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dc.contributor.advisorVivek Farias and David Simchi-Levi.en_US
dc.contributor.authorBraun, Marcus (Marcus D.)en_US
dc.contributor.otherLeaders for Global Operations Program.en_US
dc.date.accessioned2014-10-08T15:27:52Z
dc.date.available2014-10-08T15:27:52Z
dc.date.copyright2014en_US
dc.date.issued2014en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/90767
dc.descriptionThesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2014. In conjunction with the Leaders for Global Operations Program at MIT.en_US
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014. In conjunction with the Leaders for Global Operations Program at MIT.en_US
dc.description25en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 61-64).en_US
dc.description.abstractOver the past several years Greater Boston has witnessed the consolidation of multiple community hospitals into larger care organizations and a renewed focus on the delivery of affordable care. In order for the Beth Israel Deaconess Medical Center (BIDMc) to respond and adapt to this changing landscape it will be critical to not only understand demand and capacity across the organization's entire network, but also to recognize how the deployment of limited resources can best be improved. From a BIDMc Department of Surgery Perspective, essential business questions include: 1 How to allocate limited existing resources efficiently? 2 Which future growth opportunities should be pursued now? 3 How should a multiple-hospital network be used to meet system demand? Existing approaches employed for solving these questions often involve heuristic rules-of-thumb that fail to treat sunk costs and opportunity costs appropriately. These approaches often lead to demonstrably sub-optimal operational decisions. We have developed a framework for answering these questions in a more quantitatively rigorous fashion using mathematical programming. Our model captures each surgical case's impact on hospital resources (e.g. OR time, surgeon time, etc.) from when a patient enters the preoperative holding area to when they are released from the post anesthesia care unit. Using knowledge of resource requirements for each procedure, we compute an optimal allocation of cases subject to capacity and demand constraints. We pilot our framework by studying three surgical service lines within BIDMC: General Surgery, Colorectal Surgery, and Surgical Oncology. We explore three different approaches to more effectively using resources and determine that the most practical approach yields a potential profit increase of more than 5% over 2012 levels.en_US
dc.description.statementofresponsibilityby Marcus Braun.en_US
dc.format.extent74 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectSloan School of Management.en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.subjectLeaders for Global Operations Program.en_US
dc.titleOptimal allocation of surgical servicesen_US
dc.typeThesisen_US
dc.description.degreeM.B.A.en_US
dc.description.degreeS.M.en_US
dc.contributor.departmentLeaders for Global Operations Program at MITen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.contributor.departmentSloan School of Management
dc.identifier.oclc891385457en_US


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