Background: There is much interest in understanding how using bundled primary care payments to support a patient-centered medical home (PCMH) affects total medical costs.
Research Design and Subjects: We compare 2008-2010 claims and eligibility records on about 10,000 patients in practices transforming to a PCMH and receiving risk-adjusted base payments and bonuses, with similar data on approximately 200,000 patients of nontransformed practices remaining under fee-for-service reimbursement.
Methods: We estimate the treatment effect using difference-in-differences, controlling for trend, payer type, plan type, and fixed effects. We weight to account for partial-year eligibility, use propensity weights to address differences in exogenous variables between control and treatment patients, and use the Massachusetts Health Quality Project algorithm to assign patients to practices.
Results: Estimated treatment effects are sensitive to: control variables, propensity weighting, the algorithm used to assign patients to practices, how we address differences in health risk, and whether/how we use data from enrollees who join, leave, or change practices. Unadjusted PCMH spending reductions are 1.5% in year 1 and 1.8% in year 2. With fixed patient assignment and other adjustments, medical spending in the treatment group seems to be 5.8% (P=0.20) lower in year 1 and 8.7% (P=0.14) lower in year 2 than for propensity-weighted, continuously enrolled controls; the largest proportional 2-year reduction in spending occurs in laboratory test use (16.5%, P=0.02).
Conclusions: Although estimates are imprecise because of limited data and quasi-experimental design, risk-adjusted bundled payment for primary care may have dampened spending growth in 3 practices implementing a PCMH.
*Department of Economics, Boston University, Boston
†Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester
‡Verisk Health Inc., Waltham, MA
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S.V. received research support from The Commonwealth Fund and Verisk Health through Boston University for her analysis of this data. A.S.A. was supported by funds from NIH grant #UL1RR031982. A.S.A. and R.P.E, are senior scientists at Verisk Health Inc. where they help develop health-based predictive models. They have no ownership or financial relationship with Verisk Health Inc. other than this part-time consulting.
The authors declare no conflict of interest.
Reprints: Sonal Vats, MA, Department of Economics, Boston University, 270 Bay State Road, Boston, MA 02215. E-mail: email@example.com.