Can choice of the sample population affect perceived performance: implications for performance assessment.

Area of Science:

• Health Services Research
• Quality Improvement
• Health Informatics

Background:

• Growing emphasis on measuring healthcare provider quality.
• Lack of standardized methods for defining patient populations in performance assessments.

Purpose of the Study:

• To evaluate how different algorithms for defining patient populations affect performance assessments.
• To understand the impact of patient panel definitions on quality measurement.

Main Methods:

• Utilized administrative data from Community Health Centers (CHCs) in the northeastern US.
• Simulated performance assessments for breast, cervical, and colorectal cancer screening using three distinct patient population algorithms.
• Analyzed variations in simulated center-level performance rates based on alternative population definitions.

Main Results:

• Eligible patient populations for cancer screening varied widely across CHCs (e.g., 28-60% for breast cancer screening).
• Simulated data showed that variations in patient populations could falsely suggest significant performance differences or alter CHC rankings.
• Holding actual adherence constant, simulated adherence varied by over 15% due to differing population proportions across centers.

Conclusions:

• Quality measurement systems must carefully consider patient population definitions.
• Discrepancies in how patient populations are defined can impact pay-for-performance and public reporting accuracy.
• Standardizing patient panel definitions is crucial for reliable healthcare quality comparisons across providers and systems.