Healthcare AI Solutions

Depends on the intended use. AI intended to diagnose, treat, or prevent disease may qualify as Software as a Medical Device. We conduct regulatory pathway assessment early in every engagement to identify clearance requirements before development commitments.

Clinical validation includes retrospective performance analysis on held-out patient data, subgroup performance across demographic variables, prospective shadow-mode validation, and clinical expert review — documented in a validation report before deployment authorisation.

Yes. We integrate with Epic using App Orchard APIs, CDS Hooks, SMART on FHIR, and HL7 v2 interfaces. Cerner uses similar FHIR and HL7 pathways. Timeline depends on your specific EHR environment configuration.

Bias evaluation is mandatory — measuring performance disaggregated by race, ethnicity, sex, age, and insurance status. We implement mitigation strategies including oversampling and fairness constraints for underrepresented groups.

Training data handling follows HIPAA minimum necessary standards with de-identification where feasible, data use agreements covering training purposes, access controls, and audit logging throughout the development lifecycle.

Focused clinical AI applications with available training data typically deploy in 16–24 weeks including clinical validation. Complex systems requiring prospective validation or multi-site EHR integration may require additional time.

Yes. Deployment requires physician consent protocols, audio data handling agreements, and EHR integration for note delivery into the appropriate documentation template.

Production healthcare AI requires ongoing monitoring of clinical performance metrics, model accuracy against prospective labels, demographic subgroup performance, and clinical outcome correlation.

Clinical AI typically needs to support ICD-10-CM, CPT, SNOMED CT, LOINC, and RxNorm coding standards. We build terminology mapping as part of the data pipeline rather than expecting clean coded inputs.

Healthcare AI requires clinical validation, bias evaluation across patient populations, EHR integration, HIPAA compliance, and often FDA regulatory consideration. Development timelines account for clinical validation cycles general AI does not require.

Focused clinical AI applications (a single risk model or documentation tool) typically range $150,000–$400,000 including clinical validation. Multi-site or FDA-pathway systems range higher. We scope exact cost after reviewing your data availability and regulatory pathway, not from a generic price list.

Yes — every system we build is designed as a decision-support layer, not a replacement for clinical judgment. Outputs are presented with confidence indicators and supporting evidence, and clinicians retain final authority on every decision the AI informs.

Yes, measurably — ambient documentation, prior authorisation automation, and CDI systems we've built have reduced documentation time by up to 78% and recovered millions in previously-missed revenue. We size the realistic administrative-cost impact during discovery against your own volume and staffing data.

Both — a single-specialty practice typically starts with one focused tool (e.g. ambient documentation); a health system engagement usually spans multiple sites and a full clinical validation programme. Engineering discipline and compliance standards are identical at either scale.

Every engagement is covered by mutual NDA before we discuss your specific clinical workflows or request any data access. Discovery itself typically works from de-identified samples and architecture documentation, not full PHI access, until a scoped agreement is in place.

Yes — every engagement is built to HIPAA technical safeguards (encryption, access controls, audit logging, minimum-necessary access) from the architecture phase, with BAA-compliant cloud infrastructure — AWS HealthLake for FHIR-native data storage, Azure Health Data Services, or Google Cloud Healthcare API — and compliance documentation delivered as part of the engagement.

Patient data is encrypted in transit and at rest, access is scoped per engineer on a need-to-know basis, and every access event is logged and auditable. Security posture is validated through penetration testing and HIPAA security assessment before production deployment.