title: Risk-adjusted payment and performance assessment for primary care.
abstract: 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.
  
  
  METHODS
  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 R(2)'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 R(2)'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.
authors.affiliation: Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA 01655, USA. arlene.ash@umassmed.edu
authors.forename: Arlene S
authors.initials: AS
authors.lastname: Ash
authors.forename: Randall P
authors.initials: RP
authors.lastname: Ellis
mesh_terms: D017677:Age Distribution; D019985:Benchmarking / Q000191:economics / Q000379:methods; D003191:Comprehensive Health Care / Q000191:economics / Q000458:organization & administration*; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data; D010043:Outcome and Process Assessment, Health Care / Q000458:organization & administration; D018802:Patient-Centered Care / Q000458:organization & administration; D011320:Primary Health Care / Q000191:economics / Q000458:organization & administration*; D019984:Quality Indicators, Health Care / Q000191:economics; D012052:Reimbursement, Incentive; D020379:Risk Adjustment / Q000379:methods*; D017678:Sex Distribution