ArticleBMC health services research2026
Dialoguing with algorithms: experiences of generative AI use in navigating complex health decisions among older adults with multimorbidity: a qualitative study.
Article in BMC health services research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundGenerative AI has reshaped healthcare, yet multimorbid older adults face digital divide barriers limiting its safe application. Existing qualitative evidence on their AI-assisted complex health decision-making remains scarce, lacking integrated theoretical interpretation.
objectivesGuided by an integrated framework combining the Technology Acceptance Model, Senior Technology Acceptance Model and Burden of Treatment Theory, this study explored older adults' perceived usefulness and ease of generative AI for health decisions, mobilizable personal-social resources, and how technology perceptions shape dynamic usage behaviors and decision quality.
methodsFrom September to December 2025, 16 multimorbid seniors were recruited via purposive maximum variation sampling from an urban community health center in eastern China. Face-to-face semi-structured interviews were conducted, with reflexive thematic analysis following COREQ guidelines.
resultsBased on interviews with 16 participants, four themes were identified: (1) Perceived usefulness, which was mainly reflected in four dimensions: initial situational triggers prompting AI consultation, convenient access to information, integrated support for multimorbidity management, and enhanced decision-making confidence. (2) Perceived ease of use: Participants' perceptions of the ease of use of generative AI showed significant individual differences and context dependence. (3) Resource mobilization: Successful use of generative AI rarely depended on independent individual effort but required the mobilization of diverse personal resources and social support. (4) Behavioral intention and actual use: The generative AI usage behavior of older adults with multimorbidity exhibited dynamic evolutionary characteristics; its trajectory was not linear acceptance and continuous use, but a complex process involving attempts, interruptions, resumption, or permanent abandonment.
conclusionsThis study reveals that AI use among older adults with multimorbidity is not merely a technology adoption behavior, but an adaptive strategic choice made under the pressure of managing multiple chronic conditions. Only by fully considering the special needs of patients with multimorbidity in technology design and service provision can the safe and effective application of generative AI in elderly chronic disease management be truly realized. TRIAL REGISTRATION NUMBER: Not applicable. This study did not involve clinical trials.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.