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Traceable Transcription

Traceable AI Clinical Notes, Built Defensible

Othisis drafts SOAP notes and clinical documents from ambient visit audio and links each line back to its source audio or uploaded PDFs, so clinicians can verify before sign-off.

Explore Traceable AI Documentation
Clinician reviewing an AI-drafted clinical note with each line linked to its source audio

AI Clinical Documentation Is Fast, But Rarely Traceable


  

You can't defend what you can't trace back
When a payer or auditor challenges a note, unsourced AI-generated text has no anchor. The clinician is left reconstructing from memory what the AI inferred.

 

Confidence scoring is absent from most scribe outputs
Standard ambient scribes produce clean-looking drafts with no indication of where AI certainty ends. Low-confidence inferences sit beside high-confidence facts, indistinguishable at sign-off.

 

PDF summarization without traceability creates a clinical black box
Condensed outside records that can't be cross-referenced to the source document force clinicians to re-read the original before trusting the summary.

ICD-10 coding cues with no confidence signal increase denial risk
When AI surfaces diagnosis codes without evidence-grading, underdocumented codes enter claims submissions. Reviewers catch what the scribe glossed over, and the practice absorbs the reversal.

How Othisis Covers the Clinical Documentation Encounter, Start to Finish

Pre-Visit

  • Ingest uploaded PDFs from referrals and prior records

  • Summarize prior histories with confidence scores flagging conversion quality

  • Surface medication discrepancies across uploaded outside records. Available as configured workflows for multi-provider and enterprise deployments.

  • Compares the problem list against recent specialist correspondence and flags differences. Available as configured workflows for multi-provider and enterprise deployments.

  • Structure pre-visit brief from unstructured external documentation

During Visit
  • Ambient capture runs in the background without interrupting consultation
  • Transcription captures patient-stated symptom severity and treatment history
  • Source audio tied to every generated note segment after the encounter ends
  • No real-time display to clinician - full focus on the patient
  • Ambient scribe captures clinical reasoning stated aloud for future defensibility
  • Session ends; full transcript becomes the traceable foundation for all outputs
After Visit

  • Structured draft note generated after the session ends, not during

  • Clinician reviews draft with full transcript visible side-by-side

  • Click any note line to trace it back to the exact audio or PDF source

  • ICD-10 coding cues generated with green/yellow/red confidence grading

  • Insurance summary, patient summary, and referral letter drafted for review

  • Clinician approves, edits, and signs off before any output enters the record

How Othisis Supports Traceable Clinical Documentation Workflows

Traceable documentation tied to the encounter
Structures the note narrative into familiar clinical sections
Captures high-density medical language without derailing the visit
Adapts to referral-driven documentation and longitudinal follow-up

The focus remains on producing documentation that’s familiar, easy to review, and safer to finalize with clinician approval required before sign-off.

Document Intelligence for Traceable AI Clinical Notes

Specialty-Aware Document Intelligence (Before & During Visit)
High-Fidelity Clinical Documentation
Accuracy, Traceability & Risk Controls
Time, Throughput & Sustainability
Designed for Clinics Managing High-Volume Documentation
Specialty-Aware Document Intelligence (Before & During Visit)
 
  • Referral letters, prior SOAP notes, discharge summaries, and outside specialist correspondence
  • Scanned and faxed records processed for structured extraction, with conversion-quality confidence flagging sections that need a manual read
  • Medication lists, problem lists, and procedure history reconciled across multiple source documents
  • Unstructured patient histories ingested and converted with PDF-to-text confidence scoring
High-Fidelity Clinical Documentation
 
  • Structured SOAP notes drafted from ambient encounter transcript, clinician-reviewed before finalization
  • Patient summaries formatted for patient-facing communication and clinical handover
  • Insurance summary drafts built from what was documented in the visit
  • Referral letters generated with relevant clinical history drawn from verified source records
  • ICD-10 coding cues surfaced from documented encounter evidence, graded by confidence level
Accuracy, Traceability & Risk Controls
 
  • Click-to-source traceability allows clinicians to verify any note statement against the original transcript line
  • PDF summary claims indexed back to the specific page and section of the source document
  • ICD-10 cues color-graded (green/yellow/red) to signal documentation strength before submission
  • Confidence scoring for PDF-to-text conversion flags low-legibility sections requiring manual review
  • Clinician sign-off required on every output before it enters the patient record or is exported
Time, Throughput & Sustainability
 
  • Post-encounter note drafting reduces after-hours documentation time without sacrificing traceability
  • Side-by-side transcript and summary view reduces time spent re-reading source documents
  • Confidence-guided review means clinicians focus attention on flagged sections, not entire notes
  • ICD-10 coding cues reduce manual code-lookup time per encounter
  • Structured insurance summaries reduce time spent reconstructing medical necessity arguments for UR
Designed for Clinics Managing High-Documentation Workflows:
  • Suitable for single-provider practices and multi-physician clinics managing shared patient records
  • Onboards in days, not months no technical configuration required per provider
  • Clinics using mixed EHR environments benefit from export-ready outputs that fit existing chart workflows
  • HIPAA-compliant infrastructure with encryption, audit logs, and access controls from day one

Explore Othisis for Traceable AI Clinical Notes

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Frequently Asked Questions

Yes. Othisis indexes every generated note line back to the original audio segment or source PDF section. Clicking on any statement in the draft opens the exact transcript point or document reference it was drawn from, so sign-off is based on verified content, not assumption.

Yes. Othisis applies confidence scoring at two levels: PDF-to-text conversion quality (flagging sections where scanned or faxed documents may not have extracted cleanly) and ICD-10 coding cues (graded green, yellow, or red based on how strongly the documented encounter supports the suggested code). Low-confidence sections are surfaced for clinician review before finalization.

No. Othisis generates the full transcript and structured draft after the session ends, not during it. The clinician reviews the draft alongside the complete transcript before any output is approved, edited, or exported - nothing enters the record without explicit clinician sign-off.

This is where traceability has the most direct operational value. Othisis allows clinicians to click through to the exact patient statement, examination finding, or prior record reference that supports the original documentation decision - replacing memory-based reconstruction with dated, sourced evidence during appeals and audit reviews.