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Risk analysis and assessment based on Sigma metrics and intended use

INTRODUCTION: In order to ensure the quality in clinical laboratories and meet the low risk requirements of patients and clinicians, a risk analysis and assessment model based on Sigma metrics and intended use was constructed, based on which differential sigma performance (σ) expectations of 42 anal...

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Detalles Bibliográficos
Autores principales: Xia, Yong, Xue, Hao, Yan, Cunliang, Li, Bowen, Zhang, ShuQiong, Li, Mingyang, Ji, Ling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Croatian Society of Medical Biochemistry and Laboratory Medicine 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6039164/
https://www.ncbi.nlm.nih.gov/pubmed/30022882
http://dx.doi.org/10.11613/BM.2018.020707
Descripción
Sumario:INTRODUCTION: In order to ensure the quality in clinical laboratories and meet the low risk requirements of patients and clinicians, a risk analysis and assessment model based on Sigma metrics and intended use was constructed, based on which differential sigma performance (σ) expectations of 42 analytes were developed. MATERIALS AND METHODS: Failure mode and effects analysis was applied to produce an analytic risk rating based on three factors, each test of which was graded as follows: 1) Sigma metrics; 2) the severity of harm; 3) intended use. By multiplying the score of Sigma metrics by the score of severity of harm by the score of intended use, each was assigned a typical risk priority number (RPN), with RPN ≤ 25 rated as low risk. Low risk was defined as acceptable standards; the sigma performance expectations were calculated. RESULTS: Among the 42 analytes, tests with σ ≥ 6, 5 ≤ σ < 6, 4 ≤ σ < 5, 3 ≤ σ < 4, σ < 3 were 21, 5, 5, 6, and 5, respectively; there were 7 high-risk tests, 8 of them medium risk tests. According to the risk assessment conclusion, 13 tests had sigma performance expectations ≥ 6; 15 test items had sigma performance expectations ≥ 5, while 3 test items had sigma performance expectations ≥ 4; 11 test items had sigma performance expectations ≥ 3. CONCLUSIONS: Constructing the risk analysis and assessment model based on Sigma metrics and intended use will help clinical laboratories to identify the high-risk tests more objectively and comprehensively. Such model can also be used to establish the sigma performance expectations and meet the low risk requirements of patients and clinicians.