Skip to content
Skip to content

Claims Automation Pipeline

Overview

BrainSAIT's claims automation pipeline transforms manual revenue cycle processes into intelligent, AI-powered workflows. This document describes the architecture, components, and implementation of our automation platform.


Pipeline Architecture

graph LR
    A[Data Ingestion] --> B[Validation]
    B --> C[AI Processing]
    C --> D[NPHIES Submission]
    D --> E[Response Handling]
    E --> F[Analytics]

Pipeline Stages

Stage 1: Data Ingestion

Sources: - Hospital Information System (HIS) - Electronic Medical Records (EMR) - Practice Management System (PMS) - Excel uploads - API integrations

Formats Supported: - FHIR R4 bundles - HL7 v2 messages - CSV/Excel files - PDF documents - Custom APIs

Capabilities: - Multi-format parsing - Real-time streaming - Batch processing - Error handling


Stage 2: Validation Layer

Pre-Submission Checks:

Business Rules

  • Patient eligibility verification
  • Prior authorization validation
  • Timely filing compliance
  • Benefit coverage check
  • Network status verification

Data Quality

  • Required field completeness
  • Data type validation
  • Format standardization
  • Duplicate detection

Coding Validation

  • ICD-10-AM accuracy
  • CPT/HCPCS validity
  • Modifier appropriateness
  • Bundling/unbundling rules
  • Code-to-code logic

Validation Output:

{
  "status": "valid|invalid|warning",
  "errors": [],
  "warnings": [],
  "suggestions": [],
  "confidence": 0.95
}


Stage 3: AI Processing

ClaimLinc Analysis

Functions: 1. Risk Scoring - Predict rejection probability 2. Code Suggestion - Recommend optimal codes 3. Documentation Review - Identify missing info 4. Payer Optimization - Apply payer-specific rules

Machine Learning Models:

Model Purpose Accuracy
Rejection Predictor Risk scoring 92%
Code Suggester ICD-10/CPT 95%
Document Analyzer Missing info 89%
Payer Router Optimization 94%

DocsLinc Processing

Capabilities: - OCR for scanned documents - NLP for clinical notes - Entity extraction - Structured data output


Stage 4: NPHIES Submission

Submission Process:

  1. Bundle Generation
  2. Create FHIR Claim resource
  3. Include Coverage reference
  4. Add supporting information
  5. Attach documents

  6. Authentication

  7. mTLS certificate
  8. OAuth 2.0 token
  9. Provider credentials

  10. API Call

    POST /claim-submit
    Content-Type: application/fhir+json
    Authorization: Bearer {token}
    

  11. Response Handling

  12. Parse ClaimResponse
  13. Extract adjudication
  14. Log transaction

Retry Logic: - Exponential backoff - Max 3 retries - Circuit breaker pattern


Stage 5: Response Handling

Response Types:

Response Action
Accepted Update status, await adjudication
Rejected Route to correction queue
Pended Monitor and follow up
Error Log and retry

Rejection Handling: 1. Classify rejection type 2. Generate correction recommendations 3. Queue for resubmission 4. Notify relevant staff 5. Track resolution


Stage 6: Analytics & Reporting

Real-Time Dashboards: - Submission volume - Acceptance rates - Rejection patterns - SAR recovery

Reports: - Daily submission summary - Weekly denial analysis - Monthly performance review - Payer comparison

KPIs Tracked:

Metric Description Target
First-Pass Rate Claims accepted first try > 95%
Denial Rate Claims rejected < 5%
Days to Payment Average collection time < 30
Clean Claim Rate No errors at submission > 98%

Technical Implementation

System Architecture

graph TB
    subgraph "Ingestion Layer"
        A[API Gateway]
        B[File Processor]
        C[Stream Handler]
    end

    subgraph "Processing Layer"
        D[Validation Engine]
        E[AI/ML Services]
        F[FHIR Generator]
    end

    subgraph "Integration Layer"
        G[NPHIES Connector]
        H[Payer APIs]
    end

    subgraph "Data Layer"
        I[Claims Database]
        J[Analytics Store]
    end

    A --> D
    B --> D
    C --> D
    D --> E
    E --> F
    F --> G
    G --> H
    D --> I
    E --> J

Technology Stack

  • Backend: Python, Node.js
  • AI/ML: TensorFlow, PyTorch
  • Database: PostgreSQL, MongoDB
  • Queue: Redis, RabbitMQ
  • API: FastAPI, GraphQL
  • Infrastructure: Kubernetes, Docker

Integration Points

HIS/EMR Integration

Cloudpital EMR Integration

BrainSAIT provides native integration with Cloudpital's cloud-based EMR system, enabling seamless claims automation:

Integration Architecture:

graph LR
    A[Cloudpital EMR] --> B[BrainSAIT API Gateway]
    B --> C[ClaimLinc Validation]
    C --> D[NPHIES Submission]
    D --> E[Response to Cloudpital]

Real-Time Data Sync: - Automatic encounter capture from Cloudpital - Real-time charge posting and validation - Bi-directional claim status updates - Integrated denial management workflow

Pre-Built Cloudpital Connector:

from brainsait.integrations import CloudpitalConnector

# Initialize Cloudpital connection
cloudpital = CloudpitalConnector(
    api_endpoint="https://api.cloudpital.com",
    credentials=credentials
)

# Auto-fetch unbilled encounters
encounters = cloudpital.get_unbilled_encounters(
    date_range="last_7_days"
)

# Process through BrainSAIT pipeline
for encounter in encounters:
    claim = claim_linc.process_encounter(encounter)
    if claim.validation_score > 0.95:
        cloudpital.submit_to_nphies(claim)

Benefits of Cloudpital Integration: - ✅ Zero manual data entry - ✅ Real-time claim validation - ✅ Automated coding suggestions - ✅ Integrated denial workflow - ✅ 98%+ clean claim rate

Generic HIS/EMR Integration

For non-Cloudpital systems, we support standard methods:

Methods: - HL7 FHIR R4 - HL7 v2 ADT/SIU - Direct database - File exchange

Data Elements: - Patient demographics - Encounter details - Diagnoses - Procedures - Charges

Payer Integration

Bupa Arabia: - Real-time eligibility - Prior authorization - Claims submission

Tawuniya: - Benefit verification - Claim status inquiry - ERA retrieval

GlobeMed: - TPA portal integration - Utilization management - Care coordination


Deployment Options

Cloud Deployment

  • AWS, Azure, or GCP
  • Kubernetes orchestration
  • Auto-scaling
  • High availability

On-Premise

  • Docker containers
  • Local database
  • VPN connectivity
  • PDPL compliance

Hybrid

  • Sensitive data on-premise
  • Processing in cloud
  • Secure tunnels

Security & Compliance

Data Protection

  • Encryption at rest (AES-256)
  • Encryption in transit (TLS 1.3)
  • Key management (HSM)

Access Control

  • Role-based access
  • Multi-factor authentication
  • Audit logging

Compliance

  • PDPL requirements
  • HIPAA alignment
  • CCHI standards

Performance Metrics

Metric Value
Claims per hour 10,000+
Average latency < 500ms
Uptime 99.9%
Error rate < 0.1%


Last updated: November 2025