| Autor: |
Dana R. Sax, E. Margaret Warton, Oleg Sofrygin, Dustin G. Mark, Dustin W. Ballard, Mamata V. Kene, David R. Vinson, Mary E. Reed |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Journal of the American College of Emergency Physicians Open, Vol 4, Iss 4, Pp n/a-n/a (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2688-1152 |
| DOI: |
10.1002/emp2.13003 |
| Popis: |
Abstract Objectives Efficient and accurate emergency department (ED) triage is critical to prioritize the sickest patients and manage department flow. We explored the use of electronic health record data and advanced predictive analytics to improve triage performance. Methods Using a data set of over 5 million ED encounters of patients 18 years and older across 21 EDs from 2016 to 2020, we derived triage models using deep learning to predict 2 outcomes: hospitalization (primary outcome) and fast‐track eligibility (exploratory outcome), defined as ED discharge with |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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