We build ambient AI scribe and clinical documentation platforms that listen to patient encounters, generate structured SOAP notes in real time, and sync directly into your EHR — cutting documentation time without cutting corners on accuracy or compliance.
For health systems, telehealth companies, specialty clinics, EHR vendors, and healthcare AI startups building the next generation of clinical documentation tools.
🔒 HIPAA-ready architecture · BAA available · NDA on request
Patient presents with intermittent lower back pain, onset 3 days ago, worsens with prolonged sitting...
1. Mechanical lower back pain — likely musculoskeletal. 2. NSAID trial + PT referral. 3. Follow-up in 2 weeks if no improvement.
✓ Synced to Epic — ICD-10: M54.5 · CPT: 99213
Trusted by health systems, clinics & healthcare AI startups
Every additional minute a clinician spends typing into an EHR is a minute not spent looking at a patient. Across primary care, specialty practices, and hospital systems, documentation has become the single largest driver of after-hours work — commonly called "pajama time" — and one of the most consistently cited causes of physician burnout.
Studies show primary care physicians spend close to six hours a day inside the EHR, with nearly a third of that happening outside normal clinic hours. For every 15 minutes spent face-to-face with a patient, physicians spend roughly nine minutes documenting. An AI medical scribe doesn't just save time — it gives clinicians their evenings, and their attention during the visit, back.
See how much time your team could get backSources: AMA National Physician Burnout Study (2024), peer-reviewed EHR burden studies (PMC)
An AI medical scribe is software that listens to (or reads a transcript of) a patient encounter — in person, on video, or over the phone — and automatically converts that conversation into structured clinical documentation: SOAP notes, HPI, assessment and plan, procedure notes, or specialty-specific templates. Unlike basic dictation software, a modern AI scribe uses ambient speech recognition plus clinical natural language processing (NLP) to understand medical terminology, distinguish clinician speech from patient speech, and organize the conversation into the sections a chart actually needs.
The best implementations go further than transcription. They apply clinical reasoning models fine-tuned on medical language to suggest ICD-10 and CPT codes, flag missing documentation elements required for reimbursement, and push the finished note directly into the EHR — Epic, Cerner/Oracle Health, athenahealth, or whatever system your organization already runs — so the clinician's job shrinks from "write the note" to "review and sign."
Ambient AI scribes have moved from novelty to standard of care faster than almost any other clinical technology — adoption crossed 40% of US and Canadian outpatient settings in 2026, and health systems that were early are already seeing measurable retention and margin benefits. Here's what a well-built platform actually changes.
Cutting note-drafting time by 70%+ translates into real clinical capacity — more patients seen, or simply providers going home on time.
Documentation is the top-cited driver of physician burnout. Removing it is one of the highest-leverage retention levers a health system has.
When clinicians aren't typing during the visit, patients report feeling more heard — and satisfaction scores follow.
Structured, consistent documentation reduces missing elements that trigger claim denials or expose the practice in an audit.
AI-suggested ICD-10/CPT codes anchored to what was actually said in the visit close the gap between documentation and reimbursement.
For EHR vendors and telehealth platforms, a native ambient scribe is quickly becoming table stakes for enterprise deals.
The AI medical scribing market has gone from an early-adopter experiment to a near-standard workflow tool in under three years, driven by chronic clinician shortages, rising documentation burden, and the maturity of ambient speech and clinical NLP models capable of handling real medical conversation.
Projected to reach $8.93B by 2035
Range across major research firms through 2034–2035
Share of physicians using AI scribes daily at early-adopter health systems (UCSF)
Share of US/Canada outpatient settings using ambient documentation tools in 2026
Sources: Grand View Research, Towards Healthcare, Fortune Business Insights (2026 industry forecasts)
From a focused ambient-scribe MVP to a full clinical documentation suite embedded across your EHR — we build the whole stack, in-house, with clinicians and compliance officers involved from week one.
Mobile and desktop apps that listen to in-room or telehealth encounters and draft structured notes in real time.
Bi-directional sync with Epic, Cerner/Oracle Health, athenahealth, and other major EHRs — notes land where clinicians already work.
Custom SOAP, HPI, and procedure-note templates tuned to cardiology, psychiatry, orthopedics, and more.
Medical-grade transcription and entity extraction tuned for accents, overlapping speech, and clinical shorthand.
Fine-tuned language models trained on your specialty's documentation style, not generic consumer chat models.
Automatic structuring into Subjective, Objective, Assessment, and Plan — editable before it ever reaches the chart.
Surface relevant guidelines, drug interaction checks, and coding suggestions inline with the note as it's drafted.
Consent-first ambient capture designed around real exam-room and telehealth workflows, not a lab demo.
Accurate capture and translation for multilingual patient populations and international care teams.
AI-suggested ICD-10 and CPT codes grounded in the actual encounter, reducing under-coding and denials.
Native scribe layers for video-visit platforms — one workflow whether the visit is in-room or remote.
iOS and Android apps clinicians can carry between rooms, with offline capture and background sync.
Usage, accuracy, and time-saved reporting for practice administrators and CMIOs.
HL7/FHIR-based integration work, including custom interface engines where off-the-shelf connectors fall short.
HIPAA-aligned architecture, audit logging, and encryption built in from day one, not retrofitted before launch.
SLA-backed monitoring, model retraining, and continuous feature delivery post-launch.
A production-grade AI scribe platform is really three connected products: the tool the clinician uses in the room, the console the practice runs, and the integrations that make it invisible to IT. We build all three.
The day-to-day tool a physician, nurse practitioner, or PA actually uses — designed to disappear into the visit, not add another screen to manage.
Everything a practice manager, compliance officer, or CMIO needs to run the platform with confidence and prove it's being used correctly.
The plumbing that lets IT and security sign off — deep EHR integration, identity, and infrastructure controls.
Documentation needs differ wildly between a 10-minute primary care visit and a 45-minute psychiatric intake. We tune templates, vocabulary, and note structure to each specialty rather than shipping one generic note format.
From the moment a clinician opens the encounter to the moment a coded, structured note lands in the chart — here's the pipeline we build under the hood.
Clinician starts the encounter; ambient audio capture begins with patient consent logged.
Medical-grade ASR transcribes the conversation, separating clinician and patient speech.
NLP models extract symptoms, history, and plan into structured clinical entities.
A specialty-tuned model drafts the SOAP note with suggested codes.
Provider reviews, edits, and signs — the human stays firmly in the loop.
Signed note pushes into the EHR via HL7/FHIR, closing the encounter.
The hardest part of building AI documentation software usually isn't the AI — it's the plumbing. Every health system runs a different EHR, a different version of that EHR, and a different set of custom fields and templates layered on top. We've built integration engines against the systems that cover the vast majority of US and international care settings, using HL7 v2, FHIR R4, and CDA as the common backbone, with custom interface work where a client's instance needs it.
Where a true API integration isn't available, we build compliant, auditable alternatives — including RPA-style write-back and structured document delivery — so the note still lands where the care team expects it, without breaking your existing workflows or compliance posture.
Ask us about your specific EHR setupClinical software carries a higher bar than most software — clinician usability, coding accuracy, and compliance all have to be right the first time. Our process is built around getting real clinician feedback early, not after launch.
Clinical workflows, EHR environment, specialty focus, and compliance requirements workshop.
System design, EHR integration approach, model selection, compliance roadmap.
Clinician-facing UI designed with real providers in usability sessions, not just internal review.
Agile sprints across ambient capture, NLP/note generation, admin console, and EHR interfaces in parallel.
Note-accuracy testing against real (de-identified) encounters, security testing, and pilot-clinician review.
Cloud provisioning on HIPAA-eligible infrastructure, CI/CD setup, phased pilot rollout.
Ongoing SLA-backed maintenance, accuracy monitoring, and model retraining as usage scales.
We combine best-in-class speech and language models with healthcare-specific infrastructure — not a generic chatbot stack repackaged for medicine.
Plenty of dev shops can wire a speech-to-text API to a chatbot and call it a "medical scribe." Very few can ship something a compliance officer will sign off on, a CMIO will trust with Epic, and a physician will actually want to use during a visit. That gap is where Idea Usher works — we've built healthcare and AI software long enough to know that the hard part was never the demo, it's the last 20% that makes it production-ready.
Across clinical, fintech, and enterprise AI products
Real ambient capture and note generation, not a slide deck
Scope slippage is on us, not billed back to you
Same engineers from architecture through post-launch support
We know what an HPI is, why an assessment-and-plan needs to map to billing codes, and why "good enough" transcription isn't good enough for a chart.
HIPAA-aligned architecture, BAA-ready infrastructure, and audit logging are built in from sprint one — not bolted on before a demo.
We test with real clinicians in usability sessions before launch, because a scribe that slows down a visit is worse than no scribe at all.
Model fine-tuning, EHR integration, mobile apps, and admin dashboards — one accountable team, not three subcontractors passing blame.
You get a number you can take to your board or investors — and we own the risk of hitting it, not you.
SLA-backed support, ongoing model retraining, and a shared roadmap after launch — we stay accountable for how it performs in the real world.
No sales deck, no generic pitch — a 30-minute call with someone who can speak to your specific EHR, specialty, and compliance requirements.
Book Your Free Strategy CallClinical documentation software fails in production for one of two reasons: the AI wasn't built by people who understand medical workflows, or the compliance work was an afterthought. We treat both as first-class requirements from day one.
| Criteria | Freelancers | Generic Agencies | Our Company |
|---|---|---|---|
| Clinical Workflow Expertise | ✕ | Partial | ✓ Deep |
| HIPAA & Compliance Knowledge | ✕ | Partial | ✓ Built-in |
| EHR Integration Experience | ✕ | Partial | ✓ Epic / Cerner / athena |
| Clinical NLP / LLM Fine-Tuning | Varies | ✕ | ✓ In-house |
| Security Architecture | Partial | Partial | ✓ Enterprise |
| 24/7 Support | ✕ | Partial | ✓ SLA-backed |
| Dedicated Team | ✕ | Shared | ✓ Ring-fenced |
Clinical documentation software touches protected health information (PHI) at every step — the audio itself, the transcript, the structured note, and the coding output. We architect for HIPAA and HITECH from the first sprint, not as a pre-launch checklist item, and we'll sign a Business Associate Agreement (BAA) before any PHI touches our systems.
Every ambient capture session is tied to a logged patient consent event, with configurable consent flows per state and organization policy.
Raw audio can be configured to auto-delete after note generation, with only the structured note and audit trail retained long-term.
Clear contractual and technical controls over whether client data is ever used to train shared models — opt-in only, never by default.
Whether you're a health system deploying internally or a startup selling to other providers, the right pricing model changes the entire go-to-market. We help you design and build for the model that fits.
Flat monthly fee per licensed clinician using the platform.
Most common SaaS modelUsage-based pricing tied to visit volume — scales naturally with practice size.
Good for variable-volume clinicsFlat annual licensing for health systems deploying across multiple facilities.
Best for hospital systemsDistribute through Epic App Orchard, Oracle Health Marketplace, or similar EHR app stores.
Distribution-led growthBasic transcription vs. full coding/CDS-integrated tiers at different price points.
Higher ARPU ceilingLicense the underlying engine to EHR vendors or telehealth platforms under their own brand.
B2B2C distributionOne-time onboarding, integration, and clinician training packages.
Services revenue layerPremium reporting on coding accuracy, time saved, and documentation quality.
Upsell for adminsA scribe that lives outside your existing systems is just another app to log into. We build integrations so the platform disappears into workflows your team already uses.
Problem: A 40-provider multi-specialty group was losing physicians to burnout driven largely by after-hours charting.
Solution: Ambient scribe app with specialty-tuned templates across primary care, cardiology, and orthopedics, integrated directly into their Epic instance.
Problem: A telehealth company needed native in-visit documentation without asking providers to leave their video platform.
Solution: Embedded ambient scribe layer inside their existing video SDK, with real-time note preview during the call.
Problem: A behavioral health network needed long-form intake and progress notes without recording sensitive full sessions verbatim.
Solution: Structured-summary scribe model with configurable redaction and strict data-minimization controls built to their compliance spec.
Our providers got roughly two hours of their evenings back within the first month. The Epic integration was the part I worried about most, and it was the part that just worked.
They understood HIPAA and consent workflows well enough that our compliance review took days instead of months. That's rare for a dev partner.
As a healthcare AI startup, having a team that could fine-tune the clinical model AND ship a clean clinician-facing app was exactly what we needed.
From a focused ambient-scribe MVP to a full clinical documentation suite — let's scope your platform in a free 30-minute strategy call.
🔒 BAA available · ⚡ Response within 24 hours · 🏥 Built for regulated healthcare
Cost depends on scope — a focused ambient-scribe MVP costs less than a full platform with EHR integration, coding automation, and multi-specialty templates. We provide a fixed-scope quote after a discovery call.
A focused MVP typically takes 10–14 weeks; a full platform with deep EHR integration and multi-specialty support takes 4–7 months.
We build on HIPAA-eligible infrastructure with encryption, audit logging, and access controls throughout, and we sign a Business Associate Agreement (BAA) before any PHI touches our systems. Final compliance sign-off is your organization's responsibility with counsel.
Epic, Cerner/Oracle Health, athenahealth, eClinicalWorks, NextGen, and Allscripts/Veradigm are common integrations; we also build custom HL7/FHIR interfaces for less common systems.
Accuracy depends heavily on model tuning and specialty vocabulary. We benchmark against real (de-identified) encounters during development and keep the clinician in the review-and-sign loop before any note is finalized — the AI drafts, it doesn't autonomously chart.
Yes, we build AI-suggested coding grounded in the actual encounter content, with the coder or clinician confirming before submission — this is assistive, not a replacement for certified coding review.
Yes, we build native scribe layers embedded in video-visit platforms so documentation works the same whether the visit is in-person or remote.
This is configurable per your policy — many clients choose to auto-delete raw audio shortly after note generation, retaining only the structured note and an audit trail.
Yes, we tune note templates, vocabulary, and model behavior per specialty rather than shipping one generic format across every care setting.
Yes, via SLA-backed maintenance packages covering monitoring, accuracy audits, model retraining, and continuous feature delivery.
Yes, our architecture supports multi-tenant, multi-facility deployments with centralized admin controls and per-site configuration.
Yes, NDAs are available on request before any detailed technical or business discussion.
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