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

  • Yong Xia , Xiaoxue Wang , Cunliang Yan , Jinbin Wu , Hao Xue , Mingyang Li , Yu Lin , Jie Li EMAIL logo and Ling Ji EMAIL logo
Published/Copyright: March 9, 2020

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.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Employment or leadership: None declared.

  4. Honorarium: Thanks to Bin Shen, Zhisheng Tan and Yuefeng Zhou of BD China for their professional support in the pre-analysis QIs analysis and risk assessment.

  5. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

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Received: 2019-11-18
Accepted: 2020-01-09
Published Online: 2020-03-09
Published in Print: 2020-07-28

©2020 Walter de Gruyter GmbH, Berlin/Boston

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