Optimal allocation of surgical services
Name
891385457-MIT.pdf
Description
Full printable version
Size
3.43 MB
Format
Adobe PDF
Checksum (MD5)
a03c610a9cdc043b7e1126c56b0e4fd6
Author(s)
Braun, Marcus (Marcus D.)
Advisor(s)
Vivek Farias and David Simchi-Levi.
Date Issued
2014
Publisher
Massachusetts Institute of Technology
Abstract
Over 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.
Description
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2014. In conjunction with the Leaders for Global Operations Program at MIT.
Thesis: 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.
25
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 61-64).
Subjects
Sloan School of Management.
Electrical Engineering and Computer Science.
Leaders for Global Operations Program.
MIT Department
Leaders for Global Operations Program at MIT
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Sloan School of Management
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