Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2020;12(2):135-144.
doi: 10.1007/s40506-020-00216-7. Epub 2020 Mar 19.

Using Artificial Intelligence in Infection Prevention

Fidelma Fitzpatrick  1   2 , Aaron Doherty  2 , Gerard Lacey  3
Affiliations
Review

Using Artificial Intelligence in Infection Prevention

Fidelma Fitzpatrick et al. Curr Treat Options Infect Dis. 2020.

Abstract

Purpose of review: Artificial intelligence (AI) offers huge potential in infection prevention and control (IPC). We explore its potential IPC benefits in epidemiology, laboratory infection diagnosis, and hand hygiene.

Recent findings: AI has the potential to detect transmission events during outbreaks or predict high-risk patients, enabling development of tailored IPC interventions. AI offers opportunities to enhance diagnostics with objective pattern recognition, standardize the diagnosis of infections with IPC implications, and facilitate the dissemination of IPC expertise. AI hand hygiene applications can deliver behavior change, though it requires further evaluation in different clinical settings. However, staff can become dependent on automatic reminders, and performance returns to baseline if feedback is removed.

Summary: Advantages for IPC include speed, consistency, and capability of handling infinitely large datasets. However, many challenges remain; improving the availability of high-quality representative datasets and consideration of biases within preexisting databases are important challenges for future developments. AI in itself will not improve IPC; this requires culture and behavior change. Most studies to date assess performance retrospectively so there is a need for prospective evaluation in the real-life, often chaotic, clinical setting. Close collaboration with IPC experts to interpret outputs and ensure clinical relevance is essential.

Keywords: Artificial intelligence; Epidemiology; Hand hygiene; Infection diagnosis; Infection prevention and control; Machine learning.

PubMed Disclaimer

Conflict of interest statement

Conflict of InterestDr. Lacey reports personal fees from SureWash, grants and non-financial support from Enterprise Ireland, outside the submitted work. In addition, Dr. Lacey has a patent US 2009/0087028A1 with royalties paid to Trinity College Dublin. The other authors declare no conflicts of interest relevant to this manuscript.

References

References and Recommended Reading

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance
    1. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89–94. doi: 10.1038/s41586-019-1799-6. - DOI - PubMed
    1. Scott IA, Cook D, Coiera EW, Richards B. Machine learning in clinical practice: prospects and pitfalls. Med J Aust. 2019;211(5):203–5.e1. doi: 10.5694/mja2.50294. - DOI - PubMed
    1. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–1358. doi: 10.1056/NEJMra1814259. - DOI - PubMed
    1. Storr J, Twyman A, Zingg W, Damani N, Kilpatrick C, Reilly J, et al. Core components for effective infection prevention and control programmes: new WHO evidence-based recommendations. Antimicrob Resist Infect Control. 2017;6:6. doi: 10.1186/s13756-016-0149-9. - DOI - PMC - PubMed
    1. Lopez-Garcia M, Kypraios T. A unified stochastic modelling framework for the spread of nosocomial infections. J R Soc Interface. 2018;15(143). 10.1098/rsif.2018.0060. - PMC - PubMed