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Algorithmic Prediction Of Failure Modes In Healthcare.

BACKGROUND
Preventing medical errors is crucial, especially during crises like the COVID-19 pandemic. Failure Modes and Effects Analysis (FMEA) is the most widely prospective hazard analysis used in healthcare. FMEA relies on brainstorming by multi-disciplinary teams to identify hazards. This approach has two major weaknesses: significant time and human resources investments, and lack of complete, and error free results.


OBJECTIVES
To introduce the algorithmic prediction of failure modes in healthcare (APFMH); to examine whether APFMH is leaner in resources allocation in comparison to the traditional FMEA and whether it ensures 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 the hazards predicted by APFMH and FMEA methods; the total participants' working hours invested in each process; 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 hours whereas the latter required 63. Following implementing the recommendations, the adverse events decreased by 44% annually (P=0.0026). Most adverse events were preventable, had all recommendations have been fully implemented.


CONCLUSION
In the light of our initial and limited in 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 that 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.