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A Hybrid Qualitative-Quantitative FMEA Model for Risk Management in Clinical Laboratory Automation: A Case Study
Mengqi Wei1, Mingyang Li1, Yu Lin1
1Department of Clinical Laboratory, Peking University Shenzhen Hospital, Shenzhen, China.
Background:
Total laboratory automation (TLA) system greatly enhances testing efficiency and accuracy, yet potential process-related risks may cause inaccurate results or delayed reports. Based on the ISO 15189:2022 standard, this study applied failure mode and effects analysis (FMEA) to systematically assess risks across three TLA systems at Peking University Shenzhen Hospital.
Methods:
The Roche, Abbott, and Beckman TLA systems were evaluated across the pre-analytical, analytical, and post-analytical phases. Failure modes were scored for severity (S), occurrence (O) and detectability (D) to calculate the risk priority number (RPN = S × O × D). To improve the objectivity of occurrence scoring, qualitative questionnaires were integrated with quantitative data from laboratory information system, equipment logs and quality control records. Targeted interventions were implemented for high-risk nodes, and their effectiveness was subsequently reevaluated.
Results:
Several common high-risk nodes were identified across the three systems, including sample transport delay, system malfunction, and reagent failures. Targeted interventions, such as pneumatic transport systems, data visualization dashboards, and intelligent reagent management platforms, were applied accordingly. Beckman's system-specific quality control risks were effectively mitigated through Patient-Based Real-Time Quality Control and reaction curve monitoring. Result-clinical consistency was improved in the Abbott and Beckman systems via retesting and multi-rule verification. RPN values of critical failure modes were reduced to medium or low risk.
Conclusion:
By integrating ISO 15189:2022 with the FMEA approach, a standardized risk assessment model for TLA systems was established. The qualitative-quantitative integrated scoring system enhanced the objectivity of risk evaluation, while targeted intelligent interventions substantially improved the quality and safety of laboratory automation.
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