Differing priorities create barriers to AI adoption in older adult healthcare

While artificial intelligence (AI) holds immense promise for transforming health care for older adults, a new study published in JMIR Aging reveals significant friction between those who use AI health tools and those who create and fund them. Conducted by researchers at Johns Hopkins University, the University of Iowa, and Washington University in St. Louis, the study highlights how key stakeholders-older adults, care partners, clinicians, health system leaders, developers, and investors-hold fundamentally different definitions of value, usability, and cost, creating systemic barriers to technology adoption.

Lead author Zhang Zhang, PhD (Johns Hopkins Bloomberg School of Public Health and Washington University in St. Louis), along with senior author Nancy L. Schoenborn, MD (Johns Hopkins University School of Medicine), and a multidisciplinary team, conducted semistructured interviews with 49 key stakeholders across six distinct groups. While every group agreed that cost, usability, and value drive their decisions, their underlying priorities clashed:

• Older Adults & Care Partners prioritized low out-of-pocket costs and physical/sensory accessibility.
• Clinicians focused on patient affordability and preventing workflow burnout.
• Payers & Health System Leaders evaluated overall return on investment (ROI), system integration, and impact on high-cost health events.
• Developers & Investors prioritized market size, scalability, and profit margins to offset high regulatory and development risks.

These differing perspectives create distinct points of friction. End users expressed frustration over receiving AI tools that felt like "solutions in search of a problem," pointing to developer incentives that favor adapting existing AI tools over designing around the unique needs of older adults. Meanwhile, investors and developers noted that navigating 4-to-7-year regulatory timelines and high financial risks forces them to seek massive profit margins, driving up ultimate costs for consumers.

To bridge this divide, the authors highlight actionable strategies, including early multi-stakeholder engagement, targeted public education to demystify AI for older adults, and public-private partnerships-such as the Johns Hopkins Artificial Intelligence and Technology Collaboratory (JHU-AITC) for Aging Research-to de-risk early-stage development. They conclude that, as with prior health-related technology innovations, aligning decisional priorities across stakeholders remains critical to motivating impactful AI health technologies for older adults.