AI development for healthcare

Where healthcare AI actually delivers

• Prior authorization packet assembly — Pull the clinical evidence a payer requires, assemble the packet, and flag gaps. Measurable in days-to-decision and staff hours.
• Referral and intake document processing — Faxes, PDFs, and scanned records converted to structured intake with confidence routing. Still one of the highest-ROI automations in the sector.
• Coding and revenue cycle support — Code suggestion with citation to the supporting chart text, reviewed by a certified coder — never auto-submitted.
• Clinical documentation drafting — Draft notes from encounter data for clinician review and sign-off, with the clinician always the author of record.
• Permissions-aware policy and protocol lookup — Retrieval over internal clinical and operational policy, filtered by the requesting user's role.

Non-negotiables for PHI

• BAA-covered inference — Model providers must be under a business associate agreement, or inference runs inside your own cloud account. No exceptions, no shadow prototypes on consumer endpoints.
• PHI minimization before inference — Send the minimum necessary. De-identify or tokenize where the task allows it.
• Role-filtered retrieval — The model must never surface a record the requesting user could not open in the EHR directly.
• Immutable audit logging — Every inference logged with inputs, retrieved context, model version, and the human action taken.
• Explicit non-clinical scoping — Where the intent is administrative, say so in the product surface and the documentation — scope creep into clinical decision support changes the regulatory picture entirely.

Integration reality: the hard part is the EHR

Most healthcare AI timelines are set by data access, not by modeling. FHIR APIs cover less than teams expect, HL7 v2 interfaces carry site-specific quirks, and a meaningful share of clinical content still arrives as scanned documents and faxes. Budget the majority of the program for interface work, document handling, and reconciliation — in our benchmark, data access and cleaning alone averages 29% of production AI build cost, and healthcare sits at the high end of that range.

Realistic targets

How we engage

US-based W-2 senior engineers working inside your cloud tenant, identity provider, and repositories — no offshore subcontracting on PHI workloads. Engagements start with a 4-week prototype sprint against real (de-identified where possible) data, producing an evaluation baseline and a go/no-go before production budget is committed. IP assigned as work-for-hire from the first commit, under your MSA and BAA.