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Bending the Cost Curve? Results From a Comprehensive Primary Care Payment Pilot

Vats, Sonal MA*; Ash, Arlene S. PhD†,‡; Ellis, Randall P. PhD*,‡

doi: 10.1097/MLR.0b013e3182a97bdc
Brief Reports

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.

Supplemental Digital Content is available in the text.

*Department of Economics, Boston University, Boston

Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester

Verisk Health Inc., Waltham, MA

Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website,

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:

© 2013 by Lippincott Williams & Wilkins.