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David Simchi-Levi and Vivek Farias. Sham, Gregory C. (Gregory Chi-Keung) Leaders for Global Operations Program. Leaders for Global Operations Program at MIT Massachusetts Institute of Technology. Engineering Systems Division Sloan School of Management 2012-09-27T15:29:08Z 2012-09-27T15:29:08Z 2012 2012 http://hdl.handle.net/1721.1/73397 810134351 Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2012. Cataloged from PDF version of thesis. Includes bibliographical references (p. 52). In the current healthcare environment, the cost of delivering patient care is an important concern for hospitals. As a result, healthcare organizations are being driven to maximize their existing resources, both in terms of infrastructure and human capital. Using a data-driven approach with analytical techniques from operations management can contribute towards this goal. More specifically, this thesis shows, drawing from a recent project at Beth Israel Deaconess Medical Center (BIDMC), that predictive modeling can be applied to operating room (OR) scheduling in order to effectively increase capacity. By examining the current usage of the existing block schedule system at BIDMC and developing a linear regression model, OR time that is expected to go unused can be instead identified in advance and freed for use. Sample model results show that it is expected to be operationally effective by capturing a large enough portion of OR time for a pooled set of blocks to be useful for advanced scheduling purposes. This analytically determined free time represents an improvement in how the current block system is employed, especially in terms of the nominal block release time. This thesis makes the argument that such a model can integrate into a scheduling system with more efficient and flexible processes, ultimately resulting in more effective usage of existing resources. by Gregory C. Sham. S.M. M.B.A. 52 p. eng Massachusetts Institute of Technology M.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. http://dspace.mit.edu/handle/1721.1/7582 Sloan School of Management. Engineering Systems Division. Leaders for Global Operations Program. Developing a data-driven approach for improving operating room scheduling processes Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="672d590b-eaab-47c3-9e23-05527ffe982c"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Developing a data-driven approach for improving operating room scheduling processes</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2012</PublicationDate> <Authors> <Author> <DisplayName>Sham, Gregory C. (Gregory Chi-Keung)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Sloan School of Management.</Keyword> <Keyword>Engineering Systems Division.</Keyword> <Keyword>Leaders for Global Operations Program.</Keyword> <Abstract>In the current healthcare environment, the cost of delivering patient care is an important concern for hospitals. As a result, healthcare organizations are being driven to maximize their existing resources, both in terms of infrastructure and human capital. Using a data-driven approach with analytical techniques from operations management can contribute towards this goal. More specifically, this thesis shows, drawing from a recent project at Beth Israel Deaconess Medical Center (BIDMC), that predictive modeling can be applied to operating room (OR) scheduling in order to effectively increase capacity. By examining the current usage of the existing block schedule system at BIDMC and developing a linear regression model, OR time that is expected to go unused can be instead identified in advance and freed for use. Sample model results show that it is expected to be operationally effective by capturing a large enough portion of OR time for a pooled set of blocks to be useful for advanced scheduling purposes. This analytically determined free time represents an improvement in how the current block system is employed, especially in terms of the nominal block release time. This thesis makes the argument that such a model can integrate into a scheduling system with more efficient and flexible processes, ultimately resulting in more effective usage of existing resources.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>