Scaling Access to Healthcare: The Future of AI and Wearables with Dr. Trishan Panch

What It Covers

Dr. Trishan Panch, physician-entrepreneur and Harvard School of Public Health faculty, examines how AI and wearables are filling the gap left by an overburdened healthcare system. He argues that effective healthcare supply is functionally zero for most people most of the time, and that AI-enabled monitoring, clinical reasoning tools, and vibe coding will reshape both patient care and clinical practice.

Key Questions Answered

• •Effective Healthcare Supply: For the average person, access to healthcare outside acute hospital settings is functionally zero. Primary care appointments are scarce, psychological therapy has months-long waitlists, and out-of-pocket concierge care excludes roughly 95% of the population. Wearables and AI fill this gap by monitoring health across the 364.5 days per year when patients have no clinical contact whatsoever.
• •AI Clinical Reasoning Benchmark: Google's MedPaLM research team conducted prospective, blinded randomized trials comparing AI versus clinician clinical reasoning across thousands of cases. AI performed superiorly at the population level. The analogy to self-driving cars applies: statistically safer across populations even if imperfect for every individual, making broad deployment a social and political decision rather than a purely technical one.
• •Human-in-the-Loop Care Model: Dr. Panch's company Wellframe deployed a system where AI computed each high-risk patient's health state across all chronic conditions, generated personalized care checklists, and triaged which patients needed nurse intervention. This model reduced hospital readmissions by enabling one nurse to manage a far larger panel than traditional practice, proving scalable AI-assisted chronic disease management is viable today.
• •Vibe Coding for Clinicians: Clinicians can now build functional minimum viable products using natural language prompting tools like Cursor without formal engineering training. The correct approach mirrors professional software engineers: generate a full requirements document first, break it into modular chunks, and write unit tests for each component. This method produces stable, debuggable code rather than the brittle outputs of naive single-prompt vibe coding.
• •Optimal AI Health Consultation Strategy: To get clinically useful output from AI models, use the highest-tier reasoning models available, such as ChatGPT Pro, which can take up to ten minutes to process a query. Load a dedicated project with all available health context: genome or exome data, whole-body MRI results, blood panels, wearable exports, family history, and transcripts of clinician visits. Then ask the model what follow-up questions a physician would pose before requesting a diagnosis.
• •Mental Health as Physiological Signal: Ten percent of the US population has major depression, and one-third of those cases never respond to existing treatments. Wearable physiological data, particularly HRV and sleep patterns, likely tracks treatment response in real time and may reveal that "depression" is actually multiple distinct conditions lumped together by symptom presentation. Monitoring autonomic nervous system balance through devices like WHOOP could unlock more targeted psychiatric interventions.