The future of automated infection detection: Innovation to transform practice (Part III/III)

• Published in: Antimicrobial stewardship & healthcare epidemiology : ASHE Vol. 3; no. 1; p. e26
• Main Authors: Branch-Elliman, Westyn, Sundermann, Alexander J, Wiens, Jenna, Shenoy, Erica S
• Format: Journal Article
• Language: English
• Published: England Cambridge University Press 2023
• Subjects: Artificial intelligence; Automation; Disease control; Electronic health records; Epidemics; Genomes; Genomics; Health care; Health surveillance; HIV; Human immunodeficiency virus; Identification; Infections; Machine learning; Pathogens; Prevention; Public health; Quality improvement; Real time; Review; Severe acute respiratory syndrome coronavirus 2; Technological change; United States > US
• ISSN: 2732-494X; 2732-494X
• DOI: 10.1017/ash.2022.333

Current methods of emergency-room-based syndromic surveillance were insufficient to detect early community spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) in the United States, which slowed the infection prevention and control response to the novel pathogen. Emerging technologies and automated infection surveillance have the potential to improve upon current practice standards and to revolutionize the practice of infection detection, prevention and control both inside and outside of healthcare settings. Genomics, natural language processing, and machine learning can be leveraged to improve identification of transmission events and aid and evaluate outbreak response. In the near future, automated infection detection strategies can be used to advance a true "Learning Healthcare System" that will support near-real-time quality improvement efforts and advance the scientific basis for the practice of infection control.