Medicare Risk Adjustment Development by Johns Hopkins

1. Medicare Risk Adjustment Development by Johns Hopkins Chad Abrams, MA Cabrams@jhsph.edu Johns Hopkins University School of Hygiene and Public Health 624 N Broadway #600 Baltimore, Maryland 21205 June 6, 2004 San Diego CA

2. Objectives • To provide an overview of JHU’s work on Medicare risk adjustment • To summarize what we have learned • To discuss recent findings and how the ACG-Predictive Model is being refined for the elderly

3. Long History of Working with Medicare Data Final Reports Delivered to Center for Medicare & Medicaid Services (formerly HCFA) 1996 Risk-Adjusted Medicare Capitation Rates Using Ambulatory and Inpatient Diagnoses 2000 Updating & Calibrating the Johns Hopkins University ACG/ADG Risk Adjustment Method for Application to Medicare Risk Contracting 2003 Development and Evaluation of the Johns Hopkins Univeristy Risk Adjustment Models for Medicare+Choice Plan Payment

4. Better Modeling or Better Data Quality?

5. Components of the Basic Model Selected ADGs 13 ADGs demonstrated to have a significant impact on future resource use Hospital Dominant Marker A marker indicating high probability of a future admission

6. The HOSDOM Marker • Persons with a HOSDOM diagnosis have a high probability (usually greater than 50%) of being hospitalized in the subsequent time period. • Based on two-years of Medicare claims data and careful clinical review • A single concise list of 266 “setting-neutral” diagnosis codes.

7. Examples of HOSDOM Diagnoses

8. Impact of HOSDOM on Resource Consumption Data Source: 1996-97 Medicare 5 Percent Sample

9. Other Variables Considered • Frailty Marker • A list of 75 codes that appear to clinically describe frail beneficiaries. • Divided into 11 “clusters” each representing a discrete condition consistent with frailty. • Selected Disease Conditions • Johns Hopkins Expanded Diagnosis Clusters (EDCs)

10. Percent of Beneficiaries with Frail Clusters

11. Impact of Frail on Resource Consumption

12. Results: What Have We Learned?

13. 1) The frailty variable increases explanatory power AND provides greater predictiveaccuracy Data Source: 1996-97 Medicare 5 Percent Sample

14. 2) Be careful. Higher R2and improved accuracy for top quintiles may result in substantial overpayment for first quintile. Data Source: 1996-97 Medicare 5 Percent Sample

15. 3) Sometimes the kitchen-sink approach works Data Source: 1996-97 Medicare 5 Percent Sample

16. Comparison to CMS 61-Disease Model and HCC Data Source: 1996-97 Medicare 5 Percent Sample *61-Disease Model the then “current” model as of Nov. 2001. ** HCC model results from Pope et all Dec 2000

17. The Goal-- Ideally, payment models should pay appropriately for sick individuals while at the same time removing or reducing traditional incentives for promoting biased selection

18. How are we doing? • Current technologies probably not adequate • Re-insurance and/or carve-outs are still necessary to assure adequate payment for treating high cost patients • R-squared is probably NOT the correct criteria for evaluating model performance

19. Conclusions • The type of variables included matters • In general, disease specific markers • do not provide adequate payment for the sick, and • possibly lead to substantial overpayment for healthy individuals • Markers such as “hospital dominant” (likely to lead to a hospitalization) and “frail-symptoms” (a proxy for ADLs) successfully target the sick without falsely identifying healthy