Johns Hopkins study finds deep disagreement on AI priorities for older adults

Artificial intelligence is being promoted as the next great revolution in health care, but a new study suggests that one of the populations most likely to benefit from it—older adults—may be left behind unless the technology is designed around their real-world needs. Researchers at Johns Hopkins University, the University of Iowa, and Washington University in St. Louis have identified a deep divide between the people who use AI-powered health tools and the developers, investors, and institutions responsible for bringing those tools to market.

Published in JMIR Aging, the study is based on semistructured interviews with 49 stakeholders representing six groups: older adults, care partners, clinicians, payers and health system leaders, developers, and investors. Although participants broadly agreed that cost, usability, and value determine whether a health technology succeeds, the researchers found that these terms mean very different things to different groups. That disconnect may help explain why many promising digital health products fail to gain trust or achieve widespread adoption.

For older adults and their care partners, value was closely tied to affordability, independence, and accessibility. Participants emphasized the importance of low out-of-pocket expenses and interfaces that accommodate vision, hearing, dexterity, memory, and other changes associated with aging. A system that technically functions but requires small touch targets, complex menus, or fast responses may be effectively unusable for people with sensory or motor limitations. In this context, accessibility is not a cosmetic feature; it is a basic requirement for safe interaction with an AI system.

Clinicians described a different set of pressures. They wanted technologies that could reduce costs for patients while fitting smoothly into existing clinical workflows. AI systems that generate additional alerts, duplicate documentation, or require clinicians to monitor unreliable recommendations can increase cognitive load rather than reduce it. The study indicates that clinicians are therefore evaluating AI not only by its diagnostic or predictive accuracy, but also by whether it prevents workflow burnout and supports professional judgment without creating new administrative burdens.

Health system leaders and payers assessed AI through a broader economic lens. Their decisions depended on whether a technology could integrate with electronic health records, improve operational efficiency, and reduce expensive health events such as avoidable hospitalizations or emergency visits. This perspective treats AI as part of a complex infrastructure rather than as a standalone application. Even a highly accurate model may have little practical value if it cannot exchange data securely, operate within reimbursement systems, or demonstrate measurable improvements across a large patient population.

Developers and investors, meanwhile, face a different economic reality. Building and validating a medical AI product can require years of research, clinical testing, regulatory review, cybersecurity work, and post-market monitoring. Participants reported that regulatory timelines of four to seven years, combined with substantial financial risk, encourage companies to pursue large markets and high profit margins. Those incentives can make it more attractive to adapt an existing general-purpose AI tool than to create a specialized product for older adults, whose needs may be more diverse and whose market may appear less immediately scalable.

That tension produces what the researchers describe as a “solution in search of a problem.” End users may receive tools built around the capabilities of available algorithms rather than around clearly defined challenges in aging and health care. Technically, an AI model can classify images, summarize clinical notes, estimate risk, or generate responses, but those functions do not automatically translate into meaningful benefits for an older person managing multiple medications, living with cognitive impairment, or relying on a family caregiver. The central engineering problem is not simply whether a model works, but whether it works within the social, physical, and financial environment of aging.

The study points toward a more coordinated development process. Early engagement among older adults, caregivers, clinicians, health systems, developers, payers, and investors could reveal conflicting assumptions before a product is built. Public education may also help older adults distinguish realistic AI capabilities from exaggerated claims, while improving confidence in technologies that are transparent and appropriately limited. The researchers further highlight public-private partnerships, including the Johns Hopkins Artificial Intelligence and Technology Collaboratory for Aging Research, as a way to reduce early development risks and support products that might otherwise be overlooked by commercial investors.

The findings arrive as AI systems become increasingly embedded in health care, from predictive analytics and remote monitoring to conversational assistants and clinical decision support. For older adults, however, adoption will depend on more than technical performance. It will require affordable pricing, inclusive design, reliable integration, understandable explanations, and evidence that the technology improves outcomes without shifting hidden costs onto patients or caregivers. The researchers argue that aligning stakeholder priorities is essential if AI is to move beyond impressive demonstrations and become a practical, trusted part of everyday care for aging populations.

News Publication Date: July 30, 2026

Web References: https://doi.org/10.2196/86148

References: Zhang Z, Cudjoe KM, Ashida S, Massare J, Chae K, Phan P, Abadir P, Arbaje AI, Unberath M, Schoenborn NL. “The Ways in Which Stakeholders Make Decisions About AI and Novel Technologies for the Health Care of Older Adults: Qualitative Interview Study.” JMIR Aging. 2026;9:e86148. DOI: 10.2196/86148.

Keywords

Artificial intelligence, older adults, aging, digital health, health care technology, machine learning, generative AI, caregivers, clinical workflow, health care accessibility, stakeholder priorities, health systems, medical innovation