Patients whose transfer to the Intensive Care Unit (ICU) is unplanned are prone to higher mortality rates than those who were admitted directly to the ICU. Recent advances in machine learning to predict patient deterioration have introduced the possibility of \emph{proactive transfer} from the ward to the ICU. In this work, we study the problem of finding \emph{robust} patient transfer policies which account for uncertainty in statistical estimates due to data limitations when optimizing to improve overall patient care. We propose a Markov Decision Process model to capture the evolution of patient health, where the states represent a measure of patient severity. Under fairly general assumptions, we show that an optimal transfer policy has a threshold structure, i.e., that it transfers all patients above a certain severity level to the ICU (subject to available capacity). As model parameters are typically determined based on statistical estimations from real-world data, they are inherently subject to misspecification and estimation errors. We account for this parameter uncertainty by deriving a robust policy that optimizes the worst-case reward across all plausible values of the model parameters. We show that the robust policy also has a threshold structure under fairly general assumptions. Moreover, it is more aggressive in transferring patients than the optimal nominal policy, which does not take into account parameter uncertainty. We present computational experiments using a dataset of hospitalizations at 21 KNPC hospitals, and present empirical evidence of the sensitivity of various hospital metrics (mortality, length-of-stay, average ICU occupancy) to small changes in the parameters. Our work provides useful insights into the impact of parameter uncertainty on deriving simple policies for proactive ICU transfer that have strong empirical performance and theoretical guarantees.
翻译:在这项工作中,我们研究找到病人转移政策的问题,这些政策在统计估计中考虑到由于数据限制而导致的不确定性,在优化改善总体病人护理时,我们提议一个Markov Decision Process Process Process Process Process Process Process Process Production Projective 比直接接受伊斯兰法院治疗的病人死亡率高。在比较一般的假设下,我们表明最佳的转移参数政策具有阈值结构,也就是说,它将所有病人从病房转移到伊斯兰法院联盟(视现有能力而定) 。在这项工作中,我们研究发现由于数据限制而导致统计估计的不确定性的问题。我们提出一个可靠的指标不确定性政策,以衡量病人健康状况的演变情况,而国家代表着病人的强度。根据比较一般的假设,我们显示,最稳健的数值,最稳健的参数的转移政策也具有临界值。我们目前最稳健的理论性政策,而我们使用最稳健的实验室的数值,我们目前的标准推算中,我们比21个的实验室的测算更稳健的实验室的数值。