Data-Driven Hospital Admission Control: A Learning Approach
提出一种数据驱动算法，通过批量学习自适应预测患者再入院风险，并基于床位占用情况优化护理单元分配，以降低整体再入院风险，适用于医院管理者和运营研究人员。
A Data-Driven Approach to Improve Care Unit Placements in Hospitals The choice of care unit upon hospital admission is a challenging task because of the wide variety of patient characteristics, uncertain needs of patients, and limited number of beds in intensive and intermediate care units. These decisions require carefully weighing the benefits of improved health outcomes against the opportunity cost of reserving higher level care beds for potentially more complex patients arriving in the future. In “Data-Driven Hospital Admission Control: A Learning Approach,” Zhalechian, Keyvanshokooh, Shi, and Van Oyen introduce a data-driven algorithm to address this challenging task. By focusing on reducing the readmission risk of patients, the algorithm is designed to (i) adaptively learn the readmission risk of patients through batch learning with delayed feedback and (ii) determine the best care unit placement for a patient based on the observed information and occupancy levels to minimize total readmission risk. The algorithm is supported by a performance guarantee, and its effectiveness is showcased using real-world hospital system data.