Case Sampling for Evaluating Hospital Postoperative Morbidity in US Surgical Quality Improvement Programs

Area of Science:

• Health Services Research
• Surgical Quality Improvement
• Patient Safety

Background:

• US surgical quality improvement (QI) programs often use data sampling for assessing hospital postoperative complication rates.
• The decreasing trend in complications and sampling methods raise concerns about the robustness of identifying outlier hospitals.

Purpose of the Study:

• To compare the effectiveness of a sampling strategy versus universal case review in identifying hospital 30-day complication rates.
• To assess the reliability of current sampling methods in surgical quality improvement.

Main Methods:

• A US hospital-level analysis compared risk-adjusted 30-day complication observed-to-expected (O-E) ratios using a sample (n=502,730) versus universal review (n=1,725,364) of noncardiac surgical cases.
• Hospitals were identified as outliers if their O-E ratio was significantly greater than 1.0.

Main Results:

• Overall 30-day complication rates were higher in the sample cohort (7.6%) than the universal review cohort (5.3%).
• Case sampling identified outlier hospitals in 15.0% of quarters, while universal review identified them in 18.2%.
• Less than half of outlier hospital quarters identified by universal review were concurrently identified using case sampling.

Conclusions:

• Case sampling in surgical quality improvement programs identified fewer than half of hospitals with excess risk-adjusted postoperative complications.
• Further research is needed to optimize data collection and utilization for effective local quality improvement efforts.