Technology is reshaping healthcare access, but progress is uneven. AI, digital tools, and data platforms have the potential to extend care to underserved communities, address workforce shortages, and improve outcomes. At the same time, gaps in infrastructure, trust, and governance risk widening disparities rather than closing them.

On May 5, NationSwell convened a group of leaders from the healthcare, technology, philanthropy, and the social sectors to unpack how AI and technology can be used to connect more communities to quality care. Together, the group focused on practical strategies for deploying technology responsibly, building partnerships that center community needs, and ensuring that innovation strengthens equity, affordability, and trust in healthcare systems. Some of the most salient takeaways from the discussion appear below:

Key Takeaways:

Ensure that technology empowers community health workers as relationship builders. AI and tech are most valuable when they augment the work of community health workers rather than substitute it. The trusted, relational role that CHWs play in their communities is the irreplaceable foundation of effective care connection. All technology deployed should be designed to protect and extend that capacity.

Design AI tools with CHWs and communities. The most responsible AI adoption in healthcare requires community health workers and the communities they serve to be active participants in tool design. Without mechanisms for feedback, bias mitigation, and accountability, technology risks widening the very health inequities it aims to address.

Prioritize data security and trust as foundational. Organizations working at the intersection of technology and community health must treat data stewardship with the same rigor as the healthcare system itself. Achieving certifications, committing to governance structures, and designing platforms that bring AI into the human loop are essential to maintaining the trust that makes community engagement possible.

Address the full picture of need, not just point-of-care data. Existing data systems often capture only what brings someone into the healthcare system, missing the co-occurring social determinants of health that shape outcomes. Continuous, relationship-based data collection with the support of technology can surface a more complete and actionable picture that enables both better resource connection and effective advocacy.

Invest in AI literacy and critical capacity for the CHW workforce. Community health workers need both the practical skills to use AI tools effectively and the critical frameworks to evaluate how those tools are designed and deployed. Approaches that build competency while also developing CHW voice in governance and advocacy are critical to ensuring that the workforce shaping communities is not left behind as technology advances.

Build toward interoperability and sustainable models. For community-based organizations to achieve lasting impact through technology, they must be able to integrate securely with healthcare payer systems. Achieving interoperability opens pathways to revenue that sustains mission-driven work in ways that philanthropic funding alone cannot.

Shift the question from “can we?” to “should we?” Across sectors, the most important orientation toward AI adoption is not simply capability, but intentionality. Keeping the focus on how technology can better serve CHWs, and continuously asking whether each application advances their interventions, is the compass that keeps this work on the right path.