Specialty notes
SOAP notes in cardiology, derm, ortho, GI, and ophthalmology formats — signed within seconds.
Learn moreScribara listens to the encounter and finishes it — the specialty-format note, ICD-10/CPT coding with an audit trail, the prior auth, the referral, and the follow-up — before the physician leaves the room.
I20.9 93000 · evidence-linkedBuilt for the workflows specialty medicine already runs on
Specialty-tuned, noise-robust speech recognition captures the encounter in real time — overlapping speakers, instruments, and dense clinical jargon.
A clinical-reasoning model drafts the note in your specialty's format and proposes codes grounded in guidelines and payer rules — with an independent verifier.
Prior auth assembled and submitted, referral written, follow-up scheduled — every consequential step reviewed by the clinician and logged.
Every edit becomes labeled data, so notes, codes, and auths increasingly read as the physician would have written them.
Generalist scribes stop at the note. Scribara closes coding, prior auth, referrals, and follow-up — specialty-first.
SOAP notes in cardiology, derm, ortho, GI, and ophthalmology formats — signed within seconds.
Learn moreICD-10/CPT/HCC with modifiers, each code linked to note evidence and a payer-rule trail.
Learn moreEvidence assembled to the payer's criteria, submitted, tracked, and appealed on denial.
Learn moreSpecialty medical imaging — lesions, retina, X-ray — fused with the spoken encounter.
Learn moreClinician-grade ASR built for noisy, multi-speaker exam rooms.
Learn moreFrom documentation toward autonomous follow-up and chronic-care management. [ASPIRATIONAL]
Learn moreScribara owns its model stack instead of renting it. A specialty ASR on NVIDIA Riva, a clinical-reasoning LLM with an independent verifier on NeMo, and medical-imaging models on Clara/MONAI — served under 300 ms with TensorRT-LLM and Triton.
Scribara runs an agentic loop with a human in the loop on every consequential action — reliability is engineered, not assumed.
The agent scopes the encounter and the work it implies — note, codes, auth, referral, follow-up.
Vocalis transcribes; Visera reads specialty imaging; structured EHR context is retrieved.
A specialty model drafts outputs grounded in guidelines and payer rules via NeMo Retriever.
An independent verifier checks every output and abstains or escalates when unsure.
Clinician approves; Scribara writes to the EHR and logs an immutable, reversible trail.
One clinical intelligence platform — population health, chronic care, synthetic data, revenue analytics, and trial matching — on one GPU-accelerated backbone.
Population health intelligence — RAPIDS analytics + NeMo risk scoring at health-system scale.
ExploreReal-time chronic care monitoring — Holoscan + Jetson edge AI between specialty visits.
ExploreSynthetic clinical data — NeMo + Clara/MONAI. [ASPIRATIONAL]
ExploreRevenue intelligence — deny-risk scoring and undercoding detection before submission.
ExploreClinical trial matching — NeMo Retriever + RAPIDS GPU connects patients to active trials.
ExploreOutcomes are reported by design partners and marked illustrative [PLACEHOLDER].
“Charting ends at the office, not at home — the note is signed before I touch the keyboard.”
“It codes the way our best biller does, and shows its work for every line.”
Healthcare buys on trust. Scribara is built to clear procurement, security review, and compliance.
PHI handled under a Business Associate Agreement.
[ASPIRATIONAL] — roadmap to Type II within 12 months.
Run owned models inside your datacenter via NVIDIA AI Enterprise. [ASPIRATIONAL]
Every agent action logged, reversible, tied to the clinician's signature.
US, EU, UK regions; per-tenant keys (BYOK option). [ASPIRATIONAL]
AI management-system conformity on the roadmap. [ASPIRATIONAL]
No. Scribara owns its model stack — specialty ASR (Riva), reasoning + verifier (NeMo), and medical imaging (Clara/MONAI) — trained on its own encounter data and served on NVIDIA compute. Frontier APIs are only a bootstrap/fallback.
An on-prem path runs the models inside your boundary via NIM on NVIDIA AI Enterprise, so PHI never leaves the building. [ASPIRATIONAL] Cloud deployments use per-tenant keys and PHI redaction at ingress.
Cardiology, dermatology, orthopedics, GI, and ophthalmology at launch, with more added as specialty models are trained.
The in-exam inference budget is under 300 ms, served with TensorRT-LLM and Triton; a signed note is ready within seconds of the last word.
See Scribara complete a specialty encounter end to end — note, codes, prior auth, and follow-up — in a 20-minute demo.