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Encompassing numerous nationwide, statewide, and institutional initiatives inthe United States, provider profiling has evolved into a major health careundertaking with ubiquitous applications, profound implications, andhigh-stakes consequences. In line with such a significant profile, theliterature has accumulated a number of developments dedicated to enhancing thestatistical paradigm of provider profiling. Tackling wide-ranging profilingissues, these methods typically adjust for risk factors using linearpredictors. While this approach is simple, it can be too restrictive tocharacterize complex and dynamic factor-outcome associations in certaincontexts. One such example arises from evaluating dialysis facilities treatingMedicare beneficiaries with end-stage renal disease. It is of primary interestto consider how the coronavirus disease (COVID-19) affected 30-day unplannedreadmissions in 2020. The impact of COVID-19 on the risk of readmission varieddramatically across pandemic phases. To efficiently capture the variation whileprofiling facilities, we develop a generalized partially linear model (GPLM)that incorporates a neural network. Considering provider-level clustering, weimplement the GPLM as a stratified sampling-based stochastic optimizationalgorithm that features accelerated convergence. Furthermore, an exact test isdesigned to identify under- and over-performing facilities, with anaccompanying funnel plot to visualize profiles. The advantages of the proposedmethods are demonstrated through simulation experiments and profiling dialysisfacilities using 2020 Medicare claims from the United States Renal Data System.

Leveraging Neural Networks to Profile Health Care Providers with Application to Medicare Claims