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These readmissions in turn impose an additional load on the capacity-limited ICU resources. <\/jats:p> 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 [Formula: see text] of an optimal discharge strategy, where [Formula: see text] is a certain natural measure of system utilization. <\/jats:p>","DOI":"10.1287\/opre.1120.1105","type":"journal-article","created":{"date-parts":[[2012,12,17]],"date-time":"2012-12-17T17:33:46Z","timestamp":1355765626000},"page":"1323-1341","source":"Crossref","is-referenced-by-count":121,"title":["Optimizing Intensive Care Unit Discharge Decisions with Patient Readmissions"],"prefix":"10.1287","volume":"60","author":[{"given":"Carri W.","family":"Chan","sequence":"first","affiliation":[{"name":"Division of Decision, Risk, and Operations, Columbia Business School, New York, New York 10027"}]},{"given":"Vivek F.","family":"Farias","sequence":"additional","affiliation":[{"name":"Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139"}]},{"given":"Nicholas","family":"Bambos","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Department of Management Science and Engineering, Stanford University, Stanford, California 94305"}]},{"given":"Gabriel J.","family":"Escobar","sequence":"additional","affiliation":[{"name":"Kaiser Permanente Division of Research, Oakland, California 94612"}]}],"member":"109","reference":[{"key":"B1","doi-asserted-by":"crossref","unstructured":"Allon G, Deo S, Lin W. 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