Abstract
The clinical microbiology laboratory generates a huge amount of high-quality data that play a vital role in clinical care. With proper extraction, cleaning, analysis, and validation pipelines, these data can serve multiple other purposes that include supporting laboratory operations, understanding local epidemiology, informing hospital-specific policies, and public health surveillance. In this review, I use one of the core activities of the microbiology laboratory, antimicrobial susceptibility testing (AST), to illustrate several potential applications of next-generation data analytics. The first involves continuous monitoring of commercial AST systems using comparisons of minimum inhibitory concentration (MIC) distributions over time to trigger re-verification when statistically significant differences are detected. An extension of this is temporal analysis of joint MIC distributions to understand performance for multidrug-resistant organisms. More sophisticated analyses involve linking microbiologic data to clinical metadata to gain insight into the clinical validity of AST data and to inform treatment policies. The elements of a robust, validated analysis engine using routine data streams already exist, but numerous challenges must be overcome to make it a reality. Most importantly, it will require the sustained collaboration and advocacy of hospital leadership, microbiologists, clinicians, antimicrobial stewardship, data scientists, and regulatory agencies. Though no small feat, achieving this vision would provide an important resource for microbiology laboratories facing a rapidly evolving practice landscape and further cement its role as an integral part of a learning health system.