Question 1

Is the AI agent HIPAA-compliant?

Accepted Answer

Yes. We deploy on AWS Bedrock under a signed BAA, in US-East or US-West, with PHI scrubbing at the tool boundary so the LLM only sees what it strictly needs to answer. De-identification, audit logs, and role-based access control applied at the server. For higher-assurance deployments we use AWS GovCloud with self-hosted Llama.

Question 2

Which models are HIPAA-eligible?

Accepted Answer

On AWS Bedrock under BAA: Anthropic Claude, Meta Llama, AWS Titan. Direct Anthropic API is also covered under enterprise tier with a signed BAA. We avoid OpenAI for HIPAA scope unless a signed BAA is in place for your tenant. For maximum control, we deploy self-hosted Llama on AWS in your VPC.

Question 3

Do you build FDA-cleared medical devices?

Accepted Answer

No. We build AI workflows that support clinical decision-making but are positioned as Software as a Service for Healthcare (SaaS) — administrative, operational, or clinical-decision-support tools that do not require FDA SaMD clearance. If your use case requires SaMD, we partner with a regulatory consultancy and scope the build accordingly.

Question 4

Can you integrate with Epic, Cerner, Athena?

Accepted Answer

Yes via FHIR R4 and HL7 v2. Epic via App Orchard. Cerner via Code/Open Developer Experience. Athena via marketplace integration. We have shipped FHIR-based agents that read appointment and clinical data into the agent context and write structured notes back to the EHR.

Question 5

How much does healthcare AI development cost?

Accepted Answer

Single-purpose agent under HIPAA BAA: $40K-$100K. Multi-tool suite: $100K-$220K. Enterprise platform with EHR integration and HITRUST alignment: $220K-$350K. HIPAA-scoped builds typically run 15-25% higher than non-HIPAA equivalents due to compliance overhead, audit logs, and BAA negotiation.

Question 6

How do you prevent hallucination on clinical content?

Accepted Answer

Three controls. (1) Citation-grounded RAG: every clinical claim links back to source document with page number. (2) Eval harness with adversarial test cases: known-wrong scenarios designed by domain experts. (3) Human-in-loop on clinical decisions: AI proposes, licensed clinician approves. We never ship clinical-decision agents without human approval gates.

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