Data-Driven Hospital Admission Control: A Learning Approach

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. The choice of care unit upon admission to the hospital 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. The care unit placement decisions involve capturing the trade-off between the benefit of better health outcomes versus the opportunity cost of reserving higher level of care beds for potentially more complex patients arriving in the future. By focusing on reducing the readmission risk of patients, we develop an online algorithm for care unit placement under the presence of limited reusable hospital beds. The algorithm is designed to (i) adaptively learn the readmission risk of patients through batch learning with delayed feedback and (ii) choose the best care unit placement for a patient based on the observed information and the occupancy level of the care units. We prove that our online algorithm admits a Bayesian regret bound. We also investigate and assess the effectiveness of our methodology using hospital system data. Our numerical experiments demonstrate that our methodology outperforms different benchmark policies. Funding: This work was supported by the National Science Foundation [Grant CMMI 1548201]. C. Shi was supported by Amazon Research Award. Supplemental Material: The online appendix is