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Preference based load balancing as an outpatient appointment scheduling aid.

Uthpala Subodhani Premarathne, Fengling Han, Ibrahim Khalil

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 11, 2013
    PubMed
    Summary

    This study introduces a preference-based load balancing strategy for online medical consultations, significantly reducing patient waiting times and improving system efficiency. The novel approach enhances throughput in distributed healthcare systems.

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    Area of Science:

    • Distributed Systems
    • Healthcare Informatics
    • Operations Research

    Background:

    • Load balancing is crucial for optimizing performance in distributed systems.
    • Online medical consultation systems face challenges in efficient patient scheduling and resource allocation.
    • Current scheduling methods may not adequately address patient preferences or doctor expertise.

    Purpose of the Study:

    • To propose and evaluate a preference-based load balancing strategy for scheduling appointments in online medical consultations.
    • To maximize system throughput and minimize patient waiting times.
    • To enhance the efficiency of distributed healthcare systems.

    Main Methods:

    • Developed a preference-based load balancing strategy incorporating patient preferences and doctor expertise.
    • Classified doctors based on clinical expertise and patient history.
    • Modeled the proposed scheme as a queuing system using Matlab SimEvents for performance analysis.

    Main Results:

    • The preference-based load balancing scheme markedly reduced average patient waiting times.
    • Significantly improved system utilization across various load conditions.
    • Demonstrated the effectiveness of preference mapping for efficient appointment scheduling.

    Conclusions:

    • Preference-based load balancing is an effective strategy for optimizing online medical consultation systems.
    • The proposed method enhances both patient experience (reduced waiting) and system resource utilization.
    • This approach offers a scalable solution for improving efficiency in distributed healthcare applications.