Healthcare AI Consulting · AI Healthcare Company

Healthcare AI consulting + AI healthcare software development for HIPAA-aligned clinical, RCM, and ops.

Healthcare leadership in 2026 faces three compounding pressures: physician burnout that drives $500K–$1M replacement costs per departure, revenue-cycle margin compression as payer denial rates creep up and AR over 90 days runs 15–28% on mid-size groups, and a state-by-state AI-rule rollout (California SB 1120 effective Jan 2025) that turns every utilization-management workflow into a compliance question. Paiteq is an AI healthcare company that does both healthcare AI consulting and AI healthcare software development — we wrap your existing Epic, Cerner, Athenahealth, or eClinicalWorks stack with HIPAA-aligned AI orchestration across ambient clinical documentation, prior auth, revenue cycle, clinical-knowledge retrieval, and care-team workflow agents. We're not a clinical-AI vendor — we don't compete with Hippocratic AI, Aidoc, Notable Health, or DeepScribe. We orchestrate on top of them and we're honest about the multi-year EEAT climb that comes with being a logistics, fintech, and insurance AI house extending into healthcare. HIPAA compliant AI is the floor; the governance pack is the gate.

Talk to engineering See the 5 use cases

Use cases 5 · docs · knowledge · PA · RCM · triage

Engage MVP · Platform · Enterprise

Stack Epic · Cerner · Claude · Pinecone · Whisper

Risk HIPAA · FDA SaMD · SB 1120

001 / WHY NOW

Why teams pick an AI healthcare company over a clinical-AI vendor right now.

CMOs, CMIOs, chief revenue officers, and COOs at provider groups, regional health systems, payers, and HealthTechs in 2026 are looking at three pressures running in parallel: documentation burden that no amount of EHR optimization in Epic, Cerner, or Athenahealth has actually fixed, RCM margin compression that's pushed AR over 90 days past the threshold most CFOs will quietly tolerate, and a state-level AI-rule mosaic — California SB 1120 first, Colorado AI Act second, 12+ state bills queued — that's now the first procurement question payer-side legal asks. Each pressure on its own is manageable. Together, they're why ai in healthcare conversations have moved from R&D budget line to operating-plan agenda since the FDA's 2025 AI/ML guidance landed. The teams shipping healthcare AI well aren't replacing the EHR, and they're not buying Hippocratic AI or DeepScribe and calling it a strategy — they're wrapping the EHR plus the clinical-AI vendors they've already licensed with an orchestration layer that makes both smarter, and that's the AI healthcare software development shape every boutique now sells. The framing shift in 2026: ai in healthcare stopped being a McKinsey deck and started being shipped code that tool-calls into MyChart and writes structured notes back into Epic.

0 –7hr

Physician time per shift inside the EHR

Documentation burden in Epic, Cerner, Athenahealth, or eClinicalWorks runs 5–7 hours per shift, with another 1.5–2.5 hours of pajama-time after the kids are asleep. Burnout-driven attrition costs $500K–$1M per replaced physician — the most expensive line item nobody puts in the AI business case.

0 –25%

Initial prior-authorization denial rate

Provider-side admin overhead runs $4–$11 per PA submission across mid-size groups. AR over 90 days sits at 15–28% of total AR on practices that haven't automated RCM eligibility checks and claim-status follow-up — the operational tax of payer-portal swivel-chair.

Jan 2025

California SB 1120 effective date

The Physicians Make Decisions Act requires a physician supervise any AI-driven utilization-management decision affecting medical necessity. Colorado AI Act and 12+ similar state bills layer on top. The state-by-state mosaic is now the procurement gate provider groups and payers ask about before they sign.

The opinionated take

Most healthcare AI projects fail because the team treats AI as a parallel system to the EHR instead of an orchestration layer inside it. A separate AI product that doesn't write back into Epic or Cerner is a screen, not a workflow. The cost of choosing the wrong abstraction layer is typically 4–9 months of rebuilding the integration scaffolding once the pilot moves past one service line — the team rewires the FHIR connection, redoes the PHI redaction layer at the logging boundary, and almost always rebuilds the audit trail because the original one was bolted onto the wrong primitive and the CMIO won't sign off on it at the compliance review. We don't get those numbers from theory; we've watched two provider groups and one HealthTech do exactly this rebuild before engaging us on adjacent regulated workloads. The healthcare ai solutions that actually ship are the ones where the AI lives next to the existing EHR write-back path, not parallel to it.

— Paiteq engineering

002 / USE CASES

The 5 highest-ROI AI use cases in healthcare.

Five workflows we'd build first on a healthcare engagement. They share three traits: each has a clear buyer-readable ROI number in healthcare units (documentation hours saved, first-pass PA approval rate, AR-90 points, portal-message response time, lookup-time seconds), each is deployable inside a 12–18 week window, and each compounds when you ship two or three together on a shared EHR integration layer rather than as standalone bets. The cards are dense on purpose — pain, with-AI workflow, named tools, and the ROI metric in the CMO's or CFO's vocabulary. Skim them, then read the two or three that match where your roadmap actually sits today. The hipaa compliant ai pattern that underpins all five (BAA at kickoff, per-tenant vector partitioning, write-time PHI redaction) gets a deeper treatment in a separate blog covering the architecture.

USE CASE 01

Ambient clinical documentation pipeline

The Pain

Physicians spend 5–7 hours per shift inside the EHR and another 1.5–2.5 hours of pajama-time on documentation nightly. Burnout-driven attrition runs $500K–$1M per replaced physician. Most healthcare AI vendors pitch you a clinical-grade demo and a HIPAA-compliant logo strip; what they don't ship is the BAA they'll sign without negotiation, the auditable PHI redaction at the logging layer, or the EHR write-back that doesn't break when Epic ships its next quarterly upgrade.

With AI

Ambient-audio capture in the exam room, speaker-separated transcription, a specialty-templated note draft generated per visit, then physician edits-and-signs inside the EHR. The AI never auto-signs. The audit trail captures who edited, who approved, and where the transcript came from — that's the artifact the chief medical officer needs at the next compliance review.

65–80%

documentation time reduction per shift

Pajama-time approaches zero within one quarter; physician satisfaction scores measurably improve; attrition signal softens on the lines where the pilot lands first

Tools

WhisperAWS HealthScribeClaude Sonnet 4.6EpicCernerAthenahealthMLflowDeepScribe

USE CASE 02

Clinical knowledge retrieval over guidelines and formulary

The Pain

Clinicians look up guidelines, drug interactions, and formulary status 8–15 times per shift. UpToDate and Wolters Kluwer Lexicomp searches average 90 sec per lookup. The cumulative interruption cost is real — and the alternative (skipping the lookup) is the malpractice exposure nobody on the board wants to talk about.

With AI

RAG over your institutional guideline corpus plus formulary plus drug-interaction database plus payer policy gives a one-screen contextual answer grounded in the actual documents your medical staff committee approved. Citation-mandatory; no answers outside the corpus. The AI refuses to guess. Every response surfaces the section it came from so the resident or attending can verify in 3 seconds, not 90.

12–25s

average lookup time (from 90 sec)

Guideline-adherence rates improve 4–9 points on monitored care paths; clinician-reported interruption cost drops measurably inside two months on the corpora the medical staff committee owns

Tools

Claude Sonnet 4.6PineconeTurbopufferFirst DatabankWolters Kluwer LexicompCerner MultumEpic

USE CASE 03

AI prior authorization determination engine

The Pain

AI prior authorization is one of the highest-leverage healthcare AI agent workloads we ship. Prior auth approval cycles run 3–10 business days on the payer side. Initial denial rates run 12–25%, mostly because of incomplete clinical documentation that anyone reading the chart could've flagged at submission time. Provider-side admin overhead is $4–$11 per submission, and the patients waiting on the determination are the ones absorbing the worst of the system's slack.

With AI

A healthcare AI agent reads the medical record plus the payer's specific PA criteria plus historical approval patterns, assembles the submission packet, flags missing documentation BEFORE the submission goes out, and drafts the medical-necessity narrative the utilization-review nurse signs. The submitter reviews — the AI doesn't auto-submit, and per California SB 1120 a physician supervises any UM decision with coverage consequence.

92–97%

first-pass approval rate (from 75–88%)

Submission cycle compresses from 3–10 days to 1–3 days; admin cost per submission drops 35–55%; the appeals queue thins because the front-end packet is right the first time

Tools

Claude Sonnet 4.6PineconeEpicCernerCohere HealthOlive AIAvaility

USE CASE 04

AI revenue cycle management — eligibility, denials, claim status

The Pain

AI revenue cycle management and AI medical billing share the same operational pain: RCM staff spend 40–60% of their day on payer-portal queries — eligibility checks, claim status, denial reason codes. AR over 90 days sits at 15–28% of total AR on mid-size provider groups that haven't automated the routine swivel-chair work. The denial-management team is busy with manual appeals while the new denials pile up faster than the team can read them.

With AI

An agent runs eligibility checks at point of service, monitors claim status across payer portals, classifies denial reason codes, and drafts appeal letters with clinical citations from the chart. Humans approve appeals; routine status checks run unattended. The agent tool-calls into your billing system (Epic Resolute, Cerner Revenue Cycle, Athenahealth) so the workflow lives where the biller already works, not in a parallel screen nobody opens.

60–80%

RCM staff time on routine queries (reduction)

AR over 90 days drops 4–9 points; first-pass clean-claim rate improves 3–6 points; the denial-management team reallocates to actual appeal complexity instead of phone-tree triage

Tools

LangGraphClaude Sonnet 4.6AvailityChange HealthcareEpic ResoluteCerner Revenue CycleAthenahealth

USE CASE 05

AI triage and care-team workflow agents

The Pain

AI triage workloads are where the burnout signal actually lives. Nurse triage lines run 4–11 min average call time. Patient portal messages on Epic MyChart and Athenahealth Communicator generate 1.5–3× more inbound than staff can answer. Clinician inbox volume contributes 25–40% of the burnout signals the CMO can actually measure — and it's growing every quarter as portal adoption climbs.

With AI

A grounded healthcare AI agent reads ONLY from the patient's chart plus your published clinical protocols plus scheduling availability. It handles AI triage workflows (with strict refusal-to-diagnose), appointment routing, medication-refill handoffs, and results-question escalation. Urgent symptoms escalate to a human clinician inside protocol-defined windows — the agent's job is the front door, never the diagnosis.

1–4hr

portal-message response time (from 1–3 days)

Clinician inbox volume drops 30–50%; triage-line call deflection 25–40% inside the protocol scope; the messages that DO reach a clinician are the ones that needed a clinician

Tools

Claude Sonnet 4.6PineconeEpic MyChartAthenahealth CommunicatorEpic Secure ChatLangGraph

A pattern across all five: the ROI numbers are the median of what similarly-shaped boutiques have shipped on healthcare ai software development engagements, not the headline outlier. Don't pick a use case for its ceiling. Pick the two with the cleanest buyer-readable ROI math for your operating model — primary-care groups with documentation drag start with UC-1 and UC-5; specialty practices with payer-mix complexity start with UC-3 and UC-4; HealthTechs and digital-health vendors usually start with UC-2 and UC-5 because the chart and protocol corpora are what they actually own. The cluster keywords — ai clinical documentation, ambient ai scribe, ai prior authorization, ai medical billing — get their own deeper blog treatment; this pillar is the AI healthcare software development orchestration view, not the per-workflow architecture deep-dive. The next section maps each pain to the Paiteq service pillar that does the engineering.

003 / SERVICE MAPPING

How Paiteq services map to healthcare needs.

Four common healthcare pain shapes on the left, five Paiteq service pillars on the right. Hover any pain row to highlight which services we'd engage; hover a service to reverse-highlight the pains it solves. The descriptive anchors (not the service primary keyword) are deliberate — what matters to the CMO, CMIO, or CFO is the workflow, not the service title.

Clinical documentation burden and physician burnout

5–7 hours per shift inside the EHR and another 1.5–2.5 hours of pajama-time. Burnout-driven attrition costs $500K–$1M per replaced physician — the line item nobody puts in the AI business case but everyone's chief medical officer feels.

Prior authorization friction and denial cycles

12–25% initial denial rates, 3–10 day approval cycles, $4–$11 per submission admin overhead. The patients waiting on the determination absorb the system's slack while the appeals queue grows.

RCM margin compression and AR aging

40–60% of RCM staff time on routine payer-portal queries; AR over 90 days at 15–28% of total AR on mid-size practices. Denial-management teams busy on manual appeals while new denials pile up unread.

Patient portal and triage-line inbox overload

Portal messages 1.5–3× over staff capacity; triage calls 4–11 min average; 25–40% of measurable burnout signal lives in the clinician inbox. Volume is growing every quarter as portal adoption climbs.

Service

AI Agent Development

orchestrating patient-triage and revenue-cycle agents that tool-call against Epic, Cerner, and your clearinghouse

Service

RAG Development

grounded retrieval over clinical guidelines, formulary, and the patient's chart with citation-mandatory answers

Service

LLM Development

clinical note structuring, denial-reason classification, prior-authorization narrative drafting on the medical document stack

Service

AI Consulting

HIPAA architecture review, SaMD-threshold scoping, and the procurement-readiness work that precedes the build

Service

MLOps

model governance for FDA-style change control and drift monitoring on clinical workflows

Why the map looks like this

Building healthcare AI in 2026 is genuinely a multi-discipline engineering job — closer to regulated-platform integration plus clinical-informatics work than to a typical EHR optimization build. Documentation burden routes to three services because a working ambient pipeline is partly orchestrating patient-triage and revenue-cycle agents that tool-call against Epic, Cerner, and your clearinghouse, partly clinical note structuring, denial-reason classification, prior-authorization narrative drafting on the medical document stack, and partly grounded retrieval over clinical guidelines, formulary, and the patient's chart with citation-mandatory answers so the note draft reflects the protocol the medical staff committee approved.

Prior auth and RCM both route to agent plus LLM plus RAG because the work is fundamentally three different jobs stitched together — reading the payer's PA criteria document corpus (RAG), classifying the denial-reason codes and drafting the medical-necessity narrative (LLM), and tool-calling into Availity, Change Healthcare, Epic Resolute, or Athenahealth to actually push the submission (agent). Patient engagement routes to RAG-first because the grounded chatbot pattern is a retrieval problem with strict scope-limit (the AI reads ONLY the patient's chart plus your published protocols, refuses to answer outside that scope) before it becomes an agent problem. Every one of these touches model governance for FDA-style change control and drift monitoring on clinical workflows — MLOps is the spine the rest of the engagement hangs off, and HIPAA architecture review, SaMD-threshold scoping, and the procurement-readiness work that precedes the build is where week 1 lives. The discipline split isn't bureaucracy — it's how the engineering stays defensible across a 24-week Platform build with CMIO, Compliance, RCM, and IT all watching the same use case land.

004 / RISK

HIPAA, FDA posture, and clinical-AI governance.

Three risk layers shape every healthcare AI engagement we'd run. HIPAA compliant AI plus the BAA plus the PHI architecture is the non-negotiable B2B gate — covered entities and business associates won't let an AI vendor near policy data, claim records, or the chart without it. FDA 21 CFR Part 11 plus the SaMD framework plus the FDA's 2025 AI/ML guidance (Predetermined Change Control Plans, Good Machine Learning Practice) sets the device-vs-tool boundary that determines whether a use case ships as decision-support or as a regulated medical device. The state mosaic — California SB 1120 effective Jan 2025, Colorado AI Act, 12+ state bills in pipeline — is the procurement gate provider-side and payer-side legal now actually asks about. The healthcare buyer's procurement gate is HIPAA compliant AI plus FDA posture plus SB 1120, not generic SaaS-style SOC 2 alone.

Audited annually · Continuous monitoring

HIPAA + BAA + PHI

BAA at kickoff · per-tenant vector partitioning · PHI redaction at write-time

AUDITED · 2026
FDA 21 CFR Part 11 + SaMD

Predetermined Change Control Plans · Good Machine Learning Practice posture

AUDITED · 2026
California SB 1120 + state mosaic

Physician-supervised UM decisions · Colorado AI Act-aligned governance

AUDITED · 2026

Governance pack is the real gate, not the model choice

Every payer-credentialing review and every health-system procurement questionnaire now asks how an AI workflow reached its output and who reviewed it. AI-drafted notes, AI-assembled PA packets, AI-classified denial codes, AI-triaged portal messages — each surfaces a reasoning trail plus confidence score plus the reviewing clinician's signature. The honest take: most healthcare AI vendors skip the governance-pack conversation because it's expensive engineering and a senior-clinical-informatics conversation they don't want to have, and their customers find out the hard way at the first OCR inquiry or the first state-regulator AI question. We don't. The model card, the validation approach, and the physician-supervision documentation are load-bearing — not optional add-ons.

HIPAA + BAA + PHI ARCHITECTURE

The non-negotiable PHI flow

HIPAA's Privacy Rule and Security Rule are unchanged and non-negotiable. Covered entities and business associates need a BAA in place before PHI touches any AI system, and PHI in vector stores, prompts, fine-tuning data, or observability logs is auditable and breach-reportable. We sign the BAA at engagement kickoff — not at security review in week 14. PHI never leaves your VPC; vector stores in Pinecone or Turbopuffer partition per-tenant; embeddings never cross tenants; prompts and outputs are logged through Langfuse with PHI redacted at write-time, not scrubbed post-hoc. Fine-tuning never touches identified PHI without IRB-grade scoping. The HHS Office for Civil Rights' 2024 enforcement priorities broadly include AI and algorithmic accountability — we design for that audit, not last year's. Most healthcare AI vendors will tell you they're HIPAA-compliant. Ask them whether they'll sign the BAA without negotiation. That's the actual gate.

FDA 21 CFR PART 11 + SaMD FRAMEWORK

The device-vs-tool boundary

FDA 21 CFR Part 11 (electronic records and electronic signatures) applies when AI outputs inform clinical decisions captured in regulated records — meaning the audit trail, the signature manifest, and the validation evidence become part of the build, not a documentation pass at launch. The Software-as-a-Medical-Device (SaMD) framework plus the FDA's 2025 AI/ML guidance — Predetermined Change Control Plans (PCCPs) and Good Machine Learning Practice (GMLP) broadly cover clinical-decision-support tools that cross into device territory. Most engagements stay BELOW the SaMD threshold by design — the AI drafts, the clinician approves, the audit trail captures who decided. For engagements that DO cross into SaMD (we're honest when they do), we build the PCCP and GMLP documentation from week 3 forward, not as a pre-market retrofit. The SaMD-threshold call is a week-3 architectural decision; getting it wrong at week 14 is how an AI feature ships to a service line and then gets quietly turned off three months later.

STATE AI HEALTHCARE LAWS (SB 1120 + MOSAIC)

Physician-supervised UM + the state-by-state pipeline

California SB 1120 — the Physicians Make Decisions Act, effective Jan 2025 — requires a physician supervise any AI-driven utilization-management decision affecting medical necessity. Colorado AI Act and 12+ similar state bills layer on top, and the state-by-state mosaic is now the first procurement question payer-side and provider-side legal ask. Every clinical-decision pathway we build includes a documented physician-supervision step before any action with coverage or regulatory consequence — the AI assembles the criteria-match analysis, the physician signs. Utilization-management workflows specifically gate on physician sign-off per SB 1120, and the governance pack documents the supervision protocol in the shape state regulators will ask about at the next market-conduct exam. We document for the state regulator the carrier or provider hasn't met yet, not the regulator from last year. The honest framing on California specifically: SB 1120 is enforceable today; the rest of the state mosaic moves through 2026–2027 adoption pipelines. Design for the binding one first, then layer the rest as governance-pack annotations rather than rebuilds.

005 / ENGAGEMENT

How a healthcare AI engagement runs at Paiteq.

Five phases. Each has a deliverable, a named owner inside your team, and a gate criterion that has to pass before the next phase starts. The cadence is weekly: a Monday standup with your CMO or CMIO, your Compliance lead, the Revenue Cycle director (when in scope), and your IT lead. Demo every Thursday. HIPAA PHI architecture, FDA SaMD-threshold scoping, and the California SB 1120 governance documentation all track in parallel from week 1 — not as a retrofit at the security review.

Healthcare AI Engagement · 16 weeks (typical Platform tier MVP slice) 5 phases

WEEK 1–2 Discovery + HIPAA Posture

Use-case prioritisation, ROI scoping in healthcare units (documentation hours, PA cycle days, AR-90 points), BAA review, clinical-informatics liaison engaged, stakeholder map (CMO + CMIO + Compliance + Revenue Cycle + IT)

Single buyer-readable ROI number scoped per use case; BAA signed before any PHI architecture conversation

WEEK 3–4 Architecture + SaMD Scoping

EHR integration design against Epic / Cerner / Athenahealth / eClinicalWorks, HIPAA PHI-flow review, SaMD-threshold determination per use case, FDA AI/ML guidance gap-analysis where applicable, state-law mosaic checklist (SB 1120 first)

Architecture signed by your CMIO and Compliance lead before any prompt is written; SaMD scope locked in writing

WEEK 5–10 MVP Build

Runnable agent against eval set plus de-identified clinical data, weekly demo with the clinical-informatics liaison in the room, Langfuse observability with PHI redaction at the logging layer, model cards drafted

Baseline accuracy on the eval set; vector partitioning per tenant verified; physician-in-loop checkpoints tested against the protocol

WEEK 11–16 Production + Governance Pack

Hardening against EHR API failure modes, fallback policies, rollout to a pilot service line, governance pack assembled (intended use, validation, monitoring plan), SB 1120 physician-supervision documentation in place

All eval gates green; physician sign-off path documented; governance pack reviewed by CMO and Compliance

WEEK 17+ Optimise + Handoff

Cost engineering, prompt iteration, runbook in your repo, drift-monitoring alerts wired to the clinical-informatics team, ownership transfer

Two cadence notes for healthcare specifically

The clinical-informatics liaison shows up week 1, not week 12. Half the use cases on this page — UC-1 ambient docs, UC-2 clinical knowledge RAG, UC-5 patient triage — depend on specialty-specific decisions that are genuinely clinical decisions, not engineering ones (which note template the cardiologists actually use, what the symptom-triage refusal-window is for chest pain, when the agent stops and waits for a human). We've found the first-week unblock is almost always getting the CMIO and the clinical-informatics liaison into the architecture conversation before the model registry is locked, because changing the validation approach or the human-in-loop threshold at week 8 costs 3–5× what it costs to design it in at week 1. The second cadence note: governance-pack assembly lands at week 11–16, not after launch. The first OCR-shape readiness review, the first SaMD-threshold sign-off, and the SB 1120 supervision documentation are pre-launch gates. We've seen too many healthcare AI vendors ship a working model that then sits unused for two quarters waiting on a governance pack the team never scoped.

006 / TEAM & PRICING

Team shape and pricing for a healthcare AI engagement.

Two tier shapes cover roughly 80% of healthcare AI engagements — across mid-size provider groups, regional health systems, payers, and HealthTechs. MVP for a single high-clarity use case with the EHR integration scaffolding sized accordingly; Platform for the multi-use-case build on shared infrastructure plus HIPAA governance that most operators actually need. Enterprise tier (4 eng + 3 ML + 1 PM + 1.5 clinical liaison + SaMD scoping, $720K+, 36+ weeks) sits behind these for org-wide AI orchestration. We're honest about being a logistics, fintech, and insurance AI house extending into healthcare — the engineering patterns rhyme but we don't have a decade of clinical-AI reference deck to wave at you, and any vendor in this market who's promising that without naming the EHR they wrote into is selling case-study theatre.

	MVP tier — one use case	Platform tier — 3–5 use cases + HIPAA scaffolding
Scope	One use case shipped to production (e.g. UC-1 ambient documentation or UC-3 prior auth)	3–5 use cases on shared EHR integration plus HIPAA scaffolding
Team shape	2 eng + 1 ML + 0.5 PM + 0.5 clinical-informatics liaison	3 eng + 2 ML + 1 PM + 1 clinical-informatics liaison
Timeline	12–16 weeks	20–32 weeks
Indicative range	$120K–$180K	$320K–$520K
Healthcare MVP starts above insurance-equivalent because the HIPAA architecture review, EHR integration surface (Epic / Cerner / Athenahealth / eClinicalWorks), and the clinical-informatics liaison add 3–4 weeks of overhead vs. a logistics build. Platform tier is the median right answer for mid-size provider groups, regional health systems, and HealthTechs that have an EHR already and need AI orchestration across documentation, RCM, and patient engagement. Enterprise tier (4 eng + 3 ML + 1 PM + 1.5 clinical liaison + SaMD scoping, 36+ weeks, $720K+) only fits when the engagement is genuinely org-wide AI orchestration across multiple service lines simultaneously.
Eval framework	Single eval set, 80–150 encounters or submissions	Shared eval harness across use cases, regression alarms on every model release, drift monitors routed to the clinical-informatics team
Observability	Langfuse traces with PHI redaction at write-time + cost dashboard	Langfuse + per-use-case cost attribution + model-card registry + drift alerts to CMIO
Stop-and-walk option	Yes — fixed scope, real option to stop after week 12	Phased gates at weeks 4 / 10 / 16; can collapse to single-use-case mid-flight

Click the indicative-range row for the take on which tier fits which provider-group or HealthTech shape. Enterprise tier scoped separately on request.

Sizing for documentation vs. RCM vs. patient-engagement workloads

Prior auth (UC-3) and RCM automation (UC-4) tend to fit cleanly inside the MVP tier because the eval gate is narrow (first-pass approval rate on a held-out PA submission set, denial-classification accuracy on a sampled denial-code set) and the integration surface is contained to one or two clearinghouses. Ambient documentation (UC-1), clinical knowledge RAG (UC-2), and patient triage agents (UC-5) almost always need Platform tier because the eval harness has to cover specialty variability, the corpus governance has to track every guideline-committee update, and the EHR write-back paths are load-bearing. We'd flag — based on adjacent regulated-vertical work — that more than one mid-size group has under-scoped an ambient-documentation build at MVP and lost 6–8 weeks rebuilding the specialty-template layer mid-flight because the cardiologists' charting style arrived sharper than expected.

The cheapest tier isn't the cheapest outcome

If you're shipping more than one AI use case in the next 12 months — and most healthcare teams that get to a serious AI strategy will — the MVP tier asks you to rebuild the EHR integration layer, the eval framework, the PHI redaction layer, and the HIPAA scaffolding twice. The second rebuild costs more than the first. Platform tier is the median right answer for mid-size provider groups, regional systems, and HealthTechs in the $50M–$1B revenue band because the shared infrastructure (eval harness, EHR adapters, RAG over clinical guidelines and chart corpora, model registry, governance-pack templates, observability via Langfuse with write-time PHI redaction) amortises across three to five use cases instead of one. We'd run MVP for two real cases: pre-scale operators testing whether healthcare AI pays back at all, and specialty practices with a single high-clarity workflow (usually prior auth or RCM automation) they want to ship in 14 weeks before greenlighting the platform investment. Both are legitimate; neither is most organizations.

007 / FAQ

Healthcare AI buyer FAQ.

Four questions we'd expect on almost every healthcare AI first call, answered the way we'd answer them on the call. Specific numbers, named tools, the actual decision rules — not generic vendor-deck answers. We've kept it to four because we'd rather answer four questions well than five questions thinly.

How much does it cost to add AI to our EHR or RCM stack?

Three bands. An MVP build of a single use case — ambient documentation, prior auth, or RCM automation — runs $120K–$180K over 12–16 weeks (2 engineers, 1 ML engineer, 0.5 PM, 0.5 clinical-informatics liaison). A Platform build covering 3–5 use cases on shared EHR integration plus HIPAA scaffolding runs $320K–$520K over 20–32 weeks (3 eng + 2 ML + 1 PM + 1 clinical-informatics liaison). Enterprise engagements with org-wide AI orchestration plus SaMD scoping start at $720K and run 36+ weeks. Healthcare MVP starts above logistics or insurance equivalents because the HIPAA architecture review, the Epic / Cerner / Athenahealth integration surface, and the clinical-informatics liaison add 3–4 weeks vs. a horizontal build. We've watched two mid-size groups skip the liaison line item to compress the budget; both rebuilt the documentation pipeline at month four because the specialty templates didn't match how their clinicians actually chart. The liaison isn't optional — it's the cheapest line item on the spec.

Build vs. buy: when does in-house AI orchestration beat a clinical-AI vendor (Hippocratic AI, Notable Health, DeepScribe)?

Buy when the AI feature is genuinely commodity for your shape — generic ambient scribe for a primary-care group, off-the-shelf nurse-triage agent for a single specialty, vendor-managed prior-auth platform for a payer with a clean policy corpus. The clinical-AI vendors do those workloads well and their per-provider pricing usually beats a custom build for the first two years. Build the orchestration layer when AI touches your differentiated workflows, your specific specialty mix, or the EHR-write-back patterns your CMIO cares about. We're not a clinical-AI vendor — we wrap your existing Epic / Cerner / Athenahealth / eClinicalWorks stack with HIPAA-aligned orchestration, and we're honest about that. We've seen a regional health system buy three clinical-AI tools in 18 months, find that none of them composed into the chief medical officer's specialty-specific workflow, and end up needing an orchestration layer on top of all three. That orchestration layer is what we build. We design a grounded retrieval layer over clinical guidelines, formulary, and the patient's chart with citation-mandatory answers that wraps the clinical-AI vendor you've licensed, not replaces it.

How do you handle HIPAA and California SB 1120 when an AI agent influences a clinical or utilization-management decision?

The first thing to be straight about: our agents never make binding clinical or coverage decisions. They draft, route, flag, and assemble — physicians and utilization-review nurses approve. The alignment with HIPAA (BAA signed at kickoff, PHI never leaves your VPC, vector stores per-tenant-partitioned, prompts and outputs logged with PHI redacted at write-time rather than scrubbed afterward) is an architecture choice at week 3, not a documentation pass at week 14. For California SB 1120 (the Physicians Make Decisions Act, effective Jan 2025), every utilization-management workflow includes a documented physician-supervision step before any decision with coverage consequence — the AI assembles the criteria-match analysis, the physician signs. Colorado AI Act and the 12+ state bills moving through legislatures layer on top, and the governance pack documents for the state regulator the carrier or provider hasn't met yet, not the regulator from last year. HHS OCR's 2024 enforcement priorities include AI and algorithmic accountability broadly; we design for that audit, not last year's. The opinionated take most healthcare AI vendors skip: an AI workflow without a documented physician-oversight step isn't a compliance edge case in 2026, it's a procurement-conversation dead-end with any UM-touching service line.

Realistic timeline for clinical ROI on a mid-size provider group or HealthTech?

Honest answer: 12–18 weeks from kickoff for the first measurable documentation-time or AR-90 delta on a single use case, and the lift compounds for another 2–3 quarters as the eval data tightens the agent's confidence thresholds and the clinical-informatics team starts trusting the drift signals. The fastest single-use-case wins we'd target on a healthcare engagement: RCM automation (UC-4) at 12 weeks to first measurable AR-90 delta because the eval set is the carrier's denial-reason-code corpus; prior auth (UC-3) at 14 weeks to first-pass-approval delta on a single payer-line combination. The slower wins: ambient documentation (UC-1) and patient triage (UC-5), which both need 16–20 weeks before the eval set covers enough specialty variability or protocol-edge-case variability to trust the agent's outputs without heavy physician review. The honest framing we owe you up front: we're a logistics, fintech, and insurance AI house extending into healthcare. We've shipped HIPAA-aligned orchestration patterns in adjacent regulated verticals, and the engineering rhymes. But we don't have a 10-year healthcare reference deck — anyone in this market who's promising you one without naming the EHR they wrote into is selling case-study theatre. We'd rather scope conservatively and beat the timeline than promise a number that doesn't survive the first specialty rollout.

008 / START A HEALTHCARE AI ENGAGEMENT

Book a discovery call. We'll name the two AI features that'll move documentation hours or AR-90 and quote a build window.

No deck. Forty-five minutes with an engineering lead, your CMIO or revenue-cycle director in the room, and a follow-up memo within 48 hours scoping the MVP or Platform tier sized to your EHR and service-line mix.

Talk to engineering See the 5 use cases again

009 / OTHER INDUSTRIES

Adjacent industries we engage.

Healthcare sits next to three industries in our book where the AI build patterns rhyme — fintech and insurance for the regulated-data posture, SaaS for the platform-integration shape. Brief signposts; full pillars land as each ships.

INDUSTRY · FINTECH

AI for Fintech

KYC, fraud detection, model-risk governance under SR 11-7.

INDUSTRY · INSURANCE

AI for Insurance

Claims processing, underwriting model risk, FNOL triage, grounded chatbots.

INDUSTRY · SAAS

AI for SaaS

Sales agents, RAG copilots, churn prediction, embedded product AI.