Using Machine Learning to Reduce Burden on Infection Control Staff

Abstract

Summary Surveillance of health care–associated infection (HAI) is the foundation of infection control and one of the first steps in infection prevention. Traditionally, however, surveillance is performed by infection control professionals (ICPs) who manually review patients’ records, searching for defined criteria. Such an approach leaves room for subjective interpretation, resulting in low interrater reliability. Moreover, depending on the surveillance method used — for instance, a search based on antimicrobial results — it may have low sensitivity. In Brazil, leaders at Tacchini Hospital and Qualis, a startup that offers infection control advisory and antimicrobial stewardship, have developed a machine-learning–algorithm robot that has been demonstrated to be a reliable tool for identifying patients with HAIs using a semiautomated method. The performance of this infection surveillance assistant (ISA) robot shows optimal sensitivity, specificity, accuracy, and negative predictive values, and the precision (positive predictive value) is acceptable. The ISA robot identified more patients with HAIs than did the infection control manual surveillance reference. The time spent on patient review was also reduced compared with that spent on manual surveillance. The robot detected HAI in one of every two or three patients reviewed in the interface. The years of the Covid-19 pandemic have highlighted the problem of the shortage of health care professionals, including ICPs. Tacchini Hospital and Qualis aim to increase infection control efficiency, enabling these professionals to spend more time on inpatient wards, implementing care bundles, than handling office activities, such as manual surveillance. In this study, the authors describe the implementation of semiautomated surveillance in a single center, but expanding the model for different patient scenarios and multiple centers should be the future for external validation of machine-learning surveillance. Such models have the potential for generalization because they do not depend only on fixed rules for HAI classification, but they can also learn from data sets in different patient population settings.