Multi-Party Transcription AI Scribe

VISION CLINICS

AI Patient Instructions, Built From the Encounter

Patient instructions that weren't drawn from the actual visit aren't instructions; they're generic templates with the patient's name on them.

Othisis captures the full clinical encounter and generates patient instruction drafts grounded in what was discussed: the diagnosis explained, the medication changes made, the follow-up timeline agreed, and the warning signs to watch for. Every item in the patient education handout traces back to the encounter transcript. Clinicians review and approve before anything is handed to the patient.

This matters most for practices managing chronic disease follow-ups, post-procedure discharges, and high-volume appointment schedules where verbal instructions are rarely retained in full. When a patient leaves without written instructions that reflect their actual consultation, not a boilerplate printout, non-adherence, preventable readmissions, and missed follow-up appointments follow.

Explore AI Patient Instructions

Patient Instruction Documentation Is Generic, Inconsistent, and Hard to Audit

Handouts don't reflect what was actually discussed

Generic patient education templates are distributed without modification. Medication changes, individualized warnings, and agreed follow-up timelines from the encounter are absent.

Verbal instructions aren't documented anywhere

When post-visit instructions are given verbally only, there's no record of what the patient was told. Disputed non-adherence has no documented basis to reference.

Discharge instructions contradict the updated medication regimen

Printed handouts use the pre-visit drug list. Undocumented med changes made during the encounter don't reach the patient-facing summary, creating a post-discharge medication discrepancy.

Follow-up timelines aren't captured in writing at the point of care

Unsigned follow-up instructions leave patients without a documented return date. Recall rates drop. Interval history gaps appear at the next visit.

End-to-End Workflow Coverage for AI Patient Instruction Generation

Pre-Visit

Ingest prior notes, carer correspondence, and guardian-submitted histories from uploaded PDFs
Extract relevant third-party reported history from referral documents
Identify unsigned histories submitted by carers or legal guardians in outside records
Surface discrepancies between carer-reported and patient-stated medication history
Flag prior correspondence from social workers or allied health included in referral PDFs
Flag prior correspondence from social workers or allied health included in referral PDFs
Generate a structured pre-visit brief from multi-source external documentation

During Visit

Ambient capture runs without interruption across all speakers in the room
Clinician, patient, carer, and interpreter voices captured throughout the consultation
Multi-party consent discussions captured verbatim for documentation accuracy
No real-time display to clinician full attention remains on the consultation
Ward round contributions from nursing staff and allied health captured in ambient audio
Session ends; transcript becomes the traceable foundation for all post-encounter outputs

After Visit

Structured SOAP note drafted from full multi-party transcript after the encounter ends
Speaker contributions traceable to the exact audio segment in the post-encounter transcript
Clinician reviews draft with full speaker-attributed transcript visible alongside
Referral letter and patient summary drafted from verified, multi-source encounter content
ICD-10 coding cues generated from documented encounter content, graded by confidence level
All outputs require clinician sign-off nothing enters the record without explicit approval

How Othisis Supports Multi-Speaker Clinical Documentation

Patient instruction drafts generated from the encounter, not from pre-loaded templates.

Source traceability links every handout item to the transcript line it came from.

Medication changes, follow-up timelines, and structured notes for patient delivery.

Draft-first output. Every patient instruction requires clinician review and approval before distribution.

Every patient education handout Othisis generates is a draft that requires clinician review before it reaches the patient. The focus is on producing instructions that are specific to this visit, traceable to what was actually said, and safe to finalise, with the clinician accountable for what is handed over.

Document Intelligence for AI Patient Instructions

Specialty-Aware Document Intelligence (Before & During Visit)

Specialty-Aware Document Intelligence

Encounter transcripts, prior discharge summaries, specialist letters, and chronic disease management plans
Uploaded PDFs, investigation results, procedure reports, and prior patient-facing handouts
Active medication list drawn from the documented encounter, including changes made during the visit
Self-management plans and lifestyle modification instructions were discussed during the consultation
Follow-up schedules and specialist referral plans captured as discrete, documentable items
Supporting PDF context from outside records
Clinician reasoning and plan context captured during the visit

High-Fidelity Clinical Documentation

Structured patient instruction drafts with diagnosis explanation, medication list, and follow-up date
Post-procedure discharge instructions generated from the procedure encounter, not from a generic template
Chronic disease self-management handouts that reflect the specific targets discussed, HbA1c, blood pressure, weight, activity
Medication change summaries formatted for patient comprehension alongside the updated clinical note
Patient summary structured for portal delivery or printed handout, generated from the same encounter source
Clinics receiving large PDF packets before the encounter

Accuracy, Traceability & Risk Controls

Click-to-source traceability: every item in the patient instruction draft links back to the transcript segment that produced it
Medication discrepancies between the pre-visit drug list and changes made during the encounter are highlighted before the handout is finalized
Confidence scoring identifies instruction content where the source extraction was uncertain, before the clinician's sign-off
Clinician review and approval are required before any patient instruction or education handout is distributed; approval is logged
Contradictions between the patient's stated understanding and the documented clinical plan are surfaced for resolution during review
Preserves clinician judgment rather than replacing it

Time, Throughput & Revenue Efficiency

Time, Throughput & Sustainability

Patient instruction drafts are ready for clinician review immediately after the encounter ends, not between sessions
Practices running 20–40 appointments per day eliminate the gap between verbal instructions and documented written handouts
After-hours instruction writing is removed when drafts are generated and reviewable before the session closes
Non-adherence follow-up consultations are reduced when patients leave with written instructions grounded in their actual visit
Recall and follow-up attendance improves when take-home documentation includes the agreed return date and specific action items
Helps clinicians finish documentation within clinic hours more often

Designed for Ophthalmology & Optometry Practices

Designed for Clinics Managing High-Volume Documentation

Suited for GP practices, chronic disease clinics, procedural day units, and multi-specialty groups with consistent patient education needs
Compatible with read-only EHR environments, no write integration required to begin generating patient instruction drafts
Onboarding requires no IT-side EHR configuration; practices can begin producing encounter-specific patient handouts within days
Multi-provider setups benefit from a consistent patient instruction structure regardless of which clinician conducted the visit
Data encryption, access controls, and audit logs ensure patient instruction records meet HIPAA compliance requirements at every stage
Clinics handling large volumes of outside records and PDFs