Tingting Li, Yuting He, Yuanhao Chen, Shunwang Cao, Yi Wang, Chunmin Kang, Hongmei Wang, Cheng Zhang, Chang Wen, Peifeng Ke
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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).</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":10390,"journal":{"name":"Clinical chemistry and laboratory medicine","volume":" ","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Failure Mode and Effects Analysis (FMEA) integrating quality indicators for risk assessment of the total testing process in human papillomavirus genotyping testing: a proactive risk analysis model for molecular diagnostics.\",\"authors\":\"Tingting Li, Yuting He, Yuanhao Chen, Shunwang Cao, Yi Wang, Chunmin Kang, Hongmei Wang, Cheng Zhang, Chang Wen, Peifeng Ke\",\"doi\":\"10.1515/cclm-2025-0598\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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).</p><p><strong>Results: </strong>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. 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Failure Mode and Effects Analysis (FMEA) integrating quality indicators for risk assessment of the total testing process in human papillomavirus genotyping testing: a proactive risk analysis model for molecular diagnostics.
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.
期刊介绍:
Clinical Chemistry and Laboratory Medicine (CCLM) publishes articles on novel teaching and training methods applicable to laboratory medicine. CCLM welcomes contributions on the progress in fundamental and applied research and cutting-edge clinical laboratory medicine. It is one of the leading journals in the field, with an impact factor over 3. CCLM is issued monthly, and it is published in print and electronically.
CCLM is the official journal of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) and publishes regularly EFLM recommendations and news. CCLM is the official journal of the National Societies from Austria (ÖGLMKC); Belgium (RBSLM); Germany (DGKL); Hungary (MLDT); Ireland (ACBI); Italy (SIBioC); Portugal (SPML); and Slovenia (SZKK); and it is affiliated to AACB (Australia) and SFBC (France).
Topics:
- clinical biochemistry
- clinical genomics and molecular biology
- clinical haematology and coagulation
- clinical immunology and autoimmunity
- clinical microbiology
- drug monitoring and analysis
- evaluation of diagnostic biomarkers
- disease-oriented topics (cardiovascular disease, cancer diagnostics, diabetes)
- new reagents, instrumentation and technologies
- new methodologies
- reference materials and methods
- reference values and decision limits
- quality and safety in laboratory medicine
- translational laboratory medicine
- clinical metrology
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