How do I process claims documents and route low-confidence fields for review?

A practical way to do this is to use an extraction + confidence threshold + human review workflow.

1) Ingest the claim document

Common inputs:

• PDF, scanned images, emails, faxes
• Attachments or claim packets

Typical steps:

• Detect document type
• Split multi-page packets
• Run OCR if needed

2) Extract the required fields

Use an IDP/OCR or document AI model to extract fields such as:

• Claim number
• Policy number
• Insured name
• Date of loss
• Provider / claimant details
• Amounts, diagnosis, line items

For each field, keep:

• Extracted value
• Confidence score
• Source location on the page, if available

3) Apply confidence rules

Set thresholds per field type.

Example:

• High confidence: >= 0.95 → auto-accept
• Medium confidence: 0.80–0.95 → auto-accept or secondary validation
• Low confidence: < 0.80 → route to review

You can also make thresholds stricter for critical fields like:

• Claim number
• Policy number
• Total amount
• Date of loss

4) Validate against business rules

Don’t rely on confidence alone. Run checks like:

• Policy number format is valid
• Date of loss is not in the future
• Amounts are numeric and within expected range
• Claimant name matches policy holder or account data

If validation fails, route to review even if confidence is high.

5) Route low-confidence fields for human review

Create a review queue with:

• Document ID
• Field name
• Extracted value
• Confidence
• Page/snippet highlight
• Suggested alternatives if available

The reviewer can:

• Confirm
• Correct
• Reject
• Add notes

6) Use field-level routing, not document-level only

A document can be mostly good but still need review for one or two fields.

Example:

• Claim form extracted successfully
• Claim number = 98%
• Date of loss = 62%
• Total amount = 91%

Only send Date of loss to review.

7) Feed corrections back into the system

Store reviewer decisions to:

• Improve models
• Refine confidence thresholds
• Add custom validation rules
• Reduce future manual review

Example workflow

1. Receive claim PDF
2. OCR + extract fields
3. Score confidence per field
4. Validate business rules
5. Auto-post high-confidence fields
6. Send low-confidence or invalid fields to a work queue
7. Reviewer approves/corrects
8. Final data is written to claims system

Implementation patterns

You can build this with:

• Document AI / OCR platform
• Workflow engine
• Human-in-the-loop review UI
• Integration to claims system via API

Simple routing logic

for each extracted field:
    if confidence < threshold[field] or fails_validation(field):
        send_to_review(field)
    else:
        auto_accept(field)

If you want, I can also give you:

1. a sample workflow architecture,
2. pseudo-code, or
3. a Python implementation for routing low-confidence claim fields.