Insights
Rethinking the relationship between AI and human care.
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AI for community health workers
How AI can extend the reach of CHWs without diluting the trust and cultural knowledge that make them effective.
Community health worker technology →
What it takes to build technology that community health workers will actually use — and that supervisors will trust.
Healthcare conversational AI →
Conversational AI in healthcare has a trust problem. Drawing the human/AI boundary explicitly is how it earns a place.
AI patient engagement →
Engagement programs only create value when people can be reached. AI changes the economics of reaching them.
Medicaid member engagement →
Why Medicaid and Medi-Cal populations are the hardest to reach — and where an AI extender helps most.
Healthcare workforce capacity →
You can't hire your way out of the healthcare workforce shortage. The question is how to make every hour go further.
AI care navigation →
Navigation is where care plans stall — the referrals that aren't closed, the appointments that aren't kept, the barriers no one asked about.
Public health AI →
Public programs are responsible for whole populations with a fraction of the staff. Technology can extend them without replacing the human relationships they depend on.
Human-centered healthcare AI →
AI is usually judged by what it can replace. In healthcare, its greatest opportunity is what it lets people do more of.
Social determinants of health technology →
Most health outcomes are decided outside the clinic. Technology for social needs has to meet people where those needs actually surface.
Responsible conversational AI →
In healthcare, responsible AI isn't a values statement — it's a set of design decisions a buyer can inspect.
Public-private-community collaboration →
Society's hardest healthcare problems don't sit inside one sector. The tri-sector model is how HumAInity is built to work.
Measuring social impact →
If scale is supposed to increase impact rather than distance, you have to measure both.