Ambient scribe for visits and telehealth
In-room and virtual visit capture with a clinician review screen, embedded in your product or launched from the EHR with SMART on FHIR.
Clinical Documentation
AI Agents for Clinical Documentation
Ambient capture, source-linked drafts and signed-note write-back, with the clinician deciding every word.
We build AI clinical documentation agents that capture the visit with consent, draft the note in your clinic's template with every statement linked to its source, suggest ICD-10-CM and E/M codes, and write the signed note back to Epic, Oracle Health, athenahealth or eClinicalWorks.
Workflow scoping · Evaluation design · Human oversight · Integration planning
Updated September 2026
What AI agents for clinical documentation do
AI agents for clinical documentation turn a patient encounter into a signed note with less typing. The agent captures the visit with consent, transcribes it with speaker labels, drafts the note in the specialty's template, links every statement to its source, suggests codes, and waits for the clinician to accept, edit and sign before writing the note to the EHR. It is live in our builds for behavioral health and nursing.
How a clinical documentation AI agent works, from consent to signed note
- 1
Consent and capture
The clinician starts a session on a phone, desktop or the telehealth platform. The patient's consent to recording is captured and stored with the session, following the all-party consent rules in states such as California and Florida.
- 2
Transcription and speaker labels
Medical speech recognition produces a transcript with speaker diarization, checked against the session roster so clinician, patient and family statements are attributed correctly.
- 3
Draft in the clinic's template
The language model drafts the note in the format the specialty uses: SOAP or H&P for medical visits, DAP or BIRP for behavioral health, procedure notes, or discrete nursing assessment fields. Templates are versioned and owned by clinical leads.
- 4
Every sentence linked to its source
Each statement in the draft links back to the transcript segment or chart data it came from. Statements without a source are flagged for the clinician rather than silently kept.
- 5
Coding suggestions
ICD-10-CM diagnosis codes and an E/M level based on medical decision making under the 2021 office visit guidelines are suggested with the supporting text. The clinician or coder confirms them.
- 6
Clinician review and signature
The clinician accepts, edits or rejects each section, then signs. Nothing enters the legal record without that signature, and supervised clinicians route to a co-signer.
- 7
Write-back and retention
The signed note is written to the EHR, and audio is deleted on the schedule your policy sets. The draft, edits and sign-off are kept in the audit trail.
What we build for AI clinical documentation
Nursing voice-to-chart
Nurses speak an assessment and get discrete fields back for vitals, pain, skin and head-to-toe findings, confirmed field by field before they reach the flowsheet.
Behavioral health documentation
Progress notes, intake assessments and treatment plan updates, with psychotherapy note separation and 42 CFR Part 2 handling for substance use disorder programs.
Chart summarization and pre-charting
Summaries of prior notes, results and medications before the visit, with each fact linked to the source document.
EHR write-back for clinical documentation AI
Where the note lands decides whether clinicians use it. We map the draft and signed note to what each EHR accepts.
Epic
SMART on FHIR launch from the encounter, FHIR DocumentReference for notes where the organization enables it, and flowsheet rows through Observation for discrete nursing data.
Oracle Health
Millennium FHIR R4 DocumentReference for clinical notes and SMART launch inside PowerChart.
athenahealth
athenahealth's clinical document and encounter APIs alongside its FHIR endpoints.
eClinicalWorks and other EHRs
FHIR reads for context and HL7 v2 MDM document messages for write-back where FHIR writes are not offered.
How we evaluate clinical documentation AI
Omissions
Clinically relevant facts said in the visit but missing from the draft, scored by clinicians on a representative note set per specialty.
Unsupported statements
Anything in the draft with no source in the transcript or chart. These are tracked separately from omissions because the risk is different.
Attribution errors
Statements assigned to the wrong speaker, such as a family member's report written as the patient's.
Edit effort and note quality
How much clinicians change before signing, and note quality scored with an instrument such as PDQI-9. Every model or prompt change runs the full suite before release.
Build vs buy an AI medical scribe
When to buy
Ambient scribe products such as Microsoft Dragon Copilot, Abridge and Suki cover common ambulatory specialties with standard templates. They fit when your clinicians work in one EHR the vendor supports well.
When to build
Build when documentation is part of your own product, when the specialty format is unusual (behavioral health, nursing, therapy, home health), when you need discrete fields rather than prose, or when per-seat pricing does not fit your model.
Delivery plan and timeline
- 1
Discovery (2 to 3 weeks)
Templates, specialties, consent policy, retention, EHR write path and the evaluation note set.
- 2
Build (5 to 7 weeks)
Capture, transcription, drafting, source linking, review screen and EHR write-back in a sandbox.
- 3
Evaluation and pilot (4 to 6 weeks)
Clinician scoring on the note set, then a pilot group with weekly review of edits and flags.
- 4
Rollout
Specialty by specialty, with template owners and a rollback switch.
What Drives the Cost of Clinical Documentation AI
Specialties and templates
Each specialty format needs its own template, test notes and clinician scoring.
EHR write path
FHIR note writes are faster to build than HL7 v2 or flowsheet mapping.
Speech stack
Hosted speech recognition under a BAA or self-hosted models, and in-room audio hardware.
Evaluation effort
Clinician time for scoring and the size of the note set per specialty.
Proof from our clinical documentation work
Behavioral health AI scribe, live
Built into a US behavioral health practice group's clinical platform. It drafts progress notes in SOAP, DAP or BIRP, assessment and treatment plan drafts with ICD-10 suggestions, and the licensed clinician accepts or edits each section. Client name withheld.
Nurse voice-to-chart, live
A mobile app for a long-term care operator that turns a spoken assessment into discrete fields the nurse confirms before they reach the chart. Client name withheld.
Security and contracts
We sign BAAs, and Nirmitee is ISO 27001:2022 certified.
AI Agents for Clinical Documentation FAQs
What is clinical documentation AI?
Clinical documentation AI captures a patient encounter, transcribes it, and drafts the clinical note in the clinic's template, with each statement linked to its source. The clinician reviews, edits and signs the note, which is then written back to the EHR. Ambient scribes are the best-known form.
What is the difference between an AI scribe and an AI agent for clinical documentation?
An AI scribe turns a conversation into a draft note. A documentation agent also calls tools: it pulls prior notes, medications and results for context, suggests ICD-10-CM and E/M codes, routes notes for co-signature and writes the signed note to the EHR, all under the clinician's approval.
Does AI clinical documentation replace the clinician's signature?
No. The clinician reviews every section, accepts, edits or rejects it, and signs. Nothing enters the legal record without that signature, and supervised clinicians route to a co-signer. The audit trail records the draft, every edit and the sign-off.
How do you prevent hallucinations in AI-generated notes?
Every statement must link to a transcript segment or chart record, and statements without a source are flagged for the clinician. We evaluate omissions, unsupported statements and attribution errors separately on a clinician-scored note set, and every model or prompt change runs the full suite before release.
Which EHRs can AI documentation write back to?
Epic, Oracle Health, athenahealth and eClinicalWorks. We use FHIR DocumentReference where the EHR accepts note writes, athenahealth's clinical document APIs, flowsheet Observations for discrete nursing data, and HL7 v2 MDM messages where FHIR writes are not offered.
Is patient consent required for ambient clinical documentation?
Your policy should require it, and all-party consent states such as California and Florida require consent to record. We capture consent at the start of each session, store it with the session record, and give clinicians a quick way to pause or stop capture.
Can clinical documentation AI suggest billing codes?
Yes. It suggests ICD-10-CM diagnoses and an E/M level based on medical decision making under the 2021 office visit guidelines, with the supporting text highlighted. Suggestions go to the clinician or coder to confirm; they are never submitted automatically.
Does it work for behavioral health and nursing documentation?
Yes, and both are areas we have built live. Behavioral health uses DAP, BIRP or SOAP progress notes with psychotherapy note separation and 42 CFR Part 2 handling. Nursing uses discrete assessment fields that nurses confirm before they reach the flowsheet.
Is AI clinical documentation HIPAA compliant?
It is when capture, transcription, drafting and storage all run under BAAs, audio is encrypted and deleted on a set schedule, access is role-based, and every draft and edit is logged. We sign BAAs, use BAA-covered model endpoints with no training on your data, and Nirmitee is ISO 27001:2022 certified.
Should we buy an ambient scribe or build our own?
Buy when your clinicians work in one supported EHR with common ambulatory templates. Build when documentation is part of your product, your specialty format is unusual, you need discrete fields rather than prose, or per-seat pricing does not fit your model.
How long does it take to build clinical documentation AI?
Around 11 to 16 weeks to a pilot: 2 to 3 weeks of discovery, 5 to 7 weeks of build, and 4 to 6 weeks of evaluation and pilot. Each additional specialty adds template work and clinician scoring rather than a new build.
More Agents
Related AI Agents
Explore complementary solutions for your practice.
Scope your clinical documentation AI
Bring your specialties, templates, EHR and consent policy. We will map the capture flow, write-back path and evaluation note set for your pilot group.