Open AccessTheoretic and practical contribution of bayesian inference to statistical process control: The experience of the Lyon Hospitals Board hemostasis laboratory
Author(s) -
Frédéric Sobas,
Emilie Jousselme,
Mathilde Beghin,
Marie-Odile Geay Baillat,
Yves Perucchetti,
Christophe Nougier
Publication year - 2020
Publication title -
annales de biologie clinique
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.167
H-Index - 27
eISSN - 1950-6112
pISSN - 0003-3898
DOI - 10.1684/abc.2020.1578
Subject(s) - statistical process control , quality management , quality (philosophy) , computer science , external quality assessment , bayesian probability , inference , control (management) , process (computing) , bayesian inference , operations research , medical physics , medicine , operations management , mathematics , engineering , artificial intelligence , operating system , management system , philosophy , epistemology
Laboratories need to set up effective overall management of their internal quality control (IQC) and external quality assessment (EQA) results as key elements in statistical process control. Quality targets need to be defined, with methods to ensure durable control with respect to the relevant specifications. The hemostasis laboratory of the Lyon Hospitals Board (HCL, Lyon, France) uses model 3 from the Milan consensus conference, which is the state of the art in terms of quality targets, and uses a common EQA provider supplying as many real patient samples as possible. Giving priority to adopted methods, the lab optimizes the use of manufacturers' prior data: maximum acceptable inter assay coefficient of variation (CV) and prior IQC target values. Bayesian inference brings the method under control with respect to the manufacturers' prior data without the need for a preliminary phase. It links the IQC and EQA plans by the maximum acceptable CVs defined by the manufacturer.