Build a Multi-Agent System for Emergency Department Optimization with Amazon Bedrock AgentCore

Why Multi-Agent Systems Are Critical for Emergency Department Throughput Emergency departments (EDs) face increasing pressure to reduce wait times, improve patient outcomes, and manage costs. A key challenge is the lack of real-time coordination between triage, bed management, and staff scheduling—each function operates in silos, leading to bottlenecks. Traditional approaches rely on manual updates or custom-built middleware that take months to develop and maintain. As patient volumes grow, the need for adaptive, real-time coordination becomes critical. Multi-agent systems (MAS) offer a solution by deploying multiple specialized AI agents that communicate and collaborate autonomously. Each agent focuses on a specific domain—triage severity assessment, bed availability, or staff allocation—while a supervisor agent oversees the workflow and resolves conflicts. This architecture not only ac

celerates decision-making but also adapts to changing conditions, such as sudden influxes of patients or staffing shortages. Amazon Bedrock AgentCore Multi-Agent Collaboration: A Managed Orchestration Layer As of May 22, 2026, Amazon Bedrock AgentCore's multi-agent collaboration capability is generally available, providing a fully managed platform to build and orchestrate multi-agent systems. Previously, teams had to build custom orchestration frameworks using tools like AWS Step Functions or third-party libraries, resulting in significant engineering overhead. Bedrock AgentCore eliminates this by offering a built-in supervisor agent that coordinates collaborator agents, manages inter-agent messaging, and handles error recovery out of the box. This managed layer reduces development time from months to weeks, allowing healthcare organizations to focus on agent logic and domain-specific to

ols rather than infrastructure. The capability is built on the same security and compliance foundation as AWS, including HIPAA-eligible services, making it suitable for protected health information (PHI) workloads. Architecture Overview: Triage, Bed Management, and Staff Scheduling Agents The proposed architecture for ED optimization consists of one supervisor agent and three collaborator agents: - Triage Agent : Assesses patient acuity and priority based on incoming data (symptoms, vitals, history) and assigns an initial severity score. - Bed Management Agent : Tracks bed availability across the ED and hospital, including real-time occupancy, cleaning status, and predicted discharge times. - Staff Scheduling Agent : Monitors current staffing levels, shift schedules, and skill mix, and recommends adjustments based on patient load and acuity. These agents communicate through the superviso

r, which maintains a shared state (e.g., current patient queue, bed map, staff roster) and triggers actions when thresholds are exceeded. For instance, if the bed management agent signals a bed shortage, the supervisor can ask the staff scheduling agent to call in additional nurses or adjust shift assignments. Agent Specification and Tool Configuration Each agent requires specific action groups, knowledge bases, and integrated tools: Triage Agent - Action Group : Use a FHIR-based API to query patient registration data (age, chief complaint, vital signs) and a triage severity model (e.g., ESI score). - Knowledge Base : Clinical guidelines for symptom prioritization, integrated from an AWS HealthLake store containing anonymized historical data. - Tools : FHIR API (for read/write of patient records), a classification LLM fine-tuned on triage decision trees, and an event trigger to alert the

supervisor on high-acuity cases. Bed Management Agent - Action Group : Retrieve current bed status from the hospital's ADT (Admission, Discharge, Transfer) system via HL7v2 or FHIR. Update occupancy count when beds are assigned or released. - Knowledge Base : Hospital floor plan, bed types (ICU, med-surg, observation), and expected discharge times from EHR. - Tools : ADT system connector, a discrete-event simulation model for bed forecasting, and a notification service to the supervisor when bed availability drops below capacity thresholds. Staff Scheduling Agent - Action Group : Query the workforce management system for current staffing, break schedules, and skill certifications. Send shift adjustment recommendations to the scheduling team via email or chat. - Knowledge Base : Staff availability rules, union constraints, and minimum nurse-to-patient ratios. - Tools : Workforce manageme

nt API, a roster optimization model (e.g., integer programming), and a supervisor alert for staffing gaps. Inter-Agent Communication Protocols for Real-Time Decision Making Inter-agent communication follows a publish-subscribe pattern through the supervisor. Each collaborator agent publishes status