2026-09-21T23:11:05Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1114932022-01-28T17:58:21Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Retscf Levi and Duane Boning.
Hoffmann, Jordan S
Leaders for Global Operations Program.
Leaders for Global Operations Program at MIT
Massachusetts Institute of Technology. Department of Mechanical Engineering
Sloan School of Management
2017-09-15T15:36:36Z
2017-09-15T15:36:36Z
2017
2017
http://hdl.handle.net/1721.1/111493
1003322591
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2017.
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 124-129).
Despite efforts to address capacity constraints with a massive expansion less than five years ago, the Emergency Department (ED) at Massachusetts General Hospital (MGH) is again displaying consistent and serious symptoms of overreacting, including rising patient wait times and routine activation of capacity-related emergency management protocols. As MGH grapples with these challenges, it is imperative to understand precisely what is driving the congestion. In this thesis, will show there has been significant volume growth and ii) study whether these visits resulted in inpatient admissions that could have utilized alternative care pathways while preserving patient safety and quality of care. After collaborating with hospital staff to analyze ED patient volume in 2015, we conclude that avoidable admission candidates who transferred to MGH from other facilities occupied nearly 6 percent of the hospital's General Medicine capacity. Furthermore, the utilization growth associated with these patients was equivalent to 1.3 percent of all General Medicine beds. meaning transfers alone can account for the overcrowding symptoms mentioned above. In a second analysis. applying unsupervised and supervised learning methods to short-stay inpatients reveals that even generalized order data can reliably predict conditions associated with avoidable admissions. Building on this insight, we then develop a scoring method to identify avoidable admission candidates without requiring manual case review by a physician.
by Jordan S. Hoffmann.
M.B.A.
S.M.
129 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Sloan School of Management.
Mechanical Engineering.
Leaders for Global Operations Program.
Reducing a voidable admissions through the Emergency Department at Massachusetts General Hospital
Reducing a voidable admissions through the ED at MGH
Thesis
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Publication
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