Predicting medicine inpatient discharges at Massachusetts General Hospital
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2021
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| Online Access: | https://hdl.handle.net/1721.1/129850 |
| _version_ | 1826202068154580992 |
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| author | Starobinski, Keren S.(Keren Sarah) |
| author2 | Retsef Levi. |
| author_facet | Retsef Levi. Starobinski, Keren S.(Keren Sarah) |
| author_sort | Starobinski, Keren S.(Keren Sarah) |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020 |
| first_indexed | 2024-09-23T12:01:22Z |
| format | Thesis |
| id | mit-1721.1/129850 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T12:01:22Z |
| publishDate | 2021 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1298502021-02-20T03:22:42Z Predicting medicine inpatient discharges at Massachusetts General Hospital Starobinski, Keren S.(Keren Sarah) Retsef Levi. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020 Cataloged from student-submitted PDF of thesis. Includes bibliographical references (pages 115-118). At Massachusetts General Hospital, inpatients often experience significant non-clinical delays in patient care, and frequently wait in the Emergency Department or in inpatient-floor hallways before receiving bed assignments. Such delays result in overcrowding in the Emergency Department, heightened dissatisfaction among patients, and an increase in overall patient length-of-stay. Delays in bed assignments primarily occur because of the discrepancy between the timing of admissions, which generally occur throughout the day, and the timing of discharges, which typically occur in the afternoon. Furthermore, although bed managers know about scheduled admissions in advance, there is no standardized protocol that allows bed managers at the Admitting Department to identify which patients are ready to leave the hospital. In this project, we develop a discharge prediction tool that identifies medicine and neurology inpatient discharges that will occur within the next 24 hours. The goal is to use this tool to enable a more proactive bed-management process at MGH, provide the hospital staff with a methodical way to identify discharges, and ameliorate overcrowding challenges in the Emergency Department. The model was trained using the data of 60,993 inpatients who were hospitalized sometime between May 2016 and September 2018. The prediction algorithm achieved a 0.830 mean AUC-ROC (SD 0.002), 47.6% precision (24 hours), 67.4% precision (48 hours), and 43.8% recall using a decision threshold of 0.31. For inpatients who were on cardiology floors within the Department of Medicine, the model achieved 58.3% precision (24 hours), 74.3% precision (48 hours), and 63.5% recall using 0.31 as the decision threshold. Since the model used data that is accessible in most hospital information systems, it can be applied to other hospitals as well. by Keren S. Starobinski. M. Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2021-02-19T20:19:01Z 2021-02-19T20:19:01Z 2020 2020 Thesis https://hdl.handle.net/1721.1/129850 1237564991 eng MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 118 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Starobinski, Keren S.(Keren Sarah) Predicting medicine inpatient discharges at Massachusetts General Hospital |
| title | Predicting medicine inpatient discharges at Massachusetts General Hospital |
| title_full | Predicting medicine inpatient discharges at Massachusetts General Hospital |
| title_fullStr | Predicting medicine inpatient discharges at Massachusetts General Hospital |
| title_full_unstemmed | Predicting medicine inpatient discharges at Massachusetts General Hospital |
| title_short | Predicting medicine inpatient discharges at Massachusetts General Hospital |
| title_sort | predicting medicine inpatient discharges at massachusetts general hospital |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/129850 |
| work_keys_str_mv | AT starobinskikerenskerensarah predictingmedicineinpatientdischargesatmassachusettsgeneralhospital |