Our foundation
Built on clinical science, not assumptions.
Every scoring model and clinical workflow in AcuityAI is grounded in peer-reviewed research and validated against real clinical data. We document our evidence base, limitations, and validation approach so your clinical and informatics teams can evaluate it properly.
FHIR R4
Standards compliance
Full FHIR R4 resource coverage
OpenEMR
EHR integration
Native workflow delivery
Real-time
Risk scoring
At point-of-care capture
Validation
What we validate and how.
Clinical scoring models
Risk scoring models are validated against labeled clinical datasets before deployment. Validation metrics, cohort characteristics, and known limitations are documented for clinical review.
FHIR resource mapping
All FHIR resource mappings are validated against HL7 FHIR R4 specifications and tested against real clinical data structures before production use.
Workflow integration
OpenEMR workflow touchpoints are validated in staging environments that mirror production configurations before deployment.
Ongoing monitoring
Model performance is continuously monitored in production for drift, missingness, and scoring anomalies — with alerts and review pathways built in.
Our approach
Our approach to transparency.
We do not make efficacy claims we cannot support. We document what our models do, how they were validated, what their limitations are, and how they are monitored in production. We believe clinical teams should be able to evaluate AI infrastructure the same way they evaluate any clinical tool.
We document model limitations, not just capabilities.
We monitor production behavior continuously.
We build human oversight into every scoring pathway.
We do not make efficacy claims we cannot support.
Want to review our validation documentation?
Talk to our clinical informatics team about model validation and evidence documentation.
Talk to an Integration Lead