Shield Litigation Risk Prediction System
Orchestrates 13 specialized AI agents through claim intake, multi-model processing, and agent collaboration with debate resolution. Uses XGBoost ensemble with SHAP explainability, game-theoretic settlement optimization, and human-in-the-loop approval.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Agentic AI Risk Assessment - Workflow configuration interface with claim intake, preset selection, and enhanced features including GLP-1 fraud detection and telehealth analysis
AI Processing - Agent pipeline showing 14 specialized agents with real-time reasoning trace and provider intelligence analysis including litigation history and behavior patterns
Agentic Output - Shield activation recommendation with 87% litigation probability, financial exposure analysis, optimal settlement calculation, and explainable AI risk factor importance
Technical Dashboard - System performance metrics displaying ML model accuracy, inference latency, agent processing distribution, token usage analysis, and resource utilization
AI Agents
Specialized autonomous agents working in coordination
Orchestration Agent
Complex multi-agent workflows require intelligent task routing, priority management, and error recovery across heterogeneous agent types.
Core Logic
Coordinates workflow execution using GPT-4-Turbo with task routing, agent coordination, and priority management. Generates execution plans, agent assignments, and dependency graphs. Implements retry policies with exponential backoff (max 3 retries, 2x multiplier, 1000ms initial delay).
Data Extraction Agent
Raw claim data arrives in inconsistent formats with missing fields, validation errors, and PHI that requires careful handling.
Core Logic
Uses Claude-3-Haiku for efficient PHI extraction, code validation, and data normalization. Detects missing fields, standardizes formats, and generates quality scores. Outputs normalized claims with validation results and data completeness metrics.
Litigation Risk Predictor
Predicting which claims will escalate to litigation requires analyzing complex patterns across provider behavior, claim characteristics, and historical outcomes.
Core Logic
Employs XGBoost Ensemble v2.3.1 with probability calibration (Brier score optimization). Generates litigation probabilities with confidence intervals, SHAP feature importance values, and similar case matches via KNN. Provides calibrated probabilities for accurate risk assessment with 120-second timeout for complex cases.
Provider Intelligence Agent
Provider litigation behavior varies dramatically; some providers never sue while others have established patterns of aggressive balance billing.
Core Logic
Analyzes litigation history, behavior patterns, and settlement outcomes using Claude-3.5-Sonnet. Generates provider aggression scores, behavior pattern classifications, and network relationship analysis. Predicts settlement probability based on historical provider negotiation patterns.
Medical Necessity Evaluator
Defending against balance billing requires demonstrating that payment amounts are reasonable based on clinical necessity and standard of care.
Core Logic
Uses fine-tuned GPT-4-Medical for medical necessity scoring, clinical guideline matching, and documentation review. Generates defensibility indices, identifies guideline matches, and highlights documentation gaps that could weaken legal defense.
Financial Exposure Calculator
Determining optimal settlement amounts requires balancing potential litigation costs, probability of various outcomes, and provider behavior models.
Core Logic
Implements custom neural network with game-theoretic settlement optimization. Performs exposure quantification with confidence intervals, ROI projections, and scenario modeling. Uses reserve estimation algorithms for financial planning and optimal settlement calculations.
Legal Strategy Agent
Healthcare billing disputes involve complex regulatory requirements that vary by state, service type, and payer arrangement.
Core Logic
Applies Claude-3.5-Sonnet for regulation matching, legal precedent analysis, and defense strategy formulation. Searches applicable regulations by state jurisdiction, generates compliance verification reports, and produces legal strategy documents. Outputs applicable regulation summaries and recommended defense approaches.
Decision Synthesis Agent
Multiple agents produce findings that may conflict or require weighted aggregation to reach a final recommendation.
Core Logic
Synthesizes outputs from all agents using GPT-4-Turbo for multi-agent consensus building. Aggregates confidence scores using weighted averaging, generates unified recommendations, creates comprehensive audit trails, and produces human-readable explanations of the decision rationale.
GLP-1 Fraud Detector
The explosive growth in GLP-1 obesity medications has created new fraud vectors including compounding fraud, telehealth pill mills, and dosage manipulation.
Core Logic
Detects compounding fraud, telehealth pill mill patterns, off-label abuse, dosage anomalies, multiple prescriber patterns, and geographic clustering. Analyzes prescriber patterns against baseline metrics, identifies suspicious provider exposure, and generates recommended enforcement actions.
Telehealth Fraud Analyzer
Telehealth expansion has enabled new fraud schemes including impossible travel, rapid consultations, phantom visits, and modifier abuse.
Core Logic
Implements XGBoost with LLM hybrid approach for fraud pattern detection. Identifies impossible travel patterns, rapid consultation flags, phantom visits, upcoding, and unbundling. Analyzes geographic spread and timeframes for provider behavior clustering. Calculates composite telehealth risk scores.
Prior Authorization Intelligence Agent
CMS 2026 mandates require electronic prior authorization with specific interoperability requirements that affect claim validity and defense strategies.
Core Logic
Performs FHIR compliance verification, real-time PA status checking, and denial pattern analysis using GPT-4-Turbo with FHIR parser. Validates CMS 2026 compliance across electronic submission, real-time response, patient access API, and provider directory API requirements. Generates interoperability scores and appeal recommendations.
SDOH Risk Analyzer
Social determinants of health affect claim patterns, member engagement, and litigation risk in ways that pure clinical analysis misses.
Core Logic
Integrates census data with ML ensemble for comprehensive SDOH assessment across economic stability, education access, healthcare access, neighborhood factors, and social context. Analyzes ZIP code demographics, provider network density, health literacy indices, and community health indicators. Generates health equity impact assessments and targeted intervention recommendations.
Regulatory Intelligence Monitor
Healthcare regulations change frequently across federal, state, and local jurisdictions, affecting claim validity and litigation defense strategies.
Core Logic
Monitors regulatory changes using Legal LLM with rule engine integration. Tracks No Surprises Act compliance status, upcoming regulatory deadlines, and jurisdiction-specific requirements. Generates real-time compliance alerts, impact assessments with financial implications, and required action timelines.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization