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Physician reactions to quantitative individual performance reports
Howard Beckman1, Anthony L Suchman, Kathleen Curtin
1Rochester Individual Practice Association and the Departments of Medicine and Family Medicine, University of Rochester School of Medicine and Dentistry, New York 16623, USA.
Summary
Primary care physicians found pay-for-performance programs difficult to use for behavior change, citing data concerns. However, these programs also positively influenced quality, satisfaction, and practice efficiency.
Area of Science:
- Health Services Research
- Medical Economics
- Physician Behavior
Background:
- Pay-for-performance (PFP) programs are increasingly implemented to incentivize quality and efficiency in healthcare.
- Understanding physician experiences is crucial for effective PFP program design and implementation.
Purpose of the Study:
- To explore primary care physicians' experiences with the introduction and evolution of an individual physician pay-for-performance program.
- To identify physicians' perceptions of program impact on clinical practice, behavior, and professionalism.
Main Methods:
- Qualitative study involving 30 primary care physicians.
- Audiotaped focus groups conducted 13 and 26 months after program initiation.
- Thematic analysis of transcribed focus group data to identify key themes.
Main Results:
- Physicians struggled to use performance profiles for behavior modification, questioning data accuracy and external influences (specialists, patients).
- Concerns were raised regarding the validity of quality measures and the impact of PFP on professionalism.
- Despite skepticism, physicians reported positive influences on quality improvement, patient satisfaction, and practice efficiency.
Conclusions:
- Primary care physicians express skepticism towards individual physician PFP programs, primarily due to data and validity concerns.
- PFP programs can drive improvements in healthcare quality, patient satisfaction, and operational efficiency.
- Program design must address physician concerns regarding data accuracy and perceived fairness to enhance effectiveness and adoption.
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