Rationing of healthcare resources is a challenging decision that policy makers and providers may be forced to make during a pandemic, natural disaster, or mass casualty event. Well-defined guidelines to triage scarce life-saving resources must be designed to promote transparency, trust, and consistency. To facilitate buy-in and use during high-stress situations, these guidelines need to be interpretable and operational. We propose a novel data-driven model to compute interpretable triage guidelines based on policies for Markov Decision Process that can be represented as simple sequences of decision trees ("tree policies"). In particular, we characterize the properties of optimal tree policies and present an algorithm based on dynamic programming recursions to compute good tree policies. We utilize this methodology to obtain simple, novel triage guidelines for ventilator allocations for COVID-19 patients, based on real patient data from Montefiore hospitals. We also compare the performance of our guidelines to the official New York State guidelines that were developed in 2015 (well before the COVID-19 pandemic). Our empirical study shows that the number of excess deaths associated with ventilator shortages could be reduced significantly using our policy. Our work highlights the limitations of the existing official triage guidelines, which need to be adapted specifically to COVID-19 before being successfully deployed.

翻译：医疗资源配给是政策制定者和医疗服务提供者在疫情、自然灾害或大规模伤亡事件中可能被迫面临的艰难决策。必须设计明确的分诊指南来分配稀缺的救生资源，以促进透明度、信任度和一致性。为在高压情境下推动采纳与使用，这些指南需具备可解释性与可操作性。我们提出一种新型数据驱动模型，用于基于马尔可夫决策过程的策略计算可解释分诊指南，该策略可表示为简单的决策树序列（"树策略"）。具体而言，我们刻画了最优树策略的特性，并提出一种基于动态规划递归的算法以计算优质树策略。我们运用该方法，基于蒙特菲奥里医疗机构的真实患者数据，为COVID-19患者制定了新颖简洁的呼吸机分配分诊指南。同时，我们将所提指南与2015年制定的纽约州官方指南（远早于COVID-19疫情）进行了性能对比。实证研究表明，采用我们的策略可显著降低因呼吸机短缺导致的超额死亡人数。本研究揭示了现有官方分诊指南的局限性——这些指南需针对COVID-19进行专门调整才能有效部署。