%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 26
%N 
%P e60794
%T Investigating Older Adults’ Perceptions of AI Tools for Medication Decisions: Vignette-Based Experimental Survey
%A Vordenberg,Sarah E
%A Nichols,Julianna
%A Marshall,Vincent D
%A Weir,Kristie Rebecca
%A Dorsch,Michael P
%+ College of Pharmacy, University of Michigan, 428 Church St, Ann Arbor, MI, 48109, United States, 1 734 763 6691, skelling@med.umich.edu
%K older adults
%K survey
%K decisions
%K artificial intelligence
%K vignette
%K drug
%K pharmacology
%K pharmaceutic
%K medication
%K decision-making
%K geriatric
%K aging
%K surveys
%K attitude
%K perception
%K perspective
%K recommendation
%K electronic heath record
%D 2024
%7 16.12.2024
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: Given the public release of large language models, research is needed to explore whether older adults would be receptive to personalized medication advice given by artificial intelligence (AI) tools. Objective: This study aims to identify predictors of the likelihood of older adults stopping a medication and the influence of the source of the information. Methods: We conducted a web-based experimental survey in which US participants aged ≥65 years were asked to report their likelihood of stopping a medication based on the source of information using a 6-point Likert scale (scale anchors: 1=not at all likely; 6=extremely likely). In total, 3 medications were presented in a randomized order: aspirin (risk of bleeding), ranitidine (cancer-causing chemical), or simvastatin (lack of benefit with age). In total, 5 sources of information were presented: primary care provider (PCP), pharmacist, AI that connects with the electronic health record (EHR) and provides advice to the PCP (“EHR-PCP”), AI with EHR access that directly provides advice (“EHR-Direct”), and AI that asks questions to provide advice (“Questions-Direct”) directly. We calculated descriptive statistics to identify participants who were extremely likely (score 6) to stop the medication and used logistic regression to identify demographic predictors of being likely (scores 4-6) as opposed to unlikely (scores 1-3) to stop a medication. Results: Older adults (n=1245) reported being extremely likely to stop a medication based on a PCP’s recommendation (n=748, 60.1% [aspirin] to n=858, 68.9% [ranitidine]) compared to a pharmacist (n=227, 18.2% [simvastatin] to n=361, 29% [ranitidine]). They were infrequently extremely likely to stop a medication when recommended by AI (EHR-PCP: n=182, 14.6% [aspirin] to n=289, 23.2% [ranitidine]; EHR-Direct: n=118, 9.5% [simvastatin] to n=212, 17% [ranitidine]; Questions-Direct: n=121, 9.7% [aspirin] to n=204, 16.4% [ranitidine]). In adjusted analyses, characteristics that increased the likelihood of following an AI recommendation included being Black or African American as compared to White (Questions-Direct: odds ratio [OR] 1.28, 95% CI 1.06-1.54 to EHR-PCP: OR 1.42, 95% CI 1.17-1.73), having higher self-reported health (EHR-PCP: OR 1.09, 95% CI 1.01-1.18 to EHR-Direct: OR 1.13 95%, CI 1.05-1.23), having higher confidence in using an EHR (Questions-Direct: OR 1.36, 95% CI 1.16-1.58 to EHR-PCP: OR 1.55, 95% CI 1.33-1.80), and having higher confidence using apps (EHR-Direct: OR 1.38, 95% CI 1.18-1.62 to EHR-PCP: OR 1.49, 95% CI 1.27-1.74). Older adults with higher health literacy were less likely to stop a medication when recommended by AI (EHR-PCP: OR 0.81, 95% CI 0.75-0.88 to EHR-Direct: OR 0.85, 95% CI 0.78-0.92). Conclusions: Older adults have reservations about following an AI recommendation to stop a medication. However, individuals who are Black or African American, have higher self-reported health, or have higher confidence in using an EHR or apps may be receptive to AI-based medication recommendations. 
%R 10.2196/60794
%U https://www.jmir.org/2024/1/e60794
%U https://doi.org/10.2196/60794