Healthcare AI company,
shipped on your EHR — not on a slide deck.

An `ai healthcare company` like ours ships production AI workflows on your EHR stack — not slide decks, not pilots that die at month 4. The day-to-day work: scope which workflow moves a P&L line (most often ambient scribe, prior auth, billing-coding suggestion, triage, or patient intake), get a BAA signed, build the integration against your Epic / Cerner / athenahealth / Veeva tenant, pick the right model per workflow (Sonnet 4.6 for clinical narrative, Haiku 4.5 for high-volume routing, GPT-5.4-mini for structured extraction), bake in PHI scrubbing pre-prompt and audit-logging on every inference, ship behind a clinician-in-loop flag, then operate the workflow long enough to prove cost-of-ownership before scaling. We do not sell a product; we ship one workflow at a time and report cost-per-decision monthly. If you want a `healthcare ai development company` that delivers a live integration in six to eight weeks, this is it.

Yes — the operator-grade specifics: (1) BAA available and signed before any PHI is in scope, with our infrastructure providers (Anthropic, OpenAI, Cloudflare, AWS where used) on BAA where the workflow requires it. (2) PHI scrubbing pre-prompt — we deidentify before the model sees the payload using regex + named-entity scrubbing + a clinical deidentification helper tuned to your specialty. (3) Audit log on every inference — request ID, model, token counts, scrubbing actions, retrieved-context references, response, and the clinician/biller who reviewed it, written to your audit-log destination. (4) De-identification helpers for any analytics workflow leaving the BAA perimeter, with the standard 18-identifier safe-harbor stripping plus expert-determination review where required. What we don't claim: SOC 2 Type II or HITRUST CSF certification, or HIPAA-compliant chatgpt as a consumer product (consumer ChatGPT is not BAA-eligible; only the enterprise API on a signed BAA is). If your compliance team needs SOC 2 or HITRUST as a hard gate, we'll say so up front.

Yes — all four, and we've called out the failure mode of each in §6 above. Quick recap. Epic: FHIR R4 + OAuth2, with R&R (Routing & Registration) approval the long pole — budget 4–8 weeks of sandbox-to-prod sign-off before any production write-back. Cerner / Oracle Health: Millennium variant fragmentation is the headline risk; what works on tenant A often needs a shim on tenant B, so plan for per-tenant validation. athenahealth: open API + webhooks are friendly, but rate limits are tighter than Epic and webhook reliability gets uneven under heavy write — design idempotent handlers and a reconcile job that reads back from the API. Veeva: this is the one buyers most often misunderstand — Veeva is life-sciences CRM, not an EHR. If you need patient-chart write-back, Veeva pairs with an EHR via an integration partner; alone it covers MedAffairs, HCP outreach, PromoMats, and MLR-aware content workflows. `Ai integration in healthcare` is mostly the integration layer engineering, not the model — picking the right model is the easy 10%; the 90% is the FHIR contract, the auth scope, and the audit log.

An `ai medical scribe` workflow runs in two cost buckets. Model layer on Sonnet 4.6 with prompt caching warm: roughly $0.06–$0.18 per encounter for a 15–25-minute visit. Infrastructure layer (audio pipeline, PHI scrubbing, FHIR write-back, audit-log writes): typically $0.04–$0.10 per encounter. Total: roughly $0.10–$0.28 per encounter — a 40-encounter-per-day clinician runs about $4–$11/day. On a 5-clinician pilot, model + infra sits in the $400–$1,200/month range. The honest cost ceiling is engineering: getting the consent flow right, the structured fields aligned to your EHR's specialty templates, the clinician-in-loop UI to a place clinicians actually trust. That work is the engagement, not the per-encounter spend.

Yes, and this is where the highest-CPC keywords in the cluster ($103 for `prior authorization ai`, $64 for `ai medical billing`) tell you exactly how much buyers value moving these metrics. The pattern that ships: an agent reads the chart, retrieves the relevant payer policy (or the relevant payer's denial-pattern history from your last 12 months of EOBs for revenue-cycle work), drafts the auth packet or appeal letter with cited policy references, and routes to a human reviewer with the rationale pre-written. The reviewer confirms or overrides — the agent never submits autonomously. On the prior-auth side, we typically see 50–60% reviewer-time reduction on routine auths in the first 90 days; complex / cross-policy auths still go to senior reviewer with the draft packet pre-attached. On revenue-cycle, denial-rate reduction of 2–4 percentage points is realistic in the first six months if your denial data is clean. `Ai revenue cycle management` is plumbing, not magic — clean EOB data is the prerequisite, and if your billing system can't export 12+ months of structured EOB data, the audit will say so before the pilot.

Three places we'll say no — covered in §8 above and worth repeating. (1) Autonomous diagnosis without physician sign-off. Final clinical judgment stays with the physician; AI we ship is decision-support that surfaces evidence and drafts a recommendation a clinician confirms. Anything labeled "diagnostic AI" that proposes to make the call without a clinician is either regulated as a medical device (FDA clearance, not a six-week engagement) or shouldn't ship. (2) Pediatric edge cases on generic adult-tuned models. Adult dosing, adult symptom rubrics, and adult risk thresholds do not transfer; pediatric workflows need a narrower surface, tighter clinician-in-loop gate, and pediatric-specific evidence retrieval. (3) Mental-health crisis intervention via chatbot. Acute suicide-risk or psychiatric-crisis interactions should never be handled by a chatbot; we ship hard escalation triggers on risk phrases that stop the bot, display the crisis hotline + a human handoff, and log the trigger for clinician review. Beyond those three, we'll also say no on any workflow where the regulatory posture is unclear, where the data isn't clean enough to build an honest baseline, or where the metric won't move within the pilot window.

No — augment not replace. Diagnosis and final clinical judgment stay with physicians. The AI we build for healthcare organizations is decision-support: it listens to the visit and drafts the note for the clinician to sign off (ambient scribe); it reads the chart and policy and drafts the prior-auth packet for a reviewer to confirm (prior-auth automation); it suggests CPT/ICD codes for the biller to review (billing assist); it routes a symptom intake to the right visit type with a clinician escalating any borderline case (triage assist). In every pattern we ship, the physician (or appropriate clinical reviewer) signs off on the consequential decision and the AI's output goes to them with the rationale pre-written — saving 60–90 minutes a day on documentation, 40+ percent of reviewer time on routine prior-auths, 6+ minutes per intake at the front desk. That's the realistic claim a healthcare AI development company should make: doctors keep doing the diagnosis; the AI handles the documentation, the lookups, the drafts, and the routine routing.

Three tiers, pricing-locked across the cluster. (1) `Healthcare ai consulting` audit: $3K fixed, 1–2 weeks. We shadow clinical ops + IT + RCM, score candidate workflows by clinician hours returned × time-to-ship × regulatory risk, and deliver a 90-day roadmap with per-workflow cost bands and an honest "these won't pay back yet" list. (2) Pilot to production: $10–25K fixed, 4–8 weeks. One workflow shipped end-to-end on your EHR stack, BAA-scoped, audit-logged, clinician-in-loop, with steady + surge-mode config tested and a walk-away point at week 6 — if the metric won't move, we stop before production hardening and you don't pay phase 2. (3) Continuous healthcare AI team: from $5K/month, no annual contract. Embedded PM + AI engineer + clinical analyst shipping the next workflow on your roadmap, with per-workflow monthly cost-of-ownership reporting and a surge-readiness review before flu season and open enrollment. Most `ai healthcare company` engagements we run start with the audit, ship the first workflow on the pilot, and move to monthly for workflows two through five. Cost-per-decision reported monthly on every shipped workflow — no per-decision number, no engagement.