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:
- Bundle Generation
- Create FHIR Claim resource
- Include Coverage reference
- Add supporting information
-
Attach documents
-
Authentication
- mTLS certificate
- OAuth 2.0 token
-
Provider credentials
-
API Call
-
Response Handling
- Parse ClaimResponse
- Extract adjudication
- 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% |
Related Documents¶
- Claim Lifecycle
- ClaimLinc Agent
- NPHIES API Reference
- DevOps CI/CD
- Cloudpital Integration - Complete integration guide
- Cloudpital RCM - Revenue cycle features
Last updated: November 2025