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• W3092491824 startingPage "e0239947" @default.
• W3092491824 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." @default.
• W3092491824 created "2020-10-15" @default.
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• W3092491824 date "2020-10-08" @default.
• W3092491824 modified "2023-10-12" @default.
• W3092491824 title "US primary care in 2029: A Delphi survey on the impact of machine learning" @default.
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• W3092491824 doi "https://doi.org/10.1371/journal.pone.0239947" @default.
• W3092491824 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7544100" @default.
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