DocsLinc Agent¶

Overview¶

DocsLinc is BrainSAIT's AI agent specialized in medical document processing. It extracts clinical information from unstructured documents, converts them to structured data formats, and integrates seamlessly with claims processing workflows.

Core Capabilities¶

1. Document Ingestion¶

Supported Document Types: - Clinical notes - Discharge summaries - Lab reports - Radiology reports - Operative notes - Prescription records - Referral letters

Supported Formats: - PDF (native and scanned) - Images (JPEG, PNG, TIFF) - Word documents - HL7 CDA documents

2. Information Extraction¶

Clinical Data Elements: - Patient demographics - Chief complaints - History of present illness - Physical examination findings - Diagnoses - Procedures performed - Medications - Lab values - Treatment plans

3. Code Suggestion¶

Coding Support: - ICD-10 diagnosis codes - CPT procedure codes - SNOMED CT concepts - LOINC lab codes

Architecture¶

graph TB
    A[Input Documents] --> B[OCR Engine]
    B --> C[NLP Processor]
    C --> D[Entity Extractor]
    D --> E[Code Mapper]
    E --> F[FHIR Generator]

    G[Clinical Models] --> D
    H[Code Systems] --> E

Processing Pipeline¶

Stage 1: Document Preprocessing¶

Tasks: - Format detection - Image enhancement - Orientation correction - Noise reduction - Page segmentation

Stage 2: OCR Processing¶

Technologies: - Tesseract OCR - Custom medical OCR models - Arabic language support - Handwriting recognition

Accuracy Targets: - Printed text: > 99% - Handwritten: > 90% - Arabic text: > 95%

Stage 3: NLP Analysis¶

Techniques: - Named Entity Recognition (NER) - Clinical relation extraction - Negation detection - Temporal reasoning - Section identification

Stage 4: Code Mapping¶

Process: 1. Extract clinical concepts 2. Map to standard terminologies 3. Suggest most specific codes 4. Provide alternatives

Use Cases¶

Claims Documentation¶

Scenario: Extract clinical data for claim justification

Input: Discharge summary PDF

Output:

{
  "patient": {
    "name": "Mohammed Al-Ahmad",
    "mrn": "12345"
  },
  "encounter": {
    "type": "inpatient",
    "admission": "2024-01-10",
    "discharge": "2024-01-15",
    "los": 5
  },
  "diagnoses": [
    {
      "text": "Osteoarthritis, right knee",
      "icd10": "M17.11",
      "type": "principal"
    },
    {
      "text": "Hypertension",
      "icd10": "I10",
      "type": "secondary"
    }
  ],
  "procedures": [
    {
      "text": "Total knee replacement",
      "cpt": "27447",
      "date": "2024-01-12"
    }
  ]
}

Prior Authorization¶

Scenario: Extract clinical justification for auth requests

Process: 1. Identify medical necessity evidence 2. Extract relevant test results 3. Document conservative treatment history 4. Generate authorization package

Clinical Coding Assist¶

Scenario: Help coders with complex cases

Process: 1. Present extracted clinical concepts 2. Suggest applicable codes 3. Show supporting documentation 4. Allow coder refinement

Integration Points¶

ClaimLinc Integration¶

DocsLinc provides structured clinical data to ClaimLinc:

sequenceDiagram
    participant Doc as Document
    participant DL as DocsLinc
    participant CL as ClaimLinc

    Doc->>DL: Upload
    DL->>DL: Process
    DL->>CL: Structured data
    CL->>CL: Build claim

EMR Integration¶

HL7 FHIR Output: - DocumentReference - DiagnosticReport - Observation - Condition - Procedure

API Endpoints¶

Process Document:

POST /api/docslinc/process
Content-Type: multipart/form-data

file: [document file]
type: "discharge_summary"
output_format: "fhir"

Response:

{
  "document_id": "doc-123",
  "status": "completed",
  "confidence": 0.95,
  "extraction": {...},
  "codes": {...},
  "fhir_resources": [...]
}

Key Features¶

Multi-Language Support¶

• English documents
• Arabic documents
• Mixed language documents
• Medical terminology handling

Confidence Scoring¶

Each extracted element includes: - Confidence score (0-1) - Source location - Supporting context

Audit Trail¶

• Original document stored
• All extractions logged
• Review annotations tracked
• Version history maintained

Performance Metrics¶

Quality Assurance¶

Confidence Thresholds¶

Human-in-the-Loop¶

For low confidence extractions: 1. Flag for review 2. Present alternatives 3. Collect corrections 4. Retrain models

Configuration¶

Document Types¶

document_types:
  discharge_summary:
    sections:
      - admission_info
      - diagnoses
      - procedures
      - medications
      - discharge_instructions
    required_fields:
      - patient_name
      - principal_diagnosis
      - discharge_date

Extraction Rules¶

extraction_rules:
  diagnoses:
    patterns:
      - "diagnosis: {text}"
      - "impression: {text}"
    negation_handling: true
    temporal_context: true

Best Practices¶

Document Quality¶

1. Clear, legible scans (300+ DPI)
2. Proper orientation
3. Complete pages
4. Minimal noise/artifacts

Processing Optimization¶

1. Batch similar documents
2. Use appropriate document type
3. Validate output format needs
4. Review low-confidence extractions

Related Documents¶

• ClaimLinc Agent
• RadioLinc Agent
• Automation Pipeline
• FHIR R4 Profile

Last updated: January 2025