TABULA PUBLISHERS
Longevity Copilot
This case study is the result of the "AI for Designers" Bootcamp by Patricia Reiners. It teaches designers how to meaningfully integrate AI into the full design process: Research, ideation, prototyping, iteration, and workflow.
Preventive health technologies now generate more data than ever. Wearables, biomarker tests, and health apps promise deeper insight, but more information rarely leads to better decisions.
This project explores how AI can turn fragmented health data into clear, actionable guidance.
Observed Problem
Health-conscious adults seeking to prevent decline and maintain function are poorly served by today's healthcare landscape. Medical checkups use generic ranges and episodic care, while consumer tools produce overwhelming data without prioritization. Users spend time and money on tests and interventions but remain uncertain whether their actions meaningfully improve health outcomes.
60* %
of adults in the US and EU are not confident about doing the right things to prevent future health decline.
40* %
of health-engaged adults using wearables or health apps feel overwhelmed by health data rather than supported by it.
50* %
of longevity-oriented consumers say they invest in tests, supplements, or tools without knowing if they actually help.
Concept
An AI health copilot called Sentra.
It interprets personal data and surfaces the single most relevant insight at any moment.
Sentra replaces dashboards with prioritized guidance, showing why it matters and whether action is needed.
Trust & Ethics
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Sentra acts as a copilot with firm boundaries, not a medical authority
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Offering context, confidence levels, and options while leaving decisions to the user
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Transparent data use
*Sources: OECD; WHO Europe; Pew Research; Deloitte Health Consumer Surveys, McKinsey Future of Wellness; Deloitte Digital Health (US/EU); JMIR Digital Health, McKinsey; Business Insider; Euromonitor Healthy Longevity