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AI Powered OCR and Data Extraction

Turn healthcare documents into structured, usable data

Upload forms through a secure, dedicated web application or place documents in an approved workflow for a Rinova AI Agent to process. Rinova classifies each document, extracts the fields you need, assigns confidence, and returns the data in the format or system destination your team chooses.

Discuss your document workflowSee supported documents

From document upload to a review-ready dataset

Simulated workflow

Synthetic sample. The public site never accepts a real document.

CMS-1500 professional claim form

Choose a synthetic sample above. Dropping a file here does nothing — customer documents belong in the secure, dedicated application.

  1. Document enters the queue
  2. Type identified and extraction template applied
  3. Printed and handwritten fields processed
  4. Fields scored and shown beside the source
  5. High-confidence fields pass, low-confidence fields enter human review
  6. Result exported or written to the approved destination

A printed form produced by a billing system. Every field is machine readable, so confidence is high across the template.

Extracted fields

6 fields

  • Payer nameSimulated Health PlanHigh confidence
  • Member identifierSIM-MBR-00418High confidence
  • Date of serviceDay 1 of the sample periodHigh confidence
  • Procedure codeSIM-CPT-01High confidence
  • Diagnosis codeSIM-ICD-A1High confidence
  • Billed amount$182.00High confidence

Result exported or written to the approved destination

Use a dedicated application or an embedded agent workflow

  • Dedicated web application

    Rinova maps the document types and fields the customer expects, then provides secure access to a dedicated application. Authorized users select the document type, upload documents in bulk, review processing status, and download the results in the agreed format.

  • AI Agent workflow

    The customer scans or places documents in an agreed folder, drive, EHR location, or other approved source. The agent ingests the files, extracts the required information, and sends the result to an export or directly into a configured downstream workflow. An EOB, for example, can feed an approved payment-posting process.

Configured for the forms your organization receives

8 document types shown.

  • CMS-1500 and HCFA professional claim forms
  • CMS-1450 and UB-04 institutional claim forms
  • Explanations of benefits
  • Enrollment documents
  • Care coordination documents
  • Claim attachments and supporting forms
  • Non-claim operational forms
  • Customer-specific healthcare documents

Rinova can configure additional document types after reviewing the sample, required fields, volume, and output.

Return the data where it can be used

  • CSV
  • Excel
  • JSON
  • Plain text
  • Customer-defined industry format
  • Approved EHR or workflow destination

Output structure is defined during implementation, including field names, data types, confidence values, validation rules, and exception handling.

Confidence at the field level

Computer-generated fields usually produce the strongest confidence. Handwritten or unclear fields receive confidence based on Rinova's sampling, sorting, and scoring methods. The output identifies which fields can proceed and which should be reviewed by a person.

  • High confidence

    The field can pass to the output under the organization's configured threshold.

  • Review recommended

    The field is returned, but a person should confirm it before it is used downstream.

  • Unable to determine

    The agent does not guess. The field is marked and sent to the human review lane.

Status is always carried by text and an icon as well as color, so confidence is never communicated by color alone.

Customer access by design

The dedicated application is encrypted and configured so that only the customer's authorized team can access uploaded documents and downloaded results.

When a customer requires it, Rinova can configure the OCR application so Rinova personnel cannot access customer-uploaded documents. The access model is adapted to the customer's security requirements, workflows, and preferences.

Document automation questions

Yes. Handwritten fields can be extracted and assigned confidence, but accuracy depends on legibility, scan quality, layout, and the document type. Low-confidence fields can be routed for human review.

Related AI Agents

Bring us the document queue your team wants to eliminate

We will review your document types, required fields, sample volume, confidence needs, and preferred output.

Discuss your document workflow