Department of Laboratory Medicine, Peking University Shenzhen Hospital, Shenzhen, Guangdong, P.R. China.
Department of Laboratory Medicine, Peking University Shenzhen Hospital, Lianhua Road No. 1120, Futian District, Shenzhen, Guangdong, P.R. China, Phone: +86-0755-83923333-2295.
Clin Chem Lab Med. 2020 Jul 28;58(8):1223-1231. doi: 10.1515/cclm-2019-1190.
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.
背景 对总检测过程(TTP)的分析前、分析中和分析后阶段进行基于证据的实验室性能评估,对于确保患者得到安全、有效和高效的护理至关重要。为了进行风险评估,分别使用失效模式和影响分析(FMEA)和失效报告和纠正措施系统(FRACAS)等质量管理工具进行主动或被动分析。然而,FMEA 和 FRACAS 在确定现实案例评估中的评分范围和失效优先级方面面临着巨大挑战。在这里,我们通过将西格玛计量学纳入基于质量指标(QI)数据的风险评估中,开发了一种新策略,为 TTP 中的风险提供更客观的评估。
方法 收集了 1 年的 QI 数据,并应用 FRACAS 根据三个变量产生风险评级:(1)缺陷频率的西格玛计量;(2)可能的后果;(3)检测方法。每个 QI 的风险优先数(RPN)通过 5 分制评分计算,其中 RPN>50 被评为高风险。
结果 分析后阶段的两个 QI(TAT 分析物的 STAT 和及时关键值通知)的 RPN 超过 50,需要严格监测和采取纠正措施以消除高风险。九个 QI(RPN 在 25 到 50 之间)需要进一步调查和监测。在采取纠正措施后的 3 个月内,成功降低了两个已识别的高风险过程。
结论 可以实施该策略来降低已识别的风险,确保患者安全。