biostudies-literatureUnknown6(2)Aris-Brosou SBACKGROUND:Vendors in the health care industry produce diagnostic systems that, through a secured connection, allow them to monitor performance almost in real time. However, challenges exist in analyzing and interpreting large volumes of noisy quality control (QC) data. As a result, some QC shifts may not be detected early enough by the vendor, but lead a customer to complain. OBJECTIVE:The aim of this study was to hypothesize that a more proactive response could be designed by utilizing the collected QC data more efficiently. Our aim is therefore to help prevent customer complaints by predicting them based on the QC data collected by in vitro diagnostic systems. METHODS:QC data from five select in vitro diagnostic assays were combined with the corresponding database of customer complaintsJMIR medical informaticse34https://www.ebi.ac.uk/biostudies/studies/S-EPMC5974458biostudies-literaturePredicting the Reasons of Customer Complaints: A First Step Toward Anticipating Quality Issues of In Vitro Diagnostics Assays with Machine Learning.PMC5974458Liu HAris-Brosou SKim JLi LfalsePredicting the Reasons of Customer Complaints: A First Step Toward Anticipating Quality Issues of In Vitro Diagnostics Assays with Machine Learning.BACKGROUND:Vendors in the health care industry produce diagnostic systems that, through a secured connection, allow them to monitor performance almost in real time. However, challenges exist in analyzing and interpreting large volumes of noisy quality control (QC) data. As a result, some QC shifts may not be detected early enough by the vendor, but lead a customer to complain. OBJECTIVE:The aim of this study was to hypothesize that a more proactive response could be designed by utilizing the collected QC data more efficiently. Our aim is therefore to help prevent customer complaints by predicting them based on the QC data collected by in vitro diagnostic systems. METHODS:QC data from five select in vitro diagnostic assays were combined with the corresponding database of customer complaints2018-01-01T00:00:00Z2018 May2025-04-18T17:32:22.633Z2019-10-19T07:04:41ZS-EPMC59744582976479610.2196/medinform.9960