2019/07/01 by Tony Badrick, Andreas Bietenbeck, Mark A Cervinski +4 · 101 citations
Decision Sciences · Engineering · Medicine · #Analyte #Artificial intelligence #Clinical Laboratory Practices and Quality Control #Computer science #Control (management) #Control chart #Control limits #Data mining #Engineering #External quality assessment #Hemodynamic Monitoring and Therapy #Machine learning #Medical physics #Medicine #Meta-analysis and systematic reviews #Operations management #Patient data #Population #Process (computing) #Quality (philosophy) #Quality assurance #Reliability engineering #Risk analysis (engineering) #Truncation (statistics)
paper · pdf · doi:10.1373/clinchem.2019.305482
published in Clinical Chemistry 65(8), 962-971 (American Association for Clinical Chemistry)
openalex publication_date 2019/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
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.