Clinical AI Governance Solutions for Hospitals & Vendors
Ensure equitable care, mitigate clinical liability, and satisfy ACA 1557, Colorado AI Act, and EU AI Act mandates.
For Healthcare AI Vendors
Understand how your model performs across patient groups through a retrospective benchmark on de-identified outputs using our Composite Fairness Index (CFI), and talk with us about Synod Certified Fairness (SCF), an assurance designation in development for 2027 that is not yet operational.
- ✔ Retrospective model benchmarking for vendor algorithms (API integration is on the 2027 roadmap).
- ✔ Documented subgroup performance evidence of the kind ACA 1557 and EU AI Act Article 9 contemplate. Not a compliance determination.
- ✔ Independent retrospective performance reviews for model governance.
For Hospital Networks & Systems
Equip Chief Medical Officers, Chief Patient Safety Officers, and Risk Managers with visibility into AI performance across de-identified historical model outputs.
Surface demographic performance gaps early enough for a governance committee to act on them.
Explore Hospital Solutions →Built for Every Stakeholder in Healthcare
Select your role below to explore tailored benefits, risk mitigation, and operational value.
Protect Patient Safety & Standardize Clinical AI Governance
Chief Medical Officers and clinical leaders need confidence that algorithms shaping care recommendations are unbiased and reliable across all patient demographics.
- ✔ Identify where clinical decision models diverge for specific patient subgroups, from a retrospective audit of historical data.
- ✔ Review documented subgroup performance evidence to inform clinical governance discussions.
- ✔ Apply standardized equity metrics to the predictive tools under review.
Population-Specific Clinical AI Bias Solutions
Pediatric Patients
Examines adult-trained AI models that may underperform in pediatric triage and pain scoring. Part of our participation in the Connected Minds Pediatric Research Collaboration with York University and Université de Sherbrooke.
Women
Detects gender bias in emergency department pain management and flags atypical cardiac presentation under-triage (women are 30-40% less likely to receive equivalent analgesia).
Black Patients
Audits triage and pain algorithms to eliminate the documented 23% pain assessment gap and 18-22% emergency undertriage rate.
Indigenous Patients
Supports local retrospective audits designed around First Nations OCAP® (Ownership, Control, Access, and Possession) principles, protecting data sovereignty while auditing chronic disease models.
Older Adults
Audits clinical AI in long-term care and care transitions for frailty, dementia, and multimorbidity calibration gaps.
Regulatory Compliance
Produces documented subgroup performance evidence relevant to ACA Section 1557, Colorado AI Act SB24-205, EU AI Act Article 9 and Canadian Bill C-27. Not a compliance determination.