Bridging the Gap: AI, Digital Health, and the Golden Opportunity in Geriatric Care
The demographic landscape of the United States is undergoing a monumental shift. By 2050, older adults will constitute the largest demographic group in the nation (Verghese et al., 2024). This "graying" of America presents a unique set of challenges and an unprecedented opportunity for innovation. For those in the digital health and AI space, the geriatric care market isn't just a burgeoning frontier; it's a critical area of need where thoughtful innovation can dramatically improve quality of life.
A recent article in Health Affairs by Verghese et al. (2024) provides a foundational roadmap for navigating this complex market. However, to truly succeed, we must look beyond the obvious challenges and uncover the more nuanced, often-overlooked human factors at play. Let's build upon that roadmap with additional evidence to reveal the deeper insights crucial for creating technology that older Americans will actually adopt and trust.
The Data Dilemma: Garbage In, Biased Out
High-quality, diverse data is the lifeblood of AI. The Health Affairs article correctly identifies data silos and privacy as major hurdles. But a more insidious, often overlooked problem is the inherent ageism baked into the data itself. Most large-scale health datasets are heavily skewed towards younger, healthier populations. AI models trained on this data often fail older adults, whose bodies and health profiles are fundamentally different.
For example, an AI algorithm designed to predict fall risk might be trained on data that doesn't adequately account for factors like polypharmacy (the use of multiple medications) or sarcopenia (age-related muscle loss), leading to dangerous inaccuracies (Ghassemi et al., 2021). The result? A supposedly "intelligent" system that fails the very people it's meant to help.
The Creative Insight: Don't just seek more data; seek representative data. Innovators must proactively partner with geriatric centers and community organizations to build datasets that reflect the complex realities of aging. The authors' call for a National Geriatric Data Trust is a starting point, but the onus is on developers to ensure their models are rigorously tested for age-related bias before they ever reach the market (Verghese et al., 2024).
Beyond Access: Tackling the 'Tech Anxiety' Divide
We often frame the digital divide for seniors as a simple problem of access—they don't have smartphones or broadband. While that's a factor, the data tells a more complicated story. As of 2024, 69% of Americans aged 65 and older own a smartphone, and 83% use the internet (Pew Research Center, 2024). The real barrier isn't just access; it's usability and trust, often manifesting as "tech anxiety."
Many digital health tools are designed by and for a younger demographic, featuring complex interfaces, small fonts, and non-intuitive navigation that can be frustrating for older users. This design-by-exclusion approach reinforces the feeling that "this technology isn't for me."
The Overlooked Insight: The solution is co-design. The field of "gerontechnology" emphasizes actively involving older adults in the technology design process from day one (Peek et al., 2014). Instead of asking "How can we get seniors to use our app?", the question should be "How can we build a tool, with seniors, that solves their problems in a way they find empowering?" This collaborative approach not only leads to a better product but also builds the trust and ownership necessary for long-term adoption.
The Invisible User: Designing for the Caregiver Dyad
A critical oversight in many digital health solutions for seniors is the failure to recognize the role of the caregiver. Very often, the primary user of a remote monitoring dashboard, a medication reminder app, or a telehealth platform is not the older adult themselves, but their adult child or professional caregiver. This caregiver is the one troubleshooting connectivity issues, interpreting alerts, and coordinating with providers.
Ignoring the caregiver experience is a recipe for failure. If a tool adds more stress and confusion to an already overburdened caregiver, it will be abandoned, regardless of how brilliant its algorithm is.
The Creative Insight: Design for the patient-caregiver dyad. Your user interface should have two distinct modes or views: a simplified one for the older adult and a more detailed, data-rich one for the caregiver. The technology must facilitate communication and collaboration between them, not create another layer of complexity. As research by the AARP shows, family caregivers are increasingly leveraging technology and are desperate for solutions that genuinely make their monumental task easier, not harder (AARP, 2020).
Building Algorithmic Trust: The Human-in-the-Loop Imperative
For any technology to be adopted, users must trust it. This is especially true for AI in healthcare, where decisions can have life-or-death consequences. For older adults, who may be less familiar with AI, the "black box" nature of many algorithms—where even the developers can't fully explain how a conclusion was reached—is a major source of distrust.
The Health Affairs article emphasizes community engagement, which is vital. But trust must also be engineered into the product itself.
The Overlooked Insight: Embrace Explainable AI (XAI) and the "human-in-the-loop" model. An AI tool that gives a recommendation should also be able to explain its reasoning in simple terms (e.g., "We recommend a follow-up visit because your blood pressure readings have been 15% higher than your baseline this week"). This transparency empowers patients and providers. Furthermore, the final decision should always rest with a human clinician. AI should be positioned as a powerful co-pilot, not an autonomous pilot. This approach reassures patients that they are not ceding their well-being to a machine, but rather enhancing the capabilities of their trusted care team (Ghassemi et al., 2021).
The Path Forward
The opportunity to improve the lives of older Americans through AI and digital health is immense. But success requires more than just clever code and a solid business plan. It demands a deeper, more empathetic understanding of the end-user in all their complexity.
By tackling ageist data bias, designing with—not just for—seniors, empowering the invisible caregiver, and building trust through transparency, we can move beyond simply creating new technology. We can create a future where innovation truly serves the needs of a generation that deserves our best and most thoughtful work.
References
AARP. (2020). Caregiving in the U.S. 2020. https://www.aarp.org/content/dam/aarp/ppi/2020/05/full-report-caregiving-in-the-united-states.doi.10.26419-2Fppi.00103.001.pdf
Ghassemi, M., Vane, M., & D'Amour, A. (2021). The provider-AI relationship: A new public-interest framework. Health Affairs, 40(3), 478–484. https://doi.org/10.1377/hlthaff.2020.01509
Peek, S. T. M., Wouters, E. J. M., van Hoof, J., Luijkx, K. G., Boeije, H. R., & Vrijhoef, H. J. M. (2014). Factors influencing the adoption of technology for aging in place: A systematic review. International Journal of Medical Informatics, 83(4), 235–248. https://doi.org/10.1016/j.ijmedinf.2014.01.004
Pew Research Center. (2024, January 31). Internet, Broadband Fact Sheet. https://www.pewresearch.org/internet/fact-sheet/internet-broadband/
Verghese, J., Stern, A. D., & Glickman, A. (2024). Artificial Intelligence And Geriatric Health Care. Health Affairs Forefront. https://www.healthaffairs.org/content/forefront/artificial-intelligence-geriatric-health-care