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US primary care in 2029: A Delphi survey on the impact of machine learning

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  • Charlotte Blease
  • Anna Kharko
  • Cosima Locher
  • Catherine M DesRoches
  • Kenneth D Mandl

Abstract

Objective: To solicit leading health informaticians’ predictions about the impact of AI/ML on primary care in the US in 2029. Design: A three-round online modified Delphi poll. Participants: Twenty-nine leading health informaticians. Methods: In September 2019, health informatics experts were selected by the research team, and invited to participate the Delphi poll. Participation in each round was anonymous, and panelists were given between 4–8 weeks to respond to each round. In Round 1 open-ended questions solicited forecasts on the impact of AI/ML on: (1) patient care, (2) access to care, (3) the primary care workforce, (4) technological breakthroughs, and (5) the long-future for primary care physicians. Responses were coded to produce itemized statements. In Round 2, participants were invited to rate their agreement with each item along 7-point Likert scales. Responses were analyzed for consensus which was set at a predetermined interquartile range of ≤ 1. In Round 3 items that did not reach consensus were redistributed. Results: A total of 16 experts participated in Round 1 (16/29, 55%). Of these experts 13/16 (response rate, 81%), and 13/13 (response rate, 100%), responded to Rounds 2 and 3, respectively. As a result of developments in AI/ML by 2029 experts anticipated workplace changes including incursions into the disintermediation of physician expertise, and increased AI/ML training requirements for medical students. Informaticians also forecast that by 2029 AI/ML will increase diagnostic accuracy especially among those with limited access to experts, minorities and those with rare diseases. Expert panelists also predicted that AI/ML-tools would improve access to expert doctor knowledge. Conclusions: This study presents timely information on informaticians’ consensus views about the impact of AI/ML on US primary care in 2029. Preparation for the near-future of primary care will require improved levels of digital health literacy among patients and physicians.

Suggested Citation

  • Charlotte Blease & Anna Kharko & Cosima Locher & Catherine M DesRoches & Kenneth D Mandl, 2020. "US primary care in 2029: A Delphi survey on the impact of machine learning," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-18, October.
    • Handle: RePEc:plo:pone00:0239947
    DOI: 10.1371/journal.pone.0239947
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    References listed on IDEAS

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    1. Andy Stirling, 2007. "A General Framework for Analysing Diversity in Science, Technology and Society," SPRU Working Paper Series 156, SPRU - Science Policy Research Unit, University of Sussex Business School.
    2. Effy Vayena & Alessandro Blasimme & I Glenn Cohen, 2018. "Machine learning in medicine: Addressing ethical challenges," PLOS Medicine, Public Library of Science, vol. 15(11), pages 1-4, November.
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    1. Vo, Vinh & Chen, Gang & Aquino, Yves Saint James & Carter, Stacy M. & Do, Quynh Nga & Woode, Maame Esi, 2023. "Multi-stakeholder preferences for the use of artificial intelligence in healthcare: A systematic review and thematic analysis," Social Science & Medicine, Elsevier, vol. 338(C).

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