Quality indicators in laboratory medicine: a 2020–2023 experience in a Chinese province

质量(理念) 医学实验室 医学 医学教育 环境卫生 传统医学 护理部 认识论 哲学
作者
Lichao Zhang,Kefeng Jiang,Jialing Chen,Zhixiong Zhang,Liyan Zhang,Meiyining Xu,Jiaqi Li,Bing Gu
出处
期刊:Clinical Chemistry and Laboratory Medicine [De Gruyter]
标识
DOI:10.1515/cclm-2024-1457
摘要

The identification of reliable quality indicators (QIs) in the total testing process (TTP) is a pivotal step in quantifying laboratory service quality. This study comprehensively evaluated the performance quality and explored the factors affecting laboratory quality in Guangdong Province, China, by analyzing the results of QIs. The Guangdong Clinical Laboratory Center organized an external quality assessment program for QIs, and the Clinet-EQA system was used to distribute questionnaires and collect data. The results of the QIs are expressed as percentages, sigma, or minutes. The optimum, desirable, and minimum quality specifications (QSs) were defined based on the percentiles of the QIs. Furthermore, the QIs were evaluated in different disciplines and hospital grades. A total of 335 laboratories in Guangdong Province reported complete data from 2020 to 2023, and QI performance progressively improved over the years. The performance of 11 QIs attained the minimum acceptable standard (sigma value ≥3), and most QIs across the diverse disciplines and hospital grades exhibited statistically significant differences. Compared to the QSs published by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Working Group on Laboratory Errors and Patient Safety (WG-LEPS), the QSs for the 15 QIs in Guangdong Province in 2023 were stricter or roughly equivalent, except for the percentage of intra-laboratory turnaround time for emergency potassium tests. From 2020 to 2023, the QIs in the post-analytical phase achieved the best performance. It is essential for laboratories to reinforce the construction of their information infrastructure, thereby guaranteeing the accurate collection of reliable data and enabling effective long-term monitoring.
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