Digital Health That Doesn’t Leave Communities Behind

Designing for Sustainability from the Start

Sean Sylvia, Ph.D.

DHEPLab, UNC Gillings School of Global Public Health

2025-12-11

The Leapfrog Opportunity

The Choice LMICs Face

Option 1: Follow the HIC Path

• Safe, proven trajectory
• Limited by legacy infrastructure
• Ceiling set by institutional debt

Option 2: Leapfrog to the Frontier

• Skip legacy systems entirely
• Higher potential productivity
• But significant uncertainty

The Question

How do we capture the leapfrog opportunity while managing the risk?

[~45 sec] Open with opportunity, not problems. LMICs have a unique chance to skip the mistakes of HIC health IT. Mobile penetration is high, CHW networks exist—the infrastructure is there. But leapfrogging comes with uncertainty. The question is: how do we design systems that capture this opportunity while managing the risk?

AI is Different

The Complexity

• Data shapes decisions → decisions change behaviors
• Behaviors generate new data → feedback loops
• Yesterday’s patterns ≠ tomorrow’s reality
• Unknown unknowns we can’t predict upfront

The Core Insight

Health systems are information-processing and decision-making factories.

Digital systems transform the factory floor—not just how care is delivered, but what care, when, and to whom.

We must learn about the system before deploying—and design to learn continuously after.

[~90 sec] Health systems are fundamentally information-processing machines. Digital health doesn’t just make processes faster—it changes what decisions get made, when, and for whom.

But here’s the complexity: these are sociotechnical systems with feedback loops. Data shapes decisions, decisions change behaviors, and those changed behaviors generate new data. The system you’re trying to improve keeps shifting under your feet.

This means unknown unknowns—emergent effects we can’t predict upfront. You can’t retrofit sustainability onto a digital health system after deployment. The sociotechnical has to be designed in from the start. We need to learn about the system before deploying, and design systems that continue learning after.

ECAM: Four Principles for Sustainable AI

ENGAGE

Stakeholders First

• Map stakeholders before building
• CHWs, patients, officials
• Co-design, not consultation

CALIBRATE

Local Context

• Train on local populations
• Local languages, diseases
• Don’t import HIC models

AUDIT

Bias Continuously

• Equity audits by subgroup
• Gender, geography, SES
• Check for hallucination

MONITOR

Adapt Continuously

• Human override mechanisms
• Retraining triggers
• Performance dashboards

Templin et al. (2024) PLOS Digital Health | Templin et al. (2025) npj Digital Medicine

[~120 sec] Based on our work synthesizing challenges and evaluation frameworks for AI in healthcare, we propose four principles—ECAM.

Engage: Before you write a line of code, map your stakeholders. Community health workers, patients, local officials. Co-design, not just consultation.

Calibrate: Don’t import models trained on US or European populations. Calibrate to local languages, disease patterns, care pathways.

Audit: Build in equity audits from day one. Test across subgroups—gender, geography, socioeconomic status. Check for hallucination.

Monitor: Design for feedback. Human override mechanisms. Continuous retraining triggers. Performance dashboards.

Baked In, Not Bolted On

“Sustainable AI requires community engagement baked into the design, not bolted on.”

[~60 sec] Let me leave you with the core message. We can build technically brilliant AI systems that fail in the real world—because they weren’t designed with communities, for communities. Sustainable AI isn’t about bolting on community engagement after the fact. It has to be baked in from the start.

Thank You

dhep.org

Advancing health through economics, policy, and digital innovation

Sean Sylvia, Ph.D.

sean.sylvia@unc.edu

UNC Gillings School of Global Public Health

[~15 sec] Thank you. Please visit dhep.org to learn more about our work, and feel free to reach out. I look forward to the discussion.