AI Multi-Agent DME Fraud Detection & Cost Optimization System
Deploys a 10-agent AI system with deep technical capabilities including ML fraud prediction (LSTM, Isolation Forest, XGBoost), real-time market intelligence, geographic risk assessment, and multi-criteria decision synthesis. Processes orders in seconds with high fraud detection accuracy, identifies cost savings opportunities, and provides what-if scenario analysis with actionable recommendations.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Order analysis demo selection interface presenting 5 pre-configured scenarios including routine approvals, high-risk reviews, cost optimization opportunities, and critical fraud cases with detailed member and provider information
Real-time multi-agent analysis monitoring at 56% completion showing 10 agents processing with detailed technical metrics including token usage, memory consumption, and workflow step progression
Executive analysis results view displaying APPROVE decision with 92% confidence, fraud risk score of 25/100, $78 cost savings opportunity, 96% compliance score, and projected $936 annual savings with AI agent performance summary
Technical analysis view with comprehensive system telemetry showing 170.2K tokens processed, 7.68GB memory usage, 45 model inferences, and detailed agent technical profiles with model specifications and algorithm implementations
AI Agents
Specialized autonomous agents working in coordination
Master Orchestrator
Coordinating 9 specialized agents with complex dependencies requires sophisticated workflow management, parallel execution optimization, and intelligent result aggregation.
Core Logic
Manages workflow execution with dependency graph resolution enabling parallel execution of independent agents (fraud-detection, clinical-validation, geographic-risk, market-intelligence run concurrently). Handles agent handoffs with structured payloads, monitors progress across all agents, synthesizes 9 agent outputs into unified recommendations, and maintains high consensus levels across agent findings.
Data Ingestion Agent
Incoming DME orders contain unstructured data, missing fields, non-standard formats, and require enrichment from external sources before analysis can proceed.
Core Logic
Model: healthneuron-data-validator-v2 (INT8 quantization, 8192 context window). Performs JSON schema validation, NPI registry lookups via NPPES, HCPCS code parsing against CMS database, and address normalization using USPS API. Outputs enriched order JSON with data quality scores.
Fraud Detection Agent
Sophisticated fraud schemes (upcoding, phantom billing, kickbacks) require advanced pattern recognition across multiple dimensions that manual review cannot achieve at scale.
Core Logic
Model: healthneuron-fraud-detector-v3 (FP16, 16384 context, 1536 embedding dimensions). Algorithms: Isolation Forest for anomaly detection, Graph Neural Networks for provider network analysis, Benford's Law for billing pattern validation, velocity pattern detection. Data sources: historical claims, OIG Exclusion List, LEIE Database, provider claims history. Achieves high fraud detection accuracy.
Clinical Validation Agent
Validating medical necessity against LCD/NCD criteria requires deep clinical knowledge, accurate diagnosis-equipment mapping, and step therapy verification.
Core Logic
Model: healthneuron-clinical-nlp-v2 (FP16, 32768 context, 1536 embeddings). Implements LCD/NCD criteria matching using CMS policy database, ICD-10 to HCPCS mapping with medical necessity scoring, clinical NLP extraction for notes analysis. Integrates with SNOMED-CT and RxNorm terminology. Generates clinical assessments with LCD compliance reports and medical necessity scores.
Cost Optimization Agent
DME cost optimization opportunities are missed because alternatives aren't systematically evaluated—rental vs. purchase trade-offs, supplier pricing variations, and clinically equivalent alternatives.
Core Logic
Model: healthneuron-cost-optimizer-v1 (INT8, 8192 context). Algorithms: alternative equipment matching using clinical equivalence scoring, rental vs. purchase calculation, supplier price comparison against contract pricing DB and Medicare fee schedule, bundle optimization. Identifies savings opportunities with implementation complexity ratings (low/medium/high).
Compliance Check Agent
CMS compliance requirements span prior authorization, documentation completeness, billing code validation, and supplier enrollment—non-compliance results in denials and audit exposure.
Core Logic
Model: healthneuron-compliance-engine-v2 (INT8, 16384 context, temperature 0.05 for deterministic output). Rule engine evaluation against CMS billing guidelines, prior authorization verification, documentation completeness checking (prescription, clinical notes, F2F encounter, supplier agreement), billing code validation with modifier requirements. Achieves high compliance confidence.
Market Intelligence Agent
Without real-time market data, organizations overpay for equipment, miss contract optimization opportunities, and cannot benchmark pricing against market rates.
Core Logic
Model: healthneuron-market-intel-v3 (FP16, 32768 context, 1024 embeddings). Performs real-time price aggregation from supplier catalog APIs, supplier network graph analysis, contract optimization using ML, and market trend forecasting. Outputs price benchmark reports with percentile rankings, supplier scorecards (delivery reliability, quality rating, pricing tier), and alternative supplier recommendations with savings projections.
Predictive Analytics Agent
Reactive fraud detection catches schemes after damage is done. Predicting fraud likelihood, behavioral anomalies, and future patterns enables proactive intervention.
Core Logic
Model: healthneuron-predictor-v4 (FP16, 65536 context, 2048 embeddings—largest model in system). Algorithms: LSTM for fraud prediction trained on historical claims, Gradient Boosting for risk modeling, behavioral clustering for member/provider profiling, time-series anomaly detection, ensemble learning. Generates fraud likelihood scores, behavioral profiles, member/provider risk trajectories, and financial projections.
Geographic Risk Agent
Fraud concentrates in geographic hotspots, and location anomalies (unusual delivery addresses, provider-member distances) indicate scheme activity that requires spatial analysis.
Core Logic
Model: healthneuron-geo-risk-v2 (INT8, 16384 context, 512 embeddings). Algorithms: geospatial fraud clustering, distance anomaly detection using Haversine formula, regional pattern recognition, hotspot prediction model. Integrates GIS database, USPS address validation, fraud hotspot registry, and regional claims data. Outputs location risk scores, hotspot maps, distance analysis, and regional risk reports.
Decision Synthesis Agent
Nine specialized agents produce diverse findings that must be aggregated into coherent, actionable decisions with appropriate confidence levels and clear recommendations.
Core Logic
Model: healthneuron-decision-engine-v4 (FP16, 65536 context, 2048 embeddings). Implements Multi-Criteria Decision Analysis (MCDA), weighted scoring model aggregating all agent outputs, confidence aggregation with calibration against historical decision outcomes, and risk-adjusted decision trees. Generates final decisions (APPROVE/DENY/REVIEW/APPROVE_WITH_MODIFICATIONS), confidence scores calibrated against appeal rates, recommendation rationale, and complete audit trails.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
7 technologies
Architecture Diagram
System flow visualization