Risk adjustment is often necessary for outcome quality indicators (QIs) to provide fair and accurate feedback to healthcare professionals. However, traditional risk adjustment models are generally oversimplified and not equipped to disentangle complex factors influencing outcomes that are out of a healthcare professional’s control. We present VIRGO, a novel variational Bayes model trained on routinely collected, large administrative datasets to risk-adjust outcome QIs. VIRGO uses detailed demographics, diagnosis, and procedure codes to provide individualized risk adjustment and explanations on patient factors affecting outcomes. VIRGO achieves state-of-the-art on external datasets and features capabilities of uncertainty expression, explainable features, and counterfactual analysis capabilities. VIRGO facilitates risk adjustment by explaining how patient factors led to adverse outcomes and expresses the uncertainty of each prediction, allowing healthcare professionals to not only explore patient factors with unexplained variance that are associated with worse outcomes but also reflect on the quality of their clinical practice.

结果质量指标 (QI) 通常需要进行风险调整，以便为医疗保健专业人员提供公平和准确的反馈。然而，传统的风险调整模型通常过于简单化，无法理清影响医疗保健专业人员无法控制的结果的复杂因素。我们提出了 VIRGO，一种新颖的变分贝叶斯模型，在常规收集的大型管理数据集上进行训练，以调整结果 QI 的风险。 VIRGO 使用详细的人口统计数据、诊断和程序代码来提供个性化的风险调整和对影响结果的患者因素的解释。 VIRGO 在外部数据集上实现了最先进的水平，并具有不确定性表达能力、可解释特征和反事实分析能力。 VIRGO 通过解释患者因素如何导致不良结果并表达每个预测的不确定性来促进风险调整，使医疗保健专业人员不仅能够探索与较差结果相关的无法解释的方差的患者因素，而且还可以反映其临床实践的质量。