Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Comment
  • Published:

Artificial intelligence can improve decision-making in infection management

Nature Human Behaviour volume 3, pages 543–545 (2019)Cite this article

Subjects

Antibiotic resistance is an emerging global danger. Reaching responsible prescribing decisions requires the integration of broad and complex information. Artificial intelligence tools could support decision-making at multiple levels, but building them needs a transparent co-development approach to ensure their adoption upon implementation.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

References

  1. Simon, H.A. Models of Bounded Rationality. Volume 1: Economic Analysis and Public Policy. Volume 2: Behavioural Economics and Business Organization. (MIT Press, 1985).

  2. Chen, J. H. & Asch, S. M. N. Engl. J. Med. 376, 2507–2509 (2017).

    Article  Google Scholar 

  3. Komorowski, M., Celi, L. A., Badawi, O., Gordon, A. C. & Faisal, A. A. Nat. Med. 24, 1716–1720 (2018).

    Article  CAS  Google Scholar 

  4. O’Neill, J. & The Review on Antimicrobial Resistance. Tackling Drug-Resistant Infections Globally: Final Report and Recommendations. https://amr-review.org/sites/default/files/160525_Final%20paper_with%20cover.pdf (2016).

  5. Charani, E. et al. Clin. Infect. Dis. 53, 651–662 (2011).

    Article  Google Scholar 

  6. Rawson, T. M. et al. Clin. Microbiol. Infect. 23, 524–532 (2017).

    Article  CAS  Google Scholar 

  7. Sim, I. et al. J. Am. Med. Inform. Assoc. 8, 527–534 (2001).

    Article  CAS  Google Scholar 

  8. Leibovici, L., Paul, M., Nielsen, A. D., Tacconelli, E. & Andreassen, S. Int. J. Antimicrob. Agents 30, 93–102 (2007).

    Article  Google Scholar 

  9. Lacobucci, G. Br. Med. J. 360, k221 https://www.bmj.com/bmj/section-pdf/959161?path=/bmj/360/8137/This_Week.full.pdf (2018).

  10. Rawson, T. M. et al. J. Infect. 78, 75–86 (2018).

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the National Institute of Health Research Imperial Biomedical Research Centre and the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College London in partnership with Public Health England and the NIHR Imperial Patient Safety Translational Research Centre. They also acknowledge Imperial Biomedical Research Centre (BRC). R.A. is supported by an NIHR Fellowship in Knowledge Mobilisation. T.M.R., A.H.H., and P.G. are supported by funding from the National Institute for Health Research Invention for Innovation Grant (i4i), Enhanced, Personalized and Integrated Care for Infection Management at Point of Care (EPIC IMPOC), II-LA-0214-20008. This work was produced independently. The funders had no role in in study design, data collection and analysis, decision to publish or preparation of the manuscript. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the UK Department of Health.

Author information

Authors and Affiliations

  1. National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK

    Timothy M. Rawson, Raheelah Ahmad, Christofer Toumazou & Alison H. Holmes

  2. Department of Electrical and Electronic Engineering, Imperial College London, London, UK

    Pantelis Georgiou

Authors
  1. Timothy M. Rawson
    View author publications

    Search author on:PubMed Google Scholar

  2. Raheelah Ahmad
    View author publications

    Search author on:PubMed Google Scholar

  3. Christofer Toumazou
    View author publications

    Search author on:PubMed Google Scholar

  4. Pantelis Georgiou
    View author publications

    Search author on:PubMed Google Scholar

  5. Alison H. Holmes
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Alison H. Holmes.

Ethics declarations

Competing interests

The authors have no conflicts of interest to declare.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rawson, T.M., Ahmad, R., Toumazou, C. et al. Artificial intelligence can improve decision-making in infection management. Nat Hum Behav 3, 543–545 (2019). https://doi.org/10.1038/s41562-019-0583-9

Download citation

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41562-019-0583-9

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing