CANON Named Owner Principle · every clinical AI deployment requires two named persons in the audit trail — a Governance Owner at Layer 4 and a Decision Owner at the bedside, not one substituting for the other WORKING PAPER №01 The handoff that isn’t · how clinical AI escapes accountability · Mo Johnson, MD MBA EVIDENCE Duke-Margolis 2026 · most US health systems have not named who owns the clinical AI decision when something goes wrong CANON Layer 4 · Clinical AI Governance at the bedside · the layer where the named owner has to live FRAMEWORK Clinical AI Accountability Canvas™ · the diagnostic framework distinguishing Clinical AI Governance from General AI Governance EVIDENCE Stanford MedAgentBench · agentic systems already executing clinical recommendations without a named adjudicator on the chart CANON The Two Inputs · internal data the institution audits · external data the model was trained on, rarely audited at the deployment site CITATION MedicoVigilance™ Issue 6 · The Layer With No Name · 1,627 institutional subscribers CANON The Accountability Gap · the structural failure point where AI stops and the physician starts FRAMEWORK Mind the 9 Blocks™ · the nine institutional blocks that must be in place before clinical AI deployment EVIDENCE npj Digital Medicine · the four-layer governance cascade · most institutions have built the first two layers and left Layer 4 unbuilt PORTFOLIO Tangibley Health Inc. · MedicoVigilance™ Surface 1 · the named-owner architecture deployed in clinical workflow CANON Named Owner Principle · every clinical AI deployment requires two named persons in the audit trail — a Governance Owner at Layer 4 and a Decision Owner at the bedside, not one substituting for the other WORKING PAPER №01 The handoff that isn’t · how clinical AI escapes accountability · Mo Johnson, MD MBA EVIDENCE Duke-Margolis 2026 · most US health systems have not named who owns the clinical AI decision when something goes wrong CANON Layer 4 · Clinical AI Governance at the bedside · the layer where the named owner has to live FRAMEWORK Clinical AI Accountability Canvas™ · the diagnostic framework distinguishing Clinical AI Governance from General AI Governance EVIDENCE Stanford MedAgentBench · agentic systems already executing clinical recommendations without a named adjudicator on the chart CANON The Two Inputs · internal data the institution audits · external data the model was trained on, rarely audited at the deployment site CITATION MedicoVigilance™ Issue 6 · The Layer With No Name · 1,627 institutional subscribers CANON The Accountability Gap · the structural failure point where AI stops and the physician starts FRAMEWORK Mind the 9 Blocks™ · the nine institutional blocks that must be in place before clinical AI deployment EVIDENCE npj Digital Medicine · the four-layer governance cascade · most institutions have built the first two layers and left Layer 4 unbuilt PORTFOLIO Tangibley Health Inc. · MedicoVigilance™ Surface 1 · the named-owner architecture deployed in clinical workflow
Mo Johnson, MD MBA
AUTHORS

Mo Johnson, MD MBA

Founding editor, GPe Research Publications. Physician thought leader on accountability in clinical AI.
LinkedIn

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, deployed through Tangibley Health Inc. Finance, education, and fiduciary follow as the architecture earns institutional trust.

From that synthesis came the architecture. Founder of Tangibley Health Inc., 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.

At Tangibley Health Inc., the named-owner architecture is operational: accountability per deployment, decision traceability at the point of care, 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.

ROLES AND AFFILIATIONS

  • Founding editor, GPe Research Publications
  • Founder, Tangibley Health Inc.
  • Creator, Mind the 9 Blocks™ framework
  • Author, Clinical AI Accountability Canvas™
  • Editor, MedicoVigilance™

TRAINING AND EDUCATION

  • Cardiothoracic Surgery — University of Minnesota School of Medicine
  • Postgraduate Studies in Economics, Public Policy, Business Finance, and Strategy — Brown · Columbia · IE · LSE
  • Applied Generative AI for Digital Transformation — MIT

DISCLOSURES

Mo Johnson, MD MBA discloses material institutional and operating interests in Tangibley Health Inc. (founder), MedicoVigilance™ (editor), and the Clinical AI Accountability Canvas™ (author). Disclosures are maintained per paper at the point of publication.

CONTACT

research@gperesearch.com

PUBLICATIONS