Risk assessment of the total testing process based on quality indicators with the Sigma metrics

Author:

Xia Yong1,Wang Xiaoxue1,Yan Cunliang1,Wu Jinbin1,Xue Hao1,Li Mingyang1,Lin Yu1,Li Jie2,Ji Ling3

Affiliation:

1. Department of Laboratory Medicine , Peking University Shenzhen Hospital , Shenzhen, Guangdong , P.R. China

2. Department of Laboratory Medicine , Peking University Shenzhen Hospital , Lianhua Road No. 1120 , Futian District, Shenzhen, Guangdong , P.R. China , Phone: +86-0755-83923333-2295

3. Department of Laboratory Medicine , Peking University Shenzhen Hospital , Lianhua Road No. 1120 , Futian District, Shenzhen, Guangdong , P.R. China , Phone: +86-0755-83923333-2299

Abstract

Abstract Background Evidence-based evaluation of laboratory performances including pre-analytical, analytical and post-analytical stages of the total testing process (TTP) is crucial to ensure patients receiving safe, efficient and effective care. To conduct risk assessment, quality management tools such as Failure Mode and Effect Analysis (FMEA) and the Failure Reporting and Corrective Action System (FRACAS) were constantly used for proactive or reactive analysis, respectively. However, FMEA and FRACAS faced big challenges in determining the scoring scales and failure prioritization in the assessment of real-world cases. Here, we developed a novel strategy, by incorporating Sigma metrics into risk assessment based on quality indicators (QIs) data, to provide a more objective assessment of risks in TTP. Methods QI data was collected for 1 year and FRACAS was applied to produce the risk rating based on three variables: (1) Sigma metrics for the frequency of defects; (2) possible consequence; (3) detection method. The risk priority number (RPN) of each QI was calculated by a 5-point scale score, where a value of RPN > 50 was rated as high-risk. Results The RPNs of two QIs in post-analytical phase (TAT of Stat biochemistry analyte and Timely critical values notification) were above 50 which required rigorous monitoring and corrective actions to eliminate the high risks. Nine QIs (RPNs between 25 and 50) required further investigation and monitoring. After 3 months of corrective action the two identified high-risk processes were successfully reduced. Conclusions The strategy can be implemented to reduce identified risk and assuring patient safety.

Publisher

Walter de Gruyter GmbH

Subject

Biochemistry, medical,Clinical Biochemistry,General Medicine

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