About Summit Evidence AI

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Why this newsletter exists

Summit Evidence AI exists because healthcare leaders deserve more than vendor narratives and conference slides. Every briefing starts with the evidence — research, real deployments, governance outcomes — and distills it into 10–15 minutes of useful signal for executives, data leaders, and practitioners navigating AI in high-stakes environments.

The inflection point. The hype cycle of 2023–2024 has given way to something harder and more interesting: actual production. Payers are deploying generative AI in prior authorization. Health systems are running AI-assisted clinical documentation at scale. Most leaders — even experienced ones — are navigating these decisions without clean signal.

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What is actually working in production?
Which governance frameworks survive an audit? Which vendors are real, and which are dressed-up demos? Summit Evidence exists to answer those questions, from someone who has lived on both sides of them.

Who writes this

Jason Haupt

I’m Jason Haupt, PhD—a physicist turned healthcare data scientist who has spent more than fifteen years leading AI where the stakes are real: healthcare operations, regulated workflows, and decisions that have to survive contact with production.

I served as Chief Data Scientist at a Fortune 3 healthcare organization, led AI innovation across UnitedHealth Group for four years, helped scale Surest—an AI-first health tech startup acquired by UnitedHealthcare—and also worked inside a large hospital system. I have been the enterprise buyer, the builder, and the operator.

Scaling AI innovations to tens of thousands of clinical and administrative users, building enterprise governance from scratch, and evaluated hundreds of AI vendors and use cases across clinical, operational, and administrative domains.

That perspective shapes Summit Evidence AI. The goal is not to theorize about AI from the sidelines, but to surface what actually holds up under governance, workflow adoption, vendor scrutiny, and real operational pressure. Summit Evidence is the public-facing signal layer for his advisory work at Haupt AI Advisory.

At a glance:
Fortune 3 Chief Data Scientist • Surest / payer innovation • 15+ years in healthcare AI • PhD in Physics

What you get

Each issue of Summit Evidence is built around four sections:

The Big Move A close look at what a smart organization actually did: the decision, the deployment, the outcome, and what it signals for the broader field. No vendor-sourced case studies. No press-release summaries. This is field-sourced signal from the practitioner level.
The Decision Template A concrete, usable artifact you can put into practice this week. Vendor evaluation scorecards. Governance memo templates. ROI calculation frameworks. Build vs. buy decision grids. The documents operational AI leaders actually need.
The Signal Anonymized, synthesized benchmarks and field observations. What are healthcare AI leaders actually experiencing? What patterns are emerging? This section converts anecdote into evidence.
The Ask One question per issue, answered by the community. Results are analyzed and published in the following issue — with interpretation of what the distribution reveals about the state of the field.

Who this is for

Summit Evidence is written for the leaders who are accountable for healthcare AI outcomes, not just awareness of them: Chief AI Officers, Chief Data Scientists, VPs of Analytics and Innovation, payer technology leaders, clinical informatics leaders, and product or operations leaders making real deployment decisions in regulated environments.

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For teams evaluating real vendors, building real workflows, and trying to turn AI ambition into something that can survive approvals, adoption, and production.

This is for people on the hook to make decisions, not just consume AI news.

What this isn’t

Summit Evidence is not sponsored. It does not accept vendor placements, run affiliate deals, or soften assessments for commercial relationships. Healthcare AI needs more honest signal — not more marketing dressed as analysis.

It exists to stay on the right side of that line—permanently.