Comparing and Monitoring Risk-Adjusted Hospital Performance Measures: A Weighted Estimating Equations Approach.

Area of Science:

• Health Services Research
• Biostatistics
• Health Care Quality Improvement

Background:

• Evaluating hospital performance is crucial for monitoring and enhancing healthcare quality.
• Risk-adjusted performance measures are increasingly public, necessitating robust statistical methods.
• Current methods using 3-year data aggregates can yield imprecise estimates and biased views of current performance.

Purpose of the Study:

• To introduce and evaluate an alternative statistical approach for hospital performance estimation.
• To address the bias-precision tradeoff in performance measures derived from historical data.
• To provide a more accurate reflection of current hospital quality.

Main Methods:

• A novel weighted estimating equations (WEE) approach was developed.
• WEE combines historical data by down-weighting past information using estimating functions.
• The WEE method was compared against two current practices using a real-world dataset.

Main Results:

• The WEE approach demonstrated a significant reduction in the uncertainty interval width, up to 65% in a case study.
• This precision improvement was particularly notable for hospitals with fewer cases.
• The WEE method offers a more precise estimate of present performance by leveraging all available historical data.

Conclusions:

• The weighted estimating equations (WEE) approach provides a more precise estimate of current hospital performance.
• This method effectively balances bias and precision by utilizing all historical data, unlike fixed-window methods.
• The advantages of WEE are applicable to various aspects of medical performance evaluation, including surgical and practitioner performance.