Evidence map›Paper›PMID 34612839›Full record

ArticleJMIR research protocols2021

Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach.

David Wiljer, Mohammad Salhia, Elham Dolatabadi, Azra Dhalla, Caitlin Gillan, Dalia Al-Mouaswas, Ethan Jackson, Jacqueline Waldorf, Jane Mattson, Megan Clare and 7 more

Open access · goldAbstract read
In one paragraph

Article in JMIR research protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 15% of its field
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

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.

  1. Pooled it
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  13. A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence.International journal of environmental research and public health · 2022
    Review
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

17 authors at 2 institutions in 1 country.

David WiljerUniversity Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-2748-2658
Mohammad SalhiaMichener Institute of Education at University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0003-0841-1456
Elham DolatabadiVector Institute, Toronto, ON, Canada.ORCID https://orcid.org/0000-0003-2236-2611
Azra DhallaVector Institute, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-9806-3664
Caitlin GillanUniversity Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-1612-7423
Dalia Al-MouaswasMichener Institute of Education at University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-5620-0832
Ethan JacksonVector Institute, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-8988-302X
Jacqueline WaldorfMichener Institute of Education at University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0003-1922-0575
Jane MattsonMichener Institute of Education at University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-8121-6126
Megan ClareMichener Institute of Education at University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-2778-6002
Nadim LalaniVector Institute, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-7570-4681
Rebecca CharowUniversity Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-8553-4006
Sarmini BalakumarInstitute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-6061-5929
Sarah YounusUniversity Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-0728-7628
Tharshini JeyakumarUniversity Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-4482-3637
Wanda PeteanuMichener Institute of Education at University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-0119-3364
Walter TavaresUniversity Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-8267-9448
University Health Network · CAVector Institute · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSignificant investments and advances in health care technologies and practices have created a need for digital and data-literate health care providers. Artificial intelligence (AI) algorithms transform the analysis, diagnosis, and treatment of medical conditions. Complex and massive data sets are informing significant health care decisions and clinical practices. The ability to read, manage, and interpret large data sets to provide data-driven care and to protect patient privacy are increasingly critical skills for today's health care providers.

objectiveThe aim of this study is to accelerate the appropriate adoption of data-driven and AI-enhanced care by focusing on the mindsets, skillsets, and toolsets of point-of-care health providers and their leaders in the health system.

methodsTo accelerate the adoption of AI and the need for organizational change at a national level, our multistepped approach includes creating awareness and capacity building, learning through innovation and adoption, developing appropriate and strategic partnerships, and building effective knowledge exchange initiatives. Education interventions designed to adapt knowledge to the local context and address any challenges to knowledge use include engagement activities to increase awareness, educational curricula for health care providers and leaders, and the development of a coaching and practice-based innovation hub. Framed by the Knowledge-to-Action framework, we are currently in the knowledge creation stage to inform the curricula for each deliverable. An environmental scan and scoping review were conducted to understand the current state of AI education programs as reported in the academic literature.

resultsThe environmental scan identified 24 AI-accredited programs specific to health providers, of which 11 were from the United States, 6 from Canada, 4 from the United Kingdom, and 3 from Asian countries. The most common curriculum topics across the environmental scan and scoping review included AI fundamentals, applications of AI, applied machine learning in health care, ethics, data science, and challenges to and opportunities for using AI.

conclusionsTechnologies are advancing more rapidly than organizations, and professionals can adopt and adapt to them. To help shape AI practices, health care providers must have the skills and abilities to initiate change and shape the future of their discipline and practices for advancing high-quality care within the digital ecosystem. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/30940.

Indexed as

adoptionartificial intelligenceeducationhealth care providerslearningmHealthpatient care

Identifiers

PMID34612839
PMCPMC8529463
OpenAlexW3203584273

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.