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Estimate the hidden deployment cost of predictive models to improve patient care

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An Author Correction to this article was published on 14 April 2020

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Although examples of algorithms designed to improve healthcare delivery abound, for many, clinical integration will not be achieved. The deployment cost of machine learning models is an underappreciated barrier to success. Experts propose three criteria that, assessed early, could help estimate the deployment cost.

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Author information

Authors and Affiliations

  1. Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA

    Keith E. Morse

  2. Department of Clinical Informatics, Lucile Packard Children’s Hospital, Palo Alto, CA, USA

    Keith E. Morse

  3. Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA

    Steven C. Bagley & Nigam H. Shah

Authors
  1. Keith E. Morse
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  2. Steven C. Bagley
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  3. Nigam H. Shah
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Correspondence to Keith E. Morse.

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The authors declare no competing interests.

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Morse, K.E., Bagley, S.C. & Shah, N.H. Estimate the hidden deployment cost of predictive models to improve patient care. Nat Med 26, 18–19 (2020). https://doi.org/10.1038/s41591-019-0651-8

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