AI Medical Scribe & Clinical Documentation Agent

An AI Clinical Documentation Agent generates, quality-reviews, and submits clinical documentation while keeping physician accountability intact. Ambient intelligence captures the encounter in real time, the note is reviewed for CDI gaps, and discharge summaries and prior auth documents are auto-generated. Physicians review and sign in 5 to 10 minutes versus 30 to 90 minutes writing from scratch.

One tap activates ambient capture — the physician consults normally, no dictation required. Clinical NLP identifies symptoms, findings, diagnoses, and the care plan in real time, then assembles a structured SOAP note. The physician opens their EHR, finds the draft waiting, edits any inaccuracies, and signs. Post-encounter documentation: 3–8 minutes versus 20–45 minutes from memory.

Peer-reviewed studies show strong accuracy: DAX reported note quality equivalent to or better than physician-written notes in 76% of cases; Abridge's UPMC deployment showed 95%+ capture of key clinical facts. The physician-in-the-loop design covers gaps — the physician reviews and adds any reasoning not verbalised. The model fine-tunes on your organisation's documentation patterns over time.

Certified integrations for Epic (App Orchard, SMART on FHIR R4), Oracle Cerner (HealtheIntent, CDS Hooks), Meditech Expanse, athenahealth, and Allscripts/Veradigm. For all platforms, the agent reads patient context via authenticated FHIR API and writes signed notes back to the chart as the official medical record.

CDI analysis runs before the physician signs, flagging: unspecified ICD-10 codes where higher specificity is supportable, CC/MCC implied by labs or treatment but not documented, secondary diagnoses not recorded, and HEDIS/CMS Star Rating documentation gaps. Each opportunity surfaces as an ACDIS-compliant query with supporting clinical evidence attached.

Encounter audio is AES-256 encrypted in real time and not retained beyond the configured period. Notes are delivered via authenticated HL7 FHIR API and not stored in Bonami infrastructure after the session. All PHI access is logged to an immutable audit trail. Model improvement uses de-identified data only. A BAA is executed before every deployment.

Yes. The HCC capture module compares current encounter documentation against the patient's historical HCC code set and flags chronic conditions missing from the current note. Organisations deploying AI HCC capture see 8–15% improvement in risk score capture — $200–$600 per member per year in additional capitated revenue.

ROI across four dimensions: (1) Capacity recovery — 50–72% documentation reduction saves 40,000–60,000 physician-hours annually in a 200-physician org. (2) Attrition reduction — 2% attrition drop avoids $2M–$4M in replacement costs. (3) CDI uplift — a 3% CMI improvement at a 15,000-admission hospital = $4M–$8M additional reimbursement. (4) Denial reduction — improved documentation quality recovers $500K–$2M annually. Most organisations recover implementation cost within the first operating quarter.