Healthcare AI that meets HIPAA before it meets the model.
We build compliant, production-grade AI systems for health systems, digital health platforms, and medical device companies - from clinical decision support to EHR-integrated agentic workflows.
What we build
Clinical Decision Support
AI systems that surface relevant evidence, flag anomalies, and support differential diagnosis at the point of care - without adding cognitive load to clinicians.
Patient Engagement AI
Intelligent intake, pre-visit screening, post-discharge follow-up, and care gap outreach - personalised at scale across patient populations.
Diagnostic Imaging AI
CNN-based pipelines for radiology, pathology, and ophthalmology - flagging, triaging, and annotating medical images to accelerate radiologist throughput.
Administrative Automation
Prior auth processing, coding assistance, scheduling optimisation, and documentation generation - removing the operational burden from clinical staff.
Compliance architecture
Every system we build for healthcare starts with a compliance architecture review. Here is what that means in practice.
PHI Handling
- De-identification pipelines (Safe Harbor + Expert Determination)
- Encryption at rest (AES-256) and in transit (TLS 1.3)
- Data minimisation by design - only what the model needs
- BAA-ready vendor agreements
Audit Logging
- Immutable access logs per HIPAA §164.312(b)
- Query-level logging for every PHI access event
- Tamper-evident log storage with 7-year retention
- Automated anomaly alerting on unusual access patterns
Access Control
- Role-based access with clinical role mapping
- Break-glass emergency access with mandatory review
- MFA enforced across all data access paths
- Session timeout policies aligned to clinical workflow
EHR integration
We integrate with the systems your clinical teams already use - via HL7 v2, FHIR R4, and direct API where supported.
HL7 v2
Message parsing, ADT event processing, and MLLP transport for legacy hospital systems.
FHIR R4
RESTful resource access, smart-on-FHIR app launch, and CDS Hooks for real-time clinical integrations.
Direct DB / ETL
Where API access is limited, we work with data engineering teams on HIPAA-compliant ETL from warehouse exports.
FDA & regulatory considerations
SaMD Classification
We help determine whether your AI system qualifies as Software as a Medical Device under FDA 21 CFR Part 820, and what class (I/II/III) applies to your intended use.
510(k) Awareness
For Class II SaMD, we build design history files, risk analysis documentation, and technical files aligned to 510(k) premarket notification requirements.
Clinical Validation Requirements
We support analytical validation, clinical validation studies, and reader studies required to demonstrate substantial equivalence or De Novo pathway eligibility.
IEC 62304 Alignment
Our software development lifecycle documentation maps to IEC 62304 software safety classifications - essential for regulated medical device software.
"Agentic triage system deployed across 3 hospital networks - 34% reduction in ER wait times."
From intake to routing in under 3 seconds. Built on FHIR, fully HIPAA-compliant, integrated with Epic ADT events. Clinical validation completed across 18 months of retrospective data before go-live.
Why regulated AI is harder
What happens when healthcare runs on AI that was built right.
34%
reduction in average ER triage time
< 3s
intake-to-routing latency on agentic systems
92%
EHR data field extraction accuracy via FHIR R4
18 mo
average retrospective validation period before go-live
Healthcare AI built without compliance or clinical context.
PHI processed through off-the-shelf LLMs without data processing agreements
Models trained on public datasets with no clinical validation
Black-box outputs surfaced directly to clinicians with no explainability
Integrations cobbled over HL7 v2 with fragile middleware scripts
No monitoring for model drift post-deployment
HIPAA-first architecture, clinically validated before it touches a patient.
BAA in place, PHI de-identified before model ingestion, AES-256 at rest
Retrospective validation on real patient populations before any deployment
SHAP attribution, confidence intervals, and plain-language summaries on every output
FHIR R4 native integration with CDS Hooks for real-time clinical event triggers
Production monitoring with drift alerts and automatic performance escalation
Healthcare AI questions we get asked every week.
Can you use GPT-4 or Claude for healthcare applications?
Yes - but only with proper data governance in place. For PHI-containing inputs, a BAA with the model provider is mandatory under HIPAA. We structure pipelines to de-identify or anonymise data before it reaches any third-party LLM, using Safe Harbor or Expert Determination methods. Many healthcare use cases also benefit from on-premise or VPC-deployed models where PHI never leaves your infrastructure.
What is the difference between CDS Hooks and SMART on FHIR?
CDS Hooks is a standard for real-time decision support - your AI fires when specific EHR events occur (e.g. a clinician places an order). SMART on FHIR is an app launch framework that lets external applications run inside the EHR with authorised access to patient data. We use both: CDS Hooks for inline alerting and SMART on FHIR for richer clinical AI applications embedded in Epic or Cerner.
Does your AI system qualify as a medical device?
That depends entirely on intended use and risk classification. Administrative AI (scheduling, coding, prior auth) typically falls outside SaMD classification. Clinical AI that influences diagnosis or treatment decisions may require FDA clearance under 21 CFR Part 820. We help clients assess this early and build documentation (design history files, risk analysis, clinical validation studies) aligned to the applicable regulatory pathway.
How long does clinical validation take?
Retrospective validation - testing a model against historical patient records - typically takes 4–12 weeks depending on data access and data quality. Prospective studies and controlled pilots add 6–18 months depending on the clinical setting and endpoint. We help design validation protocols that are appropriately rigorous without delaying go-live unnecessarily.
Can you integrate with our on-premise Epic instance?
Yes. Epic's on-premise deployments support HL7 v2 (MLLP), FHIR R4 via Epic's FHIR APIs, and CDS Hooks for real-time integrations. We have direct experience with Epic's app configuration, OAuth 2.0 authorisation flow, and the nuances of SMART on FHIR app gallery submission if required.
What compliance-ready healthcare AI architecture looks like in practice.
HIPAA §164.312
Technical safeguard controls implemented across all systems that store, process, or transmit PHI - access control, audit logging, encryption, and transmission security.
AES-256 Encryption
All PHI encrypted at rest using AES-256. Encryption keys managed via AWS KMS or HashiCorp Vault with rotation policies and access auditing.
TLS 1.3 In Transit
All data in transit encrypted with TLS 1.3. Certificate pinning on mobile clients. No PHI transmitted over unencrypted channels under any circumstance.
BAA-Ready Vendors
We only use sub-processors that offer signed Business Associate Agreements. AWS, Azure, and GCP HIPAA eligibility fully documented in your compliance evidence pack.
IEC 62304 SDLC
For SaMD, our software development lifecycle is documented per IEC 62304 - software safety classification, unit tests, integration tests, and traceability to requirements.
Penetration Testing
Annual third-party penetration testing of all healthcare AI systems. Results documented, findings remediated, and evidence provided for your security review processes.