将检测过程中的质量指标进行西格玛度量分析，作为评估实验室性能的有效工具。
Sigma metric analysis of quality indicators across the testing process as an effective tool for the evaluation of laboratory performance.
作者信息
Swetha N K, Kusuma K S, Sahana K R, Shobha C R, Abhijith D, Akila P, Suma M N
机构信息
Assistant Professor (Biochemistry), JSS Medical College, JSSAHER, Shivarathreeshwaranagar, Mysore, India.
Professor (Biochemistry), JSS Medical College, JSSAHER, Shivarathreeshwaranagar, Mysore, India.
出版信息
Med J Armed Forces India. 2023 Dec;79(Suppl 1):S150-S155. doi: 10.1016/j.mjafi.2022.04.010. Epub 2022 Jun 13.
BACKGROUND
Laboratories across the world are successfully using quality indicators (QIs) to monitor their performance. We aimed to analyze the effectiveness of using the peer group comparison and statistical tools such as sigma metrics for periodic evaluation of QIs and identify potential errors in the preanalytical, analytical, and postanalytical phases.
METHODS
We evaluated the monthly QIs for 1 year. A total of 11 QIs were evaluated across the three phases of the total testing process, using percentage variance, and sigma metric analysis.
RESULTS
Our study observed that based on sigma metric analysis, the performance was good for all the QIs except for the number of samples with the inappropriate specimen hemolyzed samples, clotted samples, and turnaround time (Sigma value < 3). The percentage variance of QIs in all the phases was plotted in a Pareto chart, which helped us in identifying turnaround time and internal quality control performance are the key areas that contribute to almost 80% of the errors among all the QIs.
CONCLUSION
Laboratory performance evaluation using QIs and sigma metric analysis helped us in identifying and prioritizing the corrective actions in the key areas of the total testing process.
背景
世界各地的实验室都在成功地使用质量指标（QIs）来监测其性能。我们旨在分析使用同行组比较和统计工具（如西格玛指标）对质量指标进行定期评估的有效性，并识别分析前、分析中和分析后阶段的潜在误差。
方法
我们评估了1年的月度质量指标。在总检测过程的三个阶段共评估了11个质量指标，采用百分比方差和西格玛指标分析。
结果
我们的研究发现，基于西格玛指标分析，除了标本溶血、凝血样本数量和周转时间不合适的样本数量外（西格玛值<3），所有质量指标的性能都良好。在帕累托图中绘制了所有阶段质量指标的百分比方差，这有助于我们确定周转时间和内部质量控制性能是导致所有质量指标中近80%误差的关键领域。
结论
使用质量指标和西格玛指标分析进行实验室性能评估有助于我们识别总检测过程关键领域中的纠正措施并确定其优先级。
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