Estimating the Net Benefit of Improvements in Hospital Performance: G-Computation With Hierarchical Regression Models

It is important to be able to estimate the anticipated net population benefit if the performance of hospitals is improved to specific standards.

The objective of this study was to show how G-computation can be used with random effects logistic regression models to estimate the absolute reduction in the number of adverse events if the performance of some hospitals within a region was improved to meet specific standards.

A retrospective cohort study using health care administrative data.

Patients hospitalized with acute myocardial infarction in the province of Ontario in 2015.

Of 18,067 patients hospitalized at 97 hospitals, 1441 (8.0%) died within 30 days of hospital admission. If the performance of the 25% of hospitals with the worst performance had their performance changed to equal that of the 75th percentile of hospital performance, 3.5 deaths within 30 days would be avoided [95% confidence interval (CI): 0.4-26.5]. If the performance of those hospitals whose performance was worse than that of an average hospital had their performance changed to that of an average hospital, 6.0 deaths would be avoided (95% CI: 0.7-47.0). If the performance of the 75% of hospitals with the worst performance had their performance changed to equal that of the 25th percentile of hospital performance, 11.0 deaths would be avoided (95% CI: 1.2-79.0).

G-computation can be used to estimate the net population reduction in the number of adverse events if the performance of hospitals was improved to specific standards.