Population Health Analytics Multi-Agent System
Deploys 8 specialized AI agents in a collaborative orchestration framework with autonomous decision-making. Agents share findings through real-time message bus, engage in structured debates, and trigger autonomous actions while escalating high-impact decisions for human approval.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Agent Orchestration - Live multi-agent collaboration showing 8 active AI agents with real-time reasoning chains, tool executions, and inter-agent communication messages
Agentic Actions - Human-in-the-loop approval workflow with autonomous decision tracking and real-time pattern detection for GLP-1 medication trends and unusual ER utilization
Executive Summary - Population health analysis results showing $2.4M potential savings across 3,847 members with 12-month cost projections and key clinical risk findings
AI-Generated Recommendations - Prioritized intervention programs with ROI analysis, investment requirements, and time-to-value estimates for diabetes care, CKD intervention, and medication adherence
AI Agents
Specialized autonomous agents working in coordination
Central Orchestrator
Complex multi-agent analysis requires central coordination to manage workflows, handle agent handoffs, synthesize results, and ensure quality.
Core Logic
Coordinates all agents using GPT-4-Turbo with 8192 max tokens and 0.3 temperature for balanced creativity and consistency. Manages workflow phases from configuration through completion, coordinates agent-to-agent communication, synthesizes final results, and performs quality assurance checks. Capabilities include workflow management, agent coordination, result synthesis, and quality assurance.
Data Engineer Agent
Population health analysis requires ingesting, validating, and transforming data from multiple sources with varying quality and formats.
Core Logic
Handles data extraction, schema validation, quality checks, and data transformation using GPT-4-Turbo with 0.1 temperature for high precision. Generates data quality reports including completeness, accuracy, timeliness, and consistency scores. Identifies field-level issues with severity ratings and affected record counts.
Clinical Analyst Agent
Identifying clinical risk requires validated medical algorithms, evidence-based guidelines, and comorbidity analysis that general-purpose models cannot provide.
Core Logic
Analyzes clinical data using GPT-4-Turbo with medical-domain prompts for risk scoring, comorbidity analysis, and clinical guideline application. Identifies high-risk members with primary conditions, projected costs, intervention opportunities, and urgency classifications (immediate, soon, monitor). Generates findings with clinical evidence and confidence scores.
Financial Modeler Agent
Accurate cost forecasting requires actuarial techniques, trend analysis, and uncertainty quantification that simple projections cannot achieve.
Core Logic
Performs actuarial-grade cost forecasting using GPT-4-Turbo with 0.1 temperature for numerical precision. Generates financial projections with confidence intervals, monthly baseline vs. optimized comparisons, and cost driver analysis. Calculates savings opportunities, ROI projections, and budget impact assessments.
Intervention Planner Agent
Designing effective care programs requires matching member needs to available interventions, predicting outcomes, and optimizing resource allocation.
Core Logic
Designs targeted intervention programs using GPT-4-Turbo with 0.4 temperature for creative program design balanced with evidence-based approaches. Performs member-to-program matching, outcome prediction, and resource allocation optimization. Generates recommendations with expected ROI, investment requirements, time-to-value estimates, and implementation steps.
Compliance Auditor Agent
Healthcare data analysis must maintain strict HIPAA compliance while creating comprehensive audit trails for regulatory examination.
Core Logic
Ensures HIPAA compliance using GPT-4-Turbo with 0.0 temperature for deterministic, policy-consistent outputs. Performs PHI detection, validates data handling against policy rules, and maintains detailed audit logging. Tracks every data access, agent decision, and action with timestamps, actors, and data classification levels.
Real-Time Insights Agent
Static analysis misses emerging trends, anomalies, and opportunities that require continuous monitoring and proactive alerting.
Core Logic
Monitors data streams for patterns using Claude-3.5-Sonnet with 8192 max tokens for comprehensive context. Performs pattern recognition, anomaly detection, and trend forecasting. Generates real-time insights classified by type (trend, anomaly, opportunity, risk, compliance) and severity (info, warning, critical). Triggers autonomous alerts and proactive outreach within configured thresholds.
Autonomous Decision Coordinator
Scaling population health requires autonomous action on routine decisions while ensuring human oversight for high-impact situations.
Core Logic
Makes autonomous decisions within defined confidence thresholds using Claude-3.5-Sonnet with 0.1 temperature for consistent, low-risk decisions. Routes decisions through approval workflows based on impact and confidence levels. Executes approved actions (notifications, referrals, alerts, workflows, data updates, outreach) and incorporates learning feedback to improve future decisions. Tracks all decisions with full rationale and approval history.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization