Evidence map›Paper›PMID 39009038›Full record

ArticleJournal of medical Internet research2024

Leadership for AI Transformation in Health Care Organization: Scoping Review.

Abi Sriharan, Nigar Sekercioglu, Cheryl Mitchell, Senthujan Senkaiahliyan, Attila Hertelendy, Tracy Porter, Jane Banaszak-Holl

Abstract readScoping Review
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–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

13 citing papers in PubMed.

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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

7 authors.

Abi SriharanKrembil Centre for Health Management and Leadership, Schulich School of Business, York University, Toronto, ON, Canada.ORCID 0000-0002-8191-7050
Nigar SekerciogluInstitute for Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.ORCID 0000-0001-6461-8801
Cheryl MitchellGustavson School of Business, University of Victoria, Victoria, ON, Canada.ORCID 0000-0002-4291-1203
Senthujan SenkaiahliyanInstitute for Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.ORCID 0009-0007-9236-8783
Attila HertelendyCollege of Business, Florida International University, Florida, FL, United States.ORCID 0000-0001-6174-0289
Tracy PorterDepartment of Management, Cleveland State University, Cleveland, OH, United States.ORCID 0000-0001-9703-9929
Jane Banaszak-HollDepartment of Health Services Administration, School of Health Professions, University of Alabama Birmingham, Birmingham, OH, United States.ORCID 0000-0002-6896-600X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe leaders of health care organizations are grappling with rising expenses and surging demands for health services. In response, they are increasingly embracing artificial intelligence (AI) technologies to improve patient care delivery, alleviate operational burdens, and efficiently improve health care safety and quality.

objectiveIn this paper, we map the current literature and synthesize insights on the role of leadership in driving AI transformation within health care organizations.

methodsWe conducted a comprehensive search across several databases, including MEDLINE (via Ovid), PsycINFO (via Ovid), CINAHL (via EBSCO), Business Source Premier (via EBSCO), and Canadian Business & Current Affairs (via ProQuest), spanning articles published from 2015 to June 2023 discussing AI transformation within the health care sector. Specifically, we focused on empirical studies with a particular emphasis on leadership. We used an inductive, thematic analysis approach to qualitatively map the evidence. The findings were reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews) guidelines.

resultsA comprehensive review of 2813 unique abstracts led to the retrieval of 97 full-text articles, with 22 included for detailed assessment. Our literature mapping reveals that successful AI integration within healthcare organizations requires leadership engagement across technological, strategic, operational, and organizational domains. Leaders must demonstrate a blend of technical expertise, adaptive strategies, and strong interpersonal skills to navigate the dynamic healthcare landscape shaped by complex regulatory, technological, and organizational factors.

conclusionsIn conclusion, leading AI transformation in healthcare requires a multidimensional approach, with leadership across technological, strategic, operational, and organizational domains. Organizations should implement a comprehensive leadership development strategy, including targeted training and cross-functional collaboration, to equip leaders with the skills needed for AI integration. Additionally, when upskilling or recruiting AI talent, priority should be given to individuals with a strong mix of technical expertise, adaptive capacity, and interpersonal acumen, enabling them to navigate the unique complexities of the healthcare environment.

Indexed as

Artificial IntelligenceLeadershipDelivery of Health CareHumansAIAI implementationartificial intelligencehealth carehealth care organizationinnovationleadershipmanagementorganizationstrategy

Identifiers

PMID39009038
PMCPMC11358667

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.