BACKGROUND: Mass spectrometry provides a powerful platform for performing quantitative, multiplexed assays in the clinical laboratory, but at the cost of increased complexity of analysis and quality assurance calculations compared to other methodologies. METHODS: Here we describe the design and implementation of a software application that performs quality control calculations for a complex, multiplexed, mass spectrometric analysis of opioids and opioid metabolites. RESULTS: The development and implementation of this application improved our data analysis and quality assurance processes in several ways. First, use of the software significantly improved the procedural consistency for performing quality control calculations. Second, it reduced the amount of time technologists spent preparing and reviewing the data, saving on average over four hours per run, and in some cases improving turnaround time by a day. Third, it provides a mechanism for coupling procedural and software changes with the results of each analysis. We describe several key details of the implementation including the use of version control software and automated unit tests. CONCLUSIONS: These generally useful software engineering principles should be considered for any software development project in the clinical lab.
BACKGROUND: Mass spectrometry provides a powerful platform for performing quantitative, multiplexed assays in the clinical laboratory, but at the cost of increased complexity of analysis and quality assurance calculations compared to other methodologies. METHODS: Here we describe the design and implementation of a software application that performs quality control calculations for a complex, multiplexed, mass spectrometric analysis of opioids and opioid metabolites. RESULTS: The development and implementation of this application improved our data analysis and quality assurance processes in several ways. First, use of the software significantly improved the procedural consistency for performing quality control calculations. Second, it reduced the amount of time technologists spent preparing and reviewing the data, saving on average over four hours per run, and in some cases improving turnaround time by a day. Third, it provides a mechanism for coupling procedural and software changes with the results of each analysis. We describe several key details of the implementation including the use of version control software and automated unit tests. CONCLUSIONS: These generally useful software engineering principles should be considered for any software development project in the clinical lab.