癌症患者临终关怀中的种族差异已有充分记录，但文献中发现的差异的存在、方向和程度是相互矛盾的。目前识别种族差异的方法汇总数据以产生单值质量度量，该度量排除了重要的患者质量要素，因此缺乏识别可操作的公平改善见解的信息。我们的目标是开发一种可解释的机器学习方法，阐明医疗保健差异并提供更多可操作的质量改进信息。我们将临床信息与工程系统建模和数据科学相结合，使用美国医疗保险临终关怀中心针对 4 月至 12 月死亡的晚期（预后不良）癌症患者队列的数据，开发每家医院每个患者组的使用时间概况2016 年。我们计算了有色人种和白人的群体概况之间的差异，以识别种族差异特征。利用机器学习，我们对各医院的种族差异特征进行了聚类，并将这些聚类与经典的质量指标和医院特征进行了比较。我们通过 362 家医院的 45,125 名患者确定了 7 个集群； 4个集群（n = 190家医院）显示有色人种的临终关怀利用率高于白人，2个集群（n = 106）显示白人比有色人种的临终关怀利用率更高，1个集群（n = 66）显示没有差异。医院内的种族差异行为无法通过质量衡量来预测，这表明差异的真实形状如何通过质量衡量的视角被扭曲。这种方法根据用于计算质量指标的相同数据，通过算法阐明了临终关怀种族差异的形状。© 2023。Springer Nature Limited。

Racial disparities in hospice care are well documented for patients with cancer, but the existence, direction, and extent of disparity findings are contradictory across the literature. Current methods to identify racial disparities aggregate data to produce single-value quality measures that exclude important patient quality elements and, consequently, lack information to identify actionable equity improvement insights. Our goal was to develop an explainable machine learning approach that elucidates healthcare disparities and provides more actionable quality improvement information. We infused clinical information with engineering systems modeling and data science to develop a time-by-utilization profile per patient group at each hospital using US Medicare hospice utilization data for a cohort of patients with advanced (poor-prognosis) cancer that died April-December 2016. We calculated the difference between group profiles for people of color and white people to identify racial disparity signatures. Using machine learning, we clustered racial disparity signatures across hospitals and compared these clusters to classic quality measures and hospital characteristics. With 45,125 patients across 362 hospitals, we identified 7 clusters; 4 clusters (n = 190 hospitals) showed more hospice utilization by people of color than white people, 2 clusters (n = 106) showed more hospice utilization by white people than people of color, and 1 cluster (n = 66) showed no difference. Within-hospital racial disparity behaviors cannot be predicted from quality measures, showing how the true shape of disparities can be distorted through the lens of quality measures. This approach elucidates the shape of hospice racial disparities algorithmically from the same data used to calculate quality measures.© 2023. Springer Nature Limited.