人口
计算机科学
质量(理念)
分析物
质量保证
控制(管理)
控制限值
截断(统计)
医学物理学
患者数据
可靠性工程
风险分析(工程)
数据挖掘
控制图
医学
机器学习
外部质量评估
人工智能
过程(计算)
工程类
运营管理
哲学
化学
环境卫生
物理化学
认识论
数据库
操作系统
作者
Tony Badrick,Andreas Bietenbeck,Mark A Cervinski,Alex Katayev,Huub H. van Rossum,Tze Ping Loh
出处
期刊:Clinical Chemistry
[American Association for Clinical Chemistry]
日期:2019-07-01
卷期号:65 (8): 962-971
被引量:95
标识
DOI:10.1373/clinchem.2019.305482
摘要
For many years the concept of patient-based quality control (QC) has been discussed and implemented in hematology laboratories; however, the techniques have not been widely implemented in clinical chemistry. This is mainly because of the complexity of this form of QC, as it needs to be optimized for each population and often for each analyte. However, the clear advantages of this form of QC, together with the ongoing realization of the shortcomings of "conventional" QC, have driven a need to provide guidance to laboratories to assist in deploying patient-based QC. This overview describes the components of a patient-based QC system (calculation algorithm, block size, truncation limits, control limits) and the relationship of these to the analyte being controlled. We also discuss the need for patient-based QC system optimization using patient data from the individual testing laboratory to reliably detect systematic errors while ensuring that there are few false alarms. The term patient-based real-time quality control covers many activities that use data from patient samples to detect analytical errors. These activities include the monitoring of patient population parameters such as the mean or median analyte value or using single within-patient changes such as the delta check. In this report, we will restrict the discussion to population-based parameters. This overview is intended to serve as a guide for the implementation of a patient-based QC system. The report does not cover the clinical evaluation of the population.
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