Clinical AI is making decisions. Most health systems cannot tell you who owns them. That gap has a name. The accountability gap. It lives at a specific moment in every clinical workflow that nobody has designed for. The moment the AI stops and the physician starts.
Here is how Mo Johnson, MD MBA arrived at the work.
The path begins in cardiothoracic surgery at the University of Minnesota School of Medicine. The handoff happens more times than can be counted, and every time the decision belongs to a named person. That observation is the seed of the publication.
Postgraduate studies in economics, public policy, business finance, and strategy at Brown, Columbia, IE, and LSE.
Applied Generative AI for Digital Transformation at MIT.
Clinical AI data infrastructure work through applied research at GPe Research. GPe’s purpose is adjudication for regulated sectors. Healthcare is the first sector. Finance, education, and fiduciary follow as the architecture earns institutional trust.
From that synthesis came the architecture. Founder of MedicoVigilance™ and the Clinical AI Accountability Canvas™. Creator of the Mind the 9 Blocks™ framework. Nine blocks. One score. One named owner when AI gets it wrong.
The framework is simple. Every health system leader can close their accountability gap with it.
The named-owner architecture operationalizes accountability per deployment, with decision traceability at the point of care and an audit trail before escalation.
Without it, AI produces outputs. With it, AI produces decisions.
That distinction explains why a decade of AI investment has not moved the needle on outcomes that actually matter.
