Why AI Fax Ingestion Matters
Fax intake is still a major source of order problems.
Handwritten or hard-
to-read forms
Missing
ICD-10 codes
Incomplete patient
demographics
Missing insurance
information
Missing provider
NPI
Multi-page faxes with
mixed documents
Manual data entry
errors
Delayed issue
detection
What SignalDX Can Process
Built for real-world lab documents.
Requisitions
PDFs
Forms
Cards
Notes
Documents
Records
Packets
What SignalDX Extracts
Turn unstructured documents into structured order data.
Patient Data
- Name
- Date of birth
- Gender
- Contact information
- Demographics
- Insurance details
Provider Data
- Ordering provider
- NPI
- Clinic name
- Location
- Contact details
Order Data
- Ordered tests
- Panels
- ICD-10 codes
- CPT codes
- Modifiers
- Place of service
Supporting Documentation
- Clinical notes
- Attachments
- Referral information
- Payer-required documents
More Than OCR
AI extraction + normalization + issue detection.
- Document classification
- Patient matching
- Provider matching
- Payer recognition
- Test and panel recognition
- ICD-10 normalization
- CPT normalization
- Missing field detection
- Duplicate detection
Before / After: Replace Manual Fax Review With AI-Assisted Intake
Before SignalDX
After SignalDX
Examples SignalDX Can Flag From Faxed Orders
Missing ICD-10
The requisition does not include a diagnosis code needed for review or billing.
Missing Provider NPI
The ordering provider is listed, but key provider details are incomplete.
Unclear Test Selection
The selected test or panel is ambiguous and may need review.
Missing Insurance Details
The order does not include enough payer or subscriber information.
Documentation Gap
Supporting clinical documentation may be missing from the packet.
Payer Mismatch
The payer on the order may not align with the insurance card or extracted info.
How It Works: From Fax to Review Queue
1. Document Enters SignalDX
A fax, scanned PDF, or uploaded document is received.
2. AI Reads & Classifies
The document type, pages, attachments, and relevant fields are identified.
3. Data Extracted & Normalized
Patient, provider, payer, test, ICD, CPT, and documentation details are structured.
4. Issues Detected
Missing data, inconsistencies, coding problems, and reimbursement risks are flagged.
5. Order Moves Into Workflow
Clean orders move forward. Risky orders are routed to the right review queue.
Works With the Full SignalDX Platform
AI fax ingestion powers upstream issue detection.
Issue Detection
Flag missing or inconsistent order data
Eligibility Verification
Use extracted insurance details to check coverage
Policy Intelligence
Evaluate payer and medical necessity risk
Provider Corrections
Request missing or corrected information
Risk Scoring
Prioritize orders by reimbursement impact