Healthcare · Document AI

Messy charts in. Clean data out.

Drop a scanned referral, a handwritten chart or a faxed record. The AI reads it, pulls the structured fields, scores its confidence and flags anything that needs a human. Press extract.

Incoming record

referral_fax.pdf

PATIENT REFERRAL · Northside Family Practice Re: Robert A. Chen, DOB 03/14/1968 Referring: Dr. Patel · Date: 06/22/2026 Reason: recurrent atrial fibrillation, please evaluate for ablation. Pt reports palpitations x 3 weeks. Hx: HTN, type 2 diabetes. Meds: metoprolol 50mg, metformin 1000mg. Allergies: sulfa. Insurance: Manulife group #88231. BP 148/92, HR 110 irregular.

Extracted fields

structured JSON

Patient nameRobert A. Chen99% conf

Date of birth1968-03-1498% conf

Reason for referralRecurrent A-fib · eval for ablation95% conf

Symptom duration3 weeks97% conf

MedicationsMetoprolol 50mg, Metformin 1000mg94% conf

AllergiesSulfa96% conf

InsuranceManulife · group #8823192% conf

⚑ Flagged for review: HR 110 irregular + BP 148/92 noted. Routed to triage nurse.

Hours of manual data entry, gone.

The impact

Faxes and charts, turned into clean data on arrival.

As more record types route through the extractor, daily throughput climbs while the team only reviews the flagged edge cases.

Typical projection Clinic back office · industry benchmark

Records keyed per day

35→220

▲ Per staff member

Time per record

9 min→40 s

▲ Read, structure, flag

Field-level accuracy

~92%→98%

▲ Low-confidence routed to human

Records processed per day

▲ +528%Last 90 days

Before With TRIVOX

Projection, not a guaranteed result. Typical estimated range for businesses in this sector based on typical volume. Individual results vary. Shown as system capability, not a specific client case.