Evidence map›Paper›PMID 30892270›Full record

ArticleJournal of medical Internet research2019

Artificial Intelligence and the Future of Primary Care: Exploratory Qualitative Study of UK General Practitioners' Views.

Charlotte Blease, Ted J Kaptchuk, Michael H Bernstein, Kenneth D Mandl, John D Halamka, Catherine M DesRoches

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 156 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
156citing papers in PubMed, 10 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

156 citing papers in PubMed, 10 syntheses or guidelines pooled it.

  1. Atencion primaria · 2026
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96 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Charlotte BleaseGeneral Medicine and Primary Care, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.ORCID 0000-0002-0205-1165
Ted J KaptchukGeneral Medicine and Primary Care, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.ORCID 0000-0002-2253-1940
Michael H BernsteinSchool of Public Health, Brown University, Providence, RI, United States.ORCID 0000-0001-7221-0697
Kenneth D MandlComputational Health Informatics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States.ORCID 0000-0002-9781-0477
John D HalamkaBeth Israel Deaconess Medical Center, Boston, MA, United States.ORCID 0000-0003-2305-6755
Catherine M DesRochesOpen Notes, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.ORCID 0000-0001-5373-855X

Funding

The Harvard Clinical and Translational Science CenterUL1TR002541 · NCATS · HARVARD MEDICAL SCHOOL · PI NADLER, LEE MARSHALL · 2018 to 2022
$93.0M
Substance Abuse Intervention/Outcome Research TrainingT32DA016184 · NIDA · BROWN UNIVERSITY · PI Damaris J. Rohsenow, JENNIFER W. TIDEY · 2003 to 2026
$6.1M
NCATS NIH HHS UL1 TR002541NIDA NIH HHS T32 DA016184
6 · The paper itself

Abstract

backgroundThe potential for machine learning to disrupt the medical profession is the subject of ongoing debate within biomedical informatics and related fields.

objectiveThis study aimed to explore general practitioners' (GPs') opinions about the potential impact of future technology on key tasks in primary care.

methodsIn June 2018, we conducted a Web-based survey of 720 UK GPs' opinions about the likelihood of future technology to fully replace GPs in performing 6 key primary care tasks, and, if respondents considered replacement for a particular task likely, to estimate how soon the technological capacity might emerge. This study involved qualitative descriptive analysis of written responses ("comments") to an open-ended question in the survey.

resultsComments were classified into 3 major categories in relation to primary care: (1) limitations of future technology, (2) potential benefits of future technology, and (3) social and ethical concerns. Perceived limitations included the beliefs that communication and empathy are exclusively human competencies; many GPs also considered clinical reasoning and the ability to provide value-based care as necessitating physicians' judgments. Perceived benefits of technology included expectations about improved efficiencies, in particular with respect to the reduction of administrative burdens on physicians. Social and ethical concerns encompassed multiple, divergent themes including the need to train more doctors to overcome workforce shortfalls and misgivings about the acceptability of future technology to patients. However, some GPs believed that the failure to adopt technological innovations could incur harms to both patients and physicians.

conclusionsThis study presents timely information on physicians' views about the scope of artificial intelligence (AI) in primary care. Overwhelmingly, GPs considered the potential of AI to be limited. These views differ from the predictions of biomedical informaticians. More extensive, stand-alone qualitative work would provide a more in-depth understanding of GPs' views.

Indexed as

Artificial IntelligenceFemaleGeneral PractitionersHumansMaleMiddle AgedPrimary Health CareQualitative ResearchSurveys and QuestionnairesUnited Kingdomartificial intelligenceattitudesfuturegeneral practicemachine learningopinionsprimary carequalitative researchtechnology

Identifiers

PMID30892270
PMCPMC6446158

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.