The governance and evidence infrastructure for consequential AI and agent decisions in healthcare.
Govern · Monitor · Verify · Preserve Evidence
Know what was authorized. Verify what happened. Preserve the evidence. Know who is accountable.
Defensible AI Decisions designed for a mobile workforce.
Click to watch the Darwin videoAI is moving from answers to actions. Policies, inventories, and configurations describe what AI should do; they do not necessarily prove what happened when AI acted.
Hospitals are deploying AI they don't control, generating data they don't own, creating liability they can't defend.
Vendor AI runs in third-party clouds. Hospitals don't control the audit trails, can't reconstruct decisions during litigation, and watch governance data leave their jurisdiction entirely. When connectivity fails, oversight fails with it.
D&O carriers are already requiring AI governance questionnaires for coverage binding, with hard mandates expected within 18 months (of 2026). Rating agencies are adding AI governance to credit methodology. The regulatory environment is removing federal guardrails, but tort liability, insurance scrutiny, and bond rating pressure remain unchanged.
The gap between AI deployment velocity and governance readiness is the largest unpriced risk in healthcare today. And right now, hospitals don't even own the evidence they'd need to defend themselves.
Three market forces converging on a single timeline.
Federal deregulation is removing the compliance frameworks that anchored traditional governance approaches, eliminating established differentiation while leaving hospitals exposed to unchanged tort liability and insurance scrutiny. Hospitals now face unregulated AI deployment with no sovereign infrastructure to prove what happened, when, and why.
D&O insurance carriers are moving from questionnaires to mandatory governance endorsements within 18 months (of 2026). Hospitals without documented AI governance (governance they control, not a vendor report) face premium increases, specific exclusions, or loss of coverage entirely.
Meanwhile the governance standards Medigram helped author are appearing in the documents that shape what health systems buy against. The market infrastructure we spent a decade building is becoming the market requirement.
Eight disciplines. One encoded system.
Healthcare AI governance requires simultaneous fluency in eight disciplines. Most health systems cannot staff that intersection. Medigram has encoded it.
Effective governance at the point of clinical AI does not fail because institutions lack intelligence or resources. It fails because it sits at the convergence of clinical risk, security, infrastructure, finance, law, data architecture, standards, and peer-reviewed literature simultaneously. These are distinct professional literacies, each requiring years of formation. No single hiring decision, no cross-functional committee, and no vendor relationship naturally brings all eight together at once.
The result is a structural gap that moves at committee speed while liability accumulates at litigation speed. Health systems that attempt to assemble this capability from scratch face years of formation time they do not have, against a regulatory and capital markets timeline that is already in motion.
Medigram encoded this intersection through years of standards authorship, clinical operations experience, and infrastructure deployment before releasing a commercial product. The result is commissioned infrastructure that delivers all eight disciplines simultaneously, before the institution needs to hire, assemble, or train for any of them. The capability is already in the system.
The staffing reality of the AI-driven organization
Operating at the intersection of clinical, security, governance, and standards disciplines simultaneously requires roles most health systems have not yet built. The fully loaded cost of assembling that capability internally runs into seven figures annually before recruiting, tooling, or the formation time required before the team operates at the intersection rather than alongside it.
Medigram is that infrastructure. The commissioned foundation that makes the AI-driven organization operable, defensible, and sustainable.
Darwin creates and preserves the governed record behind consequential AI decisions.
Fluent is not verified.
Darwin governs the decision before it proceeds. Not after it is questioned.
The return-to-play call, made where it's made
Return-to-play decisions don't happen at a workstation. They happen on a sideline, in a training room, in a hallway outside the locker room, with the phone in my hand as the only computer present. Every input behind the call now has an algorithm in it somewhere: recovery scores, workload models, wearable trends. If he re-injures, the grievance won't ask whether I was right. It will ask how the decision was made and whether anyone can prove it. With Darwin, the governed record is created at the moment and place I decide: what informed the call, what the system was permitted to contribute, and me as the accountable authority, sealed then, not reconstructed later at a desk. When the inputs aren't sufficient, it holds rather than guesses, and the hold is on the record. The judgment stays mine. The evidence exists from the second I made it, from the device I made it on.
The day after, answered from wherever I'm standing
The morning after a contested AI-assisted decision, the committee doesn't ask what the model said. It asks how the decision was made: who was accountable, what the AI actually did, where the evidence is. My decisions don't happen at a desk either; they happen at the bedside, in a corridor between units, on call at home with a phone. Before Darwin, proving how a decision was made meant going back to a workstation and assembling screenshots, emails, and memory, none of it contemporaneous. Now the governed record is produced where the decision is, with its provenance intact: the inputs, the boundaries the system operated within, the named clinician who owned the call. It works the way downtime documentation works, a defined fallback rather than an improvisation. I don't practice differently, and I don't practice tethered. The evidence already exists on the day someone asks, created at the point of decision instead of manufactured under scrutiny.
The decision point in medicine is mobile. The evidence point has to be too.
Together, these fields establish evidence provenance and name the accountable authority for every governed decision.
Interactive Platform Demonstration: This stakeholder-focused interface represents Medigram's award-winning governance infrastructure, demonstrated at AIMed 2025 and built on our autonomous agent fleet. Select your role below to see how governance becomes actionable intelligence for each executive function.
Click on your title to see what matters most to your role.
Each score reflects your institution's current governance posture across the six TIPPSS dimensions of IEEE UL 2933 and the gap Medigram closes. DETAILS → on any row — or on a letter badge for a plain-language definition.
Select your role above to see governance through your lens.
Built for institutional scale with a permanently lean operating model. The agent fleet is the team. Designed for the power law era.
Development inspired by enterprise health systems and pro sports organizations.
Traditional AI governance and Medigram's decision-level governance, verification, evidence, and defensibility are not the same territory.
| Traditional AI Governance | Medigram |
|---|---|
| Inventory AI | Govern consequential decisions |
| Define policy | Establish authorized remit |
| Assess risk | Verify behavior |
| Monitor models | Monitor systems + agents + humans |
| Maintain documentation | Preserve decision evidence |
| Demonstrate compliance | Establish defensibility |
| Governance system of record | Governed Decision Record |
Born from a decade of secure clinical communication informed data infrastructure and AI standards leadership. Evolved into the governance outcomes hospitals need without hiring governance teams.
If the hospital didn't generate it, store it, and control it, it isn't governance. It's a report from someone else's server.
Medigram's foundational architecture is built on a single premise: governance data belongs to the hospital. Not third-party vendors. Not the cloud provider. The institution making the clinical decisions owns the evidence trail. On their premises, under their control, on their timeline.
Darwin is built so that judgment calls do not degrade under pressure. In healthcare, a clinical decision cannot be undone the way a transaction can be reversed, so Darwin either meets the standard for a decision to proceed, or it does not proceed. That standard is upheld consistently, not asserted once at launch.
Darwin addresses resource exhaustion and token-level attacks, a class of threat that current agentic AI governance frameworks identify as a risk category but do not specify technical mitigations for. TTIC's published certification requirements identify this attack surface as a Tier 1 requirement. Darwin addresses it.
TTIC on the resource exhaustion certification requirement →Built aligned to the governance standards your compliance teams already reference. Medigram provides the operational infrastructure for the Hospital AI Operations Governance Standard we helped write.
The governance model Medigram runs
was built in public, with the field.
At AIMed 2025, Medigram demonstrated the reference execution of the governance model developed by TTIC — the Trustworthy Technology and Innovation Consortium. CEO Sherri Douville was named AIMed AI Champion of the Year, awarded by physicians, health system leaders, and clinical researchers. Medigram's work has been recognized in the same industry context as organizations including Cleveland Clinic and the American Medical Association.
TTIC is an independent consortium of health systems, academic medical centers, and clinical technology leaders advancing trustworthy AI governance standards. Medigram's CEO founded and chairs TTIC. TTIC maintains independent governance, standards, and decision-making boundaries to preserve the integrity of its consortium activities. The relationship is not a vendor endorsement — it is an architecture: TTIC develops the governance model; Medigram is the reference execution of it as production infrastructure.
The clinical AI community's recognition of this work — through AIMed, IEEE, and CHIME AI Principles — reflects the trust of the institutions that understand what is actually at stake.
Sherri Douville
Operates at the intersection of clinical, technical, regulatory, and standards leadership in healthcare AI. A career spent not just advising on governance frameworks, but writing them, then building them into production.
Having held Series 7 and Series 66 securities licenses, Ms. Douville brings a unique ability to connect technical architecture to financial incentives and outcomes in healthcare finance. With deep enterprise healthcare experience, advanced technical acumen, and recognized national leadership in AI governance, she built Medigram to close the gap between AI deployment and the infrastructure required to govern it responsibly.
She also leads the AI Governance Infrastructure ecosystem that Medigram occupies — chairing TTIC, co-authoring IEEE and ANSI standards, and contributing to the standards that health systems and their capital markets partners increasingly reference when evaluating clinical AI.
Dr. Arthur Douville
Former CMO at two health systems with a track record of building and scaling multiple clinical service lines. Grounds the company's technical architecture in real-world clinical operations and physician workflows.
Dr. Art Douville serves as Chief Medical Officer of Medigram and serves as Co-Chair of Clinical Integration for the Trustworthy Technology & Innovation Consortium (TTIC), providing clinical expertise and supervision. His focus is on governance frameworks that guide AI involvement in patient care while protecting the physician's exercise of clinical judgement.
We proved what was possible. The highest Praxen RAISE score recorded to date. Independently published Here is what we learned. Read the story ↗
Move fast with intention. The governed decision platform is live. Every AI-assisted decision sealed at the moment it happens. Independently verified at RAISE 4.0 Strong. In production. Learn about Darwin ↗ See the evidence ↗
For the Advanced Leader Who
Urgently Needs to Protect
Enterprise, People, and Capital.
Your innovative partners want to move faster.
Your risk-averse partners want proof before they approve anything.
Darwin gives you the record that satisfies both conversations without slowing down either one.
Start where you are →Built for every stakeholder who owns the risk.
Select your role to understand exactly what Medigram delivers and why it matters to you.
The institutions that govern AI now will define what healthcare leadership looks like in the capital markets era.
Capital markets positioning, physician retention, and bond rating defense.
We commission the governance control plane that turns AI operations into managed exposure and exportable proof.
Liability containment, audit-grade evidence, and underwriter positioning.
We govern agent behavior across both doors with identity boundaries, decision authority, and audit-ready evidence by default.
EHR and non-EHR governance, shadow AI coverage, forensic evidence chains.
We make agentic automation operable at enterprise scale without turning it into a staffing program.
Commissioned infrastructure, gated readiness, bounded operational lift.
AI vendor agreements contain governance and liability language that warrants careful review. Medigram ensures your institution's posture is defensible before that language is tested.
Contract language, spoliation risk, FCA exposure, forensic defensibility.
An AI agent is already calling your patients. The governance question is not whether this happens. It is whether your board can prove it happened within defined boundaries.
Two-door governance, decision authority policy, rating agency posture.
We keep the system from overreacting or missing deterioration, and we can show exactly why it did what it did.
Alert calibration, cross-specialty handoffs, post-sepsis CRS protocol.
We protect your license by making you a competent physician in the loop, not a signature on an algorithm you cannot defend.
License protection, informed clinical judgment, ISO 42001 alignment.
When AI is involved in your care, you deserve to know it is operating within defined boundaries and that a clinician is always in the loop.
Clinician in the loop, explainable decisions, defined boundaries.
Medigram is the aircraft carrier for healthcare AI: credit-risk and evidence infrastructure that lets a health system deploy AI safely without turning it into a liability event or a staffing program.
Category creator, standards authorship, commissioned infrastructure at enterprise scale.
The governance gap shows up first at the bedside. Post-sepsis multisystem monitoring is where siloed workflows fail and governed AI infrastructure proves its value.
Post-sepsis cardiorenal syndrome represents one of the most analytically demanding patterns in acute care, where cardiac and renal dysfunction do not resolve on the same timeline, do not route to the same specialty, and do not generate a single alert that captures the composite risk. Peer-reviewed literature identifies the key biomarker cluster and the synthesis logic that standard siloed workflows cannot execute in real time.
Standard workflows are not built for this. Cardiology and nephrology receive separate alerts, on separate timelines, from separate systems. The synthesis that determines whether a patient needs continued monitoring, safe discharge, or escalation does not happen automatically in any EHR. It happens in the mind of a physician who happens to be reviewing the right labs at the right time. Or it does not happen at all.
Medigram brings both specialties' signals into a single governed decision, with documented rationale at every step. Each decision is captured in structured, auditable form satisfying peer review, regulatory review, and litigation discovery from the same evidence infrastructure.
Medigram clinical logic is based on published evidence and designed to support physician decision-making, not replace it. Clinical judgment and institutional protocols govern all treatment decisions.
Accountable for Health, MSSP Results 2024
Rangaswami et al., Circulation 2019
Sources: Burdick et al., BMJ Health Care Informatics 2020; Gadre et al., HCUP NRD; Xu et al., Renal Failure 2025; Zarbock et al., Nature Reviews Nephrology 2023; Rangaswami et al., Circulation 2019; Accountable for Health MSSP 2024.
Request Access
Not everyone is at the same stage. Choose how you want to engage.
Tell us about yourself (Health systems, MedTech, and Pro Sports executives: CFO, CIO, CTO, CISO, CDO, CEO, CMIO, GC, Chief AI Officer, Chief Architect, VP Infrastructure, VP/SVP Player Health and Performance, Head Athletic Trainer, Team Physician/Medical Director, VP/SVP Basketball/Football Operations, General Manager, SVP Player Health and Safety)