Algorithmic prediction of failure modes in healthcare
| Autor: | Vered H. Eisenberg, Ortal Sharlin, Merav Barbi, Talia Levy, Ayala Kobo-Greenhut, Nitza Peer, Izhar Ben Shlomo, Zimlichman Eyal, Yael Adler |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Process (engineering)
Computer science failure modes APFMH 03 medical and health sciences 0302 clinical medicine Brainstorming Health care Humans Operations management AcademicSubjects/MED00860 030212 general & internal medicine Original Research Article Healthcare Failure Mode and Effect Analysis Israel Human resources algorithmic prediction FMEA Risk Management Medical Errors business.industry SARS-CoV-2 030503 health policy & services Health Policy Public Health Environmental and Occupational Health healthcare COVID-19 Emergency department General Medicine Identification (information) Resource allocation 0305 other medical science business Failure mode and effects analysis Algorithms |
| Zdroj: | International Journal for Quality in Health Care |
| ISSN: | 1464-3677 1353-4505 |
| DOI: | 10.1093/intqhc/mzaa151 |
| Popis: | Background Preventing medical errors is crucial, especially during crises like the COVID-19 pandemic. Failure Modes and Effects Analysis (FMEA) is the most widely used prospective hazard analysis in healthcare. FMEA relies on brainstorming by multi-disciplinary teams to identify hazards. This approach has two major weaknesses: significant time and human resource investments, and lack of complete and error-free results. Objectives To introduce the algorithmic prediction of failure modes in healthcare (APFMH) and to examine whether APFMH is leaner in resource allocation in comparison to the traditional FMEA and whether it ensures the complete identification of hazards. Methods The patient identification during imaging process at the emergency department of Sheba Medical Center was analyzed by FMEA and APFMH, independently and separately. We compared between the hazards predicted by APFMH method and the hazards predicted by FMEA method; the total participants’ working hours invested in each process and the adverse events, categorized as ‘patient identification’, before and after the recommendations resulted from the above processes were implemented. Results APFMH is more effective in identifying hazards (P Conclusion In light of our initial and limited-size study, APFMH is more effective in identifying hazards (P |
| Databáze: | OpenAIRE |
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