Question 1

Can AI-generated clinical outputs create HIPAA or liability exposure?

Accepted Answer

PHI that passes through an AI inference call must be handled under a BAA with the model provider (OpenAI, Anthropic, and Google offer enterprise BAA options). For liability, AI-generated clinical recommendations must be positioned as decision-support, not autonomous clinical decisions — provider review is mandatory. We design all AI components with this posture.

Question 2

Do you train custom models on our patient data?

Accepted Answer

We do not train models on PHI unless the PHI has been de-identified per HIPAA Safe Harbor or Expert Determination methods. In practice, we achieve excellent task-specific performance through prompt engineering, few-shot learning, and retrieval-augmented generation (RAG) — approaches that do not require training on your patient data.

Question 3

How do you prevent AI hallucinations in clinical contexts?

Accepted Answer

Our standard approach: citation-grounded retrieval (all AI outputs reference the source documents they drew from), confidence thresholds (low-confidence outputs route to human review rather than presenting as definitive), structured output schemas (constrain the model to produce machine-readable outputs that can be validated), and audit trails (every AI output is logged with its input, model version, and timestamp).

Question 4

What AI models are suitable for HIPAA-covered workloads?

Accepted Answer

Models with enterprise BAA agreements: OpenAI (ChatGPT Enterprise / API with BAA), Anthropic Claude (Enterprise with BAA), Google Cloud Vertex AI (with BAA). Open-source models (Llama 3, Mistral) deployed on your own HIPAA-compliant infrastructure are also viable — PHI stays within your environment. We assess model selection in Discovery based on your use case and compliance posture.

Question 5

How do we explain AI clinical recommendations to regulators or payers?

Accepted Answer

Documentation is the answer. We deliver an AI audit trail showing inputs, model version, and output for every AI-generated recommendation, plus a model card documenting the model's training data, known limitations, and the human-in-the-loop controls. This is what an OIG audit or payer review needs to evaluate AI-generated decisions.

AI & ML Services

Four pillars of this capability.

Clinical NLP & Text Processing

Predictive & Diagnostic Models

LLM Integration & Prompt Engineering

AI Safety & Hallucination Mitigation

Deliverables for every engagement.

AI & ML Services by industry.

The productized version of this capability.

Frequently asked questions.

Ready to build this capability?