Healthcare AI, after the demo.
Architecture, integrations, safety, testing and buying decisions — written from the point where AI has to survive real clinics, real staff and real patient data.
Architecture & Data
RAG in Healthcare: The Useful Version
How healthcare RAG should handle patient scope, provenance, structured data, dates, permissions, retrieval quality and clinical source truth.
Read guide →Architecture & DataFHIR for AI: Healthcare Architecture Guide
How FHIR can support healthcare AI and agents: resources, search, provenance, terminology, tool design and the limits of FHIR alone.
Read guide →Safety & Compliance
LLMs, PHI & HIPAA: Architecture Guide
How to think about PHI, BAAs, LLM providers, logging, RAG, retention and healthcare AI architecture under HIPAA.
Read guide →Safety & ComplianceHealthcare AI Guardrails: Production Guide
Practical guardrails for healthcare AI: source truth, tool permissions, confirmation, escalation, identity, medical boundaries and audit logs.
Read guide →Safety & ComplianceTesting Healthcare AI Agents
A practical evaluation framework for healthcare AI agents: scenarios, tool calls, context, safety, multilingual behavior and regression testing.
Read guide →Clinic Workflows
Healthcare AI Use Cases: What to Automate
A practical map of healthcare AI use cases by workflow, value, risk and implementation difficulty — not another list of AI buzzwords.
Read guide →Clinic WorkflowsAI Medical Scribe: Production Guide
What an AI medical scribe actually needs beyond transcription: audio capture, terminology, note generation, review, EHR write-back and evaluation.
Read guide →Buying & Planning
AI Receptionist for Medical Practices
A practical buyer's guide to medical AI receptionists: booking, context, integrations, safety, escalation, identity and real-world edge cases.
Read guide →Buying & PlanningBuild vs Buy Healthcare AI
When to buy healthcare AI SaaS, build custom software, use an internal team or combine them — based on workflow, integrations, risk and economics.
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