1. Copyright 2003, Johns Hopkins University, 10/19/2003 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, 2004San Diego CA

2. Copyright 2004, Johns Hopkins University, 5/20 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. Copyright 2004, Johns Hopkins University, 5/20 3 Long History of Working with Medicare Data Final Reports Delivered to Center for Medicare & Medicaid Services (formerly HCFA) 1996Risk-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 2003Development and Evaluation of the Johns Hopkins Univeristy Risk Adjustment Models for Medicare+Choice Plan Payment

4. Copyright 2004, Johns Hopkins University, 5/20 4 Better Modeling or Better Data Quality? Project: Year-1 diagnoses used to predict year-2 Medicare expenditures Explanatory Power Of JHU Model Project 1: 1991-19925.5 Project 2: 1995-19968.4 Project 3: 1996-19979.1

5. Copyright 2004, Johns Hopkins University, 5/20 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. Copyright 2004, Johns Hopkins University, 5/20 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. Copyright 2004, Johns Hopkins University, 5/20 7 Examples of HOSDOM Diagnoses 491.21: Obstructive Chronic Bronchitis with Acute Exacerbation 518.81: Acute Respiratory Failure 584.9: Acute Renal Failure, Unspecified 198.5: Secondary Malignant Neoplasm, Bone 785.4: Gangrene 518.4: Acute Lung Edema, Unspecified 789.5: Ascites 571.5: Cirrhosis of Liver without mention of alcohol 403.91: Hypertensive Heart Disease with Renal Failure 284.8: Aplastic Anemia

8. Copyright 2004, Johns Hopkins University, 5/20 8 Impact of HOSDOM on Resource Consumption Percent Of Pop. Year 2 Costs (relative weight) 0 HOSDOMs90.7%0.82 1 HOSDOM7.4%2.45 2 HOSDOM1.5%4.25 3 HOSDOMs0.3%5.74 4 HOSDOMs0.05%6.59 5+ HOSDOMs0.01%7.86 Data Source: 1996-97 Medicare 5 Percent Sample

9. Copyright 2004, Johns Hopkins University, 5/20 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. Copyright 2004, Johns Hopkins University, 5/20 10 Percent of Beneficiaries with Frail Clusters ClusterDescription Percent of all Elderly Percent of Dual- Eligibles 1 Malnutrition0.08%0.20% 2 Dementia0.82%2.64% 3 Impaired Vision0.25%0.65% 4 Decubitus Ulcer1.08%3.05% 5 Incontinence of Urine0.04%0.05% 6 Loss of Weight2.51%4.40% 7 Incontinence of Feces0.15%0.20% 8 Obesity (morbid)0.03%0.07% 9 Poverty0.00%0.01% 10 Barriers to Access of Care0.02%0.05% 11 Difficulty in Walking2.88%4.37%

11. Copyright 2004, Johns Hopkins University, 5/20 11 Impact of Frail on Resource Consumption Number of Frail Clusters Percent of all Elderly Year 2 Costs (relative weight) 093.8%0.9 15.7%1.9 20.5%3.0 30.04%4.0 40.005%3.5

12. Copyright 2003, Johns Hopkins University, 10/19/2003 Results: What Have We Learned?

13. Copyright 2004, Johns Hopkins University, 5/20 13 1) The frailty variable increases explanatory power AND provides greater predictive accuracy Data Source: 1996-97 Medicare 5 Percent Sample

14. Copyright 2004, Johns Hopkins University, 5/20 14 2) Be careful. Higher R2 and improved accuracy for top quintiles may result in substantial overpayment for first quintile. Data Source: 1996-97 Medicare 5 Percent Sample

15. Copyright 2004, Johns Hopkins University, 5/20 15 3) Sometimes the kitchen-sink approach works Data Source: 1996-97 Medicare 5 Percent Sample

16. Copyright 2004, Johns Hopkins University, 5/20 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. Copyright 2004, Johns Hopkins University, 5/20 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. Copyright 2004, Johns Hopkins University, 5/20 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. Copyright 2004, Johns Hopkins University, 5/20 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