Evidence map›Paper›PMID 42525445›Full record

ArticleJMIR aging2026

The Ways in Which Stakeholders Make Decisions About AI and Novel Technologies for the Health Care of Older Adults: Qualitative Interview Study.

Zhang Zhang, Thomas Km Cudjoe, Sato Ashida, Jacqueline Massare, Kacey Chae, Phillip Phan, Peter Abadir, Alicia I Arbaje, Mathias Unberath, Nancy L Schoenborn

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In one paragraph

Article in JMIR aging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Zhang ZhangDepartment of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, NC, United States.ORCID https://orcid.org/0000-0001-9344-6578
Thomas Km CudjoeDivision of Geriatric Medicine and Gerontology, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, United States.ORCID https://orcid.org/0000-0002-2590-209X
Sato AshidaDepartment of Community and Behavioral Health, College of Public Health, University of Iowa, Iowa City, IA, United States.ORCID https://orcid.org/0000-0001-5644-8523
Jacqueline MassareDivision of Geriatric Medicine and Gerontology, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, United States.ORCID https://orcid.org/0009-0009-9189-3249
Kacey ChaeDivision of General Internal Medicine, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, United States.ORCID https://orcid.org/0009-0008-2785-2947
Phillip PhanDivision of Geriatric Medicine and Gerontology, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, United States.ORCID https://orcid.org/0000-0002-1366-1604
Peter AbadirDivision of Geriatric Medicine and Gerontology, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, United States.ORCID https://orcid.org/0000-0002-8186-0066
Alicia I ArbajeDepartment of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, NC, United States.ORCID https://orcid.org/0000-0002-3224-3942
Mathias UnberathDepartment of Computer Science, Johns Hopkins University, Baltimore, MD, United States.ORCID https://orcid.org/0000-0002-0055-9950
Nancy L SchoenbornDivision of Geriatric Medicine and Gerontology, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, United States.ORCID https://orcid.org/0000-0001-5053-5132

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) has the potential to improve health among older adults, yet how different stakeholders decide to develop, finance, and adopt AI innovations is not well understood.

objectiveThis study aimed to understand the decision-making of different stakeholders regarding AI and technologies for the health care of older adults.

methodsWe conducted semistructured interviews with 15 older adults and care partners, 15 clinicians, 8 health system or insurance leaders, 5 investors, and 6 technology developers. Data were analyzed using thematic content analysis.

resultsAll stakeholders considered cost, value, and usability important in adopting AI health technologies but emphasized different aspects of each concept. Older adults and care partners prioritized out-of-pocket costs and ease of use, whereas payers emphasized disease prevalence and implementation feasibility. Developers and investors focused on profitability and scalability, resulting in tension with end users' priorities. Participant suggestions included problem-driven design, greater stakeholder engagement, public-private partnerships, and educating older adults about AI.

conclusionsAs with prior health-related technology innovations, aligning decisional priorities across stakeholders is critical to motivate impactful AI health technologies for older adults.

Indexed as

Artificial IntelligenceDecision MakingStakeholder ParticipationAgedFemaleHumansInterviews as TopicMaleMiddle AgedQualitative ResearchAIAI adoptionartificial intelligenceartificial intelligence adoptiondecision-makingnovel technologyolder adultsperceived usefulnesstechnology acceptance modelusability

Identifiers

PMID42525445
PMCPMC13469834

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Registered trials

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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.