Industries · Healthcare

Healthcare software that clinicians and patients can trust.

Fragmented EHRs, HL7 interfaces held together by hand and mounting clinician admin make healthcare slow, costly and hard to join up. CodeFacts builds interoperable, HIPAA-conscious platforms and applies AI carefully — with clinicians in the loop — so care teams spend less time on screens and more on patients.

The moment

What's changing in healthcare.

The pressure

Providers face rising demand, workforce shortages and burnt-out clinicians, while data sits trapped across EHRs, PACS, labs and legacy departmental systems. Regulators now expect FHIR-based interoperability and information sharing, and patients expect the same digital access they get from every other service. The organisations that win turn safe, well-governed clinical data into better access and lighter administrative load.

The problems

What makes healthcare hard.

The specific, structural challenges we build against — not generic pain points.

EHR interoperability

Epic, Cerner and departmental systems don't talk cleanly; data is exchanged through brittle HL7 v2 interfaces.

Clinician admin burden

Documentation, coding and prior-authorisation work pull clinicians away from patients and drive burnout.

Poor patient access

Booking, records and results are hard to reach, so patients call, wait and miss appointments.

Privacy and compliance

HIPAA and GDPR-class rules demand auditability and access control that older systems can't fully evidence.

Fragmented legacy estate

Decades of departmental systems and point solutions leave no single, reliable view of the patient.

Unsafe use of AI

Clinical AI carries real patient-safety, bias and accountability risk if deployed without oversight.

How technology solves it

From problem to platform.

Each challenge has an engineering answer. This is how we turn them into working systems.

Challenge

Systems that don't interoperate

How we solve it

FHIR-first integration and an interoperability layer that maps HL7 v2, FHIR and legacy feeds into a consistent clinical data model.

Challenge

Documentation overload

How we solve it

Ambient and generative clinical documentation that drafts notes and codes for clinician review, never auto-filing.

Challenge

Hard patient access

How we solve it

Patient portals and telehealth for booking, records, results and secure messaging, tied back into the EHR.

Challenge

Compliance exposure

How we solve it

HIPAA-conscious architecture with encryption, role-based access, full audit trails and private or in-region deployment.

Challenge

No single patient view

How we solve it

A governed clinical data platform that consolidates records, imaging metadata and labs into one trusted source.

Challenge

Risky AI

How we solve it

Human-in-the-loop clinical AI with clear provenance, monitoring and escalation, scoped to decision support not autonomous care.

What we build

Systems built for healthcare.

Interoperability layer

FHIR and HL7 integration engine that connects EHRs, PACS, labs and devices.

Patient portals & telehealth

Web and mobile access for booking, records, results and virtual visits.

Clinical documentation AI

Ambient scribing and coding assistance that keeps the clinician in control.

Care coordination tools

Shared workflows and task management across teams and settings.

Clinical data platform

A governed, auditable source of truth for analytics and population health.

Legacy modernisation

Incremental replacement of departmental systems without disrupting care.

What good looks like

Outcomes we engineer for.

Clinical data that flows across systems via FHIR

Less documentation time back to clinicians

Faster, simpler patient access and follow-up

Auditable, compliant handling of health data

Questions we hear.

Can you integrate with our EHR?+

Yes — we integrate with Epic, Cerner and others through FHIR APIs and HL7 v2 interfaces, mapping to a consistent data model.

How do you keep patient data compliant?+

HIPAA and GDPR-class controls are designed in — encryption, role-based access, audit trails and private or in-region deployment.

Is it safe to use AI in clinical settings?+

We scope AI to decision support with clinicians in the loop, clear provenance and monitoring — it drafts and suggests, people decide.

Will this add to clinician workload?+

The opposite is the goal — we automate documentation and admin so clinicians spend more time with patients.

Where should we start?+

Usually a contained, high-value use case such as documentation or patient access that proves value without disrupting care.

Building something in healthcare?