Reading: Machine Learning in Health Care: A Critical Appraisal of Challenges and Opportunities

Download

A- A+
Alt. Display
  • Login has been disabled for this journal while it is transferred to a new platform. Please try again in 48 hours.

Commentary / Editorial

Machine Learning in Health Care: A Critical Appraisal of Challenges and Opportunities

Authors:

Mark Sendak ,

Duke Institute for Health Innovation, US
X close

Michael Gao,

Duke Institute for Health Innovation, US
X close

Marshall Nichols,

Duke Institute for Health Innovation, US
X close

Anthony Lin,

Duke University School of Medicine, US
X close

Suresh Balu

Duke Institute for Health Innovation; Duke University School of Medicine, US
X close

Abstract

Examples of fully integrated machine learning models that drive clinical care are rare. Despite major advances in the development of methodologies that outperform clinical experts and growing prominence of machine learning in mainstream medical literature, major challenges remain. At Duke Health, we are in our fourth year developing, piloting, and implementing machine learning technologies in clinical care. To advance the translation of machine learning into clinical care, health system leaders must address barriers to progress and make strategic investments necessary to bring health care into a new digital age. Machine learning can improve clinical workflows in subtle ways that are distinct from how statistics has shaped medicine. However, most machine learning research occurs in siloes, and there are important, unresolved questions about how to retrain and validate models post-deployment. Academic medical centers that cultivate and value transdisciplinary collaboration are ideally suited to integrate machine learning in clinical care. Along with fostering collaborative environments, health system leaders must invest in developing new capabilities within the workforce and technology infrastructure beyond standard electronic health records. Now is the opportunity to break down barriers and achieve scalable growth in the number of high-impact collaborations between clinical researchers and machine learning experts to transform clinical care.
How to Cite: Sendak M, Gao M, Nichols M, Lin A, Balu S. Machine Learning in Health Care: A Critical Appraisal of Challenges and Opportunities. eGEMs (Generating Evidence & Methods to improve patient outcomes). 2019;7(1):1. DOI: http://doi.org/10.5334/egems.287
567
Views
104
Downloads
25
Citations
5
Twitter
  Published on 24 Jan 2019

Galley file missing.

Please contact support [at] ubiquitypress.com

comments powered by Disqus