Case Study – Agentic AI
Revolutionizing claims processing - from bottleneck to advantage.
Overview
This case study highlights the transformation of healthcare Revenue Cycle Management through automation of remittance processing. The client struggled with manual EOB handling, payer format variability, and reconciliation inefficiencies, leading to delays and errors.
An AI-powered solution was implemented to automate EOB ingestion, data extraction, normalization, and conversion into EDI 835 transactions. It also introduced validation, reconciliation checks, and intelligent workflow orchestration.
The result was significantly faster processing, improved accuracy, higher straight-through rates, and reduced manual effort, enabling scalable and efficient RCM operations.
Industry
Healthcare Revenue Cycle Management
Processes Automated
Automation of EOB ingestion, OCR based data extraction, ST/SE transaction balancing, remittance reconciliation, exception handling, and EOB to EDI 835 conversion for downstream claims adjudication and payment posting workflows.
Current Challenges
The client faced significant operational inefficiencies due to:
- Manual processing of paper and scanned EOBs
- Handling 3000+ payer specific EOB format variations
- Delays in coordination of benefits and secondary claim workflows
- Inconsistent CARC/RARC mapping and adjustment interpretation
- Errors in payment posting and claim reconciliation
- High manual dependency for exception management and payer follow ups
- Lack of standardized remittance data for auto posting systems
These challenges impacted straight through processing rates, reimbursement timelines, and operational scalability.
Automation Objectives
The objective was to build an intelligent remittance automation framework capable of:
- Automatically ingesting and classifying EOB documents
- Extracting line level adjudication details using AI driven document processing
- Normalizing payer specific remittance structures
- Converting remittance data into HIPAA compliant EDI 835 transactions
- Performing ST/SE validation and transaction balancing checks
- Detecting reconciliation gaps, denial inconsistencies, and unapplied adjustments
- Automating COB workflows and downstream claims updates
- Increasing straight through processing while minimizing manual intervention
Agentic AI Solution Summary
We implemented an AI powered EOB automation solution combining intelligent document processing, workflow orchestration, and EDI transformation capabilities.
The solution leveraged OCR and AI based extraction models to process structured and semi structured EOB documents across multiple payer formats. Extracted remittance data was normalized and mapped into ANSI X12 835 compliant transaction sets.
The automation framework performed:
- CLP, CAS, SVC, REF, and NM1 segment level data extraction and validation
- CARC/RARC interpretation and adjustment normalization
- ST/SE transaction balancing and integrity validation
- Detection of missing payer payments, duplicate claims, and reconciliation mismatches
- Automated secondary and tertiary claim workflow initiation
- Exception routing for unresolved adjudication scenarios
- Auto generation of EDI 835 files for downstream payment posting systems
- Synchronization with claims management and revenue cycle platforms
AI agents were utilized to interpret payer specific remittance patterns, resolve policy ambiguities, and determine next best actions across reconciliation workflows.
Outcomes
- Reduced EOB processing turnaround from days to seconds
- Achieved near 100% straight through processing for standard remittance workflows
- Improved first pass auto posting and reconciliation accuracy
- Accelerated reimbursement cycles and cash flow visibility
- Reduced manual effort across payment posting and denial handling operations
- Improved EDI 835 transaction quality through automated balancing and validation checks
- Enabled operations teams to focus exclusively on high value exceptions and complex COB cases
