Evidence map›Paper›PMID 38875561›Full record

ArticleJMIR AI2023

Preparing for an Artificial Intelligence-Enabled Future: Patient Perspectives on Engagement and Health Care Professional Training for Adopting Artificial Intelligence Technologies in Health Care Settings.

Tharshini Jeyakumar, Sarah Younus, Melody Zhang, Megan Clare, Rebecca Charow, Inaara Karsan, Azra Dhalla, Dalia Al-Mouaswas, Jillian Scandiffio, Justin Aling and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JMIR AI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 3 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

17 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  3. Public participation in healthcare students' education: An umbrella review.Health expectations : an international journal of public participation in health care and health policy · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Tharshini JeyakumarUniversity Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-4482-3637
Sarah YounusUniversity Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-0728-7628
Melody ZhangUniversity Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-6341-1024
Megan ClareMichener Institute of Education, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-2778-6002
Rebecca CharowUniversity Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-8553-4006
Inaara KarsanUniversity Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-9182-0300
Azra DhallaVector Institute, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-9806-3664
Dalia Al-MouaswasMichener Institute of Education, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-5620-0832
Jillian ScandiffioUniversity Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-6610-6164
Justin AlingPatient Partner Program, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-4761-1832
Mohammad SalhiaMichener Institute of Education, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0003-0841-1456
Nadim LalaniVector Institute, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-7570-4681
Scott OverholtPatient Partner Program, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0003-1461-7362
David WiljerUniversity Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-2748-2658

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs new technologies emerge, there is a significant shift in the way care is delivered on a global scale. Artificial intelligence (AI) technologies have been rapidly and inexorably used to optimize patient outcomes, reduce health system costs, improve workflow efficiency, and enhance population health. Despite the widespread adoption of AI technologies, the literature on patient engagement and their perspectives on how AI will affect clinical care is scarce. Minimal patient engagement can limit the optimization of these novel technologies and contribute to suboptimal use in care settings.

objectiveWe aimed to explore patients' views on what skills they believe health care professionals should have in preparation for this AI-enabled future and how we can better engage patients when adopting and deploying AI technologies in health care settings.

methodsSemistructured interviews were conducted from August 2020 to December 2021 with 12 individuals who were a patient in any Canadian health care setting. Interviews were conducted until thematic saturation occurred. A thematic analysis approach outlined by Braun and Clarke was used to inductively analyze the data and identify overarching themes.

resultsAmong the 12 patients interviewed, 8 (67%) were from urban settings and 4 (33%) were from rural settings. A majority of the participants were very comfortable with technology (n=6, 50%) and somewhat familiar with AI (n=7, 58%). In total, 3 themes emerged: cultivating patients' trust, fostering patient engagement, and establishing data governance and validation of AI technologies.

conclusionsWith the rapid surge of AI solutions, there is a critical need to understand patient values in advancing the quality of care and contributing to an equitable health system. Our study demonstrated that health care professionals play a synergetic role in the future of AI and digital technologies. Patient engagement is vital in addressing underlying health inequities and fostering an optimal care experience. Future research is warranted to understand and capture the diverse perspectives of patients with various racial, ethnic, and socioeconomic backgrounds.

Indexed as

adoptionartificial intelligenceattitudeeducationhealth datahealth equitypatientpatient engagement

Identifiers

PMID38875561
PMCPMC11041489

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.