Congestion reduction in the Emergency Department of Massachusetts General Hospital
Name
1057123269-MIT.pdf
Description
Full printable version
Size
9.59 MB
Format
Adobe PDF
Checksum (MD5)
9ec375e859c2b346b5f2ecc5cc4e000c
Author(s)
Ebben, Philip T
Advisor(s)
Retsef Levi and Duane Boning.
Alternative Title
Congestion reduction in the ED at MGH
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
The MGH Emergency Department (ED) and General Medicine Floor currently experience heavy patient volume and rising patient wait times, despite recent capacity expansions. While several projects have been piloted to divert patients towards alternative care paths, MGH management wants to better understand what types of patients are being admitted to the hospital and what features are deterministic of patient admission. This thesis addresses this information gap by using binary logistic regression models to assess predictive and significant patient features for admission. Our analysis uses both patient demographic information and decision point data gathered in the Emergency Department of patient visits. On out-of-sample data, our predictive model achieves an area under the receiver operating characteristic of 0.82, and we conclude that the predictive features for admission are within good clinical practice. Further analysis of patient care suggests that provision of IV antibiotics in the outpatient setting could reduce MGH admissions by approximately 307 bed-days per year, with additional possible reductions in excess of 1,000 beddays for different provisions of care. We also assess the outpatient usage of MGH patients and conclude that 75 percent of cellulitis, pneumonia and urinary tract infection patients are not seeing a clinician in the outpatient setting prior to ED presentation. This analysis indicates that more proactive management of these patients could prevent both their visit to the ED and potentially their admission. We demonstrate that statistical methods based on real time patient data. can contribute to effective healthcare planning and operations.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2018.
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 55).
Subjects
Mechanical Engineering.
Sloan School of Management.
Leaders for Global Operations Program.
MIT Department
Leaders for Global Operations Program at MIT
Massachusetts Institute of Technology. Department of Mechanical Engineering
Sloan School of Management
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