The fairness layer for clinical AI
Most hospitals cannot answer, with evidence, whether the algorithms influencing care work as well for every population they serve. We measure it. Thirty days, de-identified historical data, no EHR integration required.
Algorithmic bias determines the clinical outcome.
Two Tracks: What We Do Today, and What We Work to Prevent
Track A: Available Today • Healthcare AI Governance
A hospital data analyst executes DDFA on de-identified clinical datasets and model logs. DDFA identifies that training data systematically under-represents Black, Indigenous, and older patients, issuing an actionable representation-gap audit report before point-of-care deployment.
Track B: The Scenario We Work to Prevent • The Alice & Amara Disparity
Same hospital • Same acute appendicitis • Same AI triage tool:
- Alice (White, 7): Pain score 7, receives timely emergency analgesia, seen and discharged safely within 3–4 hours.
- Amara (Black, 7): Pain score 7, sent home with an over-the-counter script due to algorithm undertriage, readmitted within 6 hours for unmanaged acute pain.
The 3-Layer Governance Framework
Governance-First Entry
Assess your baseline without requiring IT integration. Our 15-minute Ethical AI Maturity Assessment (EAMA) reveals institutional governance gaps and maps them to concrete mitigation strategies.
Sidecar Retrospective Wedge
Audit your existing tools on historical data securely. We analyze your de-identified model logs in 30 days to expose demographic performance variations before deploying at the point of care.
HAIQ Governance Platform (in development)
Continuous monitoring and HL7 FHIR R4 integration, so assessment becomes a standing capability rather than a point-in-time engagement. This layer is on our 2027 roadmap and is not available today.
Purpose-Built for Vendors & Hospital Networks
Healthcare AI Vendors
Build trust and pass hospital procurement ethics reviews.
- ✔ Validate algorithms against our proprietary Composite Fairness Index (CFI).
- ✔ Work with us on Synod Certified Fairness (SCF), an assurance designation we are developing for release in 2027. SCF is not yet operational and is not accredited by any third party.
- ✔ Produce documented subgroup performance evidence of the kind these frameworks contemplate, including ACA Section 1557 and the EU AI Act. We do not certify compliance, and our reports are not a compliance determination.
Hospital Networks & Systems
Protect patient safety and satisfy regulatory oversight.
- ✔ Assess demographic performance variations using de-identified historical data and existing model outputs.
- ✔ Produce committee-ready reporting for hospital AI steering committees and risk managers.
- ✔ Surface demographic performance gaps early enough for a governance committee to act on them.
Stay in the Know: Latest Publications & Events
AC:Health - Healthcare Innovation Program
Participant in AC:Health - Healthcare Innovation Program. AC:Health supports founders building AI, data, and software-enabled solutions that improve healthcare delivery, patient outcomes, and system efficiency.
Learn More →DaRMoD - Vector Institute for Artificial Intelligence
Participant in Darmod. Vector’s Data Readiness, Model Development, and Model Deployment (DaRMoD) program guides startups and small businesses from an AI idea to prototype in four months.
Learn More →Sheridan CAAI Institutional Research Collaboration
Our latest milestone in institutional validation, research collaboration, and algorithm benchmarking in partnership with Sheridan CAAI.
Learn More →The Invisible Patient: What Healthcare Has Always Known About Bias and What AI Must Learn
August 28, 2026 • By Lorna McKenzie, Retired RN
A retired primary care nurse manager on what twenty years in community health taught her about systemic bias, and why clinical AI without fairness auditing risks formalizing the inequities healthcare has always known.
Build the governance layer for your clinical AI.
Connect with our team to audit your AI tools and ensure equitable, safe care for every patient population.
Schedule a 30-Minute Call →