Physician response to pay-for-performance: evidence from a natural experiment

Health Econ. 2014 Aug;23(8):962-78. doi: 10.1002/hec.2971. Epub 2013 Jul 17.

Abstract

This study exploits a natural experiment in the province of Ontario, Canada, to identify the impact of pay-for-performance (P4P) incentives on the provision of targeted primary care services and whether physicians' responses differ by age, size of patient population, and baseline compliance level. We use administrative data that cover the full population of Ontario and nearly all the services provided by primary care physicians. We employ a difference-in-differences approach that controls for selection on observables and selection on unobservables that may cause estimation bias. We implement a set of robustness checks to control for confounding from other contemporaneous interventions of the primary care reform in Ontario. The results indicate that responses were modest and that physicians responded to the financial incentives for some services but not others. The results provide a cautionary message regarding the effectiveness of employing P4P to increase the quality of health care.

Keywords: pay-for-performance; physician behavior; physician payment.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Female
  • Humans
  • Male
  • Middle Aged
  • Models, Organizational
  • Ontario
  • Physicians, Primary Care / economics*
  • Physicians, Primary Care / psychology
  • Physicians, Primary Care / trends
  • Practice Patterns, Physicians' / economics*
  • Practice Patterns, Physicians' / standards
  • Practice Patterns, Physicians' / statistics & numerical data
  • Preventive Health Services / economics*
  • Preventive Health Services / standards
  • Preventive Health Services / statistics & numerical data
  • Quality Assurance, Health Care / economics*
  • Quality Assurance, Health Care / standards
  • Quality Assurance, Health Care / trends
  • Reimbursement, Incentive / economics*
  • Reimbursement, Incentive / standards
  • Workload