Abstract
Problem definition: Access to accurate predictions of patients' outcomes can enhance medical staff's decision-making, which ultimately benefits all stakeholders in the hospitals. A large hospital network in the US has been collaborating with academics and consultants to predict short-term and long-term outcomes for all inpatients across their seven hospitals. Methodology/results: We develop machine learning models that predict the probabilities of next 24-hr/48-hr discharge and intensive care unit transfers, end-of-stay mortality and discharge dispositions. All models achieve high out-of-sample AUC (75.7%-92.5%) and are well calibrated. In addition, combining 48-hr discharge predictions with doctors' predictions simultaneously enables more patient discharges (10%-28.7%) and fewer 7-day/30-day readmissions ($p$-value $<0.001$). We implement an automated pipeline that extracts data and updates predictions every morning, as well as user-friendly software and a color-coded alert system to communicate these patient-level predictions (alongside explanations) to clinical teams. Managerial implications: Since we have been gradually deploying the tool, and training medical staff, over 200 doctors, nurses, and case managers across seven hospitals use it in their daily patient review process. We observe a significant reduction in the average length of stay (0.67 days per patient) following its adoption and anticipate substantial financial benefits (between \$55 and \$72 million annually) for the healthcare system.
Abstract (translated)
问题定义：获取患者结果准确的预测能够增强医疗工作人员的决策能力，最终造福于医院的所有利益相关者。在美国，一个大型医院网络与学术界和顾问合作，预测所有住院病人在7家医院短期和长期结果。方法/结果：我们开发机器学习模型，预测未来24小时/48小时出院和重症监护室转诊、出院死亡率和出院处理方案的概率。所有模型都实现了高样本AUC(75.7%-92.5%)且校准良好。此外，同时结合医生的预测，能够实现更多的患者出院(10%-28.7%)，更少的7日/30日 readmissions( $p$-value <0.001)。我们实现了每天早上自动提取数据并更新预测的自动化管道，并开发了易于使用的软件和色彩编码的警报系统，以向临床团队传达这些患者级别的预测(伴随解释)。管理影响：自我们开始逐渐部署工具并培训医疗工作人员以来，超过200名医生、护士和病例管理师在7家医院每天的 patient 审查过程中使用它。我们观察到随着它的采用，平均住院时间(每个病人每天0.67天)显著减少，并预计为医疗系统带来巨大的财务好处(每年介于55万到720万美元之间)。
URL
https://arxiv.org/abs/2305.15629
