Extract structured data from medical documents with source-grounded precision

Turn healthcare documents into structured data healthcare teams can trust. Nutrient Data Extraction API extracts and validates information from forms, records, and scanned documents, giving teams traceable, production-ready data while directing human attention to exceptions — not every field. Encrypted transport and access controls help support the security requirements of HIPAA-sensitive workflows.

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Patient intake form with allergies and medication tables alongside extracted structured JSON output

Trusted by enterprises, governments, and teams building document workflows at scale

USE CASES

Example medical document workflows

Patient intake

Extract patient demographics, insurance details, consent fields, checkboxes, signatures, and supporting information from intake documents.

Prior authorization and referrals

Structure provider, payer, procedure, diagnosis, date, authorization, and supporting information for review and routing.

EOBs and claims documents

Automate extraction of payer details, dates, amounts, codes, patient responsibility, payment details, and structured tables from EOBs and claims documents.

Lab results and prescriptions

Extract test names, values, reference ranges, dates, prescription details, provider information, and structured fields from lab and prescription documents.

Medical records and packets

Prepare mixed scanned and digital medical records for review, search, AI, and downstream workflow use.

WALKTHROUGH

See it in action

GOVERNED EXTRACTION

Why not rely on LLM-only extraction for medical documents?

LLMs can reason over documents, but medical workflows need deterministic, auditable output grounded in the source file — not generated answers that vary between runs.

Predictable structured output

Typed output remains tied to the source document — not generated answers that change between runs.

Confidence signals for routing and review

Flag uncertain values before they move downstream into patient records or administrative systems.

Coordinates and page references

Every extracted value is anchored to its source location for traceability and human review.

Layout-aware structure

Tables, forms, checkboxes, and key-value regions preserved — not flattened into plain text.

Human review before downstream use

Support validation steps before structured data enters patient records, claims systems, or administrative workflows.

Auditability for compliance workflows

Source context and page detail support audit trails in regulated healthcare environments.

WHAT YOU CAN EXTRACT

Structured fields from any medical document

Structured data extracted from a medical record document

Patient and provider details

Names, DOBs, NPI numbers, addresses, and identifiers from intake and referral documents.

Diagnosis and procedure codes

ICD-10, CPT, and related codes extracted with confidence scores for validation before downstream use.

Payer and insurance information

Payer names, policy numbers, group IDs, and coverage details from insurance and claims documents.

Lab values and reference ranges

Test names, result values, units, and reference ranges preserved from lab reports.

Form fields and handwritten values

Structured key-value regions, handwritten fields, and checkbox states captured with ICR — with confidence scores for review.

Tables and multirow structured data

Tables from EOBs, prior authorization forms, and claims documents, preserved with row and column context.

HOW IT WORKS

From medical document to structured output

Text

For fast extraction from born-digital records and forms with a reliable text layer.

PDF to Markdown RAG Search

Structure

For OCR-backed extraction from scanned intake forms and image-based documents.

Scans Forms Tables

Understand

For complex layouts, tables, handwriting, and richer document structure.

Lab results EOBs Legal Insurance

Agentic

For documents that require deeper visual interpretation, review, or recovery.

Handwriting Degraded scans Complex layouts

OUTPUT FORMATS

Spatial JSON. Or Markdown. From the same API.

Confidence scores, coordinates, and page context included.
Choose output: "json" or Markdown per request.

{
  "type": "keyValueRegion",
  "id": "kv_d4e5f6",
  "bounds": { "x": 82, "y": 128, "width": 260, "height": 92 },
  "confidence": 0.98,
  "page": { "pageIndex": 0, "pageNumber": 1 },
  "pairs": [
    {
      "key": { "entityType": "QUESTION", "value": "Patient name", "bounds": { "x": 82, "y": 128, "width": 100, "height": 24 }, "confidence": 0.99 },
      "value": { "entityType": "ANSWER", "value": "Sarah Chen", "bounds": { "x": 190, "y": 128, "width": 100, "height": 24 }, "confidence": 0.99 },
      "relationshipConfidence": 0.98
    },
    {
      "key": { "entityType": "QUESTION", "value": "Diagnosis code", "bounds": { "x": 82, "y": 164, "width": 100, "height": 24 }, "confidence": 0.97 },
      "value": { "entityType": "ANSWER", "value": "J45.40", "bounds": { "x": 190, "y": 164, "width": 60, "height": 24 }, "confidence": 0.97 },
      "relationshipConfidence": 0.95
    }
  ]
}

# Patient Intake Form

**Patient** Sarah Chen
**DOB** 1985-03-12
**Insurance ID** UHC-8821047

## Diagnosis

| Code | Description |
| --- | --- |
| J45.40 | Moderate persistent asthma |

**Provider** Dr. Marcus Webb
**Date** 2024-11-08

Reviewed extraction example

Inspect one reviewed run and its source evidence

This is one reviewed run on a selected public form populated with privacy-safe demo values. It is not an accuracy, production, or performance benchmark.

Reviewed prior authorization extraction with source regions and two retained mismatches

0:00

Prior authorization intake

A selected public CMS prior-authorization form populated with privacy-safe demo values and no PHI.

Exact: 9/11 selected fields
Source: 11 returned primary source regions
Review: 2 issues retained

Inspect evidence Try your document

Grounding makes mistakes reviewable. It does not make them correct.

The videos use an Extract → Source evidence → Decision gate schematic. It is a workflow concept, not shipped product UI.

“Reviewed” means the evidence audit was recorded. It does not mean a reviewer approved the result.

0.93 accuracy (vision engine), independently benchmarked

200 real-world documents. Three metrics. Results published with every release.

SOC 2 Type 2 audited

Backed by Nutrient’s SOC 2 Type 2 security practices and built for use in business-critical and compliance-sensitive workflows.

Trust and compliance

Built for medical document workflows in production

TLS encryption by default

All API communication is TLS-encrypted. Documents in transit are protected end to end.

HIPAA-compatible architecture

No document retention on paid plans, encrypted transport, and access controls designed to support HIPAA-sensitive workflows.

Medical document extraction questions

Can the API handle handwritten medical forms?

Yes. For hand-completed fields, checkboxes, and handwritten values, the API applies intelligent character recognition (ICR) to capture what standard OCR misses. Every extracted value includes a confidence score so uncertain fields can be flagged for human review before moving downstream.

What file formats are supported?

Data Extraction API processes PDFs; images (including scans and photos); and Word, Excel, and PowerPoint files. It handles scanned PDFs, fillable forms, and mixed digital/image-based documents without requiring a separate OCR pipeline.

How do I validate extracted data before it enters downstream systems?

Every extracted element includes confidence scores, page references, and coordinates so you can compare outputs, flag low-confidence fields for human review, and trace values back to the source document before they move into patient records, claims systems, or administrative workflows.

Is this a full healthcare platform or an extraction API?

Nutrient Data Extraction API is an extraction layer — not an EHR, claims platform, or workflow management system. It extracts structured data from medical documents so that data can be reviewed, validated, and routed into the systems your team already uses. See the Data Extraction API overview for the full capability set, or the comparison hub for how it stacks up against other extraction platforms.

How do I define the fields to extract from medical documents?

Use the schema generator in Studio — upload example forms, records, or claims documents and describe the document type. It then generates a JSON Schema you can use directly with /extract. You can also write the schema manually for fields like patient details, diagnosis codes, dates, and payer information. Refer to the documentation for supported field types and limits.

When should I use agentic mode for medical documents?

Use agentic mode for medical documents that require the deepest visual understanding — degraded scans, faxed records, low-quality images, handwriting, and documents with embedded images or diagrams where understand mode produces insufficient results. Agentic mode adds a vision language model (VLM) on top of the understand pipeline for complex visual content. It costs 18 credits per page.