Using interpretable survival analysis to assess hospital length of stay
Abstract Accurate in-hospital length of stay prediction is a vital quality metric for hospital leaders and health policy decision-makers. It assists with decision-making and informs hospital operations involving factors such as patient flow, elective cases, and human resources allocation, while also...
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| Format: | Article |
| Language: | English |
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BMC
2025-05-01
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| Series: | BMC Health Services Research |
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| Online Access: | https://doi.org/10.1186/s12913-025-12852-0 |
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| _version_ | 1849325877820653568 |
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| author | Yan Li Trevor Hall Fahad Razak Amol Verma Mark Chignell Lu Wang |
| author_facet | Yan Li Trevor Hall Fahad Razak Amol Verma Mark Chignell Lu Wang |
| author_sort | Yan Li |
| collection | DOAJ |
| description | Abstract Accurate in-hospital length of stay prediction is a vital quality metric for hospital leaders and health policy decision-makers. It assists with decision-making and informs hospital operations involving factors such as patient flow, elective cases, and human resources allocation, while also informing quality of care and risk considerations. The aim of the research reported in this paper is to use survival analysis to model General Internal Medicine (GIM) length of stay, and to use Shapley value to support interpretation of the resulting model. Survival analysis aims to predict the time until a specific event occurs. In our study, we predict the duration from patient admission to discharge to home, i.e., in-hospital length of stay. In addition to discussing the modeling results, we also talk about how survival analysis of hospital length of stay can be used to guide improvements in the efficiency of hospital operations and support the development of quality metrics. |
| format | Article |
| id | doaj-art-2aff3a087ec4453f8750f66d719c2f25 |
| institution | Kabale University |
| issn | 1472-6963 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Health Services Research |
| spelling | doaj-art-2aff3a087ec4453f8750f66d719c2f252025-08-20T03:48:18ZengBMCBMC Health Services Research1472-69632025-05-0125111210.1186/s12913-025-12852-0Using interpretable survival analysis to assess hospital length of stayYan Li0Trevor Hall1Fahad Razak2Amol Verma3Mark Chignell4Lu Wang5Department of Mechanical and Industrial Engineering, University of TorontoDepartment of Mechanical and Industrial Engineering, University of TorontoThe General Medicine Inpatient Initiative (GEMINI), Unity Health TorontoThe General Medicine Inpatient Initiative (GEMINI), Unity Health TorontoDepartment of Mechanical and Industrial Engineering, University of TorontoDepartment of Biomedical Engineering, University of HoustonAbstract Accurate in-hospital length of stay prediction is a vital quality metric for hospital leaders and health policy decision-makers. It assists with decision-making and informs hospital operations involving factors such as patient flow, elective cases, and human resources allocation, while also informing quality of care and risk considerations. The aim of the research reported in this paper is to use survival analysis to model General Internal Medicine (GIM) length of stay, and to use Shapley value to support interpretation of the resulting model. Survival analysis aims to predict the time until a specific event occurs. In our study, we predict the duration from patient admission to discharge to home, i.e., in-hospital length of stay. In addition to discussing the modeling results, we also talk about how survival analysis of hospital length of stay can be used to guide improvements in the efficiency of hospital operations and support the development of quality metrics.https://doi.org/10.1186/s12913-025-12852-0Interpretable machine learningSurvival analysisHospital length of stay |
| spellingShingle | Yan Li Trevor Hall Fahad Razak Amol Verma Mark Chignell Lu Wang Using interpretable survival analysis to assess hospital length of stay BMC Health Services Research Interpretable machine learning Survival analysis Hospital length of stay |
| title | Using interpretable survival analysis to assess hospital length of stay |
| title_full | Using interpretable survival analysis to assess hospital length of stay |
| title_fullStr | Using interpretable survival analysis to assess hospital length of stay |
| title_full_unstemmed | Using interpretable survival analysis to assess hospital length of stay |
| title_short | Using interpretable survival analysis to assess hospital length of stay |
| title_sort | using interpretable survival analysis to assess hospital length of stay |
| topic | Interpretable machine learning Survival analysis Hospital length of stay |
| url | https://doi.org/10.1186/s12913-025-12852-0 |
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