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 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 to visualize profiles. The advantages of the proposed methods are demonstrated through simulation experiments and profiling dialysis facilities using 2020 Medicare claims from the United States Renal Data System.

翻译：涵盖美国全国性、州级及机构层面的多项倡议，医疗服务提供者画像已发展为一项具有广泛应用、深远影响和重大后果的主要医疗健康事业。鉴于其重要地位，相关文献已积累了多项致力于提升提供者画像统计范式的研究成果。这些方法通常采用线性预测因子进行风险因素调整，以应对广泛的画像问题。尽管该方式简洁易行，但在特定情境下可能过于刻板，无法充分表征复杂且动态的因素与结局关联。以评估治疗终末期肾病医疗保险受益人的透析机构为例，关键研究目标在于探究2019冠状病毒病（COVID-19）对2020年30天非计划再入院的影响。COVID-19对再入院风险的影响在不同疫情阶段呈现显著差异。为在机构画像中高效捕捉此类变化，我们构建了融合神经网络的广义部分线性模型（GPLM）。考虑机构层面的聚类特征，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。此外，我们设计了精确检验以识别表现异常（优异或欠佳）的机构，并辅以漏斗图实现画像可视化。通过模拟实验及基于美国肾脏数据系统2020年医疗保险索赔数据的透析机构画像分析，验证了所提方法的优势。