AI Care Orchestrator - Population Health Management System
AI Care Orchestrator deploys a 6-agent system powered by Claude Sonnet 4 that orchestrates end-to-end care management workflows. The system extracts member data from FHIR-connected EHRs, calculates validated risk scores (LACE, HOSPITAL, HCC), retrieves relevant clinical guidelines via RAG, generates personalized care plans with SMART goals, and crafts culturally-appropriate outreach communications.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Mission Briefing - Configure care management workflow by selecting high-risk members and defining trigger types and priority levels
Command Center - Real-time agent execution monitoring with live trace of agent reasoning, tool invocations, and data extraction
Cognition View - Visual pipeline progress tracking through care orchestration stages with confidence propagation network
Execution Results - Comprehensive care plan output with risk assessment, personalized interventions, evidence citations, and team assignments
AI Agents
Specialized autonomous agents working in coordination
OrchestrAI - Central Orchestrator Agent
Care management workflows involve multiple specialized tasks that must be coordinated sequentially and in parallel. Without central orchestration, care teams receive fragmented insights and cannot deliver holistic, timely interventions.
Core Logic
Serves as the central coordinator using Claude Sonnet 4. Analyzes incoming care management requests, decomposes complex tasks into subtasks, delegates to specialized agents (DataMiner, RiskAnalyzer, EvidenceFinder, CarePlanner, OutreachCrafter), synthesizes results into coherent recommendations, and ensures HIPAA compliance. Uses task planner, agent delegator, and result synthesizer tools.
DataMiner - Data Extraction Agent
Member clinical data is scattered across EHRs, claims systems, pharmacy databases, and ADT feeds. Manual data gathering is slow and incomplete. Care managers lack comprehensive member profiles to make informed decisions.
Core Logic
Queries multiple healthcare data sources via FHIR R4 APIs to build comprehensive member profiles. Extracts clinical history, diagnoses, medications, and utilization patterns. Retrieves claims data for utilization analysis, pharmacy data for adherence metrics (PDC scores), and ADT events for recent hospitalizations. Identifies data quality issues and gaps. Structures data for downstream analysis.
RiskAnalyzer - Risk Assessment Agent
Identifying high-risk members for intervention requires validated risk models. Manual risk assessment is inconsistent and time-consuming. Without explainable risk scores, care teams cannot target interventions effectively.
Core Logic
Calculates multiple validated clinical risk scores including LACE Index for 30-day readmission risk, HOSPITAL Score, Charlson Comorbidity Index, and CMS-HCC risk scores. Runs proprietary ML readmission prediction models with 89% confidence. Generates SHAP-based explanations identifying top risk drivers and their relative importance. Provides confidence intervals for all predictions.
EvidenceFinder - Evidence Retrieval Agent
Care planning requires evidence-based clinical guidelines that are often buried in lengthy documents. Care managers cannot quickly find relevant protocols for specific member conditions. Similar successful cases are not leveraged for new interventions.
Core Logic
Implements RAG (Retrieval-Augmented Generation) using vector similarity search against a clinical guidelines knowledge base. Retrieves relevant protocols from ADA, AHA, KDIGO, and other authoritative sources. Finds similar historical cases with successful outcomes and applies case-based reasoning. Cites all sources with confidence scores and synthesizes evidence into actionable recommendations.
CarePlanner - Care Planning Agent
Creating personalized care plans requires synthesizing risk assessments, clinical evidence, and member-specific barriers. Generic care plans fail to address individual needs. Social determinants of health (SDOH) barriers often derail interventions.
Core Logic
Generates comprehensive, personalized care plans based on risk assessment outputs and retrieved evidence. Identifies and addresses SDOH barriers including transportation, financial constraints, and health literacy. Creates SMART goals with measurable success criteria. Assigns tasks to appropriate care team members with realistic timelines. Integrates barrier resolution solutions with success probability estimates.
OutreachCrafter - Communication Agent
Member engagement is critical for care plan success. Generic outreach messages fail to resonate with diverse populations. Language barriers, health literacy levels, and cultural preferences are often ignored, leading to low engagement rates.
Core Logic
Generates personalized, culturally-appropriate member communications optimized for engagement. Adapts language for health literacy level and member preferences. Supports multiple languages including Spanish. Incorporates motivational interviewing techniques into scripts. Analyzes historical engagement patterns to recommend optimal contact timing, channel, and approach. Predicts engagement probability for each communication strategy.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
8 technologies
Architecture Diagram
System flow visualization