Speaker
Description
In healthcare provider profiling, accurately assessing hospital performance is crucial for informed decision-making and quality improvement. Traditional approaches rely heavily on parametric regression models for risk adjustment, but these methods often fail to account for between-center heterogeneity and may produce biased estimates, especially in the presence of low event rates or small provider sample sizes. This talk reviews statistical approaches for provider profiling, offering a unified perspective across several approaches. We cast the problem in a causal inference framework and focus on balancing weight methods using constrained optimization algorithms. A case study using a dataset of nearly 43,000 congenital heart surgeries undertaken between 2016 and 2022 examining operative mortality across 115 U.S. centers illustrates issues. We describe a flexible framework with robust estimation of nuisance functions that account for between-center heterogeneity in treatments and patient confounders, particularly when positivity violations and low event rates complicate inferences. Various estimation strategies using the congenital heart data are employed, providing an implementation strategy across the different estimation approaches (funded by Grant R01HL162893 from the U.S. National Institutes of Health).
53573502324