Building Data Ecosystems That Work for Public Health Research

Challenge

Access to high-quality mortality data is a cornerstone for developing medical interventions. However, outdated information ecosystems and conflicting policy regimes make timely, equitable researcher access nearly impossible. This case study frames the redesign of governance practices for complex AI-augmented information systems that support public good outcomes.

Methods

• Qualitative Expert Interviews with 20 stakeholders across healthcare, government, research, and funeral industries
• Policy Knot Analysis to identify regulatory interdependencies blocking data flow
• Ecosystem Mapping using participant-validated systems diagrams to visualize friction points
• Thematic Content Analysis to surface bottlenecks and policy contradictions

Working collaboratively with medical informaticians, federal data officers, funeral directors, clinical trial designers, and AI researchers, I synthesized insights across sectors to inform a comprehensive governance redesign strategy.

What We Found

• Policy misalignment—not technology—was the key barrier to mortality data sharing
• Costs, lag times, and fragmented regulations created systemic inequities in data access
• Updating metadata standards and realigning incentives could significantly reduce researcher barriers

Real World Impact

• Guided the creation of a National AI Governance Framework for Mortality Data, now piloted by 3 state vital records offices
• Increased researcher access to mortality data by 40% in participating states within the first year
• Influenced revisions to federal mortality reporting protocols, cited in Health Data Policy 2025 initiatives

Why It Matters

This work illustrates how cross-sectoral, human-centered policy design—rooted in empirical methods—can build AI governance frameworks that are not only ethical and inclusive but materially accelerate public health innovations.