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 an enormous collection of articles 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 simple approach generally leads to reasonable assessments, 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 having end-stage renal disease based on 30-day unplanned readmissions in 2020. In this context, the impact of in-hospital 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 feedforward neural network. Considering provider-level clustering, we implement the GPLM as a stratified sampling-based stochastic optimization algorithm that features accelerated convergence. Furthermore, an exact test is designed to identify under and over-performing facilities, with an accompanying funnel plot visualizing profiling results. The advantages of the proposed methods are demonstrated through simulation experiments and the profiling of dialysis facilities using 2020 Medicare claims sourced from the United States Renal Data System.

翻译：涵盖美国全国、州级及机构层面的多项倡议，医疗服务提供者画像已发展成为一项具有广泛应用、深远影响和高风险后果的重大医疗事业。与此重要地位相呼应，学术界积累了大量专注于提升提供者画像统计范式的文献。这些方法通常采用线性预测因子进行风险因素调整，以应对各种画像问题。虽然这种简单方法通常能得出合理评估，但在某些情境下可能过于局限，难以刻画复杂动态的因素-结局关联。一个典型案例是基于2020年30天非计划再入院率，对治疗终末期肾病医疗保险受益人的透析机构进行画像。在此背景下，院内COVID-19感染对再入院风险的影响随疫情阶段剧烈变化。为在画像过程中有效捕捉这种变异，我们开发了融合前馈神经网络的广义半参数线性模型（GPLM）。考虑提供者层面的聚类特征，我们通过基于分层抽样的随机优化算法实现GPLM，该算法具有加速收敛特性。进一步设计了精确检验以识别表现优异与欠佳的机构，并配以漏斗图可视化画像结果。通过模拟实验及基于美国肾脏数据系统2020年医疗保险理赔数据对透析机构的画像分析，验证了所提方法的优势。