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Leveraging Neural Networks to Profile Health Care Providers with Application to Medicare Claims by Wu, Wenbo; Li, Fan; Liu, Richard; Li, Yiting; McAdams-DeMarco, Mara; Geras, Krzysztof J.; Schaubel, Douglas E.; Díaz, Iván is a scholarly article available to read on EtoBox.

What is Leveraging Neural Networks to Profile Health Care Providers with Application to Medicare Claims about?

Encompassing numerous nationwide, statewide, and institutional initiatives in the United States, provider profiling has evolved into a major health care undertaking with ubiquitous applications, profound implications, and high-stakes consequences. In line with such a significant profile, the literature has accumulated a number of developments dedicated to enhancing the statistical paradigm of provider profiling. Tackling wide-ranging profiling issues, these methods typically adjust for risk factors using linear predictors. While this approach is simple, it can be too restrictive to characterize complex and dynamic factor-outcome associations in certain contexts. One such example arises from evaluating dialysis facilities treating Medicare beneficiaries with end-stage renal disease. It is of primary interest to consider how the coronavirus disease (COVID-19) affected 30-day unplanned readmissions in 2020. The impact of COVID-19 on the risk of readmission varied dramatically across pandemic phases. To efficiently capture the variation while profiling facilities, we develop a generalized partially linear model (GPLM) that incorporates a neural network. Considering provider-level clust

Author

Wu, Wenbo; Li, Fan; Liu, Richard; Li, Yiting; McAdams-DeMarco, Mara; Geras, Krzysztof J.; Schaubel, Douglas E.; Díaz, Iván

Published

2023

Language

EN