After working in the area for the past three years, I am happy to report that the paper below is finally out in Medical Care.
Ash, Arlene S. PhD; Ellis, Randall P. PhD
The full version is currently posted as printed ahead of print, although the actual date of the publication is not yet known. You will need a OVID or a Lippincott Williams & Wilkins subscription to have access to the full paper. you can see if your university has access by visiting the site above. There is also a rich appendix with further results and tables.
Background: Many wish to change incentives for primary care practices through bundled population-based payments and substantial performance feedback and bonus payments. Recognizing patient differences in costs and outcomes is crucial, but customized risk adjustment for such purposes is underdeveloped.
Research Design: Using MarketScan’s claims-based data on 17.4 million commercially insured lives, we modeled bundled payment to support expected primary care activity levels (PCAL) and 9 patient outcomes for performance assessment. We evaluated models using 457,000 people assigned to 436 primary care physician panels, and among 13,000 people in a distinct multipayer medical home implementation with commercially insured, Medicare, and Medicaid patients.
Methods: Each outcome is separately predicted from age, sex, and diagnoses. We define the PCAL outcome as a subset of all costs that proxies the bundled payment needed for comprehensive primary care. Other expected outcomes are used to establish targets against which actual performance can be fairly judged. We evaluate model performance using R2′s at patient and practice levels, and within policy-relevant subgroups.
Results: The PCAL model explains 67% of variation in its outcome, performing well across diverse patient ages, payers, plan types, and provider specialties; it explains 72% of practice-level variation. In 9 performance measures, the outcome-specific models explain 17%-86% of variation at the practice level, often substantially outperforming a generic score like the one used for full capitation payments in Medicare: for example, with grouped R2′s of 47% versus 5% for predicting “prescriptions for antibiotics of concern.”
Conclusions: Existing data can support the risk-adjusted bundled payment calculations and performance assessments needed to encourage desired transformations in primary care.
(C) 2012 Lippincott Williams & Wilkins, Inc.
It is currently only available as a publication ahead of print, 2012 Apr 19. [Epub ahead of print]
Risk-adjusted Payment and Performance Assessment for Primary Care.
Ash AS, Ellis RP.