ArticlePloS one2020
US primary care in 2029: A Delphi survey on the impact of machine learning.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review.BMC primary care · 2025Pooled it
- Efficacy of Artificial-Intelligence-Driven Differential-Diagnosis List on the Diagnostic Accuracy of Physicians: An Open-Label Randomized Controlled Study.International journal of environmental research and public health · 2021Trial
- Leveraging machine learning approach to identify relationships between practice facilitation strategies and practice characteristics based on the implementation research logic model.Implementation science communications · 2025Article
- Generative artificial intelligence in medicine: a mixed-methods survey of UK general practitioners.BMJ digital health & AI · 2025Article
- The emergence of medical futures studies uncovers medicine and healthcare's untapped potential.NPJ digital medicine · 2024Review
- Forecasting the future of smart hospitals: findings from a real-time delphi study.BMC health services research · 2024Article
- Impact of artificial intelligence in transforming the doctor-cancer patient relationship.ESMO real world data and digital oncology · 2024Article
- An evidence-based approach to artificial intelligence education for medical students: A systematic review.PLOS digital health · 2023Article
- Uncertainty in healthcare and health decision making: Five methodological and conceptual research recommendations from an interdisciplinary team.Journal of behavioral medicine · 2023Review
- The Impact of Digital Transformation on Inpatient Care: Mixed Methods Study.JMIR public health and surveillance · 2023Review
- Computerization of the Work of General Practitioners: Mixed Methods Survey of Final-Year Medical Students in Ireland.JMIR medical education · 2023Article
- Artificial intelligence and health inequities in primary care: a systematic scoping review and framework.Family medicine and community health · 2022Article
- The Predicted Future of Psychotherapy: A Decennial e-Delphi Poll.Professional psychology, research and practice · 2022Article
- Machine learning in medical education: a survey of the experiences and opinions of medical students in Ireland.BMJ health & care informatics · 2022Article
- Will Technology and Artificial Intelligence Make the Primary Care Doctor Obsolete? Remember the Luddites.Frontiers in medicine · 2022Article
- Preparing Patients and Clinicians for Open Notes in Mental Health: Qualitative Inquiry of International Experts.JMIR mental health · 2021Article
- Machine Learning in Clinical Psychology and Psychotherapy Education: A Mixed Methods Pilot Survey of Postgraduate Students at a Swiss University.Frontiers in public health · 2021Article
- General practitioners' opinions of generative artificial intelligence in the UK: An online survey.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo solicit leading health informaticians' predictions about the impact of AI/ML on primary care in the US in 2029.
designA three-round online modified Delphi poll.
participantsTwenty-nine leading health informaticians.
methodsIn 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.
resultsA 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.
conclusionsThis 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.