AI & blockchain for healthcare
We engineer the systems clinical data flows through — patient identity, interoperable records, verifiable AI diagnostics and HIPAA/GDPR-grade pipelines — where a defect is a patient-safety event and every access has to be accountable.
For health systems modernizing records, digital-health startups launching new products, and clinical data teams extending internal engineering capability.
In healthcare, a bug is a patient-safety event
Healthcare software carries consequences most systems don’t. A mismatched patient record, a dropped result, or an opaque AI recommendation can directly affect care. That raises the bar on correctness, traceability and explainability — every record change and every model output has to be attributable, reviewable and safe to act on. Clinical safety isn’t a QA phase; it’s an architectural constraint.
The regulatory surface is unusually heavy. HIPAA, GDPR and regional health-data rules govern how patient data is stored, transmitted, de-identified and accessed — with strict consent, audit and residency requirements. Systems have to prove who touched what and why, encrypt data end to end, and hold up to security and privacy review. These obligations shape the data model and the access architecture from day one.
And healthcare is a web of legacy systems. New products must interoperate with EHRs, hospital information systems, labs and imaging through standards like FHIR and HL7 — often with inconsistent real-world implementations. Getting that integration right, without disrupting live clinical workflows, is where most of the engineering effort actually goes.
Systems we ship for healthcare
Patient identity & records
Master patient identity, consent management and tamper-evident record systems with fine-grained, auditable access control.
Interoperability layer
FHIR/HL7 integration engines that connect EHRs, labs, imaging and third-party apps without disrupting clinical workflow.
AI diagnostics & imaging
Model pipelines for imaging, triage and decision support — validated, monitored and built for clinical review.
Verifiable AI decisions
Explainable, auditable AI outputs with provenance, so clinicians and regulators can trust how a result was produced.
Medical data pipelines
Secure ingestion, de-identification and analytics pipelines that keep PHI protected and residency rules intact.
Clinical & patient apps
Portals, clinician tools and patient-facing apps built on secure APIs and integrated with the systems of record.
Six capabilities, pointed at healthcare
Every Magnus Mage service maps to a concrete need in this sector. Follow any of them for the full capability.
Verifiable records, consent management and provenance for clinical data.
Diagnostics, imaging and clinical decision support — validated and explainable.
Claims, health payments and tokenized incentives where they apply.
Clinical portals, patient apps and EHR/HIS integrations.
HIPAA-ready infrastructure, encryption and audit-grade observability.
Compliance-led architecture, security audits and interoperability strategy.
For health systems modernizing records, digital-health startups launching new products, and clinical data teams extending internal engineering capability.
What working with us changes
Lower execution risk
Safety, privacy and audit trails designed in, so security and compliance review doesn’t stall your rollout.
Faster time to launch
Proven interoperability and data-protection patterns mean you integrate with EHRs instead of reinventing them.
Long-term ownership
Documented, tested systems your own team can operate under regulation — you own all code and IP.
One accountable partner
A single team from architecture through operations, accountable for safety and uptime end to end.
Healthcare & verifiable-AI builds
Enterprise blockchain in healthcare
DeliveredPermissioned networks for regulated multi-party data · Larad consensus · the architecture behind an enterprise chain whose target sectors include healthcare
Read case study →Libonomy
Verifiable AIAI-driven consensus and verifiable decisions relevant to trustworthy clinical AI — cryptographic evidence for consequential actions, which is what trustworthy clinical AI will be held to.
Read case study →How healthcare teams work with us
Common first step — map data flows, consent and the interoperability surface before building.
Validate an AI model or integration against real clinical data and safety review before scaling.
Typical for digital-health startups launching a product end to end, from architecture to production.
Extend an in-house team with health-data, AI and integration specialists under your direction.
Common for health systems — an independent review before a records or interoperability program.
Take over a stalled build or modernize legacy clinical systems without disrupting live care.
Operate, monitor and harden production clinical systems under SLA after launch.
Healthcare questions
Talk to our team about your healthcare build.
Tell us what you’re building. We’ll come back within 2–3 business days with a scoping call — no sales runaround, straight to our team.