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Determination of optimal variable-sized multiple-block appointment systems.

B E Fries, V P Marathe

    Operations Research
    |February 10, 1981
    PubMed
    Summary
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    This study analyzes a flexible single-server multiple-block appointment system, outperforming traditional methods. The novel approach optimizes clinic scheduling by balancing patient waiting times and provider idle time for improved efficiency.

    Area of Science:

    • Operations Research
    • Healthcare Management
    • Queueing Theory

    Background:

    • Single-block appointment systems are prevalent in ambulatory care.
    • Existing research explores various scheduling models, including single-block, individual, and mixed systems.
    • There is a need for more flexible and efficient appointment scheduling models.

    Purpose of the Study:

    • To analyze a general single-server multiple-block appointment system with variable block sizes.
    • To develop analytical results and approximations for this system.
    • To compare the performance of this general system against other common scheduling models.

    Main Methods:

    • Utilized a dynamic programming approach with modifications for non-Markovian properties.
    • Developed analytical solutions and approximations to reduce computational demands.

    Related Experiment Videos

  • Evaluated system performance based on criteria including patient waiting time, provider idle time, and overtime.

    • Main Results:

    • Obtained analytical results and approximations for the general single-server multiple-block system.
    • Demonstrated that the proposed system can achieve superior performance compared to traditional systems.
    • Performance improvements are contingent on the weighting of waiting, idle, and overtime criteria.

    Conclusions:

    • The analyzed general single-server multiple-block system offers enhanced flexibility and potential for superior performance in ambulatory care scheduling.
    • This model provides a valuable alternative to conventional appointment systems.
    • Further research can explore specific parameter optimizations for diverse clinical settings.