Data, Decisions and Clinics

Data, Decisions and Clinics

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Latest — 22 Feb 2026

The Whiteboard: Making Physician Reasoning work for AI

Healthcare AI fails not from lack of data, but from fragmentation. Each system sees a piece of data, no one sees the patient. Physicians reason in connected patterns, "Knowledge Graphs" formalize that reasoning, creating a unified, governed layer that lets AI see meaning, not noise.

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Stop Waiting for Clean Data

The healthcare data integration problem is 20 years old and not going away. So why are we still building AI that assumes clean data? A case for designing AI that works in the real world, not the one we keep promising to build.

The 3 a.m. Problem: Why Healthcare Governance Can't Copy Finance

Governance by design is essential, until 3 a.m. In an emergency, a perfectly compliant blood bank workflow nearly cost a life. Healthcare AI doesn’t need less governance. It needs adaptive governance, rules that scale with risk and allow accountable overrides when seconds matter.

Why Healthcare AI Governance Isn't What You Think It Is

AI governance isn’t nested boxes or monthly committees. It’s architecture. Under HIPAA and GDPR, your “data steward” often can’t even review the data. Real governance is built into the plumbing, validation, lineage, and security enforced automatically. Otherwise, it’s theater.

The Architect Who Should Have Read JAMA

In healthcare AI, policy shifts now appear first in journals like JAMA and NEJM, then quietly become grant conditions and RFP requirements. Many tech teams miss this signal. The winners will be those who translate medical literature into architecture before it becomes mandatory.

Customer Data Platforms in Healthcare: Necessary Infrastructure, Dangerous Autonomy

Healthcare is rapidly adopting Customer Data Platforms to unify patient data and personalize engagement. Used as infrastructure, CDPs improve identity resolution, consent management, and outreach. But without clear governance, they risk optimizing for engagement over clinical need.

A thought on titles and timelines

I’m seeing a flood of self-proclaimed “AI experts” with little real depth. After decades building enterprise systems, I still hesitate to claim the title. In healthcare AI, experience matters more than hype. Projects fail from old debts and governance gaps, not lack of buzzwords.

The Two Faces of Digital Twins: Your Body vs. Your Doctor

Digital twins are moving from factories to hospitals. We’re building replicas of patients, modeling disease in real time, and of physicians, capturing decades of expertise. The twin extends capacity while preserving oversight. What will happen when they no longer need us in between?

The Data Product Revolution in Healthcare: Why AI's Future Depends on Getting This Right

Data products are the missing layer in healthcare AI. There is no harmonization at scale without them. Instead of fragmented, untrusted datasets, data products create governed, reusable, semantically clear assets with defined ownership and value. That’s how AI moves from pilots to production.

Data Harmonization in Healthcare: Building the Foundation for AI-Ready Clinical Data

We can’t talk seriously about AI in healthcare without understanding the data infrastructure behind it. From HL7 v2 to FHIR, SNOMED, and MCP, I break down how clinical data actually moves and why every healthcare leader needs to grasp this before approving the next AI pilot.