Generative AI Consulting for Healthcare

It is an implementation engagement that puts documentation and administrative copilots — note drafts, coding assist, prior-auth packets, referral letters — into production with HIPAA-aware architecture, EHR integration, and a ban on improvised medical advice. It is not a patient-access call-center bot.

Patient-access conversational AI handles scheduling, intake, and benefits on the phone or portal. Healthcare generative AI here is for staff documentation and admin paperwork grounded in the chart. The two share HIPAA and escalation rules but are different products, evals, and EHR write paths.

It can draft notes from the encounter, ambient transcript, and chart, with citations to source sections. A licensed clinician reviews and files. The system must not add problems, meds, or findings that are not in the record; that is a faithfulness failure, not a feature.

No. Intended use excludes symptoms, dosing, diagnosis, and treatment recommendations. Classifiers and output filters treat those as escalations, and the missed-escalation rate is a release gate. Answering a clinical question is a liability, not a roadmap item.

Every vendor in the inference path needs a BAA, PHI should be minimized before the model call, transport and storage are encrypted, access is logged, and retention is defined. Consumer APIs without a BAA are not an option for chart text.

Yes. Typical pattern is FHIR or vendor APIs to read the encounter and write a draft to an inbox, in-basket, or coding worklist. Auto-file to the legal medical record is out of scope for a first pilot.

A scoped documentation or admin pilot with privacy review, EHR integration, and safety evals typically takes 10–14 weeks. Subsequent specialties reuse the same controls and ship faster.

On well-bounded admin intents — note first drafts, packet assembly, coding suggestions that stick — 30–50% of the targeted workload typically moves off the human’s first-pass time, with the clinician still accountable for the filed record.