Algorithmic prediction of failure modes in healthcare

Abstract

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 < 0.0001) and is leaner in resources than the traditional FMEA: the former used 21 h whereas the latter required 63 h. Following the implementation of the recommendations, the adverse events decreased by 44% annually (P = 0.0026). Most adverse events were preventable, had all recommendations been fully implemented. CONCLUSION: In light of our initial and limited-size study, APFMH is more effective in identifying hazards (P < 0.0001) and is leaner in resources than the traditional FMEA. APFMH is suggested as an alternative to FMEA since it is leaner in time and human resources, ensures more complete hazard identification and is especially valuable during crisis time, when new protocols are often adopted, such as in the current days of the COVID-19 pandemic.

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