The interest in applying machine learning in healthcare has grown rapidly in recent years. Most predictive algorithms requiring pathway implementations are evaluated using metrics focused on predictive performance, such as the c statistic. However, these metrics are of limited clinical value, for two reasons: (1) they do not account for the algorithm’s role within a provider workflow; and (2) they do not quantify the algorithm’s value in terms of patient outcomes and cost savings. We propose a model for simulating the selection of patients over time by a clinician using a machine learning algorithm, and quantifying the expected patient outcomes and cost savings. Using data on unplanned emergency department surgical readmissions, we show that factors such as the provider’s schedule and postoperative prediction timing can have major effects on the pathway cohort size and potential cost reductions from preventing hospital readmissions.

近年来，人们对在医疗保健中应用机器学习的兴趣迅速增长。大多数需要路径实现的预测算法都是使用专注于预测性能的指标来评估的，例如c统计。然而，这些指标的临床价值有限，原因有二：(1) 它们没有考虑算法在提供者工作流程中的作用；(2) 他们没有量化算法在患者结果和成本节约方面的价值。我们提出了一个模型，用于模拟临床医生使用机器学习算法随时间选择患者，并量化预期的患者结果和成本节约。使用计划外急诊科再入院的数据，我们表明提供者的时间表和术后预测时间等因素可能对路径队列规模和预防再入院的潜在成本降低产生重大影响。