Optimizing Intensive Care Unit Discharge Decisions with Patient Readmissions

• 成果类型: Article
• 署名作者: Chan, Carri W.; Farias, Vivek F.; Bambos, Nicholas; Escobar, Gabriel J.
• 署名单位: Columbia University; Massachusetts Institute of Technology (MIT); Stanford University; Stanford University; Kaiser Permanente
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.1120.1105
• 发表日期: 2012
• 页码: 1323-1341
• 关键词: length-of-stay; emergency-department; hospital mortality; delayed transfer; RISK; ICU; admission; MODEL; IMPACT

摘要

This work examines the impact of discharge decisions under uncertainty in a capacity-constrained high-risk setting: the intensive care unit (ICU). New arrivals to an ICU are typically very high-priority patients and, should the ICU be full upon their arrival, discharging a patient currently residing in the ICU may be required to accommodate a newly admitted patient. Patients so discharged risk physiologic deterioration, which might ultimately require readmission; models of these risks are currently unavailable to providers. These readmissions in turn impose an additional load on the capacity-limited ICU resources. We study the impact of several different ICU discharge strategies on patient mortality and total readmission load. We focus on discharge rules that prioritize patients based on some measure of criticality assuming the availability of a model of readmission risk. We use empirical data from over 5,000 actual ICU patient flows to calibrate our model. The empirical study suggests that a predictive model of the readmission risks associated with discharge decisions, in tandem with simple index policies of the type proposed, can provide very meaningful throughput gains in actual ICUs while at the same time maintaining, or even improving upon, mortality rates. We explicitly provide a discharge policy that accomplishes this. In addition to our empirical work, we conduct a rigorous performance analysis for the family of discharge policies we consider. We show that our policy is optimal in certain regimes, and is otherwise guaranteed to incur readmission related costs no larger than a factor of ((p) over cap +1) of an optimal discharge strategy, where (p) over cap is a certain natural measure of system utilization. Subject classifications: dynamic programming; healthcare; approximation algorithms. Area of review: Policy Modeling and Public Sector OR. History: Received October 2009; revisions received May 2010, December 2010, August 2011, February 2012, April 2012; accepted July 2012.