Detecting the Failures Nobody Sees: Applying Pareto Analysis and Failure Mode and Effects Analysis to Equipment Reliability in a Healthcare Simulation Institute.

Background Hospitals have used failure mode and effects analysis (FMEA) for two decades to examine clinical processes before they fail, and healthcare simulation has served as a useful instrument for carrying out that analysis. The equipment inside the simulation center has attracted far less scrutiny. Audiovisual (AV) and manikin failures interrupt teaching, and when video capture is affected, they destroy the recording on which objective structured clinical examination (OSCE) grading and debriefing depend. Methods We analyzed a 24-month log of reported equipment incidents (N = 22) from the simulation institute of an offshore medical school using Pareto analysis, Fishbone (Ishikawa) diagrams with Five Whys questioning, and FMEA. The work formed the Define, Measure, and Analyze phases of a Define-Measure-Analyze-Improve-Control (DMAIC) quality improvement cycle. Severity anchors were set by institute leadership according to consequence; Occurrence was scored from observed frequency; and Detection reflected how each failure mode ordinarily comes to notice. The risk priority number (RPN) was calculated as the product of the three scores. Results AV and network problems accounted for seven incidents (31.8%) and manikin power or mechanical failures for six (27.3%), together making up 13 (59.1%) of the total. FMEA ranked capture failure during OSCEs at the top, with an RPN of 378, well ahead of manikin total shutdown at 140, even though the shutdown carried the maximum severity score. Detectability accounted for the whole of the difference. A shutdown stops the session and is obvious within seconds; a capture failure leaves the session looking entirely normal and comes to light only when someone asks for a recording that was never made. Conclusions Prioritizing equipment risk by severity alone would have ranked manikin shutdown as the highest risk, while leaving the more damaging failure unaddressed. Detectability deserves a place alongside severity in simulation operations improvement work. We set out the incident classification scheme, the scoring anchors, and a five-step workflow so that other programs can run the same analysis against their data.