Healthcare
Engineering where the margin for error is small.
EHR integrations, patient portals, telehealth platforms, and medical device software: built by engineers who understand HIPAA isn't a checkbox.
The real blockers
Healthcare engineering has a short list of things that can go wrong.
Most of them are expensive: legally, financially, and in terms of patient trust.
HIPAA isn't negotiable
Protected health information has specific technical safeguards, audit requirements, and breach notification rules. Engineers who haven't worked in healthcare often underestimate what's actually required.
Interoperability is genuinely hard
HL7, FHIR, and legacy EHR systems don't speak the same language. Building reliable integrations with Epic, Cerner, or Allscripts requires patience, domain knowledge, and careful error handling.
Clinical workflows are complex
Software that looks simple from the outside: a scheduling system, a care coordination tool: has clinical context that takes time to understand. Bad UX in healthcare has real consequences.
Regulatory timelines don't flex
If your software touches a medical device or a clinical decision, FDA oversight applies. Engineers who've never dealt with regulated software development often underestimate the documentation and validation requirements.
What we build
Real use cases, not marketing categories.
EHR and EMR integrations
HL7 v2, FHIR R4, and vendor-specific APIs for Epic, Cerner, Athenahealth, and others. Engineers who've navigated the quirks of each vendor's implementation.
Patient portals and engagement tools
Appointment scheduling, lab results, messaging, and care plan access: built to the accessibility and security standards healthcare requires.
Telehealth and virtual care platforms
WebRTC-based video consultations, async messaging, and remote monitoring integrations. Engineers who've handled the latency, privacy, and reliability challenges specific to telehealth.
Health data infrastructure
HIPAA-compliant data pipelines, claims processing systems, and analytics warehouses. Built with audit trails, encryption at rest, and access controls from the start.
AI in this industry
Predictive analytics, clinical NLP, and AI-assisted diagnosis support are changing how healthcare software gets built. Our engineers working in this space come with awareness of the explainability and validation standards that matter most: models deployed in clinical settings need documentation that a typical data science project doesn't require.
Common tech stack
What healthcare engineering teams typically use.
Python
Node.js
Java
PostgreSQL
Redis
Kafka
TerraformFAQ
Common questions
Healthcare engineering is different. We know that.
Tell us what you're building and the compliance context. We'll match accordingly.