Cargando…

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...

Descripción completa

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
_version_ 1783338631810777088
author Xia, Yong
Xue, Hao
Yan, Cunliang
Li, Bowen
Zhang, ShuQiong
Li, Mingyang
Ji, Ling
author_facet Xia, Yong
Xue, Hao
Yan, Cunliang
Li, Bowen
Zhang, ShuQiong
Li, Mingyang
Ji, Ling
author_sort Xia, Yong
collection PubMed
description 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.
format Online
Article
Text
id pubmed-6039164
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Croatian Society of Medical Biochemistry and Laboratory Medicine
record_format MEDLINE/PubMed
spelling pubmed-60391642018-07-18 Risk analysis and assessment based on Sigma metrics and intended use Xia, Yong Xue, Hao Yan, Cunliang Li, Bowen Zhang, ShuQiong Li, Mingyang Ji, Ling Biochem Med (Zagreb) Original Papers 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. Croatian Society of Medical Biochemistry and Laboratory Medicine 2018-06-15 2018-06-15 /pmc/articles/PMC6039164/ /pubmed/30022882 http://dx.doi.org/10.11613/BM.2018.020707 Text en ©Croatian Society of Medical Biochemistry and Laboratory Medicine. This is an Open Access article distributed under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
Xia, Yong
Xue, Hao
Yan, Cunliang
Li, Bowen
Zhang, ShuQiong
Li, Mingyang
Ji, Ling
Risk analysis and assessment based on Sigma metrics and intended use
title Risk analysis and assessment based on Sigma metrics and intended use
title_full Risk analysis and assessment based on Sigma metrics and intended use
title_fullStr Risk analysis and assessment based on Sigma metrics and intended use
title_full_unstemmed Risk analysis and assessment based on Sigma metrics and intended use
title_short Risk analysis and assessment based on Sigma metrics and intended use
title_sort risk analysis and assessment based on sigma metrics and intended use
topic Original Papers
url 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
work_keys_str_mv AT xiayong riskanalysisandassessmentbasedonsigmametricsandintendeduse
AT xuehao riskanalysisandassessmentbasedonsigmametricsandintendeduse
AT yancunliang riskanalysisandassessmentbasedonsigmametricsandintendeduse
AT libowen riskanalysisandassessmentbasedonsigmametricsandintendeduse
AT zhangshuqiong riskanalysisandassessmentbasedonsigmametricsandintendeduse
AT limingyang riskanalysisandassessmentbasedonsigmametricsandintendeduse
AT jiling riskanalysisandassessmentbasedonsigmametricsandintendeduse