Abstract
INTRODUCTION: We have developed a set of tools built with open-source software that includes both a database and a visualization component to collect LC-MS/MS data and monitor quality control parameters. DESCRIPTION OF TOOL: To display LC-MS/MS data we built a parsing tool using Python and standard libraries to parse the XML files after each clinical run. The tool parses the necessary information to store a database comprised of three distinct tables. Another component to this toolkit is an interactive data visualization tool that uses the data from the database. There are 5 different visualizations that present the data based on interchangeable parameters. EVALUATION OF TOOL: Using histogram visualization, we assessed how quality control parameters that feed our quality control algorithm, SMACK, which assists to improve the efficiency of data review and results, performed against the collective data. Using the newly identified QC parameter values from the toolkit, we compared the output of the SMACK algorithm; the number of QC flags changed in that there was a 1.7% (31/1944 observations) increase in flags and a 7.1% (138/1944 observations) decrease in presumed false positive flags, increasing the overall performance of SMACK which helped staff focus their time on reviewing more concerning QC failures. DISCUSSION: We have developed a customizable web-based dashboard for instrument performance monitoring for our opiate confirmation LC-MS/MS assay using data collected with each batch. The web-based platform allows users to monitor instrument performance and can encompass other instruments throughout the laboratory. This information can help the laboratory take proactive measures to maintain instruments, ultimately reducing the amount down time needed for maintenance.