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Published on: March 8, 2012
Failure Mode and Effects Analysis (FMEA) integrating quality indicators for risk assessment of the total testing
Tingting Li1, Yuting He2, Yuanhao Chen3
1Department of Laboratory Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Objectives:
To develop a proactive risk assessment model for human papillomavirus (HPV) genotyping testing by integrating Failure Mode and Effects Analysis (FMEA) with quality indicators (QIs), ensuring compliance with ISO 15189:2022 and improving diagnostic accuracy.
Methods:
A multidisciplinary team designed and performed detailed FMEA across pre-analytical, analytical, and post-analytical phases of HPV genotyping testing. To improve objectivity, we integrated Sigma metrics into the FMEA framework through a molecular diagnostics-specific model of QIs (MQI). The FMEA model systematically identified testing phases, potential failure modes, their effects, root causes, and existing controls. Risk was quantified using Severity, Occurrence (from 1-year QI data), and Detection scores (1-5 scale). Risk Priority Numbers (RPNs) were calculated (Severity × Occurrence × Detection) to prioritize failure modes, with mandatory interventions implemented for high-risk items (RPN≥40).
Results:
Five high-risk failure modes (e.g., sample misidentification, data analysis errors) were identified and successfully mitigated to acceptable levels (RPN<40) through process optimization and standardization, achieving RPN reductions of 20-80 %. We established a molecular diagnostics-specific MQI, comprising 14 pre-analytical, 25 analytical, and three post-analytical phase QIs. QI-based risk assessment of 35 evaluable QIs for HPV genotyping testing revealed one high-risk QI ("Incorrect results due to information system failures") and three medium-risk QIs, all of which were addressed through corrective actions.
Conclusions:
This study developed an integrated FMEA-QI model for HPV genotyping testing, establishing both a traditional FMEA framework and a molecular diagnostics-specific MQI. The combined approach improves risk assessment objectivity and enables multidimensional analysis compared to conventional methods.
