly from your software vendor Mapping files will be recognized by the ACG System when they are installed This process is initiated by selecting Manage mappings from the Tools menu Then click Install File and select your ACG mapping file using the file chooser ACG mapping files will have a acgm extension 4 Tip You may not be able to connect to the ACG Website if your internet connection uses a proxy server Contact your designated support person to receive updated mapping files Figure 16 Mapping File Communication Error J Failed to communicate with ACG website for mapping updates 3 times in a row Please contact technical support for assistance There will be no additional warnings Using the Software The ACGs for Windows software is a standard Windows application initiated from the Start menu Follow these steps to access the software 1 Click the Start Menu 2 Select All Programs 3 Select Johns Hopkins ACG 8 2 4 Tip To create a shortcut to the ACG Software on your desktop simply right click
134. els are especially useful for small group underwriting because the movement of one or two high risk individuals into or out of a plan can have potentially dramatic effects on costs for a small group Small employer groups are sensitive to price and have a tendency to shop for a new carrier at renewal time The initial rate process uses more data than is feasible during a typical renewal therefore the initial rate process often produces the most competitive rates Small groups exhibiting low risk can often find rates lower than with their current provider however small groups exhibiting a history of high expenditures may find going to a new insurer prohibitively expensive This type of selection bias can lead to a very high risk pool and a future inability of a plan to offer attractive rates to retain the healthy groups In order to retain the best business insurers are faced with the difficult task of offering competitive pricing for these small groups by trying to accurately match premium revenue to expected expense while complying with existing rating regulations The Johns Hopkins suite of Predictive Models provides health plans the tools necessary to leverage existing medical and pharmacy claims in order to better estimate risk and better set premiums for small group renewal There are several benefits to using predictive modeling within the underwriting process e There is greater efficiency Predictive modeling can provide an automated risk
135. ended more for the programmer analyst this chapter discusses at a high level the minimum data input requirements and other necessary data requirements for performing ACG based risk adjusted analyses Included are discussions of augmenting or supplementing diagnosis information with optional user supplied flags as well as consideration of the use of pharmacy information Chapter 5 Installing and Using ACG Software Intended for the programmer analyst this chapter discusses the technical how to of installing using importing and exporting data and reports Chapter 6 Assessing the ACG Grouper s Output Intended for those running the software this chapter is intended to provide rudimentary advice on assessing ACG output Chapter 7 Making Effective Use of Risk Scores Intended for the programmer analyst the purpose of this chapter is to provide an overview of the risk scores or weights produced by the software and to provide assistance to the user as to how results might be improved or refined via customizing and the use of local cost data Chapter 8 Final Considerations A prelude to the Reference Manual this final chapter of the Technical User Guide highlights some of the key analytical and technical issues that affect both the framing and interpretation of analyses associated with the application of diagnosis based risk adjustment in populations Much of this discussion relates to forming a population for risk adjustment determin
136. ending on data input provided to the software be used for describing differences in morbidity mix across population sub groupings Technical User Guide The Johns Hopkins ACG System Version 8 2 Assessing the ACG Grouper s Output Figure 1 Analyze Menu x Johns Hopkins ACG System 8 2 File Edit view ACG Data File Comm Summary Statistics Patients processed Patients processed 6 Diagnoses processed Unique diagnoses ent Unique unknown dia Percentage of psen Unknown diagnoses Patients with Unique matched diag PEE Tools Help Seles RUB Distribution ACG Distribution ADG Distribution Population Dist By Age Band and Morbidity shili ict 5 y MEDC By RUB Distribution Bay Dist ull Options EDC By RUB Distribution Value RxMG By RUB Distribution Standardized Morbidity Ratio By MEDC Standardized Morbidity Ratio By EDC Standardized Morbidity Ratio By Major Rx MG Standardized Morbidity Ratio By Rx MG Cost Predictions By Select Conditions Cost Predictions For Selected Rx MGs Unique unknown diac Patients with unsupp Pharmacy codes proc Unique pharmacy coc Unique unknown pha Percentage of pharm Unknown pharmacy Patients with unknow Unique matched pha Unique unknown pharmacy code sets encountered Patients with unsupported pharmacy code sets encountered Number of EDCs assigned Number of MEDCs assigned Number of ADGs assigned 9654097 Number of Rx MGs assigned 5725867 Percentage of patients
137. eni r nE EN EETAS ONR 5 16 View Manere aaa vance ta oan vides 5 16 Analyze Menu mennoniitat ie e 5 17 Figure 18 ACG Reports Available for Analysis cesceseeeeneeeeeees 5 17 Figure 19 Report CAS sai oressnsccenasasncanaspeicernssnnanenasederenssvounisesnacaariats 5 18 Resource Utilization Band RUB Distribution Analysis 5 18 The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software Table 1 RUB Distribution Analysis Report Layout ccceeeeees 5 19 ACG Distribution AGEL a saserecncanes Gsaerqseasnesiemed esapeesnnascaeaciomnianicnoaen 5 19 Table 2 ACG Distribution Analysis Report Layout c eee 5 19 ADG Dee A alySiS amissis n a aait 5 20 Table 3 ADG Distribution Analysis Report Layout cceesceeeeeee 5 20 Population Distribution by Age Band and Morbidity Analysis 5 21 Table 4 Population Distribution by Age Band and Morbidity Analysis Report LOW isis visoasiesacessxanmianmadsniscustenasmeiniaaswnadanaranieidleartates 5 21 MEDC by RUB Distribution Analysis cccaiscsssssssesasscssicscasiacscsiscssetussanerses 5 22 Figure 20 Report Options for MEDC by RUB Distribution Analysis 5 22 Figure 21 Select the Risk Assessment Variables cccesseeeeseeeeees 5 23 Table 5 MEDC by RUB Distribution Analysis Report Layout 5 24 EDC by RUB Distribution Analysis oc iiscesccsciscst sclusctevisaschdinacuteniaseabiniess 5 25 Table 6 EDC by RUB
138. ent separated by spaces A vector of zeros and ones to indicate which ADG codes this patient was assigned A 1 in the fifth position indicates the patient was assigned ADG 5 ADG is prepended to this vector as a convenience to help other database systems like Microsoft Access treat this vector as a String Note ADG15 and ADG19 are no longer in use and thus should always be zero ADG Vector Expanded Diagnosis Clusters all of the EDC codes assigned to this patient separated by spaces EDC Codes The EDC taxonomy identifies patients with specific diseases or symptoms that are treated in ambulatory and inpatient settings Major Expanded Diagnoses Clusters All of the MEDC codes assigned to this patient separated by spaces The EDC taxonomy is structured into broad clinical categories called MEDCs MEDC Codes Pharmacy Morbidity Group Codes all of the Rx Rx MG Codes MG codes assigned to this patient separated by spaces Major Pharmacy Morbidity Group Codes All of Major Rx MG Codes the Major Rx MG codes assigned to this patient separated by spaces The number of major ADGs assigned to this patient A major ADG is an ADG found to have a Major ADG Count significant impact on concurrent or future resource consumption There are separate major ADGs for pediatric and adult populations A flag for any one of 11 diagnostic clusters that represent discrete conditions consistent with frailty
139. eports by population variable were run according to the option selected 5 Review the list and distribution of data warnings 6 Examine the list of non matched ICD and pharmacy codes The goal of these analyses is to first provide an initial review of the output the second is to provide a more detailed understanding of the study population s characteristics or texture Example RUB Distribution Resource Utilization Bands RUBs represent a means of collapsing the multiple ACG categories into six iso resource groupings from very low or non users to very high ACG aggregation into RUBs is as follows e RUB 0 No Resource Use ACG 5200 e RUB 1 Low Expected Costs ACGs 0200 0300 1600 e RUB 2 Low Intermediate Expected Costs ACGs 0100 0400 0700 0900 1300 1800 2500 3400 3800 e RUB 3 Intermediate Expected Costs ACGs 0800 1400 1500 1712 1722 1732 1742 1752 1762 2600 3300 3500 3700 3900 4320 4410 4420 4510 4610 4710 4720 4910 5010 5310 5330 e RUB 4 Intermediate High Expected Costs ACGs 1711 1721 1731 1741 1751 1761 1771 1772 4330 4430 4520 4620 4730 4830 4920 5020 5040 5050 5320 e RUB S High Expected Costs ACGs 4930 4940 5030 5060 5070 5340 RUBs provide an easy means of summarizing ACG information and are useful for presentation payment and profiling applications Figure 2 Technical User Guide The Johns Hopkins ACG System Version 8 2 6 8 Assessing the ACG Grouper s
140. ered valid are eligible for export to the non matched ICD file Each mismatched code is written out one time for each person who has that code along with a corresponding person ID In this way you can use this machine readable information to generate a listing of codes and or people who have non matched codes A sample of a non matched ICD file is presented as Table 1 The non matched ICD file contains each patient identifier for whom a non matched code occurred the ICD version 9 or 10 and the corresponding ICD code At the very least you should scan the list of non matched codes to determine if any codes that should have been assigned to an ADG are listed frequently The non matched ICD codes can be exported and saved as a CSV file either tab or comma delimited as shown in Figure 4 To gain a fuller perspective of the codes that are contained in the non matched ICD file you can sort the output file by ICD code only and create a frequency distribution of all rejected non matched ICD codes Table 1 Sample of Non Matched ICD File patient_id icd_version icd_cd d514AAAAAACAADBN 9 D999 d514AAAAAACAHJZW 9 E888 d514AAAAAACAIYSE 9 E888 d514AAAAAACAOBLE 9 E888 d514AAAAAACAOTGN 9 E888 d514AAAAAACASNTD 9 E888 d514AAAAAACAUAGC 9 E888 d514AAAAAACAWYRK 9 E888 d514AAAAAACBMZYK 9 E888 d514AAAAAACBNDHW 9 7412 d514AAAAAACBPYLW 9 E812 d514AAAAAACBXTBZ 9 E826 d514AAAAAACCCGTY 9 E813 d514AAAAAACCCJSM 9 E888 d514AAAAAACCIKWQ 9 E888
141. erm enrollees are likely to undervalue expected costs for those groups In any event such analyses should be approached cautiously because of the instability associated with the shorter term enrollment In summary when performing concurrent or retrospective risk based adjustment a PMPM calculation of ACG weights for a population that includes some number of part time enrollees tends to over represent the expected costs associated with 12 month enrollees and under represent the expected costs associated with shorter term enrollees A PMPY calculation of concurrent ACG weights appears to provide a more accurate measure of the expected weight As noted earlier we believe this empirical observation represents a relatively new paradigm and we encourage analysts performing profiling Technical User Guide The Johns Hopkins ACG System Version 8 2 Making Effective Use of Risk Scores and other concurrent analyses to test whether and how such an approach could replace the PMPM approach within their organization The Johns Hopkins ACG Development Team expects to continue providing empirical findings and support material regarding this innovation Table 5 Effect of Enrollment Period on Selected ACG Specific Weights 1 3 Months Avg Cases 2 939 736 63 264 4 6 Months Avg 818 1 3 1 7 9 Months Avg Cases 5 358 gt Q Q Cases All 4 713 1 062 18 1 dee N 1 9 0 19 3 3 2 27 AJAN J
142. es The Johns Hopkins ACG System Version 8 2 Technical User Guide Making Effective Use of Risk Scores 7 7 Converting Scores to Dollars As noted above both the ACG weights and the ACG PM s PRI are expressed as relative values where the mean is centered at 1 0 assuming the scores have been appropriately rescaled The interpretation then is that individuals with scores higher than 1 0 are more expensive than average whereas those with scores less than 1 0 are less expensive than average Such relative indices can easily be converted to dollar amounts by multiplying by the underlying mean of the population to which the risk adjustment values will be applied These dollars can be used as the expected cost values for profiling and other risk adjustment applications Before converting scores to dollar amounts it is important to rescale the data one option is to just use the adjusted weights described above to account for differences between the reference population in this case the US Non Elderly Risk Assessment Variables from Johns Hopkins nationally representative database and the population to which the weights are applied e g your population of interest Rescaling is necessary to assure that the underlying mean of the weights is 1 0 A similar process is undertaken when you use your own reference population and it has somewhat different characteristics e g it is from a previous time period or benefit coverage is somewhat d
143. es where the inclusion of one patient my falsely identify a provider as an outlier physician Yet at the same time it is these very high cost or outlier patients that the ACG PM high risk case identification tool is designed to identify Thus the use of truncation depends upon the application For applications that relate to rate setting or profiling a conservative strategy would be to top code set a ceiling for per person costs to 50 000 The Johns Hopkins ACG System Version 8 2 Technical User Guide Index IN 1 Index A ACG ADG adjusted clinical groups ACGs 3 3 ambulatory encounters 4 12 analysis time frame 4 7 capitation and rate setting 3 31 command line usage 5 89 components of the toolkit 3 2 compressed data file 6 1 concurrent versus prospective applications 3 36 concurrent weights 7 5 constructing resource consumption measures 4 12 delivery status 4 11 describing a population s health 3 5 distribution analysis 5 19 excluding lab and x ray claims 4 7 guidelines 5 92 identifying special populations with augmented data inputs 4 10 license file 5 10 low birth weight 4 11 multivariate models 3 32 one system many tools 3 1 output data 5 56 predictive model 5 55 6 3 predictive modeling 3 4 pregnancy status 4 10 procedure code ranges to exclude 4 8 profiling resource use 3 13 risk assessment variables 4 13 selecting relevant diagnosis for input into the software 4 7 selecting the
144. ess common than average across the subpopulation of interest The significance indicator identifies categories that are statistical different from the age sex adjusted expected value At the user s discretion the expected values can be derived from either the population mean or the national benchmark data The methodology for this analysis is explained more fully in the EDC Chapter in the Reference Manual The report layout is as follows Table 10 Standardized Morbidity Ratio by Major Rx MG Analysis Report Layout Column Definition Name oy vee Each Major Rx MG code that was assigned to at least one patient Moree The description for Major Rx MG Cd Name The number of patients assigned this Major Rx MG in this stratification The number per 1 000 patients in the current stratification that were assigned to this Observed 1000 Major Rx MG Calculated as Patient Count total Patient Count within the same stratification for all Major Rx MGs x 1000 The number of expected observations per 1 000 after adjusting for the age sex distribution in the current stratification Calculated as total of overall age sex prevalence rate x number of patients in age sex in current stratification for all age sex combinations number of patients in the current stratification for all Major Rx MGs x 1000 SMR Observed to Expected Ratio Calculated as Observed 1000 Age Sex Expected 1000 95 Confidence The lower range of the 95 confidence interval
145. essanesasvantsncsinammamiaietenmieien 1 3 Customer Commitment and Contact Information cccccssscssssceees 1 4 The Johns Hopkins ACG System Version 8 2 l ii Getting Started This page was left blank intentionally The Johns Hopkins ACG System Version 8 2 Technical User Guide Getting Started 1 1 Introduction to The Johns Hopkins ACG System The ACG Adjusted Clinical Groups System was developed by faculty at the Johns Hopkins Bloomberg School of Public Health to help make health care delivery more efficient and more equitable Because the ACG System can be used for numerous management finance and analytical applications related to health and health care they have become the most widely used population based case mix risk adjustment methodology Precisely because of the diversity of ACG applications one size does not fit all in terms of methodology Like health management and analysis itself using case mix or risk adjustment methods involves art as well as science and these applications are particularly context and objective driven We hope this documentation will provide you with much of the guidance you will need in order to apply the ACG System to most effectively meet the risk adjustment and case mix needs of your organization Objective of the Technical User Guide The technical user guide was designed to assist analysts programmers or other personnel who are responsible for applying ACG functionality to data
146. ewed the documentation supplied with this software release we would welcome your inquiries on any topic of relevance to your use of the ACG System within your organization Technical support is available during standard business hours by contacting your designated account representative directly If you do not know how to contact your account representative please call 866 287 9243 or e mail acg dsthealthsolutions com We thank you for using the ACG System and for helping us to work toward meeting the Johns Hopkins University s ultimate goal of improving the quality efficiency and equity of health care across the United States and around the globe The Johns Hopkins ACG System Version 8 2 Technical User Guide Release Notes 2 1 2 Release Notes CIV ONVICW E E E E E E nae 2 1 Localization Enh ncemenisS scsscsssscssesssosssssvesssensnccesssoesaassosssnsssvensissnssasssesenes 2 1 Cod SSS vestiiiesintdstaaciieciaaiancsioduiduaiableidssadeadeuioiadisnagpuadianisedielaiumaelmisbancss 2 1 Fig re 1 Summary SISSY sirens sesgura ae e aoedd 2 2 Anatomical Therapeutic Chemical ATC Classification 2 2 Risk Assessment Variables ussiisa ieo o Ea 2 3 Figure 2 New File Sere thi csisascctnccs cde canes insnlassdaseuasneastesneiausasmielassiases 2 3 Figures Summary Statistics Taberner e EE Rt 2 4 Figure 4 Build Options TaD seenen ie 2 5 Figure 5 All Models File Export Option sseessessessressesseessesssssorsseeseese 2 6 Enhanced License M
147. f ACG PM scores 7 16 prospective risk scores 7 6 resource bands 7 16 resource utilization bands RUBs 5 18 7 17 software produced weights and their uses 7 1 Managing pharmacy risk 3 30 MEDC RUB distribution analysis 5 22 Medication therapy management program MTMP candicate selection 3 30 Memory RAM 5 1 Mismatch break release notes 2 9 Multivariate models 3 32 N Navigation technical user guide navigation 1 1 Technical User Guide Index IN 5 NDC coding issues 4 9 Non matched codes diagnosis 6 10 pharmacy 6 11 Non users who are eligible to use services 8 5 final considerations 8 5 O Objective of the technical user guide 1 1 Open acgd files 5 80 Operating system 5 1 Options 5 64 usage details 5 90 Overview basic data requirements 4 1 release notes 2 1 P Patient file format 5 73 sample tab 5 56 Patient clinical profile report 5 41 Patient list analysis 5 44 Patient sample 6 4 Pharmacy data file format 5 77 non matched codes 6 11 PM scores distribution 6 5 Population distribution by age band and morbidity analysis 5 21 Predictive model ACG 5 55 6 3 options 5 82 predicted resource index PM PRI score 3 32 Pregnancy status 4 10 ACG 4 10 basic data requirements 4 10 Probability score 3 26 Procedure code ranges to exclude 4 8 ACG 4 8 Profiling resource use 3 13 Prospective risk scores 7 6 Provider performance assessment 3 13 Provisional diagnosis
148. f setting health policy or demonstrating value to health purchasers As a population ages health may be expected to decline but interventions to improve population health may improve or reverse that trend The ACG System describes population health in a unique aggregate way that can be trended over time In the example below the case mix for the population demonstrates a sharp increase in case mix from 1 02 to 1 17 Using a movers analysis Resource Utilization Bands which stratify the population into low moderate and high morbidity categories can be used to show changing morbidity patterns within a population see Table 5 For example in the prior period there were 758 patients assigned to the low morbidity category 405 of these individuals stayed in the low morbidity category 329 moved to the moderate morbidity bucket and 24 moved to the high morbidity bucket For those who went from low to high their average cost went from 2 333 to 14 183 Similarly there were 2271 moderate morbidity patients in the prior period Roughly half stayed the same and slightly less then half moved to low morbidity categories but 10 moved to high morbidity categories and tripled their resource use Table 5 Movers Analysis Tracking Morbidity Burden Over Time Current Period Case Mix 1 17 Low Moderate High Morbidity Morbidity Morbidity 405 329 24 Low 12 0 9 7 0 7 Morbidity P 618 P 705 P 2 383 C 1 382 C 1 512 C 14 183 9
149. ferences in provider practice patterns In general population oriented analysis will have more flexibility and be more comprehensive if both users and non users are included Sample Size The question of what is an appropriate minimum enrollee patient sample size arises at many levels of the risk adjustment process As a general rule the larger the sample size the better Ideally the total population used to perform ACG based analysis should be larger than 20 000 individuals Also ideally there should be a minimum of 30 50 cases in each ACG cell Smaller sample sizes may be applied but users should be cautious of instability created by small cell size Sample size plays an important role in profiling provider practice patterns Even when the underlying ACG weights are calculated using a large reference population providers treating relatively few patients may be unfairly skewed simply because of the effects of random error resulting from sample size Technical User Guide The Johns Hopkins ACG System Version 8 2 Handling High Cost or Outlier Cases How high cost or outlier cases are included affects many risk adjustment applications If untruncated cost weights of very high cost individuals are included in the calculation of either concurrent or prospective risk scores there will be a tendency for the variability of all cost estimates or risk scores to increase Similarly high cost cases can create problems for physician profiling analys
150. g diabetics enrolled in a disease management program EDCs are useful in portraying the disease characteristics of a population of interest Within disease management programs if significant differences in expected resource consumption exist across the morbidity subclasses this analytic approach is useful for better targeting interventions towards subgroups at higher risk The ACG Software produces tables in which each row represents persons falling into EDC or MEDC disease specific categories the columns array these individuals into RUB co morbidity categories according to their ACG assignment Table 9 presents the percentage distribution for a series of selected EDCs across the five RUB categories Table 10 presents the expected relative resource use within each RUB and illustrates co morbidity s profound influence on resource use within individual disease groups The ACG based RUBs do a very good job of explaining variations in resource use within specific diseases For additional detail on interpreting or building similar tables please refer to the chapter entitled Expanded Diagnosis Clusters EDCs in the Reference Manual Table 9 Percentage Distribution of Each Co Morbidity Level Within an EDC Samples RUB 1 RUB 5 Very RUB 2 RUB 3 RUB 4 Very EDC Description Low Low Average High High ADMO ADM02 46 L6 i3 oo Go amp ojx N Ww uo Y RIAA a Juja n Ur pes amp lon WwW 5 zA
151. g of the ACG algorithm Please contact your distributor to license the Johns Hopkins ACG System for any use not authorized under the current license agreement The terms The Johns Hopkins ACG System ACGA System ACGA ADGA Adjusted Clinical Groups Ambulatory Care Groups Aggregated Diagnostic Groups Ambulatory Diagnostic Groups Johns Hopkins Expanded Diagnosis Clusters EDCs ACG Predictive Model Rx Defined Morbidity Groups Re MG ACG PM Dx PM Ro PM and DxRx PM are I accept the terms in the license agreement I do not accept the terms in the license agreement lt Back Finish Cancel Technical User Guide The Johns Hopkins ACG System Version 8 2 Installing and Using ACG Software Figure 11 Install the License File Upon completion of the install process the user will be prompted to load a license file License files are client specific Access to the diagnosis and or pharmacy components of the system is dependent on the licensing agreement acquired from your software vendor For information on which license file is required please contact your primary support person x 2 License does not have dx or rx modules enabled Would you like to install a new license now ele Click Yes to go to the next window Figure 12 Figure 12 Choose the License File gy Choose a License File to Install LookIn Johns Hopkins ACG 8 1 X la A Ej eB Ca lib 3 Uninstall
152. gh expenditures for each of these categories will be calculated The software automatically selects the best Technical User Guide The Johns Hopkins ACG System Version 8 2 4 2 Basic Data Requirements available model based on the available data with the minimum data elements being age gender and either diagnosis pharmacy or diagnosis pharmacy codes Optionally and at your discretion predictive model performance may be enhanced by the incorporation of the following e Total medical costs including pharmacy costs and or e Pharmacy costs Finally and discussed in more detail subsequently you may optionally augment the diagnosis stream in three key areas pregnancy status delivery and low birth weight Providing such additional user supplied flags will enhance the performance of the system and may affect the number of risk categories produced Once risk assessment variables have been assigned the output of the software is typically linked to additional user supplied data inputs to prepare additional customized reports In some cases particularly where reporting systems are already in place the software output can be exported and linked directly to existing patient specific summary files In addition to the basic data input age gender and relevant diagnosis codes and output ADGs ACGs EDCs concurrent weights RUBS and predictive modeling scores produced by the software there are several additional pieces of informatio
153. gt column names should not contain spaces gt column descriptions may contain spaces gt data types are described in the documentation for file formats in the Windows application The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 93 Install a License File C gt Progam Files Johns Hopkins ACG 8 2 jhuacg exe install license c acgdata mylic acgl The command above is typed on a single line Note Ifthe license file was installed under the Windows release prior to using the command line version then the license file does not need to be re installed and this step can be skipped Figure 46 Use the Command Line Version to Install a License File amp Command Prompt o x E C Program Files Johns Hopkins ACG 8 2 gt jhuacg install license MyLicense acgl Installing license file C Program Files Johns Hopkins ACG 8 2 MyLicense acgl C Program Files Johns Hopkins ACG 8 2 gt _ Technical User Guide The Johns Hopkins ACG System Version 8 2 5 94 Installing and Using ACG Software Create a New ACG Data File acgd C gt Program Files Johns Hopkins ACG 8 2 jhuacg new acg file c acgdata 82Sample acgd patient c acgdata My_Patient_file csv patient format tab diagnosis c acgdata My Diagnosis _file csv diagnosis format tab pharmacy c acgdata My_Pharmacy_File csv pharmacy format tab ignore prior costs The command above is typed o
154. harmacy code sets encountered Unique unknown pharmacy code sets encountered Patients with unsupported pharmacy code sets encountered Number of EDCs assigned Number of MEDCs assigned Number of ADGs assigned Number of Rx MGs assigned Percentage of patients with total cost gt 100 and no diagnoses Percentage of patients with pharmacy cost gt 100 and no pharmacy codes 0 6 Number of patients with diagnosis information and no pharmacy codes 0 Number of patients with pharmacy codes and no diagnoses 0 Number of data warnings 4621 Number of patients with data warnings 4613 Minutes To load data 18 Total cost model selected DxRx PM total cost gt total cost Pharmacy cost model selected DxRx PM rx cost gt rx cost Date loaded 2008 10 28 Created with ACG version 8 2 Created with Risk Assessment Variables US Non Elderly Created an ACG mapping version 8 1 3rd Quarter 2008 Release release dat 2008 07 07 The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 55 Which Predictive Model The Summary Statistics Tab also provides the user with information on which predictive model was used in selecting the scores predictions of total cost pharmacy cost and probability scores for high total cost and high pharmacy costs in the summary patient file The descriptions for each model are described in four sections using the following example Total Cost Model Selected DxRx
155. he Export Current Tab To Delimited Data File will export the data in the currently selected tab to a single data file comma delimited or tab delimited text file You can choose to write out a header row as the first row in the data file This row contains the names of the columns and is useful when importing the data into another database Figure 37 Export Report Tables x Johns Hopkins ACG System 8 2 File Edit Yiew Analyze Tools Help Aom x ee 4 9 fl fill S2SAMPLE acgd E ACG Distribution ACG Distribution Analysis for 825AMPLE acgd Overall Line of Business Company Product Employer Id Benefit Plan Health System Report Options ACG Cd ACG Description Frequency Freq 0100 Acute Minor Age 1 32 0 16 0200 Acute Minor Age 2 to 5 192 0 97 0300 Acute Minor Age gt 5 1 723 8 71 0400 Acute Major 0500 Likely to Recur w o Allergies 0600 Likely to Recur with Allergies Export Table 0700 0800 0900 1000 1100 1200 1300 1400 1500 1600 1711 1712 1721 1722 1731 1732 1741 1742 1751 Asthma Chronic Medical Unstable Chronic Medical Stable Chronic Specialty Stable Eye Dental Chronic Specialty Unstable Psychosocial w o Psych Unstable Psychosocial with Psych Unstable w o Psych Stable Psychosocial with Psych Unstable w Psych Stable Preventive Administrative Pregnancy 0 1 ADGs delivered Pregnancy 0 1 ADGs not delivered Pregnancy 2 3 ADGs no
156. he ACG system does accept many three digit codes and other invalid codes when their meaning is clear and their categorization is precise enough for assignment into a single ADG Rule Out Suspected and Provisional Diagnoses One of the most frequent criticisms of the ICD system is the lack of codes that allow a provider to stipulate that a particular diagnosis be designated as rule out R O suspected or provisional Providers may record diagnoses as R O on medical records even though they do not strongly suspect them because certain tests procedures or trials of therapy are used to make a more definitive diagnosis However because ICD has no rule out code or modifier diagnoses such as coronary artery disease subarachnoid hemorrhage and hiatal hernia just to name a few may remain in the patient s claim database because they were recorded on one or more of the claim forms in the course of the patient s work up With the exception of excluding diagnoses from lab and x ray claims which frequently are rule out or provisional in nature the Johns Hopkins ACG Development Team does not believe that R O or suspected diagnoses have a dramatic effect on ACG assignment One reason is that in a retrospective application R O diagnoses still affect the consumption of healthcare resources For example a patient who has R O coronary artery Technical User Guide The Johns Hopkins ACG System Version 8 2 4 6 Basic Data Requirements disease or
157. her because they have special medical circumstances or at the extreme die In addition to these circumstances as the following tables will illustrate shorter term enrollees have seemingly higher PMPM costs in large part because the denominator of the PMPM calculation is relatively smaller for those enrollees By contrast the average cost of 12 month enrollees tends to be more stable The following analysis illustrates the implications of this within the context of diagnosis based risk adjustment such as ACGs Table 3 presents a side by side comparison of the PMPM and PMPY costs of enrollee sub groups defined in terms of months enrolled during a given recent year at a large commercial HMO The table is limited to those who used services because retrospective analyses e g provider profiling are typically limited to those who actually used services The average PMPM costs for the enrollee cohorts decrease as the length of enrollment increases Those who were enrolled for 12 months used 86 95 PMPM while those enrolled for only one month used 768 92 PMPM illustrating almost a nine fold difference between twelve month and one month enrollees Viewed from this perspective it would appear that it is important to account for months enrolled when examining the pattern of costs over a given time period In contrast there is less than a two fold difference between those enrolled for 1 and 12 months on a non annualized PMPY basis As would be expected t
158. his product includes software developed by The Apache Software Foundation http www apache org This product includes the Java Runtime Environment developed by Sun Microsystems http java sun com This product includes the following open source JDOM library http www jdom org iText library http www lowagie com iText JasperReports library http www jasperforge org This page is intentionally left blank Table of Contents i Table of Contents I Get ng Started socicnnnnan a a a a e Introduction to The Johns Hopkins ACG System c ssssssssssssssssssesseeseee 1 1 Objective of the Technical User Guide ssesssesssocesooesoocssocessocesocesoosssoosee L I Technical User Guide Navigation ssccsssssssssssssssssssssssssssssesssssessseseseees L 1 Technical User Guide Topics seeesseecsocccoosccoeesoccssocecosecooesoooesocoscossessseesssess 1 2 Reference Manual Topics s ssssssesssoossoosccocscooessooessooossooccooseoossosesssoesssssssssss Led Customer Commitment and Contact Information sssesssesssocssoossoossssesss 1 4 PARE NGS e E E E E OA aa Ea E E E E E E E T E E E E Localization Enhancements s ssoesssecssscossooscosecoocssooessoocsocscosssosessseessssssssses 2 L Technical Enhancements ssssssssssssssssssssssnsssssssssssssssssssosssssssosssssssssssscsssssssssss 270 Documentation Enhancements csssccsssssscsssccssssscesscssssscsssssssssssccees 2 10 3 Selecting the Rishi
159. hose enrolled for very few months tend to have lower within plan annual average costs but this effect is less marked than the differential found when PMPM values are compared Table 3 Comparison of PMPM and PMPY Average Costs by Months Enrolled Within a HMO Population The Johns Hopkins ACG System Version 8 2 Months Enrolled 768 92 768 92 125 45 1 129 09 Cost includes total paid claims truncated at 35 000 e The population was limited to service users in a large commercial HMO population for 1996 PMPM Per member per month PMPY Per member per year Note Although 12 months were used here other extended periods could also be used to calculate per member per period weights Technical User Guide Making Effective Use of Risk Scores 7 11 When diagnoses are assigned on a concurrent basis and partial year enrollees are included in the analysis the denominator in the PMPM calculation tends to skew the relationship between actual and expected costs particularly when performing retrospective analyses such as provider performance profiles As previously described PMPM ACG weights are calculated by determining the costs associated with each ACG divided by the total member months associated with that ACG The total expected costs associated with any given individual in this case would be the PMPM ACG weight times the number of months enrolled Alternately ACG weights derived on a PMPY basis are calculated as the costs associate
160. iated with frailty This marker is one of the risk factors considered by the Dx PM and DxRx PM models e Hospital dominant condition marker Diagnoses that when present are associated with a greater than 50 percent probability among affected patients of hospitalization in the next year This marker is one of the risk factors considered by the Dx PM and DxRx PM models The standard sets of Risk Assessment Variables delivered with the software are US Non elderly and US Elderly In these sets of Risk Assessment Variables the reference concurrent weights the predictive modeling coefficients and reference prevalence rates are calculated based upon a representative population of either US Non elderly members or US Elderly members The mappings of ACGs to RUBs and the mappings of diagnosis codes to Frailty and Hospital Dominant Conditions are standard across all models at this Technical User Guide The Johns Hopkins ACG System Version 8 2 4 14 Basic Data Requirements time If your population is large and may vary from the US Non elderly or US Elderly references please contact your distributor about additional Risk Assessment Variables for your population Summary Review To recap this chapter lays out the general data requirements of the ACG System Software and outlines the key considerations for data analysts as they begin the process of gathering the necessary elements for running the software The main data elements for running the softwa
161. ices are potentially intertwined with patterns of over use or under use Risk adjusted rates based on these factors may in a circular manner lead to setting rates that are inappropriate either too high or too low Moreover when risk factors are determined by such drug use or procedural delivery patterns providers who practice efficiently could potentially be penalized for their efficiency This circularity issue is not a major concern when only diagnostic information not linked to specific types or settings of service is used as the main source of information on risk factors Underwriting The ACG predictive models calibrated for high risk case identification provide underwriters with a suite of tools to estimate future resource use based on the case mix of the enrolled population which offers an improvement over more traditional prior utilization models For example in addition to just estimating future resource use the models can also be used to help identify persons expected to convert from relatively low to relatively high resource use This not only improves the quality and accuracy of Technical User Guide The Johns Hopkins ACG System Version 8 2 Selecting the Right Tool underwriting but also provides opportunities for reducing costs for employers by getting at risk employees enrolled in timely case management interventions to help reduce both future medical expenses and illness associated absenteeism The ACG predictive mod
162. id Bodycombe Sc D Klaus Lemke Ph D Patricio Muniz M D MPH MBA Thomas M Richards Barbara Starfield M D MPH and Erica Wernery Special thanks to Lorne Verhulst M D MPA of the British Columbia Ministry of Health in Vancouver Canada for his contribution to the chapter titled Practitioner Profiling Assessing Individual Physician Performance Provider Performance Assessment Additional production assistance and original content provided by Rosina DeGiulio Meg McGinn and Amy Salls of DST Health Solutions LLC The ACG Team gratefully acknowledges the support provided by our corporate partner in helping to move this publication forward If users have questions regarding the software and its application they are advised to contact the organization from which they obtained the ACG software Questions about grants of rights or comments criticisms or corrections related to this document should be directed to the Johns Hopkins ACG team see below Such communication is encouraged ACG Project Coordinator 624 N Broadway Room 607 Baltimore MD 21205 1901 USA Telephone 410 955 5660 Fax 410 955 0470 E mail askacg jhsph edu Website http acg jhsph edu Third Party Library Acknowledgements This product includes software developed by the following companies Health Plus Technologies http www healthplustech com Karsten Lentzsch http www jgoodies com Sentintel Technologies Inc http www healthplustech com T
163. ient with multiple diagnoses can be assigned to one or more ADGs 4 Example A patient with both Obstructive Chronic Bronchitis ICD 9 CM code 491 2 and Congestive Heart Failure ICD 9 CM code 428 0 will fall into only one ADG Chronic Medical Unstable ADG 11 4 Example A patient with Candidiasis of Unspecified Site ICD 9 CM code 112 9 and Acute Upper Respiratory Infections of Unspecified Site ICD 9 CM code 465 9 will have two ADGs Likely to Recur Discrete Infections ADG 8 and Time Limited Minor Primary Infections ADG 2 respectively For more information on ADGs please refer to the chapter in the Reference Manual entitled Clinical Aspects of ACGs ICD stands for the World Health Organization International Classification of Disease coding system The number reflects the version number CM stands for Clinical Modification the version used in the United States The Johns Hopkins ACG System Version 8 2 Technical User Guide Selecting the Right Tool 3 3 Adjusted Clinical Groups ACGs ACGs are a series of mutually exclusive health status categories that are defined by morbidity age and sex They are based on the premise that the level of resources necessary for delivering appropriate health care to a population is correlated with the illness burden of that population This means that populations using the most health care resources reflect the interplay of co morbidities and cannot be ac
164. ifferent Unless rescaling is done resource use or payments may be over or under predicted Table 2 and the accompanying discussion provide a simplified example for a population with only twelve members How to Rescale and Assign Dollar Values The rescaling process consists of the following steps Step 1 Compute population mean weight Compute a separate grand mean for each of the weights either concurrent ACG weights or the ACG PM PRI generated for your population the observations represent individuals The mean for this example is shown in Table 2 at the bottom of Column B Step 2 Apply weighting factor Divide each individual weight by the rescaling factor i e the mean that you computed in Step 1 The result is the rescaled relative weight Column C Step 3 Compute population mean cost For the same population on which the weights were based compute the mean cost for the current data year For this example the mean cost was 1 265 11 Step 4 Compute cost Multiply the rescaled relative weights generated for each member of the population Column C by the average population cost generated from Step 3 to calculate an estimated individual cost Column D Technical User Guide The Johns Hopkins ACG System Version 8 2 Table 2 Estimating Costs in a Sample of Cases B C D Member Relative Weight Rescaled Weight Estimated Cost The rescaling factor functions as a summary case mix index for understanding how the rating p
165. ile c acgdah ta 82Sample acgd export file c acgdata patientexport txt m Loading mappings sing mapping version 8 2 3rd Quarter 2008 Release release date Jul 7 2068 Exporting data to c acgdata patientexport txt Successfully exported patient from c acgdata 82Sample acgd to c acgdata pat ientexport txt C Program Files Johns Hopkins ACG 8 2 gt _ Technical User Guide The Johns Hopkins ACG System Version 8 2 5 96 Installing and Using ACG Software Appendix C Java API The ACG System includes a Java API which allows clients to process data one member at a time This may be useful when building applications which provide data to the system interactively e g within a workflow system The client can utilize this API with a development environment that can interface with Java Because the API processes a single member at a time some aggregate processes will not be performed by the API and will be the responsibility of the developer In order to use prior cost as an input into the predictive model the developer will be required to calculate the total and pharmacy cost bands for input into the application Probability scores will not be calculated by the API but can be calculated by ranking the scores determining the percentile and converting to a probability score using a lookup table Other aggregate variables such as local weights and rescaled PRIs will not be available in the API A Tip Pleas
166. ill produce The Groups tab is originally populated with the default population stratifiers for the selected analysis The underlying details of a group can be displayed by selecting it i e clicking on it The currently selected group displays a Name which is the title of the section on the analysis and the categories which are the list of columns that define the stratifiers for that group You can add new groups modify groups or remove groups before an analysis is run If custom fields have been created you can build groups on these columns as well Technical User Guide The Johns Hopkins ACG System Version 8 2 5 64 Installing and Using ACG Software Figure 34 Groups Report Options Filters Options Groups Define groups to control the stratifications your report will produce This analysis will produce a separate report for each Group all in one session You can add new groups modify the current groups or remove groups to enhance the output of an analysis The categories define the columns that a group will stratify on No categories produces an overall summary Groups Overall Line of Business Categories Company Product Employer Id Benefit Plan Health System New Group Name New Group Column Name Line of Business Company Product Employer Id Remove Lx Options For some of the analyses there is an additional report option tab This provi
167. individuals with a certain type of condition may be in need of intervention or case management rather it is individuals in the far right of the table those individuals exhibiting a specific condition AND multiple co occurring conditions who are most likely to need high levels of health care services This analysis has the option to report the estimated concurrent resource use in terms of local weights or national weights Using local weights each of the rows is compared to the average of the population while using reference weights each of the rows is compared to the reference data base described by the Risk Assessment Variables in the Summary Statistics 4 Tip Selection of local versus reference weights is determined by selection of Report Options Options Weight Type and graphically illustrated in Figure 20 below Figure 20 Report Options for MEDC by RUB Distribution Analysis Report Options Filters Options Groups Set these options to control how your report is calculated Options control how your analysis is calculated See the help for more information regarding how each option impacts a given report Concurrent Weight Options Weight Type Reference Weights v Reference Weights Predictive Mode ocal Weights Model Type Prevalence Comparison Group Prevalence Type The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 23 A Tip Risk Assessment Variable
168. ing ACG Software Actuarial Cost Projections The Actuarial Cost Report represents a summary of information relevant for actuarial purposes and for differentiating groups as high medium and low risk This analysis provides a number of aggregate measures for both current and future costs expressed as a relative index scores equal to 1 0 indicate average morbidity or risk greater than 1 0 indicate greater than average morbidity burden or risk and less than 1 0 less than average The Reference CMI is a concurrent measure that compares the group case mix to the referenced benchmark used in the selected Risk Assessment Variables based on the mix of ACGs assigned to the members of the group The Local CMI is a similar measure but the comparison group is based on the population presented to the ACG System Mean Total PRI is a measure of prospective risk using the ACG predictive model to forecast total cost relative to the plan average Likewise the Mean Rx PRI measures the prospective risk of pharmacy cost relative to the plan average These resource indicators can be compared to the age sex relative risk When age sex relative risk is equal to the local CMI the risk is driven by the age and sex of the group When age sex relative risk is lower than the local CMI the risk is driven by disease burden more than the age sex mix of the group There is an additional index of the observed cost to the expected cost accounting for the local CMI as a measure of h
169. ing the observation period Based upon a national reference database with a mean of 1 0 the predicted value is expressed as a relative weight Population or sub group analyses provide comparisons to national norms Value based on best model selection The model used can be found in the Summary Statistics Rescaled Total Cost Resource Index The Total Cost Resource Index rescaled so that the local population mean is 1 0 Sub group analyses provide comparisons to local norms Probability High Total Cost ACG PM Probability Score for total cost the probability that this patient will have high total costs including pharmacy costs in the year following the observation period Unscaled Pharmacy Cost Resource Index ACG PM PRI Score for Pharmacy Costs the estimated pharmacy costs for this patient for the year following the observation period Based upon a national reference database with a mean of 1 0 the predicted value is expressed as a relative weight Population or sub group analyses provide comparisons to national norms Value based on best model selection The model used can be found in the Summary Statistics Rescaled Pharmacy Cost Resource Index The Pharmacy Cost Resource Index rescaled so that the overall population mean is 1 0 Sub group analyses provide comparisons to local norms Probability High Pharmacy Cost The Johns Hopkins ACG System Version 8 2 ACG PM Probability Score for pharmacy co
170. ing which members to include and to exclude and circumstances where sampling is appropriate Index The Johns Hopkins ACG System Version 8 2 Technical User Guide Getting Started 1 3 Reference Manual Topics For your convenience a list of the Reference Manual chapters is provided Chapter 1 Getting Started Provides a general overview of the physical organization of the manual as well as content Chapter 2 Adjusted Clinical Groups ACGs This chapter provides a brief overview of the history of the clinical origin of the ACG System and describes the minutiae of the ACG assignment algorithm Chapter 3 Clinical Aspects of ACGs Designed to provide more clinical contextual detail this chapter also explains the ACG algorithm but does so using several clinical vignettes to help elucidate how ACGs work Chapter 4 Expanded Diagnosis Clusters EDCs The first section of this chapter explains the development and evolution of the EDC methodology while the second is dedicated to demonstrating how they might be used or combined with ACGs for disease or case management applications Chapter 5 Predicting Future Resource Use with Diagnostic Data This chapter provides background information on the conceptual and clinical basis underlying predictive modeling and provides the history of the development of the ACG diagnostic based predictive model Dx PM Chapter 6 Predicting Future Resource Use with Pharmacy Data This chapter describes
171. install into opt jhuacg version The current version should install into opt jhuacg8 2 Installation can be confirmed by running the help command opt jhuacg8 2 jhuacg h Note The software requires a Java 6 Runtime this is technically Java 1 6 recently marketed as Java 6 ACG Command Line Usage Command line usage of the ACG application works the same at the Windows command prompt and at the UNIX command prompt shell All examples given are provided in Windows format Technical User Guide The Johns Hopkins ACG System Version 8 2 5 90 Installing and Using ACG Software Usage Details Create a New ACG Data File jhuacg new acg file lt file gt patient lt file gt patient format TAB COMMA lt file gt patient skip diagnosis lt file gt diagnosis format TAB COMMA diagnosis skip pharmacy lt file gt pharmacy format TABJ COMMA pharmacy skip rav lt rav code gt ignore prior costs all models Export Data from an ACG Data File jhuacg export lt type gt acg file lt file gt delim TABJCOMMA col file lt file gt export file lt file gt no headers Install a License File jhuacg install license lt file gt Install a Mapping File jhuacg install mapping file lt file gt Options new acg file lt file gt Creates a new ACG Data File called lt file gt patient lt file gt Uses lt file gt as patient source data file patient format lt file gt Uses
172. io button or the area of interest or click on the File Selection button to activate Windows explorer to find and highlight the requested file s Technical User Guide The Johns Hopkins ACG System Version 8 2 5 82 Installing and Using ACG Software Model Options By default the ACG for Windows software automatically selects the best predictive model based on the data found in the patient file that is whether or not total cost or pharmacy cost data is available for each member and the data found in the diagnoses and pharmacy input files depending on whether or not one or both are present Optionally the user may request that the software e Use a specific reference data set when assigning risk assessment variables such as reference concurrent weights reference prevalence rates and predictive modeling scores e Ignore prior cost data in the estimation of the models and or e Calculate all valid predictive models for use under the direction of technical support The selection of these options is controlled by clicking the buttons under the Risk Assessment Variables Prior Costs and the All Models section of the screen above The default settings are to calculate scores for an under age 65 population and to include prior cost in the predictive modeling algorithm In general including prior costs will improve performance of the predictive model performance It is true however that including prior costs in the model makes it l
173. ional Rescaled Weight Local Weight ADG Codes ADG Vector EDC Codes MEDC Codes A banded indicator of patient age Possible values include e lt 0 e 00 04 e 05 11 e 12 17 e 18 34 e 35 44 e 45 54 e 55 69 e 70 74 e 75 79 e 80 84 e 85 e Unknown Adjusted Clinical Groups The ACG code assigned to this patient ACGs assign persons to unique mutually exclusive morbidity categories based on patterns of disease and expected resource requirements Aggregations of ACGs based upon estimates of concurrent resource use providing a way of separating the population into broad co morbidity groupings as follows e 0 No or Only Invalid Dx e Healthy Users e 2 Low e 3 Moderate e 4 High e 5 Very High An estimate of concurrent resource use associated with a given ACG based on a national reference database and expressed as a relative value Each patient is assigned a weight based on their ACG Cd National weights that are rescaled so that the mean across the population is 1 0 A concurrent weight assigned to this patient based upon their ACG Cd using local cost data The weight for each ACG is calculated as the simple average total cost of all individuals assigned to each category Aggregated Diagnosis Groups The building blocks of the ACG System Each ADG is a grouping of diagnosis codes that are similar in terms of severity and likelihood of persistence of the health condition over time This column contains a
174. ions Description Value Patients processed Patients processed 65 years and older Diagnoses processed Unique diagnoses encountered Unique unknown diagnoses encountered Percentage of diagnoses that were unknown Unknown diagnoses encountered Patients with unknown diagnoses encountered Unique matched diagnosis code sets encountered Unique unknown diagnosis code sets encountered Patients with unsupported diagnosis code sets encountered Pharmacy codes processed Unique pharmacy codes encountered Unique unknown pharmacy codes encountered Percentage of pharmacy codes that were unknown Unknown pharmacy codes encountered Patients with unknown pharma odes encountered Unique matched pharmacy code sets encountered Unique unknown pharmacy code sets encountered Patients with unsupported pharmacy code sets encountered Number of EDCs assigned Number of MEDCs assigned iaa 1 Ea L 90054 6277 486621 6951 23 0 0 108 106 1 0 0 231564 7192 338 2 6 5981 1 0 0 355386 256716 ALU Anatomical Therapeutic Chemical ATC Classification The use of ATC codes as a data source for the pharmacy predictive models Rx MGs and Rx PM has been tested with our international partners and is now available for licensing Please contact your distributor The Johns Hopkins ACG System Version 8 2 Release Notes Release Notes 2 3 Risk Assessment Variables The ACG Software provides reference data through a number
175. is history and their resource consumption profile may differ from members who were enrolled for the entire period For the most part and so long as these new enrollees are randomly distributed across the population and population sub groupings their impact is minimal If however large numbers of enrollees are concentrated in one provider group being profiled or one employer group for which rates are being set concentration of new enrollees may bias results to make this group look healthier than they otherwise might have if complete diagnoses and claims information had been available for them In general when including individuals who are not eligible for the entire enrollment period it is recommended that results be scrutinized closely One approach would be to compare results excluding and including these individuals to help assess whether their inclusion has introduced any systematic bias Another strategy for assessing their impact would be to examine ACG distribution across the various units of analysis such as by provider A disproportionate number of persons assigned to ACGs 5100 or 5110 and 5200 1 e no diagnoses and non user ACGs may indicate the enrollee cohort entered the plan near the end of the analysis period and may lack sufficient contact with the provider The Johns Hopkins ACG System Version 8 2 Technical User Guide Final Considerations 8 5 to allow accurate overall ACG assignment Such groups can and perhaps sho
176. isk space is typically sufficient to handle one million patients If you receive an out of space message and you have adequate space for the ACG data file review the following related to the use of temporary space The ACG application will use temporary space that is approximately five times greater than the input data files to sort and merge the data files This can lead to out of space messages because the ACG application is taking advantage of the Windows TMP variable for these activities This is typically on the client s primary C drive It may be moved using the following actions in a Windows XP operating system 1 Start 2 Control Panel 3 System 4 Advanced 5 Environment variables Edit the TMP variable to a location with more available space This will change default Windows behavior e g logging statistics will be moved as well This TMP variable is machine and user specific The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 3 Installing the Software The ACG application is most commonly delivered via FTP Once the application is downloaded use Windows Explorer to navigate to the JHUACGSetup executable file and double click to begin installation If you received an installation CD insert the CD into your CD ROM drive If the installation screen does not automatically appear choose Start Run from the Windows taskbar browse to the CD ROM drive and select the JH
177. it data for all enrolled members are included in the database All plan data are quality controlled before they become part of the database and the data is HIPAA compliant M 5331 Infants 6 ADGs no Major ADGs low birth weight M 2 The Johns Hopkins ACG System Version 8 2 Technical User Guide Final Considerations 8 i 8 Final Considerations Trt OCHO si cccsviscsasractssenscasesasteseacascsanedevcavedunvesnetensdstecenvissasesvastadenistsunansease 8 1 PAPC OE Risk ACSC wcscsscorceccsnds cess cacceceasiireeriaeescnesiadisetaaasvas eencneanenaenanteans 8 1 Fig te 1 Risk Adjustment Pyramid os cscesacssedsepncsincunisadrecdsaiesenissncubeselndede 8 1 Time Frames and Basic Population Perspectives eesssessoesssecssecssocsccosses 8 2 Figure 2 Typical Timeline for Risk Adjustment sseesssesseseseeseserseee 8 3 Handling New or Part Year Enrollees cccsccccsssssssscssssscccssssssccscesees 8 4 Non Users Who are Eligible to Use Services cccccesscceseceteeeeeeeeeeeees 8 5 Sample S126 ssc ssssicncsos sans cecnssnveanssane eanmsnvtcensanstens enbenvesiactansvaaeentmacaiaieeaanens 8 5 Handling High Cost or Outlier Cases ssscoscsssssssssssssscssssssesssssesssseeses 8 6 Technical User Guide The Johns Hopkins ACG System Version 8 2 8 ii Final Considerations This page was left blank intentionally The Johns Hopkins ACG System Version 8 2 Technical User Guide Final Considerations 8 1 In
178. ive ACG ACG Label Weight 6 9 Other ADG Combinations Females Age 18 to 34 no Major 4810 ADGs 6 9 Other ADG Combinations Females Age 18 to 34 14 4820 ADGs 6 9 Other ADG Combinations Females Age 18 to 34 2 4830 ADGs 4910 6 9 Other ADG Combinations Age gt 34 0 1 Major ADGs 4920 4930 4940 5010 T T lt amp amp iad 9 104 j 5020 5030 5040 104 j 104 j 104 j 5050 5060 5070 5110 5200 5310 5311 5312 5320 no 5321 Infants 0 5 ADGs 1 Major ADGs low birth weight 5322 Infants 0 5 ADGs 1 Major ADGs normal birth weight 5330 Infants 6 ADGs no Major ADGs 5332 Infants 6 ADGs no Major ADGs normal birth weight 5340 Infants 6 ADGs 1 Major ADGs 5341 Infants 6 ADGs 1 Major ADGs low birth weight 5342 Infants 6 ADGs 1 Major ADGs normal birth weight 9900 Invalid Age or Date of Birth The source data comes from PharMetrics a unit of IMS in Watertown MA and would be shown when the user selects the US Non Elderly Risk Assessment Variables The data is comprised of paid claims from a number of managed healthcare plans The database is nationally representative of commercially insured populations with respect to region age gender and health plan type The database also includes populations that are insured by government payers The database combines medical and prescription drug data with enrollment data across multiple years and only plans that subm
179. l Considerations Evaluate the Warning Distribution The Warning Distribution Analysis produces a frequency distribution by warning A sample listing of warnings is presented in Figure 3 The frequencies reported should be examined possible data completeness issues For example an excessive number of cases receiving Warning 14 Patient has gt 0 in total costs but no diagnoses may indicate a problem with how total cost was captured Alternatively this may indicate inappropriate exclusion of diagnoses related to rule out or provisional claims In either case a review of the original input data may be necessary The Software may need to be rerun if problems are found that can be corrected Figure 3 Sample Warning Distribution x Johns Hopkins ACG System 8 2 File Edit Yiew Analyze Tools Help Mx 8 tv N Commercial_v82 acgd PECETE Warning Distribution Analysis For Commercial v 82 Overall Report Options Warning Code Warning Description Frequency Freq 7 Suspicious sex for pregnancy diagnosis or user Flag 1 327 0 04 8 Suspicious age for pregnancy diagnosis or user flag 917 0 03 12 Patient has 0 in total costs but diagnoses 18 729 0 58 13 Patient has 0 in pharmacy costs but pharmacy codes 30 989 0 96 Technical User Guide The Johns Hopkins ACG System Version 8 2 6 10 Assessing the ACG Grouper s Output Examining the List of Non Matched Diagnosis Codes All input ICD codes not consid
180. lculated Options control how your analysis is calculated See the help for more information regarding how each option impacts a given report Concurrent Weight Options Weight Type Predictive Model Options Model Type Prevalence Comparison Group Prevalence Type Local X l Local Technical User Guide The Johns Hopkins ACG System Version 8 2 5 30 Installing and Using ACG Software Standardized Morbidity Ratio by MEDC Analysis The Standardized Morbidity Ratio Analysis produces a summary by Major EDC MEDC with observed expected and o e ratio This report is useful in understanding how the prevalence of certain conditions as defined by MEDCs are more or less common than average across the subpopulation of interest The significance indicator identifies categories that are statistically different from the age sex adjusted expected value At the user s discretion the expected values can be derived from either the population mean or the national benchmark data The methodology for this analysis is explained more fully in the EDC Chapter in the Reference Manual The report layout is as follows Table 9 Standardized Morbidity Ratio by MEDC Analysis Report Layout Column Definition Name MEDC Cd Each MEDC code that was assigned to at least one patient MEDC Name The description for MEDC Cd The number of patients assigned this MEDC in this stratification The number per 1 000 patients in the current stratifi
181. le 9 98 99 percentile Pharmacy Cost A banded indicator of historic Band pharmacy costs based upon pharmacy cost percentiles Possible values include e 0 0 pharmacy costs e 1 10 percentile e 2 11 25 percentile e 3 26 50 percentile e 4 51 75 percentile e 5 76 90 percentile e 6 91 93 percentile e 7 94 95 percentile e 8 96 97 percentile e 9 98 99 percentile The Johns Hopkins ACG System Version 8 2 Technical User Guide Making Effective Use of Risk Scores 7 5 Concurrent ACG Weights A fixed set of concurrent ACG weights based upon the Risk Assessment Variables selection is available as part of the software output file see the chapter entitled Installing and Using ACG Software in this document for instructions on how to turn this option on Separate sets of weights exist for under age 65 working age populations and for over 65 Medicare eligible populations Which set of weights is applied is dependent upon the user specified options selected about which population the user is working on i e under or 65 and over The weights produced by the software are relative weights i e relative to a population mean and are standardized to a mean of 1 0 An individual weight is associated with each ACG The software supplied weights may be considered a national reference or benchmark for comparisons with locally calibrated ACG weights In some instances e g for those with limited or
182. lly provide icd codes 2 through 5 for each row Table 16 Diagnosis Data File Format Data A unique string to identify this individual Text 9567213984 01 patient The version of the ICD code in icd_cd_l icd_version_1 The ACG grouping logic currently supports Number ICD version 9 and 10 The ICD code This code cannot be longer than 6 characters You may optionally icd cd 1 include an explicit decimal If a decimal is included it must be in the fourth position If a decimal is not included then the ICD code cannot be longer than 5 characters N The ICD code ext Number N The Johns Hopkins ACG System Version 8 2 Technical User Guide Pharmacy Data File Format The default pharmacy data file format is a tab delimited or comma delimited optionally quote enclosed text file with the following columns in order This format is directly supported by Microsoft Excel and Microsoft Access and a variety of other tools This file should contain all pharmacy codes that were experienced for each patient during the observation period There can be zero 1 or more rows per Patient ID The patient ID icd version_1 and the icd_cd_1 columns are required Table 17 Pharmacy Data File Format Data Column Name Column Description Type Example patient id a string to identify this individual 9567213984 01 The date the prescription was filled in rx_fill_date CCYY MM DD format 2006 01 01 rx_code The pharmacy code 00591505210
183. lt file gt as the format definition for the patient data patient skip Skips first row from patient file diagnosis lt file gt Uses lt file gt as diagnosis source data file diagnosis skip Skips first row from diagnosis file pharmacy lt file gt Uses lt file gt as pharmacy source data file pharmacy skip Skips first row from pharmacy file rav lt rav code gt Uses lt rav code gt stated RAV for calculations US ELD US Elderly US NONELD US Non Elderly the default if no rav is specified is US NONELD all models Generates all valid predictive models ignore prior costs Ignores prior cost data export lt type gt Exports data from an ACG Data File lt type gt determine what data to export as follows The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 91 PATIENT exports patient details ADG exports ADG assignments EDC exports EDC assignments MEDC exports MEDC assignments RXMG exports Rx MG assignments MAJ RXMG exports Major Rx MG assignments DIAGNOSIS exports patient diagnoses PHARMACY exports patient pharmacy codes NM DIAGS exports non matched diagnosis codes NM PHARMACY exports non matched pharmacy codes WARNINGS exports warnings LOCAL WEIGHTS exports local weights MARKERS exports model markers MODELS exports all model outputs delim TABJCOMMA Uses a tab or comma delimiter for export If not specified TAB is used col file lt file gt Exp
184. m the ACG Data File 0 0 cee eeceesteeeeeees 5 95 Figure 48 Use the Command Line Version to Export Data 5 95 Appendix C Java APL sssnuntisnndnnectinaimunnunvainnuiuannanins 5 96 Technical User Guide The Johns Hopkins ACG System Version 8 2 5 vi Installing and Using ACG Software This page was left blank intentionally The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 1 Introduction The central element of the Johns Hopkins University ACG System Release 8 2 is a Windows based reporting application intended to facilitate implementation of the ACG System within health care settings The Windows based software is not only a flexible reporting application but also provides the ability to run the software in batch mode from the command line allowing individuals to automate or to queue up multiple jobs Additionally the software is available as a stand alone assignment module for several non Windows based UNIX platforms including Solaris SPARC AIX and HP UX RISC This chapter discusses using and installing all versions of the software 4 Tip Input and output file requirements as well as batch mode processing are identical across all supported Windows and non Windows based UNIX platforms This simplifies the use of all ACG based applications within your organization see the Appendix B of this chapter for details on batch mode processing System Requirements Th
185. markable stability Where differences in ACG weights across plans are present it is almost universally attributable to differences in covered services reflected by different benefit levels The software provided concurrent weights associated with the US Non elderly Risk Assessment Variables which were developed from a nationally representative database comprising approximately two million lives with comprehensive benefit coverage Technical User Guide The Johns Hopkins ACG System Version 8 2 If local cost data are available the ACG Software also calculates local ACG weights These local weights more accurately reflect local benefit levels and area practice patterns In general it is recommended that the reference population on which the weights are developed should be as similar as possible to the assessment population to which the weights are applied However in the absence of local cost data the national weights may prove useful for calculating reasonably representative profiling statistics reference the chapter entitled Provider Performance Assessment in the Reference Manual Prospective Risk Scores With the advent of the ACG PM it is also possible to generate prospective risk scores within the ACG Software This prospective risk score or weight is called the Predictive Resource Index or PRI Unlike the concurrent ACG weights which are linked to specific ACGs the PRI is individualized and thus conceivably every member co
186. mmary Statistics Patient Sample Local Weights Age Gender Dist Probability Dist Build Options Selection Patient File C Documents and Settings asalls My Documents 4CGs Demo 82Demo_patients csv Patient Filter None Diagnosis File C Documents and Settings asalls My Documents 4CGs Demo 82DEMO DIAGNOSES tab Pharmacy File C Documents and Settings asalls My Documents 4CGs Demo 82DEMO PHARMACY tab Risk Assessment Yariables US Non Elderly 4ll Models Best Models All Ignore Use Prior Costs Use The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software Figure 32 Analyze Menu 5 61 The Analyze menu provides access to several additional reports The report content provided is static but may be customized to the needs of the user with the application of groups and filters described below xp Johns Hopkins ACG System 8 2 File Edit Xo Bx Fal 825AMPLE acgc View Summary Statistics Patients processed Patients processed 6 Diagnoses processed Unique diagnoses ent Unique unknown diag Percentage of diagnd Unknown diagnoses Patients with unknow Unique diagnosis cod Unique unknown diag Patients with unsupp Pharmacy codes prog Tools Help RUB Distribution 2 ACG Distribution ADG Distribution Population Dist By Age Band and Morbidity MEDC By RUB Distribution EDC By RUB Distribution RxMG By RUB Distribution Standardized Morbidity Ratio By MEDC 5 Sta
187. n a single line Figure 47 Use the Command Line Version to Create a New ACG Data File amp Command Prompt BEE C Program Files Johns Hopkins ACG 8 2 gt jhuacg new acg file c acgdata 82Sample al acgd patient c acgdata My_Patient_file csu patient format tab diagnosis c a cgdata My_Diagnosis_file csu diagnosis format tab pharmacy c acgdata My_Pharm acy file csy pharmacy format tab ignore prior costs Loading mappings Using mapping version 8 2 3rd Quarter 2068 Release release date Jul 7 2608 Building ACG data file c acgdata 82Sample acgd Successfully created ACG Data File c acgdata 82Sample acgd C Program Files Johns Hopkins ACG 8 2 gt Example custom format file Patient Format File Property Definitions delim tab Columns Definitions patient id String Patient Id age Integer Age sex String Sex pharmacy_cost Double Pharm Cost total cost Double Total Cost The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 95 Export Patient Data from the ACG Data File C gt Program Files Johns Hopkins ACG 8 2 jhuacg exe export patient acg file c acgdata 82Sample acgd export file c acgdata patientexport csv The command above is typed on a single line Figure 48 Use the Command Line Version to Export Data amp Command Prompt C Program Files Johns Hopkins ACG 8 2 gt jhuacg export patient acg f
188. n on the Accreditation of Healthcare Organizations have long focused on process metrics only because there is little basis to believe that the provision of specific services should differ in populations that differ by case mix The steady rise in pay for performance initiatives and balanced scorecards for health care providers has been accompanied by the steady expansion of performance assessments to include outcome metrics There is a strong basis of evidence that health outcomes do vary by case mix and that these metrics need some form of case mix adjustment to ensure appropriate comparisons between health care providers When performance assessment is focused on specific diseases there is a tendency to look for case mix or severity adjustment that is tailored to the specific disease There are numerous risks to such a disease oriented performance assessment strategy not the least of which is that there are often insufficient numbers of cases for an accurate assessment and that such a disease orientation will encourage care practices that are not holistic Some pay for performance programs have chosen to roll up disease specific metrics into an overall summary measure that is less prone to the problem of small numbers and also broadens the quality focus In such cases ACGs used as RUBs or Dx PM risk scores will work quite effectively as case mix adjusters Indeed prior work has shown that ACGs do an excellent job of adjusting for differences in case mix
189. n that are required to produce many of the sample reports presented in the Technical User Guide in the chapter entitled Selecting the Right Tool including e Data elements necessary to stratify the population into groups for analysis such as primary care physician identifier region benefit plan or employer group and ideally the dates when the members entered left these groups e Data elements necessary to construct resource consumption measures typically dates of service service procedure codes length of inpatient stay the place of service code and the allowed charges from each claim line item and summary measures of resource consumption e g total charges ambulatory charges ambulatory encounters lab x ray use pharmacy use rates or specialty referrals e Information on enrollment status during the time period in question e Any other administrative information A layout of the standard patient summary file which could be used to perform all of the available Windows based analyses is presented in the Technical User Guide in Table 1 of the chapter entitled Installing and Using ACGs Software The Johns Hopkins ACG System Version 8 2 Technical User Guide Basic Data Requirements 4 3 Data Items Usually Required for ACG Analysis in a Managed Care Context e Unique member identifier e Relation of person to subscriber e Age e Gender e Benefit plan product or line of business identifier e g copayme
190. nal Sources of Information csccsccsscsssssssssssssecesssesesssesseees 5 85 Appendix A ACG Output Data sciiccccniiicmnciincminicmnninmns 5 85 Table 18 Column Definitions for the ACG Output File 5 85 Appendix B Batch Mode Processing cssscccssssccssssccsssccsssssssssscesees 5 89 Windows DOS ieena Ei 5 89 ONIA cosir eoo iee e EE O 5 89 ACG Command Line Usage sineresia ietiecas iei 5 89 Usage Detaily arenan EE ER EERE E 5 90 Create a New ACG Data Fil scicscscitsisccaturcsatnaiaideabinleceteitunesleiasnaueisas 5 90 Export Data from an ACG Data File sg sceiacsiseccatsiissas toatceinstiesaisssasninaats 5 90 Install a License Pile sic catcssicsnrcaeianssascpiscsbanatiesdusisraneelicebaiecdbeiouless 5 90 Installa Mapping File ests cacdasuandannngonansseseacdansadoduderacenmasnsendabveoedeaas 5 90 CIDE neries enir E e 5 90 GM deN Seriana i i e Re aih 5 92 Mestalla License Fil isis cite sie cadedpcindesian ins aE Ei 5 93 Figure 46 Use the Command Line Version to Install a License File 5 93 Create a New ACG Data File aod ccccicsccssvatinisasietessinctteseslaraciacuises 5 94 The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 v Figure 47 Use the Command Line Version to Create a New ACG Data E E aia dees N aan coo I AN A dak dala ease tilted E 5 94 Example c stomi format Piles isiscicessscactuciecedisscpieesseactioientaniseagiblsieaned 5 94 Export Patient Data fro
191. nancy categories non user categories infant While useful as a drilldown approach for understanding the why of differences between groups the number of ACGs 93 groups depending on user specified options may be slightly too cumbersome for comparing contrasting morbidity between population sub groupings To simplify things the ACG System Software will automatically assign a six level Low to High simplified morbidity category termed a Resource Utilization Bands or RUB The six RUBs are formed by combining the ACG mutually exclusive cells that measure overall morbidity burden Utilizing the RUB categories Table 2 demonstrates how a simple RUB based analysis highlights differences in the distribution of morbidity of the Group 1 and Group 2 exemplary subpopulations Confirming the impression drawn from Table 1 the Group 2 population clusters in the bands associated with higher overall morbidity burdens Table 2 Percentage Distribution of Two Subgroups by Resource Utilization Band RUB Categories 25 8 35 6 22 5 2 Healthy Users 13 9 17 5 11 1 Through use of disease specific EDCs a standardized morbidity ratio analysis is now available See the chapter entitled Expanded Diagnosis Clusters EDCs in the Reference Manual for additional details on interpreting this table Table 3 shows an example of this analysis based on the major subheadings of Expanded Diagnosis Clusters This report presents MEDC level disease pre
192. nd how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication A flag indicating if this patient has this medical condition and how it was Depression indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication A flag indicating if this patient has this medical condition and how it was Diabetes indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication A flag indicating if this patient has this medical condition and how it was Hyperlipidemia indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication A flag indicating if this patient has this medical condition and how it was Ischemic Heart Disease indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication A flag indicating if this patient has this medical condition and how it was Low Back Pain indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 41 Patient Clinical Profile Report The Patient Clinical Profile Report produces a report for one or more patients that presents a profile of their current and predicted costs along side relative predicted resource utilization and clinical indicators This report assists clients with understanding member level ri
193. ndardized Morbidity Ratio By EDC Standardized Morbidity Ratio By Major Rx MG Standardized Morbidity Ratio By Rx MG Cost Predictions By Select Conditions Cost Predictions For Selected Rx MGs Actuarial Cost Projections bility Dist Build Options Value Simple Profile 3 Unique pharmacy cog Care Management List Unique unknown pha Patient Clinical Profile Report Percentage of pharm Patient List Unknown pharmacy d bee Patients with unknow Warning List Unique pharmacy co Warning Distribution Unique unknown pharmacy code sets encountere Patients with unsupported pharmacy code sets encountered Number of EDCs assigned Number of MEDCs assigned Number of ADGs assigned Number of Rx MGs assigned Percentage of patients with total cost gt 100 and no diagnoses Number of patients with diagnosis information and no pharmacy codes Number of patients with pharmacy codes and no diagnoses Number of data warnings Number of patients with data warnings Minutes To load data Total cost model selected Pharmacy cost model selected Date loaded ateg with 4 Technical User Guide 0 55221 43180 47355 37946 3 0 Percentage of patients with pharmacy cost gt 100 and no pharmacy codes 0 0 0 nooo DxRx PM total cost gt total cost DxRx PM rx cost gt rx cost 2008 10 20 The Johns Hopkins ACG System Version 8 2 5 62 Installing and Using ACG Software Analyze Report Options Many of the re
194. need high levels of health care services This analysis has the option to report the estimated concurrent resource use in terms of local weights or national weights Note The percent distributions are calculated across each row stratification It is not likely but possible for a row to have a total of less than 100 because RUB 0 is not included in the output The report layout is as follows Table 6 EDC by RUB Distribution Analysis Report Layout Est Concurrent Resource Use RUB 1 Dist RUB 1 Est Concurrent Resource Use RUB 2 Dist RUB 2 Est Concurrent Resource Use RUB 3 Dist RUB 3 Est Concurrent Resource Use RUB 4 Dist Technical User Guide The mean of the national rescaled or local concurrent weight based upon which weight type was selected in Report Options for all patients in this stratification across all RUBs The percentage of patient assignments to this stratification in this RUB is out of the total patients in this RUB The mean of the national rescaled or local concurrent weight based upon which weight type was selected in Report Options for all patients in this stratification in this RUB The percentage of patient assignments to this stratification in this RUB is out of the total patients in this RUB The mean of the national rescaled or local concurrent weight based upon which weight type was selected in Report Options for all patients in this stratification in this RUB The per
195. ng for the local CMI as a measure of how efficiently the group utilizes services as compared to the population mean There are additional rate based measures provided to describe the factors contributing to group risk Groups with higher disease burdens will also generally tend to have higher prevalence rates of high risk members who are more likely to have chronic conditions higher rates of hospital dominant and frailty conditions and higher rates of psychosocial conditions Comparisons can be made between the group and the population mean by comparing the groups tab to the overall tab in the analysis window Concurrent versus Prospective Applications The time frame used for most rate setting and other financial analyses is a prospective or predictive one That is this year s diagnostic information is used to determine risk factors and expected resource consumption in some future period Thus the weights associated with each risk factor are calibrated to that future period But this is not the only temporal approach that organizations can use for rate setting Some ACG System users have implemented concurrent rating processes for financial exchanges In such cases this year s expected resource use among the benchmark population is attached to each ACG cell as a relative value rather than next year s resource use While we do encourage experienced actuaries and financial analysts to learn more about the advantages and challenges of these i
196. nnovative concurrent approaches we do not recommend that organizations apply concurrent approaches to payment without first simulating the impact that these methods might have on the rate setting process The Johns Hopkins ACG System Version 8 2 Technical User Guide Selecting the Right Tool 3 37 4 Example A real world example of a concurrent approach to rate setting is one being implemented in Minnesota Medicaid where plan level payments are based on concurrent ACG adjusted profiles of the plan Under this scenario payment to a health plan is the same for each individual enrollee within a particular plan however the amount paid is case mix adjusted by the plan s overall morbidity burden relative to an average across the population of 1 0 This approach assumes that the morbidity burden of large groups i e any individual health plan is fairly stable and that the group s overall morbidity does not change much by the addition exit of any one individual Additional Information For additional discussion on this and other issues related to risk adjustment as applied to financial exchanges we encourage readers to review our chapter titled Health Based Risk Adjustment Application to Premium Development and Profiling incorporated into Charles Wrightson s Financial Strategy for Managed Care Organizations Rate Setting Risk Adjustment and Competitive Advantage See http www ache org pubs wrightson cfm for ordering details or
197. no cost data these weights may also be used as a reasonable proxy for local cost data Table 6 at the end of this chapter provides a complete listing of ACGs and their corresponding nationally representative concurrent ACG weight from the US Non elderly Risk Assessment Variables See the following discussion regarding the importance of rescaling so that dollars are not over predicted or under predicted The software supplied national ACG weights are supplied in two forms unadjusted and adjusted Unadjusted ACG weights are simply the values of the national ACG weights applied to a population of interest The mean value of the unadjusted ACG weights provides a rudimentary profiling statistic If the mean of the unadjusted ACG weight is greater than 1 0 it indicates the rating population the population to which the weights are being applied is sicker than the reference population the national reference database If the mean is less than 1 0 it indicates the rating population is healthier To ensure that dollars in the system are not over or under estimated we have also made available an adjusted or standardized ACG weight that mathematically manipulates the unadjusted ACG weight to have a mean of 1 0 in the local population The steps for performing this manually are discussed in more detail subsequently Our experience indicates that concurrent also referred to as retrospective ACG weights especially when expressed as relative values have re
198. ns over an extended period such as a year are assigned to one of 32 ADGs ADGs can be considered a type of morbidity marker A person may have multiple ADGs The ADG Distribution Analysis produces a frequency distribution by ADG code Since a patient can be assigned to potentially more than one ADG code the total frequency will probably be larger than the overall patient count The report layout is as follows Table 3 ADG Distribution Analysis Report Layout ADG Cd Each ADG code that was assigned to at least one patient in this stratification ADG Description The description for ADG Cd Fredienc The number of patients with this ADG in this stratification meeting the optional q y filter criteria Freg The percentage of patients within this stratification and meeting the optional filter q criteria that were assigned this ADG ADG distributions can quickly demonstrate differences in types of morbidity categories across sub groupings within your organization An advantage of ADGs is that they can quickly identify clinically meaningful morbidity trends that may be obscured at the disease specific or relative morbidity index levels The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 21 Population Distribution by Age Band and Morbidity Analysis The Population Distribution By Age and Morbidity Analysis produces a frequency distribution by Age Band and Resource Utilization Band This analysi
199. ns Hopkins ACG System Version 8 2 3 36 Selecting the Right Tool The Actuarial Cost Report provided in Table 15 is a standard report produced by the software and represents a summary of information relevant for actuarial purposes and for differentiating groups as high medium and low risk This analysis provides a number of aggregate measures for both current and future costs expressed as a relative index scores equal to 1 0 indicate average morbidity or risk greater than 1 0 indicate greater than average morbidity burden or risk and less than 1 0 less than average The National CMI is a concurrent measure that compares the group case mix to a national benchmark based on the mix of ACGs assigned to the members of the group The Local CMI is a similar measure but the comparison group is based on the population presented to the ACG System Mean Total PRI is a measure of prospective risk using the ACG predictive model to forecast total cost relative to the plan average Likewise the Mean Rx PRI measures the prospective risk of pharmacy cost relative to the plan average These resource indicators can be compared to the age sex relative risk When age sex relative risk is equal to the local CMI the risk is driven by the age and sex of the group When age sex relative risk is lower than the local CMI the risk is driven by disease burden more than the age sex mix of the group There is an additional index of the observed cost to the expected cost accounti
200. ns for cost predictions by select conditions analysis 5 34 selecting the risk assessment variables 5 23 step 1 load your own data 5 80 step 2 load your own data 5 81 step 3 loading your own data 5 83 summary statistics 5 54 summary statistics tab 2 2 2 4 The Johns Hopkins University digital signature 2 9 use the command line version to create a new ACG data file 5 94 use the command line version to export data 5 95 use the command line version to install a license file 5 93 view the installed license 5 13 welcome to the Johns Hopkins ACG system setup 5 10 File menu 5 16 Filters 5 62 Final considerations art of risk adjustment 8 1 handling high cost or outlier cases 8 6 handling new or part year enrollees 8 4 introduction 8 1 non users who are eligible to use services 8 5 sample size 8 5 time frames and basic population perspectives 8 2 G Getting started customer commitment and contact information 1 4 introduction to the Johns Hopkins ACG system 1 1 objective of the technical user guide 1 1 reference manual topics 1 3 Groups 5 63 H Handling high cost or outlier cases 8 6 final considerations 8 6 Handling new or part year enrollees 8 4 final considerations 8 4 Health status monitoring 3 12 Help menu 5 50 Technical User Guide High risk case identification for case management 3 20 How to rescale and assign dollar values 7 7 step 1 7 7 step 2 7 7 step 3 7 7 step 4 7 7
201. nscaled Concurrent Weight within the current stratification For additional details on the calculation and interpretation of these statistics please refer to the chapter on Provider Performance Assessment in the Reference Manual The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 39 Care Management List The Care Management List produces the 1 000 patients that match the selected filters that have the highest probability of having high total costs in the year following the observation period The data is sorted in descending order by the Probability High Total Cost The user can use the filtering criteria to isolate a more targeted cohort of patients for further analysis and review For example identifying current low users with the high probability of future expense captures individuals who may have the greatest opportunity for early intervention before expenses escalate A single member or the filtered list can be sent to the Patient Clinical Profile Report for additional information The list layout is as follows Column Name Table 16 Care Management List Layout Patient ID A unique identifier for the patient Age The patient s age at the end of the observation period Sex The patient s sex Total Cost be ie medical and pharmacy cost for this patient during the observation Rescaled Total Cost The rescaled adjusted with local data estimated total costs for the year
202. nt level deductible levels utilization review provisions benefit flags such as member health or maternity e Sponsor company and or employer group identifier e Geographic area of residence e g ZIP code e Any other rating factors now used by actuaries e ADG flags yes no for each of the 32 ADGs e ACG category e EDC markers e Predictive Modeling scores e Rx MGs e Total paid allowed claims for each patient e Total paid allowed ambulatory care claims for each patient e Total paid allowed in patient care claims for each patient e Total paid allowed ancillary procedures e g pharmacy lab x ray for each patient e Utilization measures e g visits days in hospital number of lab claims e Provider ID primary care physician panel or site e Continuous enrollment flag or start stop months of eligibility e Total paid allowed pharmacy claims for each patient e Optional markers for Pregnancy Delivery Low Birth Weight Technical User Guide The Johns Hopkins ACG System Version 8 2 Coding Issues Using the International Classification of Diseases ICD Diagnosis codes are the primary data requirement of the Johns Hopkins ACG System The user must ensure to the extent possible the diagnosis codes recorded on the claims encounter records and the resulting machine readable data records are comprehensive and consistent with the source medical records For the purpose of assessing the quality of diagnosis code data a rudimen
203. ntification of low or normal birth weights among neonates due to inconsistencies in how ICD codes are commonly used the software cannot readily identify most low birth weight infants using only ICD codes from the input claims file Validation analysis across a variety of indemnity and HMOs indicated that within most plans 2 to 5 of infants were identified as low birth weight Based on vital records and other sources the actual percentage should be somewhere between 6 and 9 Because diagnoses did not seem a reliable source of the recording of birth weight analysts wishing to take advantage of this feature to appropriately categorize low birth weight infants must flag such infants before passing the data to the ACG Software and provide the software with the flag s location 4 Tip ICD 9 CM codes used to identify low birth weight 764 0 764 1 764 2 764 9 765 0 765 1 where 1 8 48 codes total Technical User Guide The Johns Hopkins ACG System Version 8 2 4 12 Basic Data Requirements Constructing Resource Consumption Measures Key to any ACG based application for either physician profiling or capitation is consideration of how the resource use measure is defined Most analyses developed to date have focused on visit rates ambulatory charges or total charges However more recent work is being conducted to assess the ACG System as a means of evaluating pharmacy use understanding specialist use and assessing quality of car
204. nts a new way of actuarial thinking which is only feasible because of the use of ICD based adjusters such as ACGs Note The per member per year notation or PMPY will be used generally to reflect a per member per period approach where the extended period may be other than a 12 month year e g 10 months or 18 months Since PMPY can be considered a paradigm shift in the manner by which such expected values are usually calculated we have attempted to provide extensive background information on why the PMPY is preferred over the traditional PMPM approach for many risk adjustment applications Including Part Year Enrollees The primary reason PMPY is preferred for risk adjustment is because of the way it handles part year enrollees Past work using data from multiple sites has demonstrated that persons who are enrolled for fewer than 12 months in a health plan during a given year tend to use more resources on a PMPM or annualized basis than those who are continuously enrolled for the entire period New previously uninsured enrollees may have higher costs as a result of previously unmet needs or they could be switching plans in the midst of a special healthcare episode e g they could be responding to a newly diagnosed condition Technical User Guide The Johns Hopkins ACG System Version 8 2 Making Effective Use of Risk Scores Shorter term enrollees as a group also exhibit higher costs in part because they include those who leave a plan eit
205. of output variables Specifically concurrent weights predictive model coefficients and reference prevalence rates are based on external data aggregated from multiple U S health plans The software currently provides two separate references one for a U S elderly population and one for a U S non elderly population In Version 8 2 these external references have been renamed Risk Assessment Variables RAVs and are now delivered with the mapping files for ease of update This change will also provide the capability to license additional references or Risk Assessment Variables in the future Please contact your distributor if you would like to discuss the creation of Risk Assessment Variables based on your population For all users the selection of reference data for model calibration has changed to a drop down box on the New File screen reference Figure 2 Figure 2 New File Screen Choose the data sources for your new ACG data file Patient Data Patient Data File C acgdata My_patient_file csv Skip First Row i e column headers in data file Use Tab Delimited File Format Use Comma Delimited File Format Use Custom File Format Diagnosis Data Diagnosis Data File c acadatalMy_diagnosis_file csv C Skip First Row i e column headers in data file Use Tab Delimited File Format C Use Comma Delimited File Format Pharmacy Data Pharmacy Data File C Skip First Row i e
206. ofile analysis 5 38 Software produced weights and their uses 7 1 Special note for ICD 10 users 4 6 diagnosis codes 4 6 Standardized morbidity ratio by EDC analysis report layout 5 28 Standardized morbidity ratio by major Rx MG analysis 5 31 Standardized morbidity ratio by MEDC analysis 5 30 Standardized morbidity ratio by Rx MG analysis 5 32 Summarizing total or ambulatory charges 4 12 ACG 4 12 Summary statistics tab 5 53 6 2 Support for larger ACGD file release notes 2 9 Support for Vista release notes 2 9 Suspected diagnosis 4 5 System requirements 5 1 The Johns Hopkins ACG System Version 8 2 IN 6 Index T percentage distribution of two subgroups by RUB categories 3 7 Tables percentage of patients with selected outcomes by ACG procedure code ranges to exclude 4 8 ACG typical place of service codes to exclude 4 8 ACG distribution analysis report layout 5 19 actuarial cost projections report layout 5 37 ADG distribution analysis report layout 5 20 amount of data and its impact on model performance 3 20 care management list layout 5 39 care management listing 3 27 classification of metformin 4 10 column definitions for the ACG output file 5 85 comparison of actual and ACG expected costs months of member enrollment PMPM versus PMPY weight calculation approaches 7 12 comparison of ADG distribution across two enrollee groups 3 6 comparison of characteristics affecting physician
207. omizing Risk Scores Using Local Cost Data cccssscssssscssssscsssseee 7 9 Resource Bad scssissssisissnsssnccsssnenccansnensaanssacanssavansiansssaanassscactisassassevassveccisseee 2 10 Final Considerations sssssssssssssssesssssssssssscssessssssssssosossssssssssssssosssssssssoosssossssesscsssse O71 Introductions a DL Art of Risk Adj stm nt siicccnsiccncssniiminmnnuinnmmnninndnnmncet el Time Frames and Basic Population Perspectives cccssccssssscssssscesssres 8 2 Handling New or Part Year Enrollees sccccsssssccssssssssccssssssccssssssssees D 4 Sample SIZE 4a ncunnecunnnnnennsannnEnnseainnianEnenMMmaeE O Handling High Cost or Outlier Cases cccsssccssssccsssccssssccsssecsssssscessees S lid Rerrera EL Technical User Guide The Johns Hopkins ACG System Version 8 2 iv Table of Contents This page was left blank intentionally The Johns Hopkins ACG System Version 8 2 Technical User Guide Getting Started Technical User Guide 1 1 1 Getting Started Introduction to The Johns Hopkins ACG System c cssssscssessssssssessssseess 1 1 Objective of the Technical User Guide ccsssccssssscsssccssssccssssscsscssees 1 1 Technical User Guide Navigation csssccscssccsssssssssscssssssssssscsssesssssessees 1 1 Technical User Guid Topics ssscsssssssssesssssensssessonesennsovsssensenssensonessensncassensors 1 2 Reference Manual WOpi cs vsesssicscsssscensssnecetesccaastvens
208. on is granted to redistribute this documentation No permission is granted to modify or otherwise create derivative works of this documentation Copies may be made only by the individual who requested the documentation initially from Johns Hopkins or their agents and only for that person s use and those of his her co workers at the same place of employment All such copies must include the copyright notice above this grant of permission and the disclaimer below must appear in all copies made and so long as the name of The Johns Hopkins University is not used in any advertising or publicity pertaining to the use or distribution of this software without specific written prior authorization Disclaimer This documentation is provided AS IS without representation as to its fitness for any purpose and without warranty of any kind either express or implied including without limitation the implied warranties of merchantability and fitness for a particular purpose The Johns Hopkins University and the Johns Hopkins Health System shall not be liable for any damages including special indirect incidental or consequential damages with respect to any claim arising out of or in connection with the use of the documentation even if it has been or is hereafter advised of the possibility of such damages Documentation Production Staff Senior Editor Jonathan P Weiner Dr P H Managing Editor Chad Abrams M A Production assistance provided by Dav
209. onnel and administrators to proactively monitor diet and other indicators that can prevent major complications a version of case management The Johns Hopkins ACG System Version 8 2 Technical User Guide Selecting the Right Tool 3 5 The ACG System has a suite of predictive modeling tools the Dx PM formerly called ACG PM based on diagnosis codes the Rx PM based on drug codes and the combined DxRx PM which uses both diagnostic and medication information to provide the most comprehensive idea of a patient s future health care use The Reference Manual chapters five through seven provide an overview of predictive modeling and its application in the healthcare arena as well as detailed information about the development and use of the ACG System s predictive modeling tools Chapter 5 Predicting Future Resource Use with Diagnostic Data focuses on clinical and conceptual challenges facing predictive modeling and introduces the diagnosis bases Dx PM while Chapter 6 Predicting Future Resource Use with Pharmacy Data provides an overview of the pharmacy based Rx PM and discusses the benefits of combining both ICD and Rx information sources in the DxRx PM The series closes with Chapter 7 Predictive Modeling Statistical Performance which discusses some key considerations in evaluating model performance and provides some simple validation statistics of the various ACG predictive models The ACG System allows you to better under
210. ook more like a prior cost model Therefore in certain instances such as a federal agency interested in using predictive modeling scores for payment you may want to exclude prior cost from the model so this option has been provided This option may also prove useful for certain disease or case management applications which may possibly prove more robust to removing the prior cost information If an elderly model is selected then all predictive modeling scores will be calibrated against an elderly managed care population aged 65 or greater The reference population includes pharmacy benefits and expenditures so that pharmacy expense can be predicted relative to a Medicare eligible population When this option is selected the national concurrent weights will also be based upon an elderly population While adjustments have been made to accommodate the occasional under age 65 enrollee if your Medicare eligible population is disabled and predominantly non elderly the non elderly option is better suited for your application The last check box calculating all valid predictive models produces a separate output file where the rows are the patients and the columns are all possible predictive modeling scores This file is useful for analysts wishing to compare the suite of ACG predictive modeling tools looking to contrast the diagnosis pharmacy and diagnosis pharmacy based predictive models 4 Tip Caution Clicking the check box to calculate all
211. open a acgd file created under The Johns Hopkins ACG System version 8 0 you will be prompted to upgrade the file This will allow you to use the current version of the software to files created under a previous release Note the software will not recalculate any of the categories or scores so the data will reflect older mapping files To update to the most current mapping files the user will need to revert to the original text files and run the import process again Load Your Own Data Case Study All input data files are required to be either tab or comma delimited with quotes In this example a custom patient data file is utilized see Custom File Format section under Using Your Own Data while the diagnosis and pharmacy input files are standard layouts Use the following steps to process new input data 1 To import data using the custom file format select File 2 Select New Figure 42 Step 1 Load Your Own Data 1 Johns Hopkins ACG System 8 2 lex Choose the type of file you wish to create New ACG File Cc Create ACG File From Imported Data Create ACG File From Sample Data New Data File Format Create Custom Patient File Format The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 81 From the New File window click the Create ACG File from Imported Data radio button and then click Next Figure 43 appears on the following page Figure 43
212. opulation e g your local population compares to the development data the US Non Elderly Risk Assessment Variables from JHU s nationally representative database The interpretation of this factor is analogous to how one interprets both relative weights and profiling indicators If the rescaling factor is greater than 1 0 as it was in the example then your population is sicker if the factor is less than 1 0 then your population is healthier than the reference population Adjustments for Inflation If you are going to use the scores for predicting future expenditures it may be appropriate to inflation adjust these values Based on Bureau of Labor Statistics results for the calendar year 2004 medical care costs rose by approximately 5 over the previous year see http data bls gov In the preceding example if you were going to apply this inflation adjustment you would multiply the mean cost computed in Step 3 by 1 05 to reflect inflation For this example the inflation adjusted mean cost for the next year would have been 1 328 37 instead of 1 265 11 Depending on the local situation it may also be appropriate to modify future cost expectations for other actuarial factors such as changes in benefit structure of cost sharing provisions Note The above discussion was meant to offer general instructional guidance on the rescaling of relative weights and inflation adjustment Given that no two analytic or actuarial applications are exa
213. or selected medical conditions This analysis allows the user to stratify a particular population by predicted risk This can be helpful in sizing programs or understanding the resource expectations for specific risk groups At the user s discretion the average predicted resource use columns may be selected to reflect either total cost including pharmacy cost or pharmacy cost only The report layout is as follows Table 12 Cost Predictions by Select Conditions Analysis Report Layout those without any of the listed conditions Avg Pred Resource The mean of the predicted cost resource index for all patients within the current Use stratification Avg Pred Resource The mean of the predicted cost resource index for all patients within the current Use Prob lt 0 4 stratification that have a probability of being high cost lt 0 4 Avg Pred Resource The mean of the predicted cost resource index for all patients within the current Use Prob 0 4 stratification that have a probability of being high cost 0 4 Avg Pred Resource The mean of the predicted cost resource index for all patients within the current Use Prob 0 6 stratification that have a probability of being high cost 0 6 Avg Pred Resource The mean of the predicted cost resource index for all patients within the current Use Prob 0 8 stratification that have a probability of being high cost 0 8 4 Tip Use the Report Options Options Model type Figure 23 below to control
214. ority of government agencies and health care organizations employ the 11 digit code format including the Johns Hopkins ACG System It follows the form 5 4 2 referring to the digit lengths of each individual sub code segment The first segment issued by the FDA identifies the labeler manufacturer code The next four digits called the product code impart information regarding drug strength dosage form and formulation The last two digits the package code refer to package size and type Together these three number sequences form the NDC number With these pieces of information one can ascertain generic name active ingredient manufacturer strength route of administration package size and trade name for any medication We suggest users process all NDC codes over the period of interest The World Health Organization s WHO Anatomical Therapeutic Chemical ATC codes may also be processed with the ACG System In the ATC classification system the drugs are divided into different groups according to the organ or system on which they act and their chemical pharmacological and therapeutic properties Drugs are classified in groups at five different levels The drugs are divided into fourteen main groups 1st level with one pharmacological therapeutic subgroup 2nd level The 3rd and 4th levels are chemical pharmacological therapeutic subgroups and the Sth level is the chemical substance The 2nd 3rd and 4th levels are often used to i
215. orts only the columns listed in lt file gt lt file gt should contain columns on separate lines Only valid for PATIENT export acg file lt file gt Uses the acg data file lt file gt to export from export file lt file gt Exports data into lt file gt no headers Does not write a row of headers into the export file install license lt file gt Installs the license in lt file gt install mapping file lt file gt Installs the mapping file lt file gt help Prints this message Technical User Guide The Johns Hopkins ACG System Version 8 2 5 92 Installing and Using ACG Software Guidelines e All filenames should be specified with an absolute pathname e All input files should be in either comma delimited or tab delimited format using optional quotes with the platform specific end of line character s CR LF on Windows LF on UNIX e By default export files will be exported as tab delimited quote enclosed using the platform specific end of line character s Use the delim option to select comma separated files e To use a patient file format that is different from the standard file format the user can either create a format file acgf in the Windows application and apply it within the command line or the user can create a custom format file for use with the command line The user needs to create a text file in the following format property value col name data type col desc Column formatting rules are
216. ososssrssisessssesnosssosss konsessies sssri 3 1 One System Many Tools Many Solutions seessesesocesooessoessseessocssoossooseoo 3 1 Introduction to the Components of the ACG Toolkit ccssscssssseees 3 2 Aggregated Diagnostic Groups A DGS sssessssssssssseessesessresseserssressessess 3 2 Adjusted Clinical Groups ACG cccssceoscensssuesteacuascsseanssannteansnpedonesstayeuneste 3 3 Expanded Diagnosis Clusters EDCs sccscincsiavesinsnsrasecssavnaaciussaiaeivanceaninss 3 3 Rx Defined Morbidity Groups Rx MGs s sssssessesessssesseseresressessrssresseese 3 4 Adjusted Clinical Group Predictive Modeling ACG PM eee 3 4 Table 1 Comparison of ADG Distribution across Two Enrollee CR assesses drones a a ew ae 3 6 Table 2 Percentage Distribution of Two Subgroups by Resource Utilization Band RUB Categories sinisinta 3 7 Table 3 Observed to Expected Standardized Morbidity Ratio SMR by Major EDC MEDC sssvsrtessconcasseocascadenaisasisunieddedoniiaaincadandods 3 8 Table 4 Observed to Expected Standardized Morbidity Ratio SMR by Rx Morbidity Group RX MG sssssseessssesessseseesessrseesessrsessee 3 10 Health Status MOnitoring ss cssssssosesscasssvesesssvessseessssenes sevsesvonessvessvecsensevseseess 3 12 Table 5 Movers Analysis Tracking Morbidity Burden Over Time 3 12 Provider Performance Assessment sesessossesoesossescosoesesoosoesossossssossossesse 3 13 Profiling R source IGG oss sic
217. ow efficiently the group utilizes services as compared to the population mean There are additional rate based measures provided to describe the factors contributing to group risk Groups with higher disease burdens will also generally tend to have higher prevalence rates of high risk members who are more likely to have chronic conditions higher rates of hospital dominant and frailty conditions and higher rates of psychosocial conditions Comparisons can be made between the group and the population mean by comparing the groups tab to the overall tab in the analysis window The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software The report layout is as follows Note The columns that are marked with a D only appear when diagnosis data is present in the model Column Name Cases Table 14 Actuarial Cost Projections Report Layout Definition Number of patients in this stratification National CMI D Average of National Unscaled Concurrent Weight in this stratification Scores lt 1 0 indicate healthier gt 1 0 indicate sicker than the reference population Local CMI D Average of Local Concurrent Weight in this stratification Useful only for sub group analysis Equal to 1 0 for the total population interpretation the same as National CMI for population sub groupings Mean Total PRI Average or Rescaled Total Cost Resource Index for patients in this stratification
218. oyer Id Employer Name Benefit Plan Health System PCP Id PCP Name PCP Group Id PCP Group Na SSXZWTXTRYQSTZTXZY 28M COMMERCIAL_B COMPANY_B PPO 2062 GROUP2062 POS_B Hs02 20606 PCP20606 2060 PCP_GRP2060 SUWZWSUVUYQSXZUYRY 32M COMMERCIAL_D COMPANY_B PPO 1573 GROUP1573 POS_B H503 15718 PCP15718 1571 PCP_GRP1571 TRZWTWXSQXZZYYZWYY 3M COMMERCIAL_D COMPANY_A PPO 1673 GROUP1673 POS_B H502 16702 PCP16702 1670 PCP_GRP1670 TSQZWTWWTYQSYZRZXZ 33F COMMERCIAL_D COMPANY_B PPO 0093 GROUP0093 POS_B HS04 00948 PCP00948 0094 PCP_GRP0094 TSTZWXZZZXZZYYXXXZ 3F COMMERCIAL_D COMPANY_B PPO 1673 GROUP1673 POS_B H505 16783 PCP16783 1678 PCP_GRP1678 TSURSWVXYYQTRYYYUY 42M COMMERCIAL_C COMPANY_A PPO 0124 GROUPO124 POS_A HS04 01261 PCPO01261 0126 PCP_GRPO126 TTRXXSSYZYQSWZREWY 31M COMMERCIAL_C COMPANY_C PPO 0103 GROUPO103 POS_B H503 01091 PCP01091 0109 PCP_GRP0109 TTRXXSSYZYQTRZSWZZ 36 F COMMERCIAL_C COMPANY_C PPO 0103 GROUPO103 POS_B HSO3 01091 PCPO1091 0109 PCP_GRPO109 TT ZSYRWVYQRRZUXZY 16M COMMERCIAL_D COMPANY_A PPO 2441 GROUP2441 POS_B HS02 24402 PCP24402 2440 PCP_GRP2440 TUSZRQZSWYQSZZXZRY 35M COMMERCIAL_C COMPANY_A PPO 0113 GROUP0113 POS_B HSOS 01125 PCPO1125 0112 PCP_GRPO112 TUXYVZQRUYQSQZUWYY 25M COMMERCIAL_B COMPANY_B PPO 2042 GROUP2042 POS_B HS01 20483 PCP20483 2048 PCP_GRP2048 TUXYVZQRUYQSRZVZQ2 26F COMMERCIAL_B COMPANY_B PPO 2042 GROUP2042 POS_B H501 20483 PCP20483 2048 PCP_GRP2048 TUYXTZQQRYQQYYXZVZ 13F COMMERCIAL_B COMPANY_A PPO 2051 GROUP2051 POS_B H501 2
219. please contact your primary support person for assistance A Tip If processing ICD 10 data pay special attention to the non matched ICD 10 codes Users are reporting higher than anticipated mismatch rates due to local implementation of ICD 10 CM encouraged by the World Health Organization Adjustments to the input data to assure conformity to ICD 10 WHO may be necessary to assure that maximal diagnostic information may be extracted from the claims data Talk to your software vendor about the possibility of including local code sets to accommodate your customization of ICD 10 The Johns Hopkins ACG System Version 8 2 Technical User Guide Assessing the ACG Grouper s Output 6 3 Which Predictive Model The Summary Statistics Tab also provides the user with information on which predictive model was used in selecting the scores predictions of total cost pharmacy cost and probability scores for high total cost and high pharmacy costs in the summary patient file The descriptions for each model are described in four sections using the following example Total Cost Model Selected DxRx PM total sot gt total Sout Risk Assessment Variables US non elderly E Indicates the type of ACG predictive model Possible values include Dx PM for diagnosis based predictive modeling Rx PM for pharmacy based predictive modeling or DxRx PM for diagnosis plus pharmacy based predictive modeling i Indicates whether o
220. ports available on the Analyze menu may be customized at the user s discretion Customization is controlled via the Report Options menu that includes up to three screens 1 Filters 2 Options and 3 Groups Each screen will be discussed in more detail below Filters Use filters to control the selection of patients from the active data file to be included in the analytical view If no filters are defined all patients will be included in an analysis A typical use for filters is to run an analysis on a sub set of a population such as a single benefit plan company product or line of business Filters can be defined on any available column in the patient data which also includes all ACG calculated elements and additional custom fields imported as part of the data file It is possible to stack filters using Boolean And or Or operators For example to run an analysis on all patients in the PPO Product that have Employer ID 2051 or all patients in the Benefit Plan POS_A fill out the filter as follows The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 63 Figure 33 Filters Report Options Filters Groups Select a previously saved filter to load it into the filter editor below Saved Filters Sample Delete Filter Choose filters to limit the data that is used to build your report Filters define the source data to include in your analysis IF you don t add any fil
221. r not and the type of prior cost information included in the calibration of the predictive model Possible values include No cost for no cost information was incorporated Total cost for total cost or Rxcost for Pharmacy cost Indicates what is being predicted Possible values include Total cost for total cost Rxcost for pharmacy cost 4 Indicates the population to which the model has been calibrated Possible values include Non elderly for less than 65 years old and Elderly for populations 65 years or older A Tip For advanced users wishing to explore the All Models File containing all possible permutations of the Dx PM the Rx PM and the DxRx PM a similar albeit not identical model identification schema has been implemented Technical User Guide The Johns Hopkins ACG System Version 8 2 6 4 Assessing the ACG Grouper s Output Patient Sample The second table produced by the ACG Software is a sample of the first 1 000 output records The table provides a means of quickly assessing whether data appears to have been loaded and processed correctly User should use this table to confirm that input data matches the equivalent output information and check the remaining output fields for consistency in column population Local Weights The third table produced by the ACG Software is the Local Weights tab The table presents patient counts total cost and concurrent weights by ACG based on local data
222. r of patients assigned this Rx MG in this stratification The number per 1 000 patients in the current stratification that were assigned to this Observed 1000 Rx MG Calculated as Patient Count total Patient Count within the same stratification for all Rx MGs x 1000 The number of expected observations per 1 000 after adjusting for the age sex Age Sex distribution in the current stratification Calculated as total of overall age sex Expected 1000 prevalence rate x number of patients in age sex in current stratification for all age sex combinations number of patients in the current stratification for all Rx MGs x 1000 SMR Observed to Expected Ratio Calculated as Observed 1000 Age Sex Expected 1000 95 Confidence The lower range of the 95 confidence interval Calculated as Low SMR 1 96 x SQRT SMR expected count 95 Confidence The upper range of the 95 confidence interval Calculated as High SMR 1 96 x SQRT SMR expected count An indication of statistical significance Contains a minus sign when the SMR is Significance significant and less than 1 contains a plus sign when the SMR is significant and greater than 1 The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 33 Cost Predictions by Select Conditions Analysis The Cost Predictions by Select Conditions Analysis describes risks and predicts expenditures in the subsequent time period f
223. r to use The defaults are already setup for import into Microsoft Access The Write Header Row option will write the first row in the export file with the column names This makes it easier to import into Microsoft Access Only one tab can be exported at a time in the delimited data file mode Finally click the File Selection button and choose a filename for the exported data Click OK on the Export Table window to begin the export Export Data Files From an active data file tab it is possible to export the entire data file to another application The Export ACG Data option will create a tab delimited text file from your ACG data This data format is directly supported by Microsoft Excel Microsoft Access and many other mainstream databases and statistical applications Using the Tools Export or La menu button simply click the File Selection button and choose a filename in which to save the exported data Click OK on the Export ACG Data window to begin the export The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 69 Figure 38 Export Data Files x Johns Hopkins ACG System 8 2 File Edit View Analyze Tools Help Rox 2 2 via 9 E 825AMPLE acgd ACG Data File Summary Statistics Patient Sample Local Weights Age Gender Dist Probability Dist Build Options Description Value Patients processe
224. racy of the predictive model will increase as more information is made available Therefore a model that uses diagnosis pharmacy and prior cost will be more predictive than a model based only on pharmacy claims without prior cost There is still good reason to implement the pharmacy only model Pharmacy data is fairly complete after 90 days and there is generally minimal lag As new enrollees are brought on to the plan rapid risk assessment can be performed on these members using Rx PM The minor differences in predictive accuracy are compensated for by the gains in time for intervention The ACG predictive modeling suite provide choices that allow you to select the model that best fits your application Using just a single month of claim s data Table 11 demonstrates the benefit of the ACG Rx PM model Table 11 Amount of Data and Its Impact on Model Performance Data and Model C Statistic 1 Month Rx 3 Months Rx 6 Months Rx 12 Months Rx 12 Months Rx Dx Prior Cost There are many ways to adapt the ACG predictive models in the pursuit of improved patient care This section provides a summary and overview of some of the recommended approaches that an organization may wish to consider in the care management and quality improvement QI domains The Johns Hopkins ACG System Version 8 2 Technical User Guide Selecting the Right Tool 3 21 ACG predictive modeling provides information at the individual patient level to
225. rations os sssscosssssosssesscasscossasvocsesssecsonsssosssscssessosesseonsseess 6 9 Ev l ate the Warning DistribUtioi sso ot aacccdacaschcomcssntdniahariaituioainiaadieass 6 9 Figure 3 Sample Warning DistOun i sicsscsccsccssscccsdussssiaccesindedsseseevesaczeans 6 9 Examining the List of Non Matched Diagnosis Codes ceeeeeeeee 6 10 Table 1 Sample of Non Matched ICD Fite os sjscs casiisiscssaieriasiiatansntescanss 6 10 Common Input File Problems sissano aias 6 11 Examining the List of Non Matched Pharmacy Codes ccceeeeee 6 11 Table 2 Sample of Non Matched Pharmacy File cccseeseeeeeeeees 6 12 FigyreA Epon Files crysceediner iai e 6 13 CONCHISION E E E E E E E 6 14 Technical User Guide The Johns Hopkins ACG System Version 8 2 6 ii Assessing the ACG Grouper s Output This page was left blank intentionally The Johns Hopkins ACG System Version 8 2 Technical User Guide Assessing the ACG Grouper s Output 6 1 Introduction This chapter is intended for the programmer analyst who will actually be running the ACG Software This chapter outlines a series of steps that will help you assess the face validity of the grouping process ACG Compressed Data File After processing the input patient diagnostic and optional pharmacy files the ACG Software generates a single output file in a compressed data file format with an acgd extension The ACG output file contains all of the input and output variable
226. re 39 Select Columns x Johns Hopkins ACG System 8 2 File Edit View Analyze Tools Help SEx r amp of fial 825AMPLE acgd ACG Data File Summary Statistics Patient Sample Local Weight e Export Column Chooser Patients processed a paene processed 65 year Select a previously saved column list to load into selected columns below Diagnoses processed Unique diagnoses encounte Saved Lists Unique unknown diagnoses Percentage of diagnoses th Select and reorder columns for export Unknown diagnoses encour Available Columns Selected Columns Patients with unknown diagi lage Patient Id Unique diagnosis code sets Sex Age Band nique unknown diagnosis Line of Business ACG Cd Patients with unsupported Company Resource Utilization Band Pharmacy codes processed Product Rescaled Total Cost Resource Index a Employer Id M __ Select Probability High Total Cost Pease Phiny col Eeoa ie Deselect Move Up Inknown pharmacy codes Patients with unknown phai bea d select an _ Move Down Unique pharmacy code sets PCP Name Deselect All Unique unknown pharmacy PCP Group Id Patients with unsupported g PCP Group Name Number of EDCs assigned Pregnant Number of MEDCs assigned Delivered Number of ADGs assigned ee Number of Rx MGs assigned Percentage of patients wil Percentage of patients wit Number of patients with diagere ormererorrene Number of patients with pharm
227. re include a unique member identifier age gender and string of diagnoses codes for the period of interest typically a year To perform ACG based analyses the output produced by the software the risk assessment variables must be linked to data files containing additional data elements necessary to stratify the population into groups for analysis linked to resource consumption measures The next chapter will walk you through the process of installing and using the software Subsequent chapters are intended to aid in validating and using the output produced by the software The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 i 5 Installing and Using ACG Software DEC UCR GI os siiss E T A 5 1 System Reguiremients vc ivacssesccasvssssssessacenassssseannveassvonsesssnantassnsantassssnarsasssansnsrs 5 1 NS PSC crscraneacaatenseansnssmannenasitess E EANET EEEN SEED 5 1 Central Processing Unit CPU ensinei 5 1 Memory EEE AVON 5s ace titantatgns aitassuctabessti e decade igucmiaseuaats 5 1 Disk Spatne neia E a 5 2 Installing th SoftWalCssssssssssssoissssssssusscssssssssssssus ssssunsasssssssssvssissesssssssossss ss 5 3 Figure 1 First Setup SGre fencnsstengnera r E iais 5 3 Figure 2 Extraction Status masrana enn RN 5 4 Figure 3 Guided NN on srnce E RE 5 4 Figure 4 Select Destination Location ssesessssesseserssressessrssressessrssees 5 5 Figur 5 Choose Shortcut Foldet a cceiccsssds
228. report automatically generated by the software you are encouraged to develop your own individual risk summary reports on each potential case over a certain threshold for instance the top 1 of individuals This target group can be separated further by case managers on the basis of various sources of information available from the ACG Software and elsewhere These additional data might include primary care provider information service history history of prior inclusion in care management programs and results from any ongoing surveys such as health risk appraisals Reference chapters five through seven focused on the ACG Predictive Models and managing care for persons at risk for high future cost for a comprehensive discussion of the ACG predictive models and their applications The Johns Hopkins ACG Case Mix System Version 8 2 Technical User Guide 3 27 ing Care Management Listi Selecting the Right Tool Table 13 uorsu z z z 6 Ay fas x id z a wipe elelefe elefelele iy fefefe efe ee mpeg as a a a T Z Z Z Z z IUO mpeg aD dAI SISUOD Q g m S a a BEE sea Ayreagy Z gt Z Z Z Z Z z Z uorrpuogo HUO yueurwo q ndsoHg B10 4351H Amqeqord Eee m ks p a Lge N O wy PI uned 6221564 16 19331125 6244137 14 6422322 14 6221471 14 19551215 6427141 16 444412 141 6442443 16 19621114 6533734 14 6646141 14 The Johns Hopkins ACG System Version 8 2 Technical User
229. ries e g 2 5 6 11 18 34 e Is the number of members assigned to ACG 5100 no valid diagnoses assigned to an ADG or ACG 5200 non users consistent with the plan s non user rate If it is not and the ICD mismatch rate is within the expected range a diagnosis coding or record justification problem may exist on the input data file Age Gender Distribution The fourth table produced by the ACG Software is an Age Gender Distribution of the local population This distribution is useful as a comparison between the given population and any external reference data source as a means of validating input data It can also be used for historical trending The Johns Hopkins ACG System Version 8 2 Technical User Guide Assessing the ACG Grouper s Output 6 5 PM Scores Distribution The fifth table produced by the ACG Software is a Predictive Modeling PM Distribution of the local population For Commercial populations results should be reviewed and compared against the reference data results below Users should expect to see a large portion of the population with PM scores below 0 80 Review of Reports Produced by the Analyze Menu There are a series of reports available in the Analyze menu Figure 1 of the software each of which may be accessed by 1 selecting Analyze from the Windows task bar and 2 selecting the desired report from the pull down menu The Analyze menu may be used not only for assessing data quality but may also and dep
230. right tool 3 1 summarizing total or ambulatory charges 4 12 tyical place of service codes to exclude 4 8 using the software 5 15 warning list 5 49 Windows desktop 5 16 ACG PM high risk case identification for case management 3 20 local calibration of scores 7 16 options 5 82 predictive model predicted resource index PM PRI score 3 32 probability score 3 26 underwriting 3 34 Actuarial cost projection 5 37 Added documentation regarding temporary disk space release notes 5 2 Addressing the impact of age on the calculation of ACG weights 7 15 Technical User Guide aggregated diagnostic group ADGs 3 2 distribution analysis 5 20 Adjustments for inflation 7 8 Age gender distribution 6 4 All file model 7 6 Ambulatory encounters 4 12 Analysis time frame 4 7 basic data requirements 4 7 Analyze menu 5 17 Analyze report options 5 62 Anatomical therapeutic chemical ATC classification release notes 2 2 Appendix ACG output data 5 85 ACGoutput data 5 85 batch mode processing 5 89 Java API 5 96 Application of regional settings release notes 2 9 Att of risk adjustment 8 1 Assessing ACG grouper output age gender distribution 6 4 comparison to reference or external data 6 8 evaluate the warning distribution 6 9 local weights 6 4 patient sample 6 4 PM scores distribution 6 5 reports produced by the analyze menu 6 5 RUB distribution example 6 7 summary statistics tab 6 2 Assessing the ACG grouper
231. rol the calculation of member specific output variables The user is asked to select the Risk Assessment Variables to be used at the time that the input files are specified The Risk Assessment variables include e Reference Concurrent Weights An estimate of concurrent resource use associated with a given ACG based on a reference database and expressed as a relative value In addition to member output these weights are used in observed to expected ratios and in reference case mix index values e Predictive Modeling Coefficients An estimate of prospective resource use associated with a given risk factor based on a reference database and expressed as a relative value These coefficients are added for each member based on the risk factors present to produce a Predicted Resource Index e Reference Prevalence Rates MEDC EDC Major Rx MG and Rx MG prevalence rates for each age sex cohort within a reference population These rates are aggregated to form the expected prevalence in the corresponding Standardized Morbidity Ratio analysis e Resource Utilization Bands Aggregations of ACGs based upon estimates of concurrent resource use providing a way of separating the population into broad co morbidity groupings Several standard analyses use the distribution across RUBs e Frailty Marker A dichotomous on off variable that indicates whether an enrollee has a diagnosis falling within any 1 of 11 clusters that represent medical problems assoc
232. rovides access to and allows for customization of the ACG based reports The columns and descriptions for each available analysis follow Figure 18 ACG Reports Available for Analysis RUB Distribution ACG Distribution ADG Distribution Population Dist By Age Band and Morbidity MEDC By RUB Distribution EDC By RUB Distribution RxMG By RUB Distribution Standardized Morbidity Ratio By MEDC Standardized Morbidity Ratio By EDC Standardized Morbidity Ratio By Major Rx MG Standardized Morbidity Ratio By Rx MG Cost Predictions By Select Conditions Cost Predictions For Selected Rx MGs Actuarial Cost Projections Simple Profile Care Management List Patient Clinical Profile Report Patient List Warning List Warning Distribution 4 Tip The following sections explain each of these analyses in more detail and this symbol will be used to highlight useful features and or customizable aspects of the analysis The reader is encouraged to review these tips along with Analyze Report Options discussed under Loading the Sample Data Set on how to take full advantage of the report customization capability of the software using the Filters Groups and Options capabilities gt Note For each analysis generated a tab displays any filtering options analysis groupings or options applied to the analysis see Figure 19 Technical User Guide The Johns Hopkins ACG System Version 8 2 5 18 Installing and Using ACG Software Figure
233. s Utilization Bands RUBs The software automatically assigns 6 RUB classes e 0 No or Only Invalid Dx e 1 Healthy Users e 2 Low e 3 Moderate e 4 High e 5 Very High The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 19 The RUB Distribution Analysis produces a frequency distribution by Resource Utilization Band The report layout is as follows Table 1 RUB Distribution Analysis Report Layout Resource Utilization Band Each RUB that was assigned to a patient within the current stratification RUB Description The description for the resource utilization band The number of patients with this RUB and in this stratification that meet the pee optional filter criteria The percentage of patients within this stratification and meeting the optional filter criteria that were assigned this RUB Freq The report is useful for providing a quick snapshot of population health and when populations sub groupings are compared by RUB distribution it is easy to identify which groups are serving patient populations with more or less severe morbidity merely by looking at the percentage with high or very high morbidity or those with very low morbidity 4 Tip If generating analyses for similar sub groups regularly filters can be saved and recalled for later analyses This feature is discussed more thoroughly under the Analyze Report Options heading ACG Dis
234. s were consumed by this group compared to how many resources they would have consumed had they utilized the average resource use of the population based on their case mix characteristics All three of these statistics are expressed as relative values with the average or normative value centered at 1 0 Scores greater than 1 0 indicate higher than average whereas those less than 1 0 indicate lower than average Tests of statistical significance can be developed to assess outlier status Clearly the use of risk adjustment provides a dramatically different basis for assessing the performance of the three profiled sites For additional information see the chapter entitled Provider Performance Assessment in the Reference Manual Table 6 Comparison of Observed to Expected Visits and Calculation of Three Profiling Ratios a fon s 1 Actual Visits per person Omenan 2 2 Plan Average 5 50 3 Actual to Group Average 0 97 1 1 26 Unadjusted Efficiency Ratio 7 8 9 1 1 Number of Expected Visits 5 54 5 Expected to Plan Average Observed to Expected Ratio Row 1 divided by Row 2 Expected based on ACG characteristics at each site Row 4 divided by Row 2 Row 1 divided by Row 4 The Johns Hopkins ACG System Version 8 2 Technical User Guide Selecting the Right Tool 3 15 Evaluating Productivity and Distributing Workload In addition to efficiency assessment case mix adjustment is vital to the evaluation of physician
235. s ACG System Version 8 2 5 50 Installing and Using ACG Software Warning Distribution Analysis The Warning Distribution Analysis produces a frequency distribution by Warning The report layout is as follows Table 20 Warning Distribution Analysis Report Layout Warning Code Each warning that was assigned to a patient within the current stratification Warning Description The description for the warning Frequency The number of patients that encountered this warning within this stratification Freq The percentage that frequency represents out of the total patients processed Tools Menu The Tools menu provides access to the export utility which exports both the data and or reports produced by the software The Tools menu also provides management functions for installing license files and updated mappings See the section on Installing the Software for more information on these functions Help Menu The Help menu provides access to quick reference information for the ACGs for Windows interface Much of the information provided below is also accessible directly from within the software The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 51 Load the Sample Dataset The ACG System Version 8 2 includes sample data representing approximately 20 000 members The sample data is provided to familiarize users with the system and it will be used here to demonstrate use of
236. s a given report Concurrent Weight Options Weight Type Reference Weights v Predictive Model Options Model Type Prevalence Comparison Group Prevalence Type Technical User Guide The Johns Hopkins ACG System Version 8 2 Installing and Using ACG Software Report Options Figure 36 Report Options x Johns Hopkins ACG System 8 2 File Edit Yiew Analyze Tools Help For each analysis generated a tab is generated which displays any filtering options analysis groupings or options applied ao Mm x R S w amp A ez5ample acgd Ef SYR By EDC Standardized Morbidity Ratio By EDC using Referenc Company Product Benefit Plan Report Options 1 Option Selection ACG Data File C acgdata 82Sample acad Filter line_of_business equals Commercial Column Groups Company Company Product Product Benefit Plan Benefit Plan Prevalence Type Reference The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 67 Export Report Tables From each analysis tab it is possible to select the Export Table option La or from Tools Export to export the complete analysis results to Microsoft Excel or to export a single tab s data to a Delimited Data File like a CSV file Choose the type of file to export to The Export All Tabs To Excel File option will export all data in the analysis saving each tab to a separate Microsoft Excel worksheet T
237. s can be used to directly compare two populations to understand differences in risk and to validate the imported data Table 4 Population Distribution by Age Band and Morbidity Analysis Report Layout Age Band Each Age Band that was assigned to a patient within the current stratification Patient Count The number of patients in the related age band and stratification RUB 0 The percent of all patients in this stratification in the related Age Band with RUB 0 RUB 1 The percent of all patients in this stratification in the related Age Band with RUB 1 RUB 2 The percent of all patients in this stratification in the related Age Band with RUB 2 RUB 3 The percent of all patients in this stratification in the related Age Band with RUB 3 RUB 4 The percent of all patients in this stratification in the related Age Band with RUB 4 RUB 5 The percent of all patients in this stratification in the related Age Band with RUB 5 Total The percent of all patients in this stratification in the related Age Band Technical User Guide The Johns Hopkins ACG System Version 8 2 5 22 Installing and Using ACG Software MEDC by RUB Distribution Analysis The MEDC By RUB Distribution Analysis produces a frequency distribution by MEDC and by Resource Utilization Band RUB A patient can be assigned to multiple MEDC codes but only one RUB This report is useful for case managers because it helps to illustrate that not all
238. s from this model could then be applied to months 13 24 to yield predictions for months 25 36 In essence modeling would occur across the lag period These longer term models could serve as provisional models for a period of interest and could be replaced once a potentially more predictive annual model becomes available Yet a third approach is that implemented by Minnesota Medicaid and the Buyers Health Care Action Group BHCAG and several other tiered network applications where group level predictions are based on historical group level concurrent profiles with a trend factor applied to generate an estimate of future resource expectations at the group level The assumption behind using group level concurrent profiles to predict future costs is that the case mix of a group at least of sufficient size will not change much over time and that projections based on concurrent profiles provide more accurate projections than individual level predictions In such an application the concurrent ACG based profiles are generally recalibrated approximately every three months and new targets are set thus mitigating the data lag problem Handling New or Part Year Enrollees Most ACG applications involve the analyst viewing a snapshot of the utilization history of plan members during a particular period of time If any members of the risk pool have been eligible to use services for a period of time that is shorter than the in scope period both their diagnos
239. s necessary to produce each of the standard reports as well as the ability to export the data for customized analyses Please refer to the Installation and Usage Chapter for more detail on each of the ACG Software input and output files Note UNIX users must transport the acgd file created by the software to a Windows platform and invoke a Windows version of the software to follow the review steps outlined in this chapter Basic Review Process The first stage in the quality control process includes an initial review of the reports automatically produced by the software These include 1 Review the Summary Statistics tab including verifying the input file s person counts diagnosis code mismatch rate and that the number of warning messages is reasonable Review the Patient Sample tab to confirm population of the each field within the acgd file and confirmation that the input of data matches the patient source file Review the Local Weights tab to validate the presence of most or all ACGs Of particular interest is a relationship between the pregnancy and newborn ACGs as well as the number of non user and no dx code ACGs Review the Age Gender Distribution tab against the known age and gender mix of the population Review the Predictive Modeling Scores Distribution tab Users should expect to see a large portion of the population with PM scores below 0 80 Review the Build Options tab Information on input files pa
240. s providing a comparison by percentage of high cost members correctly identified using prior cost Dx PM and DxRx PM models The two charts contrast the difference between making predictions using just one month of pharmacy data versus making predictions using twelve months of diagnosistpharmacy data While the Rx PM model works well on as little as one month of data the accuracy of predictive modeling improves as the quality of the underlying data as measured by diagnoses and pharmacy data improves Using Dx PM and Rx PM as independent assessments of risk can yield even more information for a care manager Technical User Guide The Johns Hopkins ACG System Version 8 2 3 22 Selecting the Right Tool Figure 1 Percent Correctly Identified as High Cost Comparing One Month of Rx to 12 Months of Dx Rx 25 50 39 25 25 Rx PM 1 month data ODxRx PM 12 months data Prior Cost E Prior Cost E Both E Both The Rx MGs can supplement the EDCs in describing the clinical conditions of the patient Depression and hypertension in particular may not be part of the diagnoses but will be captured in the prescriptions If these patients are tracked over time and there is a pattern of prescriptions without visits communication with the member and provider may be helpful The Johns Hopkins ACG System Version 8 2 Technical User Guide Selecting the Right Tool 3 23 4 Pharmacy identifies additional members with specific condi
241. scar oisudacesccussssaisiewiuducearcubaiesbiacuisanaivimncciincusses 3 13 Table 6 Comparison of Observed to Expected Visits and Calculation of Three Profiling Ratios icc cicascasicteccsncistestuiainasiseusemeseiacala 3 14 Evaluating Productivity and Distributing Workload ce eeeeeeeneees 3 15 Table 7 Comparison of Characteristics Affecting Physician PR ieena couse eh eats eestor sas a sete ee areas 3 15 Quality of Care FBS aE IICINT sca ondsccacactaeensncieemieievaleeieneiieecsaeaanees 3 16 Table 8 Percentage of Patients with Selected Outcomes by ACG PM Risk OG sccsniassiucnenssdvsalanedensisametiseahinrnamuesatenearnsadnsaiusweds 3 17 Care Management and Predictive Modeling Providing Information for Disease and Care Managers e sessessossesoesossesoosoesesooseeseee 3 18 Table 9 Percentage Distribution of Each Co Morbidity Level With gt EDC Samples hasrohnn n a 3 18 Table 10 Estimated Concurrent Resource Use by RUB by MEDC Samples inio a ai E 3 19 High Risk Case Identification for Case Management ceseeeeeeeees 3 20 Technical User Guide The Johns Hopkins ACG System Version 8 0 Selecting the Right Tool Table 11 Amount of Data and Its Impact on Model Performance 3 20 Figure 1 Percent Correctly Identified as High Cost Comparing One Month of Rx to 12 Months of Dx RX oo ee eeceecesecneeeneeeeeeeeeeeenaes 3 22 Figure 2 Percent of Patients Identified by ICD or NDC or Both 3 23 Figure
242. scular High Blood Pressure _ _ Cardiovascular Hyperlipidemia Cardiovascular Vascular Disorders Ears Nose Throat Acute Minor __ _ Endocrine Bone Disorders Endocrine Chronic Medical Endocrine Diabetes With Insulin _ Endocrine Diabetes Without Insulin Endocrine Thyroid Disorders Eye Acute Minor Curative _ Eye Acute Minor Palliative _ Female Reproductive Hormone Regulation Gastrointestinal Hepatic Acute Minor Gastrointestinal Hepatic Peptic Disease General Signs and Symptoms Nausea and Vomiting _ General Signs and Symptoms Pain General Signs and Symptoms Pain and Inflammation _ Genito Urinary AcuteMinor O Selecting the Right Tool 3 11 Approximate Observed Age Sex Standard 95 Prevalence Expected Morbidity confidence Per 1 000 Prevalence Ratio interval Rx MG Description Population per 1 000 SMR Low High Infections Acute Minor 1 143 1 121 1 164 Neurologic Migraine Headache 1 226 1 135 1 318 Neurologic Seizure Disorder 24 84 21 03 1 181 1 096 1 266 Psychosocial Attention Deficit Hyperactivity 1 280 Psychosocial Chronic Unstable Skin Acne 1 174 1 097 1 251 Skin Acute and Recurrent 92 47 78 99 1 171 1 127 1 214 Technical User Guide The Johns Hopkins ACG System Version 8 2 3 12 Selecting the Right Tool Health Status Monitoring Monitoring the health status of a population may be desirable for purposes o
243. se no services Therefore they do not have diagnosis assignments during the first 12 month risk assessment period These are a few of the challenges that the prospective capitation process faces The prototypical time line for this process and the concurrent profiling process are outlined in Figure 2 Technical User Guide The Johns Hopkins ACG System Version 8 2 Final Considerations 8 3 Figure 2 Typical Timeline for Risk Adjustment 12 Months 3 Months 3 Months 12 Months Risk measurement period also assessment Data lag period Analysis rating Risk measurement period also assessment period for retrospective profiling process period for retrospective profiling Technical User Guide The Johns Hopkins ACG System Version 8 2 8 4 Final Considerations There are numerous technical approaches for dealing with the data lag problem for prospective applications The simplest approach is to take the predictions provided by the ACG PM model This of course means that the prediction is already aged by the period of the lag An alternative is to use an historical database to determine trended resource use for successive years For example at Plan Z by going back to a time period 24 months before the target year the target year being months 25 36 it would be possible to associate future resource use based on risk scores assigned during the previous time period In this simulation months 1 12 would be used to predict months 13 24 Result
244. se options to control how your report is calculated Options control how your analysis is calculated See the help for more information regarding how each option impacts a given report Concurrent Weight Options Weight Type Predictive Model Options Model Type Prevalence Comparison Group Prevalence Type Loca W Reference Local Release Notes The Johns Hopkins ACG System Version 8 2 2 8 Release Notes Figure 7 Reference Option Selection x Johns Hopkins ACG System 8 2 File Edit View Analyze Tools Help KoH R l fill 82Sample acgd AF Actuarial Projections Actuarial Cost Projections for 825ample acqd Overall Line of Business Company Product Employer Id Benefit Plan Health System Age Band Report Options Health System a Cased Reference cmr focai CMI Mean Total PRI Mean Rx PRI High Risk Frail Chronic Psychosocial Discretionary Age Sex Relative Risk 10001 2 836 0 97 0 87 0 94 0 94 2 01 B 1 45 28 28 16 40 11 78 0 90 10002 2 114 0 91 0 84 0 92 0 96 1 37 5 1 84 29 47 18 21 9 74 0 96 10003 26 078 1 01 0 91 0 91 0 92 1 61 5 1 57 28 72 19 50 10 84 0 87 10004 1 458 1 11 1 00 1 09 1 08 2 47 2 40 30 25 20 64 11 59 0 96 10005 3 167 1 07 0 98 0 98 0 92 1 83 1 42 29 90 18 09 10 07 0 90 10006 12 583 1 12 1 02 1 06 1 15 2 07 5 1 50 33 55 21 89 11 84 0 95 10007 1 02 0 93 0 97 0 99 1 64 1 77 17 94 10 39 10008 0 95 0 85 0 87 0 92 1 73 K 1 88 18 20
245. siducaticeiawhnesa 5 52 View Results of the Grouping Process ois cisicccssesdenstascbonescetantesousonssaasies 5 52 Summary Statisties Ta Deisi siea a ETOS 5 53 Figure 26 S mmaty Statisti CS ssesung sensisse Esini 5 54 Which Predictive Model 3 scccscsanarasasdisesstenteidastdalecninadinsativemuieoaauanlansanl 5 55 Fig re 27 Patient Sample Tab cass cesiagtianasanaaaveonidadeansanteensdicnientuadansanadains 5 56 ACG Output DAA so ictcscssactsleisdscodtivastoncsniascbiatlcludesniasieuapnoleorssdacloaasineesnies 5 56 Pesto 28 Local Weights TaB css ccna essacarrecinsaccniieennawnnavenisans 5 57 Figure 29 Age Gender Distribution Tab sissisisiscsasscasisncaticessmissveientasats 5 58 Figure 30 Probability Distribution Ta ssccccsedscccerscsceresesotaccasesesenicatanns 5 59 Fewe S12 Buld Opuons Tabien na 5 60 Fgm Analyze MEM aces co eenont iaiia on r E E ERDE 5 61 Analyze Report His acon ccc ae 5 62 EE E E E E eet ar E E A ree E 5 62 Ficu FINGU uresen 5 63 E a e EA E E EA EEE EEE T EA EEE E AE E E E OA 5 63 Keue 20 sect eee a 5 64 EEIT Etc E E eames esate stems acai eta E aaa E E 5 64 Firmes OpUoN Sease 5 65 Ne rah ccot sce nee R E R 5 66 Figure 36 Report OPtONS core nnno nen aioe 5 66 Export Report TAs ios sistasesscisasssccdaansieiianensecnasssndinnsnansiaansindeoneriesbanesiaciacins 5 67 Figure 37 Export Report Taples ici csssicciesiscteusssisiessnivtlucisgieiassaluscienns 5 67 Technical User Guide The Johns Hopkins ACG System Version 8 2
246. sk Scores 7 17 Resource Utilization Bands RUBs ACGs were designed to represent clinically logical categories for persons expected to require similar levels of healthcare resources However enrollees with similar predicted or expected overall utilization may be assigned different ACGs because they have different epidemiological patterns of morbidity For example a pregnant woman with significant morbidity an individual with a serious psychological condition or someone with two chronic medical conditions may all be expected to use approximately the same level of resources even though they each fall into different ACG categories In many instances users may find it useful to collapse the full set of ACGs into fewer categories particularly where resource use similarity and not clinical cogency is a desired objective Often a fewer number of combined categories will be easier to handle from an administrative perspective ACGs can be combined into what we term Resources Utilization Bands RUBs The software automatically assigns 6 RUB classes e 0 No or Only Invalid Dx e 1 Healthy Users e 2 Low e 3 Moderate e 4 High e 5 Very High Technical User Guide The Johns Hopkins ACG System Version 8 2 Table 6 Relative Concurrent PMPY Weights and RUB Categories Relative ACG ACG Label 0100 0200 0300 0400 0500 0600 0700 0800 0900 1000 1100 1200 1300 1400 1500 1600 1710 Acute Minor Age 1 Acute Minor Age 2 to 5 Acu
247. sk and resource needs It is intended to assist with the clinical screening process Table 17 Patient Clinical Profile Report Layout Column Name Patient Id The patient s unique identifier PCP Id The primary care practitioner assigned to the patient Product The product identifier the patient is assigned to Age The patient s age in years Gender The patient s gender F Female M Male Resource Utilization Band The resource utilization band assigned to this patient Local Weight The local concurrent weight assigned to this patient This weight represents the relative expected resource utilization for this patient based upon their ACG code Chronic Condition Count The chronic condition count assigned to this patient Hospital Dominant Count The hospital dominant count assigned to this patient Frailty Flag The frailty flag for this patient Y N Total Cost The patient s total costs during the observation period The patient s pharmacy costs during the observation period Model The specific ACG model parameters used in predicting total cost and pharmacy cost Probability High Total Cost The probability that this patient will be in the top 5 percent of total cost in the subsequent year Predicted Total Cost Range Technical User Guide The predicted total cost for this patient for the subsequent year The Johns Hopkins ACG
248. some ICD coding issues of which ACG Software users should be aware Diagnosis Codes with Three and Four Digits The ICD coding scheme is structured hierarchically with the fourth or fifth digits used to further define or subdivide diseases or conditions that are described in general terms with the first three digits With the majority serving as headers for the more specific four and five digit codes that follow only a minority of three digit ICD 9 or ICD 10 codes are clinically valid as separately defined conditions Therefore these three digit codes often will not be accepted by payers on insurance claims The difficulty for the analyst is that there is no official list of valid three digit codes While the Center for Medicare and Medicaid Services Diagnosis Related Groups e g the CMS DRG grouper does contain a list of valid ICD 9 CM codes these are geared to the inpatient setting For ambulatory care services the only source of information lies with the various ICD 9 CM publications produced by the general publishing houses and software vendors and these differ on the specific codes they consider valid Many of these entities produce color coded ICD 9 books that indicate whether a code is valid for billing or if it requires a fourth or fifth digit JHU encourages you to obtain one of these books and use it to compare the results from the Non Matched ICD 9 List produced by the ACG Software Given the common use of three digit codes t
249. st the probability that this patient will have high pharmacy costs in the year following the observation period Technical User Guide Installing and Using ACG Software 5 49 Warning List The Warning List produces a list of all patients that had ACG calculation warnings The list layout is as follows Table 19 Warning List Layout Patient ID A unique identifier for this patient ACG Cd The ACG code that was assigned to this patient Age The patient s age as of the end of the observation period Sex The patient s gender The total medical and pharmacy costs for this patient during the observation Total Cost s period Pharmacy Cost The total pharmacy costs for this patient during the observation period A set of warnings that were generated for this member during the ACG grouping process The possible codes include 6 means the patient was greater than 107 years old 7 means the person was pregnant but not a female Warning Codes 8 means the person was pregnant but not of child bearing age lt 5 or gt 55 11 means there was an indication of delivery but not of pregnancy and the person was of child bearing years so the patient is assumed to be pregnant 12 means the patient had 0 total costs but had diagnoses 13 means the patient had 0 pharmacy costs but had pharmacy codes Review of data warnings is an important part of assuring data quality Technical User Guide The Johns Hopkin
250. stalling the software Select next to pick your installation options Figure 3 Guided Setup X Johns Hopkins ACG 8 2 x Introduction InstallAnywhere will guide you through the installation of Johns Hopkins ACG 8 2 Itis strongly recommended that you quit all programs before continuing with this installation Click the Next button to proceed to the next screen If you want to change something on a previous screen click the Previous button You may cancel this installation at any time by clicking the Cancel button The installation will present a default folder for installation You may accept the default by selecting Next or you may choose an alternate location for the installation The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 5 Figure 4 Select Destination Location YE Johns Hopkins ACG 8 2 amka Choose Install Folder Introduction Where Would You Like to Install Choose Install Folder C Program Files Johns Hopkins ACG 8 2 aritu Folder Restore Default Folder tion Surrirrnary Install Goraplete The application will create a shortcut folder with the icons for the application documentation and reference data in the location of your choice To accept the program default select Next Technical User Guide The Johns Hopkins ACG System Version 8 2 5 6 Installing and Using ACG Software Figure 5 Choose Shortcut Folder YE
251. stand and explain the health of populations The System s various diagnosis based risk assessment markers provide a useful means for comparing the morbidity of different subpopulations of interest to you Additional pharmacy based markers can also identify morbidity characteristics of a population Pharmacy data is typically available much sooner than diagnosis information Simple descriptive analyses like those shown in the following sample tables compare the distribution of morbidity across selected population groupings These are offered as models for how you may wish to apply our System to describe the morbidity characteristics of those cared for by your organization Technical User Guide The Johns Hopkins ACG System Version 8 2 3 6 Selecting the Right Tool Table 1 Comparison of ADG Distribution across Two Enrollee Groups Time Limited Minor 14 7 ime Limited Minor Primary Infections 32 2 Time Limited Major 5 5 Time Limited Major Primary Infections 6 1 Allergies 3 6 4 Likely to Recur Discrete 8 6 20 7 Likely to Recur Progressive 2 0 12 9 8 0 Chronic Specialty Stable Ortho 0 9 0 7 Chronic Specialty Stable Eye 2 6 No Longer in Use 0 0 0 8 Chronic Specialty Unstable ENT 0 0 1 6 0 0 0 45 21 10 8 9 3 3 5 Psychosocial Recur or Persist Stable 9 8 Psychosocial Recur or Persist Unstable 5 8 Signs Symptoms Minor 16 9 Signs Symptoms Uncertain 17 5 Group 1 Group 2 14 8
252. stopped in the event that the data does not match your license or an available code set Release Notes The Johns Hopkins ACG System Version 8 2 2 10 Release Notes Figure 9 New File Screen Select a new filename to save the ACG Data ACG File ACG File Name V Stop building after too many non matched codes encountered Max Non matches 10 000 Back lt Next gt Changes to the Output Format The use of scientific notation in the export of very small values e g local weights was reported as an issue All numeric outputs from the system will display all decimal values without the use of scientific notation Documentation Enhancements A variety of improvements have been made to facilitate implementation of the ACG System e Technical User Guide Chapter 4 Basic Data Requirements has been expanded to describe the contents of Risk Assessment Variables and to describe the implementation of pharmacy based predictive modeling using ATC Codes e Technical User Guide Chapter 5 Installing and Using ACG Software has been revised to reflect the latest application usage e Reference Manual Chapter 6 Predicting Future Resource Use with Pharmacy Data has been expanded to discuss how ATC codes have been applied within the system The Johns Hopkins ACG System Version 8 2 Release Notes Selecting the Right Tool 3 i 3 Selecting the Right Tool Introducti ti issssssssissssissssssssssosssssssssssp
253. sus prospective applications 3 36 Concurrent versus prospective calculations 7 15 Constructing resource consumption measures 4 12 basic data requirements 4 12 Converting scores to dollars 7 7 Cost predictions by Rx MG analysis 5 35 Cost predictions by select conditions analysis 5 33 Create a new ACG data file usage details 5 90 Create a new ACG data file acgd 5 94 Custom 5 94 Custom file formats 5 78 Customer commitment and contact information 1 4 getting started 1 4 Customizing risk scores using local cost data 7 9 D Data items usually required for ACG analysis in a managed care context 4 3 Delivery status 4 11 ACG 4 11 basic data requirements 4 11 Describing a population s health 3 5 Diagnosis data file format 5 76 Diagnosis codes non matched 6 10 provisional 4 5 rule out 4 5 special note for ICD 10 users 4 6 suspected 4 5 The Johns Hopkins ACG System Version 8 2 three and four digits 4 5 using ICD 9 and ICD 10 simultaneously 4 6 Disk space 5 2 Documentation enhancements release notes 2 10 E EDC disease management and care management applications 3 18 expanded diagnosis clusters 3 3 RUB distribution analysis 5 25 Edit menu 5 16 Enhanced license management release notes 2 6 Evaluate the warning distribution 6 9 Evaluating productivity and distributing workload 3 15 Examples customer format file 5 94 Excluding lab and x ray claims 4 7 ACG 4 7 Export dat
254. t 5 35 Actiarial Cost Projections saminean nie a eioi 5 36 Table 14 Actuarial Cost Projections Report Layout cece 5 37 Simple Prorile Analysis ess iscsssncearnasecadaxnneaidanesonsnddascnctennsncedisevdadunidatenial 5 38 The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 ili Table 15 Simple Profile Analysis Report Layout cceceeseeeeeeeees 5 38 Care Management LISU coreg cio seadinccs aera REE 5 39 Table 16 Care Management List Layout ices icscesysdaccevaceieseasectapieaciceseavas 5 39 Patient Chnical Profile Report sca sccssisaccinsesaveonsassavansdicsd nsaaseundasariedsinisves 5 41 Table 17 Patient Clinical Profile Report Layout 0 0 0 cceeeeeseeeeeeeeee 5 41 Patient List Analysis essiri enserra e aa EE AEE 5 44 Table 18 Patient List Analysis Report Layout cicccsssisesccussssccesovcesscaeases 5 44 Warmine Listesine a EE N Oe 5 49 Table 19 Wy annie List Lay iscs iscsi ccisnssagralusiccintnaissnanionaaeciieas 5 49 Warning Distribution Analysis cs icsccscineesdensaceisiciaomeraeaneeagegeneaeeiens 5 50 Table 20 Warning Distribution Analysis Report Layout 006 5 50 Tools ME Disnea a aera ees 5 50 Help Nieme E E TE 5 50 Load the Sample Dataset csicsssscsasssesssssosssessesscassoossessonssvessonsensssosasesessnssneas 5 51 Figure 24 Create ACG File from Sample Data ceeeeeeseerceeeeees 5 51 Figure 25 Save ACG Sample yi ioscssescssahesvestanesantn
255. t ACGs retain much of their explanatory power Less prone to manipulation Particularly in applications involving rate setting there could be incentives to manipulate risk adjustment strategies to increase payment Unlike some other disease specific risk adjusters aggressive efforts to capture additional diagnostic codes on the part of providers will have a more limited impact on ACG assignments Where code creep associated with general increases in completeness and accuracy of coding exists the simplicity of the ACG System makes it very easy to identify this trend and to implement appropriate action such as recalibration of the underlying cost weights Stability The conceptual elegance and underlying simplicity of ACGs have made the system very stable over long periods The underlying clinical truth captured by ACGs does not change dramatically with each new data set and each new application Ease of making local calibrations It is very easy to recalibrate ACG based actuarial cells to reflect local differences in patterns of practice benefit structure and provider fees Especially for capitation and rate setting tasks we encourage you to calibrate the ACG output to reflect the unique nature of the local cost structure The same simplicity that makes it possible to risk adjust using a spreadsheet makes it equally possible to accomplish recalibration using the same types of simple tools The ultimate testimony to the value of ACGs used
256. t Tol icsisssisartsiccrtarssntieccienssndenimiaianawoennanimacrl Intr od CtiONMsissssssssissssndssssii ssssssusssssssa ssssssin sasssi ssssssssssesssassdiisis ssa L One System Many Tools Many Solutions eessoossoocssosessecsssecesocesoosssosssse J L Introduction to the Components of the ACG Toolkit cssccsssssssseee 3 2 Health Status Monitorin sssissssisirsesesssissssrnissossssssosnseesrsess isoissa na LA Provider Performance ASSeSSMENL scccssscsssssccssssccsssccssssscsssssssssssssees I 13 Care Management and Predictive Modeling Providing Information for Disease and Care Managers ccsccsssscssssscssssscsssssees J 1S Managing Pharmacy RISK sisisisciaicnninsnimnnninimnausmmnn S Capitation and Rate Setting sccssscccssssssssssssssssssssssssssssesssesssssessessers 3 3 L Concurrent versus Prospective A pplications ccscccsssscssssscssssecssees O SO Technical User Guide The Johns Hopkins ACG System Version 8 2 ii Table of Contents Additional Informati i s ssssssssssessssesssssssessccsssosssssssosssosssosssssossssossosssissesss 9 97 4 Basic Data Requirements sssescossosessscossooccosscoosssoosssoosssoscossesoesssoessssscossssssess 47 OVERVIEW sassicesseetbectecesaciccaccvacus aae sa okna ane asiaani FL Coding Issues Using the International Classification of Diseases ICD jerrcsnanimnminnnas nannaa a a a Selecting Relevant Diagnoses for Input to th
257. t may be useful to ascertain using medical records if the use of R O diagnoses is higher in these instances For example this situation could occur if certain experienced diagnosticians are referred a disproportionate share of difficult patients with unclear symptoms While the only way to validate the impact of R O diagnoses is by undertaking a complex and expensive review of medical records our experience suggests that ACG applications will not be adversely impacted by a random distribution of rule out diagnosis codes Special Note for ICD 10 Users The WHO version of the ICD 10 was first incorporated into the ACG grouper in August of 2003 Users of ICD 10 are encouraged to pay special attention to the discussion on augmenting their pregnancy delivery and low birth weight information as the usefulness of ICD 10 data for these purposes is not well established in the United States 4 Tip The ACG System supports the WHO version of ICD 10 If you have a need for a country specific adaptation please contact your ACG software distributor to discuss the potential for local customization Using ICD 9 and ICD 10 Simultaneously It is possible to simultaneously use both ICD 9 and ICD 10 data collected on the same population These codes can be processed as one data stream however ICD 9 data must be stored in separate fields or columns on the input data from the ICD 10 data see the Installing and Using ACG Software chapter in the Technic
258. talling and Using ACG Software 5 57 Figure 28 Local Weights Tab The Local Weights tab provides a distribution of members and cost by ACG In addition relative weights have been calculated using the local cost data provided during the import phase These weights are calculated as the average cost per member for each ACG divided by the average cost per member overall Relative weights are presented in several standard analyses produced by the software The choice of local or national weights is also offered within these analyses xy Johns Hopkins ACG System 8 2 Ef Fie Edit view Analyze Tools Help S amp oM x R i F D fal 82S5AMPLE acad ACG Data File 8254MPLE acgd Summary Statistics Patient Sample Local Weights Age Gender Dist Probability Dist Build Options ACG Cd ACG Description Patient Count Total Cost Concurrent Weight 0100 Acute Minor Age 1 32 24 698 84 0 35 a 0200 Acute Minor Age 2 to 5 192 63 488 37 0 15 E 0300 Acute Minor Age gt 5 1 723 76814812 0 20 0400 Acute Major 613 578 998 79 0 43 0500 Likely to Recur w o Allergies 987 587 402 72 0 27 0600 Likely to Recur with Allergies 168 120 125 86 0 33 0700 Asthma 37 33 563 20 0 41 0800 Chronic Medical Unstable 68 250 730 82 1 69 0900 Chronic Medical Stable 409 385 104 44 0 43 1000 Chronic Specialty Stable 27 66 566 23 1 13 1100 Eye Dental 101 28 522 49 0 13 1200 Chronic Specialty Unstable 50 18 156 62 0 17 1300
259. tary understanding of the structure and limitations of the International Classification of Diseases ICD 9 ICD 9 CM and or ICD 10 is needed The two current editions of the International Classification of Diseases ICD 9 and ICD 10 are developed and maintained by the World Health Organization In the United States a clinical modification of ICD 9 was prepared by the National Institutes of Health NIH Known as ICD 9 CM this system has been in use since the early 1980s and is expected to be replaced by ICD 10 CM ICD 10 was adopted by the WHO in 1993 and it and its various adaptations are in use by several other countries The ICD system was designed to serve primarily as an epidemiologic tool for tabulating causes of mortality throughout the world As accountability and reporting requirements in the health care delivery and financing system have multiplied so has the integration of ICD diagnosis coding into claims management medical management and managed care system oversight ICD 9 CM employs a five digit coding scheme whereas ICD 9 uses only four digits In both systems codes with as few as three digits are sometimes valid The system is almost entirely numeric with the exception of selected codes that begin with the letter V Factors Influencing Health Status or the letter E External Causes of Injury and Poisoning There are roughly 15 000 ICD 9 CM codes but the lack of specification or agreement as to what constitutes an invalid co
260. tched pharmacy codes See Table 1 above to perform the export process Technical User Guide The Johns Hopkins ACG System Version 8 2 6 12 Assessing the ACG Grouper s Output Table 2 Sample of Non Matched Pharmacy File patient_id Rx_code_type rx_cd 0214AAAAAAAAABWB 77777777777 0214AAAAAAAAAFIH N 49502020701 0214AAAAAAAAATUS N 53489042405 0214AAAAAAAABLOY N 51552049810 0214AAAAAAAABUSI N 08884473000 0214AAAAAAAACTEF N 08290328438 0214AAAAAAAACTEF N 53885024510 0214AAAAAAAAEKQL N 00193361050 0214AAAAAAAAGSNX N 53885037410 0214AAAAAAAAIWOH N 66666666666 0214AAAAAAAAMHDY N 53885004810 0214AAAAAAAAMHDY N 53885044450 0214AAAAAAAAMPUG N 49452278001 0214AAAAAAAANEWL N 53885044450 0214AAAAAAAAPESD N 53885004810 0214AAAAAAAAQBNK N 50924038110 0214AAAAAAAAQKIY N 50924096610 0214AAAAAAAAQYNA N 00001000101 0214AAAAAAAAQYNA N 12866101800 0214AAAAAAAAQYNA N 66666666666 0214AAAAAAAARRIR N 00193394221 4 Tip Accessing the file export options can also be done by using the Tools Export or the menu button Once the Export ACG Data window is opened simply click the File Selection button and choose a filename in which to save the exported data Click OK to begin the export The Johns Hopkins ACG System Version 8 2 Technical User Guide Assessing the ACG Grouper s Output 6 13 Figure 4 Exporting Files x Johns Hopkins ACG System 8 2 a l x Ele Edt view Edk _ view Export ACG Data x n E Choose
261. te Minor Age gt 5 Likely to Recur w o Allergies Likely to Recur with Allergies Chronic Medical Unstable Chronic Medical Stable Chronic Specialty Eye Dental Chronic Specialty Unstable Psychosocial w o Psych Unstable Psychosocial with Psych Unstable w o Psych Stable Psychosocial with Psych Unstable w Psych Stable Preventive Administrative Pregnancy 0 1 ADGs 1711 Pregnancy 0 1 ADGs delivered 1712 Pregnancy 0 1 ADGs not delivered 1720 Pregnancy 2 3 ADGs no Major ADGs 1721 Pregnancy 2 3 ADGs no Major ADGs delivered 1722 Pregnancy 2 3 ADGs no Major ADGs not delivered 1730 Pregnancy 2 3 ADGs 1 Major ADGs 1731 Pregnancy 2 3 ADGs 1 Major ADGs delivered 1732 Pregnancy 2 3 ADGs 1 Major ADGs not delivered 1740 Pregnancy 4 5 ADGs no Major ADGs 1741 Pregnancy 4 5 ADGs no Major ADGs delivered 1742 Pregnancy 4 5 ADGs no Major ADGs not delivered 1750 Pregnancy 4 5 ADGs 1 Major ADGs 1751 Pregnancy 4 5 ADGs 1 Major ADGs delivered 1752 Pregnancy 4 5 ADGs 1 Major ADGs not delivered 1760 Pregnancy 6 ADGs no Major ADGs 1761 Pregnancy 6 ADGs no Major ADGs delivered 1762 Pregnancy 6 ADGs no Major ADGs not delivered 1770 Pregnancy 6 ADGs 1 Major ADGs 1771 Pregnancy 6 ADGs 1 Major ADGs delivered 1772 Pregnancy 6 ADGs 1 Major ADGs not delivered 1800 1900 2000 gt gt 4 2 3 z z a 5 Acute Minor and Acute Major Acute Minor and Likely
262. ters all source data will be included in the analysis See the help for more information E Any v of the Following conditions are true All And Any Or None Not B a X of the Following conditions are true 4ll And Any Or None Not Delete Product w Equals 1 2 n v PPO Delete j Deleted Employer Id w Equals 1 2 n gt 2051 Delete Add Criteria Add Any AlliNone Benefit Plan zj Equals 1 2 04N Posa Dakete Add Criteria Add Any All None Clear Filter Save Filter p CK cancel Note The effect of an Any line is to apply the filter criteria on each side of the Or separately In the example used a patient in the Benefit Plan POS_A would be included even if they didn t have the Employer 2051 due to the Any condition However a patient in the PPO Product would only be included if they have the Employer 2051 due to the All criteria at that level Filters can be saved and recalled for any future analysis Filters are saved within a users Windows profile so they are specific to a single computer and user Groups The analyses in the ACG System are conceptually different from reports in other systems and are best conceived as data views The primary difference is that a single analysis can generate several stratifications in one single session The Groups define the stratifications that an analysis w
263. the population s numbers are small or when the need to communicate the inner workings of the methods to a wide audience of providers is critical If you have historical claims data or other similar data sources it is generally preferable to calculate local expected resource use values for each ACG or RUB for each resource measure of interest e g total cost hospital use specialist referrals pharmacy based on actual patterns of practice within your organization If such data are unavailable or inadequate then the relative weights supplied as part of the ACG Software can be used as a proxy See the chapter entitled Making Effective Use of Risk Scores in the Technical User Guide for a detailed discussion of relevant methodological issues related to weight calculation Technical User Guide The Johns Hopkins ACG System Version 8 2 3 14 Selecting the Right Tool Table 6 presents a summary of the most common profiling statistics 1 The actual to group average resource use unadjusted efficiency ratio This is a measure of how the profiling group compares to the average population 2 The expected to plan average the case mix index or morbidity factor This provides an indication of how sick the profiling population is compared to the average population 3 The actual to expected average resource use efficiency ratios The observed to expected ratio O E Ratio provides an indication of how many health care resource
264. the software Use the following instructions to begin using the sample data from within the Johns Hopkins ACG System Desktop 1 Select File 2 Select New 3 From the New File window click the radial button for Create ACG File From Sample Data 4 Click Next Figure 24 Create ACG File from Sample Data x Johns Hopkins ACG System 8 2 le x fal co Choose the type of file you wish to create ied New ACG File Summa Create ACG File From Imported Data Create ACG File From Sample Data Patient New Data File Format Create Custom Patient File Format Cancel E CS ai 5 When prompted type the name of the file to which the ACG database will be saved Technical User Guide The Johns Hopkins ACG System Version 8 2 5 52 Installing and Using ACG Software Select a new filename to save the ACG Data ACG File ACG FileName 825AMPLE C Stop building after too many non matched codes encountered Max Non matches Backe Next gt Cancel 6 Click Next 7 Click Finish 4 Tip To open an existing data file select the folder button in the tool bar and then navigate to the destination folder View Results of the Grouping Process Once the ACG processing is complete you are returned to the ACGs for Windows desktop You can now begin to review the results of the grouping process customize the standard analyses using filters and groups or save the data for
265. the type of data to export and the file location Mae eia e amp xport Data Summary Statistic Patients and ACG Result Pharmacy Codes Patient EDC Assignments Non Matched Diagnosis Codes Patients processed Patient MEDC Assignments Non Matched Pharmacy Codes Patients processed R 3 uee Patient ADG Assignments Data Warnings Unique diagnoses amp Patient Rx MG Assignments Local Weights _ Patient Major Rx MG Assignments Model Markers Unknown diagnose Diagnosis Codes All Models Patients with unknd Unique matched diq Export Options Unique unknown dis V Write Header Row Patients with unsup Pharmacy codes pr Unique pharmacy c Comma Separated Value commas with quotes nie Leonie Select Columns Percentage of pha Export File Tab Separated Value tabs without quotes Export File Name Patients with unsup Number of EDCs as Number of MEDCs 4 Number of ADGs assigned 9654097 Technical User Guide The Johns Hopkins ACG System Version 8 2 6 14 Assessing the ACG Grouper s Output Conclusion Now that you have successfully run the ACG Software and taken some preliminary steps to validate the output it is time to begin using the ACG System The next chapter Making Effective Use of Risk Scores will provide more detail on the built in scores or weights provided with the software that be used for additional validation purposes an
266. this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication Hyperlipidemia A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication Hypertension The Johns Hopkins ACG System Version 8 2 A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication Technical User Guide Installing and Using ACG Software 5 43 A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication Ischemic Heart Disease A flag indicating if this patient has this medical condition and how it was indicated NP Not Present ICD ICD Indication Rx Rx Indication BTH ICD and Rx Indication Low Back Pain A subset of EDCs and Rx MGs assigned to the High Impact Conditions current patient and which are expected to have a significant contribution to future cost A subset of EDCs and Rx MGs assigned to the Moderate Impact Conditions current patient and which are expected to have a moderate contribution to future cost A subset of EDCs and Rx MGs assigned to the Low Impact Conditions current patient and which are expected to have
267. tient diagnosis and pharmacy as well as reference weights e g Risk Assessment Variables and options selected for the ACG predictive model are easily summarized Technical User Guide The Johns Hopkins ACG System Version 8 2 6 2 Assessing the ACG Grouper s Output The second stage in the quality control process includes producing and evaluating those reports available in the Analyze Menu reference the section entitled Review of Reports Produced by the Analyze Menu in this chapter Review of Reports Produced Automatically by the Software Summary Statistics Tab The first tab the user sees after processing the data or opening an acgd file is the Summary Statistics tab which provides a summary review of which input file s were processed a summary person count information on diagnosis code mismatch rate and information on the number of warning messages generated The first check is to verify that the number of output records people should be consistent with the general knowledge of the input data Non Grouped code percentages should generally be 1 or less for ICD codes and 10 or less for NDC codes Rates higher than this may suggest a coding or data processing problem on the part of the user It is equally useful to examine the Non Matched ICD and Non Matched Pharmacy Code List There may be codes in this list that cause concern and can be easily deleted or replaced If you are concerned that the mismatch rate is too high
268. tions Table 14 presents Predictive Ratios by Quintile for the diagnosis based Dx PM applied to commercial and Medicare populations The Johns Hopkins ACG System Version 8 2 Technical User Guide Selecting the Right Tool 3 33 Table 14 Predictive Ratios by Quintile for The Johns Hopkins ACG Dx PM Applied to Commercial and Medicare Populations Lowest Quintile Total Spending Year 1 2 Quintile Total Spending Year 1 4 Quintile Total Spending Year 1 Highest Quintile Total Spending Year 1 ci ee na Spending Ratios reflect actual year 2 costs for each year 1 quintile cohort divided by their predicted costs One important caveat is worth noting here Though not included in the results presented in Table 14 prior pharmacy cost is available as an optional risk factor in Dx PM Although inclusion of pharmacy cost information improves model performance we do NOT recommend that models using the optional pharmacy cost predictor be applied to capitation rate setting Instead we suggest that the Dx PM model relying only on ICD input variables be used for such a purpose We take this position for the same reason we believe that episode groupers that rely on procedure codes such as CPT and Rx groupers based on use of specific medications as defined by NDC codes should not be used for rate setting purposes or efficiency profiles Risk factor variables of this type which are directly defined by the providers clinical pract
269. tions as compared to diagnosis alone as demonstrated in Figure 2 Figure 2 Percent of Patients Identified by ICD or NDC or Both Hypertension CHF 14 54 32 Rx OICD E Both Hypertension Depression Rx OICD E Both Technical User Guide The Johns Hopkins ACG System Version 8 2 Selecting the Right Tool Figure 3 shows the value of evaluating members with discordant scores based on diagnosis and pharmacy Both the Dx PM and Rx PM scores were grouped into percentiles to indicate high medium and low risk Those members with high risk as defined by Dx PM were more likely to be hospitalized especially when they were low risk as defined by Rx PM The combination of scores may provide insight into the under treatment or non compliance of particular populations Figure 3 Combining Rx and Dx Predictive Modeling Scores for Targeted Intervention hospitalized with MI 90 99 Rx PM 50 89 NDC Risk lt 50 Percentile lt 50 50 89 90 99 Dx PM ICD Risk Percentile The ACG predictive models include reports providing disease specific based on selected individual and aggregated EDCs and or pharmacy based morbidity categories Rx MGs distributions of risk probability scores and average expected resource use for different risk cohorts An example of such a report for The Johns Hopkins ACG Dx PM model shown as Table 12 will be useful in helping to frame a strategy for targeting various risk cohorts within disease m
270. to unique mutually exclusive morbidity categories based on patterns of disease and expected resource requirements Resource Utilization Band Aggregations of ACGs based upon estimates of concurrent resource use providing a way of separating the population into broad co morbidity groupings as follows e 0 No or Only Invalid Dx 1 Healthy Users 2 Low 3 Moderate 4 High 5 Very High National Unscaled Weight An estimate of concurrent resource use associated with a given ACG based on a national reference database and expressed as a relative value Each patient is assigned a weight based on their ACG Cd National Rescaled Weight National weights that are rescaled so that the mean across the population is 1 0 Local Weight Technical User Guide A concurrent weight assigned to this patient based upon their ACG Cd using local cost data The weight for each ACG is calculated as the simple average total cost of all individuals assigned to each category divided by the average total cost of all The Johns Hopkins ACG System Version 8 2 5 46 Installing and Using ACG Software individuals in the source data file Aggregated Diagnosis Groups the building blocks of the ACG System each ADG is a grouping of diagnosis codes that are similar in terms of severity ADG Codes and likelihood of persistence of the health condition over time This column contains a listing of all ADG codes assigned to this pati
271. tribution Analysis The foundation of the system is the original Adjusted Clinical Group algorithm ACGs assign persons to unique mutually exclusive morbidity categories based on patterns of disease and expected resource requirements ACGs can be used in place of traditional age sex categories when attempting to account for variations in morbidity burden across two or more patient populations A person falls into one of 93 mutually exclusive ACG health status categories based on a combination of ADGs age gender and if available birth weight for newborns and delivery status for pregnant womenThe ACG Distribution Analysis produces a frequency distribution by ACG code The report layout is as follows Table 2 ACG Distribution Analysis Report Layout ACG Cd Each ACG code that was assigned to a patient ACG Description The description for ACG Cd The number of patients with this ACG in this stratification meeting the optional Technical User Guide The Johns Hopkins ACG System Version 8 2 5 20 Installing and Using ACG Software SE The percentage of patients within this stratification and meeting the optional filter criteria that were assigned this ACG ADG Distribution Analysis ACGs are based on building blocks called Aggregated Diagnosis Groups ADGs Each ADG is a grouping of diagnosis codes that are similar in terms of severity and likelihood of persistence of the health condition over time All ICD 9 codes assigned by clinicia
272. troduction The purpose of this chapter is to highlight and discuss some of the key analytical and technical issues associated with the application of diagnosis based risk adjustment in populations These issues affect both the framing and interpretation of analyses Much of this discussion relates to forming a population for risk adjustment determining which members to include and to exclude and circumstances where sampling is appropriate Art of Risk Adjustment Figure 1 Risk Adjustment Pyramid Management Applications Case Management High Disease Burden Practice Resource Management Single High Impact Disease Users Users amp Non Users Population Segment While the essential methodological underpinnings of risk adjustment are straightforward technical challenges may be experienced when putting health based risk adjustment in place within an organization Figure 1 is intended to help graphically illustrate the variety of ways in which risk adjustment is most commonly applied within healthcare organizations today Some implementations such as needs assessment or payment finance applications apply to the entire population base Other implementations such as care management or disease management interventions focus only on targeted population subgroups Depending on the application or the question being asked it is important to appropriately define the denominator or the population of interest Another
273. ts to this stratification in this RUB is out of the total patients in this RUB The mean of the national rescaled or local concurrent weight based upon which weight type was selected in Report Options for all patients in this stratification in this RUB The percentage of patient assignments to this stratification in this RUB is out of the total patients in this RUB The mean of the national rescaled or local concurrent weight based upon which weight type was selected in Report Options for all patients in this stratification in this RUB The percentage of patient assignments to this stratification in this RUB is out of the total patients in this RUB The mean of the national rescaled or local concurrent weight based upon which weight type was selected in Report Options for all patients in this stratification in this RUB Technical User Guide Installing and Using ACG Software 5 25 EDC by RUB Distribution Analysis The EDC by RUB Distribution Analysis produces a frequency distribution by EDC and by Resource Utilization Band RUB A patient can be assigned to multiple EDC codes but only one RUB This report is useful for case managers because it helps to illustrate that not all individuals with a certain condition may be in need of intervention or case management rather it is individuals in the far right of the table those individuals exhibiting a specific condition AND multiple co occurring conditions who are most likely to
274. tuarial risk assessment e Resource planning and program budgeting e Clinical analysis evaluation and research e Quality improvement and outcome monitoring e High risk case identification also known as predictive modeling Technical User Guide The Johns Hopkins ACG System Version 8 2 3 2 Selecting the Right Tool Introduction to the Components of the ACG Toolkit The Johns Hopkins ACG System is a suite of tools Each tool is designed to assist organizations with understanding the health care needs of their population Whether through simple categorical approaches complex disease classification or sophisticated predictive modeling the ACG System provides you with multiple solutions for addressing the many aspects of their business These are the components of the ACG System s toolkit Aggregated Diagnostic Groups ADGs The first step in the ACG assignment process is to categorize every ICD 9 9 CM and 10 diagnosis code given to a patient into a unique morbidity grouping known as an ADG ADGs are the building blocks of the ACG System Each ADG is a group of ICD diagnosis codes that are homogenous with respect to specific clinical criteria and their demand on healthcare services The ADG categories reflect the entire spectrum of care with certain ADGs indicating preventive care while others assigned when specialty care is more likely Patients with only one diagnosis over a time period are assigned only one ADG while a pat
275. u have a need to customize the ACG model to your environment Version 8 2 will allow you to operationalize new models within the ACG Software Please contact your distributor if you would like to discuss model customizations Code Sets The ICD 9 CM ICD 10 WHO and NDC coding standards that are currently supported by the ACG Software are updated via the web through a mapping file This allows for code maintenance to occur without a reinstallation of the software Beginning with Version 8 2 the ACG Software will not be constrained to diagnoses based on ICD 9 CM and ICD 10 WHO or pharmacy coding based on NDC classification If you use local coding variants e g Read codes in the United Kingdom or ICD 10 SGVB in Germany please contact your distributor to determine if a country specific or regional adaptation is available Access to additional code sets is controlled via the mapping file and your license file Release Notes The Johns Hopkins ACG System Version 8 2 2 2 Several additional fields were added to the Summary Statistics reference Figure 1 to Release Notes identify how many unique code sets were present in the data and used by the ACG Software Figure 1 Summary Statistics Tab x Johns Hopkins ACG System 8 2 File Edit wiew Analyze Tools Help KSaoM x ee 4 F fal 82Sample acad ACG Data File 825ample acad Summary Statistics Patient Sample Local Weights Age Gender Dist Probability Dist Build Opt
276. uld be eliminated from the analysis or be reported with appropriate caveats The specific approach used will vary for each analysis organization based on the quality of the alternatives Although new enrollees ICD codes may be incomplete risk adjustment based on a limited pool of diagnoses generally provides more accurate risk adjustment than do alternative demographic adjustments Non Users Who are Eligible to Use Services Most grouping methods and case mix measurement tools that focus on episodes of care restrict their attention to the subset of a population that actually consumes resources e g those visiting a provider or being admitted to the hospital The most common applications of these tools provider profiling and other retrospective applications are concerned exclusively with users of services since only for these members can a meaningful profile be developed However for capitation rate development and other prospective applications non users are of great importance since many if not most of the enrollees who do not use services in the current period will consume services to at least some degree in the future period Since capitation payments are made regardless of whether the member interacts with the capitated provider the characteristics of non users are important For profiling consideration of the percentage of enrollees assigned to a physician who are non users may provide information on access issues or illustrate dif
277. uld have a distinct PRI score Two PRI scores are produced one for total cost and one for pharmacy cost The PRI is interpreted in the same manner as a concurrent ACG weight i e as a relative value The software produces both an unadjusted and adjusted form of the PRI The adjustment process is identical to that used to produce the adjusted concurrent weights All Model File Optionally the user may select the All Models option when importing their data The All Models selection will produce the full set of predictive modeling variables for Dx PM Rx PM and DxRx PM We recommend contacting your software vendor for additional support in interpreting and using the All Model File The intent is to allow users a means of easily comparing and contrasting each of the predictive modeling approaches Upon contacting your software vendor an appendix will be made available that describes the columns in more detail As a bit of a preview the variable naming convention is in shorthand form and describes the type of score what is being predicted as well as what model was applied the reference or comparison population on which the model was developed and whether or not prior cost information was incorporated into the forecasts We strongly encourage users wishing to take advantage of this option to contact their software vendor A Tip Utilizing the All Model File feature may consume significant PC resources and require longer processing tim
278. upon which weight type was selected in Report Options for all patients in this stratification in this RUB The Johns Hopkins ACG System Version 8 2 5 28 Installing and Using ACG Software Standardized Morbidity Ratio by EDC Analysis The Standardized Morbidity Ratio Analysis produces a summary by EDC with observed expected and o e ratio This report is useful in understanding how the prevalence of certain conditions as defined by EDCs are more or less common than average across the subpopulation of interest The significance indicator identifies categories that are statistically different from the age sex adjusted expected value At the user s discretion the expected values can be derived from either the population mean or the national benchmark data see ACG Tip below and remember ACG Tip from above about selecting the appropriate reference benchmark data using the Risk Assessment Variables option on data input The methodology for calculating the statistics presented in this table are explained more fully in the EDC Chapter in the Reference Manual The report layout is as follows Table 8 Standardized Morbidity Ratio by EDC Analysis Report Layout Column Definition Name EDC Cd Each EDC code that was assigned to at least one patient EDC Name The description for EDC Cd The number of patients assigned this EDC in this stratification The number per 1 000 patients in the current stratification that were assigned to this Obser
279. valence of a subpopulation of interest after taking into account the age and sex mix of the group relative to either the underlying population or a national comparison group The user can determine the population to be used for comparison by using the report options when the analysis is run The analysis is also available by individual EDC thus the morbidity ratio report will assist you in isolating statistically significant demographically adjusted disease category differences within a subpopulation of interest Technical User Guide The Johns Hopkins ACG System Version 8 2 3 8 Selecting the Right Tool The diagnostic morbidity distribution reports outlined here should be useful for many clinically oriented applications within your organization These could include population clinical needs assessments and targeting where disease management or outreach programs might be developed Table 3 Observed to Expected Standardized Morbidity Ratio SMR by Major EDC MEDC Approximate Observed Age Sex Standard 95 Prevalence Expected Morbidity confidence Major EDC Per 1 000 Prevalence Ratio interval Description Population per 1 000 Low High 269 87 0 952 75 56 1 169 86 29 1 072 0 824 172 29 0 807 280 9 79 1 211 0 31 4 121 6 81 0 57 1 70 3 100 4 40 65 1 262 54 53 0 439 88 28 1 071 67 47 1 159 80 15 1 120 108 65 1 066 0 729 50 53 1 030 11 49 28 20 14 01 164 24 66 96 10 04 51 25 24 36 126 73 14 72 144 07 128
280. ve them from the diagnoses in the input data file and rerun the ACG software Remember if processing ICD 10 data special attention should be paid to the non matched ICD 10 codes A large number of users are reporting higher than anticipated mismatch rates due to local implementation of CM encouraged by the World Health Organization Adjustments to the input data to assure conformity to ICD 10 WHO may be necessary to assure that maximal diagnostic information may be extracted from the claims data Examining the List of Non Matched Pharmacy Codes To assist users with understanding potential pharmacy coding issues non matched pharmacy code file can be generated All input pharmacy codes that are not considered valid codes are eligible for export to the non matched pharmacy file A sample of a non matched pharmacy file is presented as Table 2 The non matched pharmacy codes can be exported and saved as a CSV file either tab or comma delimited This file contains each patient identifier for whom a non matched code occurred the pharmacy code type NDC or ATC and the corresponding pharmacy code At the very least you should scan the list of non matched codes to determine if any codes that should have been assigned to an Rx MG are listed frequently To gain a fuller perspective of the codes that are contained in the non matched pharmacy file you can sort the output file by pharmacy code only and create a frequency distribution of all rejected non ma
281. ved 1000 EDC Calculated as Patient Count total Patient Count within the same stratification for all EDCs x 1000 The number of expected observations per 1 000 after adjusting for the age sex Age Sex distribution in the current stratification Calculated as total of overall age sex Expected 1000 prevalence rate x number of patients in age sex in current stratification for all age sex combinations number of patients in the current stratification for all EDCs x 1000 SMR Observed to Expected Ratio Calculated as Observed 1000 Age Sex Expected 1000 95 Confidence The lower range of the 95 confidence interval Calculated as Low SMR 1 96 x SQRT SMR expected count 95 Confidence The upper range of the 95 confidence interval Calculated as High SMR 1 96 x SQRT SMR expected count An indication of statistical significance Contains a minus sign when the SMR is Significance significant and less than 1 contains a plus sign when the SMR is significant and greater than 1 4 Tip Local or reference comparisons may be used to produce this report by accessing the Report Options Options menu shown in Figure 22 below The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 29 Figure 22 Select Report Options for Standardized Morbidity Ratio by EDC Analysis Report Options Filters Options Groups Set these options to control how your report is ca
282. w for each patient that had data warnings This data is the same information presented in the Warning List Analysis The columns in this file are Patient ID ACG Code Age Sex Total Cost Pharmacy Cost Warning Codes e Local Weights This data file contains the Local Weights data that is displayed in the ACG Data File screen This data is calculated during the ACG grouping process and summarizes the local costs by ACG code The columns in this file are ACG Code ACG Description Patient Count Total Cost Concurrent Weight e Model Markers This data file contains a set of flags that are used during the ACG grouping process for each Patient ID You will need to contact technical support for assistance in using this data The columns in this file are Patient ID Demographic Markers gender age bands Dx PM Covariates frailty hospital dominant conditions prospective RUBs pregnancy w o delivery ACG markers EDC markers Rx PM Covariates Rx MG markers Cost Percentile Groups total cost bands rx cost bands The Johns Hopkins ACG System Version 8 2 Technical User Guide Installing and Using ACG Software 5 73 e All Models This data file contains all possible predictive model scores for each patient You will need to contact technical support for assistance in using this data The MODEL NAME component is repeated for every model included in the ACG system If
283. with total cost gt 100 and no diagnoses 1 8 Percentage of patients with pharmacy cost gt 100 and no pharmacy codes 0 0 Number of patients with diagnosis information and no pharmacy codes 646753 Number of patients with pharmacy codes and no diagnoses 132890 Number of data warnings 51962 Number of patients with data warnings 51479 Actuarial Cost Projections Simple Profile Care Management List Patient Clinical Profile Report Patient List Warning List Warning Distribution 11798095 8680106 A Tip All of the reports generated in the Analyze menu can be exported as Excel spreadsheets using the 1 selecting the Tools from the Windows Task bar and 2 selecting Export from the pull down menu The Johns Hopkins ACG System Version 8 2 Technical User Guide Assessing the ACG Grouper s Output 6 7 While each of these reports is discussed in additional detail elsewhere in the manual please see the Installation and Usage chapter in the Technical User Guide at a fundamental level the review process can be distilled to a few basic elements as follows 1 Evaluate the distribution of persons by ACG category for face validity 2 Verify that patients are being assigned to appropriate ACG categories when delivery status and or birth weight status is present 3 Examine the distribution by ADG against known patterns 4 Compare the EDC Major EDC Rx MG and Major Rx MG distributions against known patterns Validate that r

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