Multi-Agent DME Billing Reconciliation System
Orchestrates an 8-agent AI system with LLM-style chain-of-thought reasoning and tool calling. Agents aggregate data from multiple sources, perform intelligent record matching using semantic similarity, validate CMS compliance, detect fraud patterns, identify revenue recovery opportunities, predict future discrepancies, and execute auto-corrections—delivering comprehensive reconciliation with full audit trails.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Multi-agent reconciliation setup wizard displaying data source connections from 6 systems including EMR and billing platforms, facility configuration, and 8 AI agents ready to process reconciliation
Live reconciliation workspace showing Intelligent Matching Agent executing semantic similarity algorithms with real-time chain-of-thought, tool execution visibility, and technical performance metrics
Reconciliation analytics dashboard with processing pipeline visualization showing 789 records processed, 96.7% match rate, algorithm performance metrics, and $86,388 in projected annual savings
Detailed reconciliation results showing $18,750 immediate revenue recovery, $34,200 fraud prevented, 97.8% compliance score, severity distribution by category, and resolution status breakdown
AI Agents
Specialized autonomous agents working in coordination
Orchestration Agent
Complex reconciliation workflows require coordinated execution of multiple specialized agents, error handling, and synthesis of diverse findings into actionable insights.
Core Logic
Implements workflow coordination using task planning, agent delegation, and output synthesis tools. Manages execution sequence with dependency resolution, handles errors with fallback strategies, aggregates findings from all agents into executive-ready reports with insights and prioritized recommendations.
Data Aggregation Agent
Reconciliation data exists in multiple disconnected systems with different schemas, formats, and quality levels—making comprehensive analysis impossible without normalization.
Core Logic
Connects to multiple data sources (EMR, billing, supplier, payer, inventory) via secure API integrations. Extracts records in batches of 100 with pagination, validates against DME_BILLING_V2 schema, normalizes date formats/HCPCS codes/name casing, and removes duplicate records. Uses tools: connect_datasource, extract_records, validate_schema, normalize_data, detect_duplicates.
Intelligent Matching Agent
Delivery records and billing entries often don't match exactly due to typos, timing differences, and data entry errors—manual matching is time-consuming and misses discrepancies.
Core Logic
Performs multi-field composite matching using semantic similarity (vector embeddings) and fuzzy matching algorithms (Jaro-Winkler, Levenshtein). Applies weighted matching across HCPCS code, patient name semantic similarity, equipment description fuzzy match, and date proximity. Achieves high match rates with configurable confidence thresholds, flags discrepancies by severity.
Compliance Validation Agent
CMS billing rules are complex with 15,000+ HCPCS codes, ICD-10 mappings, modifier requirements, and LCD/NCD policies. Non-compliance leads to claim denials and audit risk.
Core Logic
Loads CMS 2025 HCPCS database and applies rule-based expert system validation. Validates HCPCS codes with modifiers, checks ICD-10 medical necessity linkage against LCD/NCD policies, applies CMS billing rules, and generates HIPAA-compliant audit trails with SHA-256 hashing. Tools: validate_hcpcs, check_icd10, verify_medical_necessity, apply_cms_rules, generate_audit_trail.
Fraud Detection Agent
Billing fraud manifests in subtle patterns—duplicate billing, upcoding, velocity anomalies—that are difficult to detect manually across thousands of transactions.
Core Logic
Deploys ensemble anomaly detection combining Z-score outliers, IQR analysis, and Isolation Forest ML models trained on historical claims. Matches anomalies against known fraud signatures for duplicate billing, upcoding, and velocity patterns. Calculates risk scores using XGBoost classifier, categorizes findings as high/medium/low risk with recommended actions.
Revenue Optimization Agent
Revenue leakage occurs from unbilled deliveries, incorrect billing rates, missed contract pricing, and suboptimal payer mix—often resulting in significant lost potential revenue.
Core Logic
Identifies unbilled items by comparing delivery records to billing entries within tolerance windows. Compares billed rates against Medicare/Medicaid fee schedules and contract pricing. Calculates immediate recovery opportunities and annual projections. Prioritizes actions and provides specific recommendations for revenue recovery.
Predictive Intelligence Agent
Reactive reconciliation only catches problems after they occur. Proactive identification of future discrepancies, compliance risks, and revenue trends enables preventive action.
Core Logic
Analyzes historical patterns using ARIMA for trend forecasting, LSTM for anomaly prediction, and XGBoost for risk classification. Identifies seasonal patterns, predicts discrepancies ahead with high confidence, forecasts quarterly revenue with growth projections, and calculates audit risk scores.
Autonomous Action Agent
Many reconciliation issues have straightforward corrections that don't require human review—but manual processing creates bottlenecks and delays resolution.
Core Logic
Evaluates findings against eligibility criteria: high confidence, low impact threshold, pre-approved rule exists, no regulatory risk. Executes auto-corrections for qualifying items, applies CMS modifier rules and rate adjustments, generates full audit trails with hash verification, queues remaining items for human review with priority sorting and SLA deadlines. All actions are rollback-capable.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
6 technologies
Architecture Diagram
System flow visualization