1 / 19

Medicare Risk Adjustment Development by Johns Hopkins

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. Objectives.

devika
Télécharger la présentation

Medicare Risk Adjustment Development by Johns Hopkins

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related