Operations Research Models for Reducing Hospital Readmissions

hospital readmission;healthcare policy;reliability;Industrial and Operations Engineering;Engineering;Industrial & Operations Engineering

Liu, XiangVan Oyen, Mark Peter ;

University of Michigan

关键词: hospital readmission;    healthcare policy;    reliability;    Industrial and Operations Engineering;    Engineering;    Industrial & Operations Engineering;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/150023/liuxiang_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Hospital readmissions are burdensome and costly to both healthcare providers and patients. In the U.S., one in five Medicare patients is readmitted within 30 days of discharge. We study how to use operations research models to reduce hospital readmissions. Our approach focuses on both the hospital operations level and the policymaker system level. We develop a delay-time optimization framework to maximize the detection of post-operative complications via post-discharge checkups. Then we study how to design a bundled payment policy to balance and incentivize pre and post-discharge readmission reduction efforts. We build a readmission prediction model using laboratory values observed during the index hospitalization. Ultimately, we provide novel methods for reducing readmissions in the continuum of care spanning between the pre- and post-discharge stages, at the hospital and policymaker levels.