Healthcare Agent Orchestrator

• Healthcare Agent Orchestrator (HAO) is a modular, distributed multi-agent system designed for real-time patient scheduling and resource optimization in hospitals.
• The system leverages autonomous agents—MHCA, Patient Agents, Resource Agents, and Scheduling Agents—to enable local decision-making and reduce global communication overhead.
• Empirical results show that HAO's grouping heuristic achieves approximately a 12% reduction in total tardiness, enhancing efficiency, scalability, and robustness compared to centralized methods.

A Healthcare Agent Orchestrator (HAO) is a modular, multi-agent system designed to coordinate and optimize complex processes in healthcare environments by leveraging distributed, software-based agents acting autonomously or cooperatively. In the context of the Distributed Optimized Patient Scheduling with Grouping (DOPSG) system (Mageshwari et al., 2012), the HAO epitomizes a decentralized approach to managing patient flow and resource allocation, yielding measurable improvements in scheduling efficiency, system scalability, and adaptability to dynamic hospital conditions.

1. Distributed Multi-Agent Architecture

The DOPSG-derived HAO implements a distributed multi-agent architecture with the following primary components:

• Master Health Checkup Agent (MHCA): Interfaces with the hospital's database or electronic health records (EHR) to collect patient information and instantiate individual Patient Agents (PAs).
• Patient Agents (PAs): Dynamically instantiated for each patient, PAs encapsulate attributes such as priority, arrival time, and specified next treatment task.
• Resource Agents (RAs): Associated with each hospital department or resource (e.g., diagnostic labs, consultation rooms), RAs are tasked with local queue management, load monitoring, and capacity negotiation.
• Scheduling Agents (SAs): Deployed per resource, SAs trigger the scheduling process, determine optimal moments and groups for patient migration, and manage inter-agent communication for distributed decision-making.

Integration of MHCA with hospital informatics ensures that manual data entry or high-level global coordination are not required, thus reducing central bottlenecks or single points of failure.

2. Agent Interactions, Roles, and Scheduling Logic

The HAO manages patient scheduling and migration through cooperative agent interactions:

• Dynamic Generation and Assignment: The MHCA generates PAs based on real-time data, assigning them to respective RAs via initial routing determined by department, priority, and medical necessity.
• Local Monitoring and Migration: Each RA continuously monitors its local patient load and resource capacity. Upon detecting overload, migration is initiated whereby RAs communicate only with immediate neighbor RAs to offload a subset of PAs (i.e., the group Gi) based on readiness or priority.
• Grouping and Communication: SAs, in collaboration with RAs, execute grouping operations—forming sets Gi of patients with similar priority, arrival times, or required treatments—thereby transferring groups instead of individual PAs for efficiency. Only minimal, local information is exchanged, reducing global communication overhead.

This structure enables distributed real-time optimization without requiring a full global view of the entire hospital state.

3. Optimization Objectives and Mathematical Formalism

The HAO's core optimization objectives are formalized explicitly in terms of patient and resource-related performance metrics. Key objective functions include:

• Maximum Completion Time:

$C_{\max} = \max_{i}(\text{CompletionTime}_i)$

• Total Weighted Tardiness:

$$\tilde{W}T_{t} = \sum_{i}(\text{weight}_i \times (\text{CompletionTime}_i - \text{DueTime}_i))$$

• Other Metrics:

Total Completion Time ($\sum C_j$), Total Tardiness ($\sum T_j$), and their weighted variants.

The HAO seeks to minimize maximum completion time (overall makespan), reduce patient waiting (tardiness), and decrease resource idle times. By assigning patient weights (priorities), the orchestrator ensures urgent and severe cases are prioritized.

4. Handling of Partial Information and Local Autonomy

A defining characteristic of the DOPSG-based HAO is its design for operation with only partial, local information:

• Localized Triggers: Migration requests are only sent to immediate neighbors, not globally broadcast, relying on each RA’s snapshot of its own and neighboring resource loads.
• Threshold-Based Decisions: Accept/decline migration is decided by each RA against its own capacity threshold ($CRA_{+1} < ThRA_{+1}$).
• No Global Patient Registry: At no point do agents maintain a full hospital-wide patient directory, thus both enhancing scalability and reducing system complexity.

This approach ensures that dynamic changes—such as sudden patient arrivals or resource downtime—can be absorbed without centralized reconfiguration.

5. Empirical Performance and Resource Utilization

Experimental evaluation (simulation with three RAs and fifty PAs) using JADE validates the HAO's effectiveness:

Metric	DOPSG-HAO	Traditional Methods
Total Tardiness	12% reduction	Baseline
Weighted Tardiness	Significantly lower	Higher
Completion Time (max)	Lower	Higher
Resource Idle Time	Lower	Higher

Communication overhead is reduced by transferring sets Gi rather than individuals. The grouping heuristic and local communication enable ~12% reduction in total tardiness relative to FCFS and WSPT baselines.

6. Scalability, Adaptability, and Flexibility

By delegating control to autonomous, interacting agents, the HAO supports:

• Scalability: Each agent operating on partial information enables direct scaling to larger hospitals or multiple departments without central rewiring.
• Incremental Extension: New RAs or SAs for additional departments/resources can be added transparently.
• Robustness to Surges: Dynamic, group-based migration distributes patient load in response to variable, bursty arrival patterns, avoiding localized congestion.
• No Central Bottleneck: The orchestrator, by design, precludes any single control point becoming a throughput limiter.

7. Comparison with Centralized and Heuristic Systems

Compared to traditional, centralized scheduling—such as First Come First Serve (FCFS) or Weighted Shortest Processing Time (WSPT):

• Centralized methods lack flexibility, are unable to respond quickly to local overload, and require comprehensive, up-to-date system knowledge.
• HAO/DOPSG achieves real-time distributed scheduling, adapts instantaneously to local changes, and minimizes global coordination. Performance metrics validate lower tardiness and better utilization across simulated scenarios.

8. Implementation and Real-World Deployment Considerations

The reference implementation leverages the Java Agent Development Framework (JADE), simulating hospital departments as RAs and simulating the real-time flow/migration of PAs. Robustness depends on ensuring consistent inter-agent communication and appropriately calibrating migration thresholds and groupings given the hospital's real-world topology and workload.

Key implementation considerations:

• Local Threshold Calibration: Performance depends on proper sizing of capacity thresholds ThRA for each RA to avoid oscillatory migration or resource starvation.
• Failure Handling: Agents must cope with unexpected failures (e.g., communication drop, RA downtime) and safely reroute patients.
• Integration: For real-world extension, the HAO must map to EHRs, patient registries, and hospital IT ecosystems, while maintaining security and audit trails.

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In summary, the HAO architecture derived from DOPSG (Mageshwari et al., 2012) constitutes a rigorously defined, distributed multi-agent system optimized for patient scheduling across multiple hospital departments, capable of minimizing waiting and idle times using only partial information. Its grouping-based, localized migration and absence of central bottlenecks result in measurable gains in efficiency, adaptability to changing hospital conditions, and operational scalability. These principles and empirical outcomes establish a concrete methodological foundation for constructing modern healthcare orchestrators in dynamic, multi-resource, multi-departmental environments.