A Comparison of statistical methods for hospital performance assessment
Journal of Hospital Administration
oregon; portland; cards; cards publication
During hospital quality improvement activities, statistical approaches are critical to help assess hospital performance for benchmarking. Current statistical approaches are used primarily for research and reimbursement purposes. In this multiinstitutional study, these established statistical methods were evaluated for quality improvement applications. Leveraging a dataset of 42,199 patients who underwent coronary artery bypass grafting surgery from 2014 to 2016 across 90 hospitals, six statistical approaches were applied. The non-shrinkage methods were: (1) indirect standardization without hospital effect; (2) indirect standardization with hospital fixed effect; (3) direct standardization with hospital fixed effect. The shrinkage methods were: (4) indirect standardization with hospital random effect; (5) direct standardization with hospital random effect; (6) Bayesian method. Hospital performance related to operative mortality and major morbidity or mortality was compared across methods based on variation in adjusted rates, rankings, and performance outliers. Method performance was evaluated across procedure volume terciles: small (< 96 cases/year), medium (96-171), and large (> 171). Shrinkage methods reduced inter-hospital variation (min-max) for mortality (observed: 0%-10%; adjusted: 1.5%-2.4%) and major morbidity or mortality (observed: 2.6%-35%; adjusted: 6.9%-17.5%). Shrinkage methods shrunk hospital rates toward the group mean. Direct standardization with hospital random effect, compared to fixed effect, resulted in 16.7%-38.9% of hospitals changing quintile mortality ranking. Indirect standardization with hospital random effect resulted in no performance outliers among small and medium hospitals for mortality, while logistic and fixed effect methods identified one small and three medium outlier hospitals. The choice of statistical method greatly impacts hospital ranking and performance outlier’ status. These findings should be considered when benchmarking hospital performance for hospital quality improvement activities.
Center for Cardiovascular Analytics, Research + Data Science (CARDS)
Wu, Xiaoting; Zhang, Min; Jin, Ruyun; Grunkemeier, Gary; Maynard, Charles; Hira, Ravi S.; Mackenzie, Todd; Herbert, Morley; He, Chang; Holmes, Sari D.; Thompson, Michael P.; and Likosky, Donald S., "A Comparison of statistical methods for hospital performance assessment" (2021). Articles, Abstracts, and Reports. 5255.