How do you avoid filing to the wrong patient?

Matching requires multiple identifiers to agree, and anything short of a confident match goes to review with candidates shown. A misfiled clinical document is a patient safety issue, so the threshold is set high and failures route to a person.

Can it handle handwritten notes?

With variable accuracy. Structured handwritten forms extract reasonably; free-text clinical handwriting remains genuinely difficult. We test against your real documents during scoping rather than promising a figure.

What accuracy should we expect?

Clean native PDFs of a consistent format extract very reliably. Poor scans and highly variable layouts are harder. We benchmark on your actual documents before committing to a threshold, and the confidence scoring means low-certainty extractions get reviewed rather than silently posted.

Do we still need someone reviewing documents?

For a much smaller share. The goal is that your team reviews the exceptions rather than every document — which typically means a fraction of the volume, handled in seconds each rather than minutes.

Document AI · Healthcare

Document AI for Healthcare

Referral letters arrive as PDFs and leave as retyped records.

Clinics receive a constant stream of referral letters, discharge summaries, test results, and insurance forms, each requiring someone to read it and enter the relevant fields. Document AI extracts and files that content against the right patient record, with review for anything ambiguous.

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Per document: SecondsPer document
Field confidence: ScoredField confidence
Full trail: AuditedFull trail

Typical stack

Azure Document AI
OpenAI Vision
Python
Postgres
Queue Workers

Use cases

What this handles for clinics and healthcare providers

Extract referral letter details and file them against the correct patient record
Process discharge summaries and results into structured record entries
Capture insurance and authorisation forms into billing without retyping
Digitise historical paper records into a searchable archive

Incoming clinical correspondence filed and structured without administrative retyping.

Context

The healthcare problem underneath it

Healthcare administration consumes an extraordinary share of clinical capacity. Booking, rescheduling, intake forms, referral letters, insurance verification, recall lists — none of it needs clinical judgment, and most of it can run without a person in the middle. We build the administrative layer and stay firmly out of diagnosis.

Reception phones jam at opening while patients wait on hold or hang up
No-shows go unfilled because nobody has time to work a waiting list
Intake forms are completed on paper and then retyped into the practice system
Referral letters and reports arrive as PDFs that someone has to read and file

What we build

Inside a document AI pipeline

We design for the ninety percent and route the rest to people. Documents that extract cleanly and pass validation flow straight through. Anything below your confidence threshold, or failing a business rule, lands in a review queue where a person fixes it in seconds — and that correction feeds back into extraction quality.

OCR and layout parsing for scans, photos, native PDFs, and email attachments
Field extraction with per-field confidence scoring rather than all-or-nothing output
Validation rules that check totals, dates, references, and cross-document consistency
Human review queues for low-confidence extractions with single-screen correction
Straight-through posting into ERP, accounting, or CRM systems
Classification that routes each document type to the right pipeline automatically

Deliverables

What ships with the engagement

Extraction schema per document type agreed with your team
Accuracy benchmark measured on a sample of your real documents
Review queue interface for exceptions
Integration writing extracted data into your system of record
Retention and audit configuration matching your compliance requirements

FAQ