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Design and analysis of a hybrid appointment system for patient scheduling: an optimisation approach

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  • Sharan Srinivas
  • Mohammad T. Khasawneh

Abstract

This paper proposes a mixed integer linear programming (MILP) model for a hybrid appointment system (HAS) that schedules patients based on their preference while minimising the total loss of the system. The HAS proposed is a combination of three scheduling methods, namely walk-ins, pre-booked, and open access. The model supports the decision maker in determining the rejection and overtime rates of the health system under consideration. The HAS is analysed for the impact of different parameters, namely proportion of patients requesting open access (OA ratio), patient no-shows, and variation of service time on the optimal rejection rate and overtime rate. The results indicated that the HAS can handle variations in clinic's operational factors (variation of at least 20% in OA ratio and service time) without significantly impacting the performance measures. Furthermore, sensitivity analysis indicated that the model is not impacted when the demand fluctuation is within 40%.

Suggested Citation

  • Sharan Srinivas & Mohammad T. Khasawneh, 2017. "Design and analysis of a hybrid appointment system for patient scheduling: an optimisation approach," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 29(3), pages 376-399.
    • Handle: RePEc:ids:ijores:v:29:y:2017:i:3:p:376-399
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    Citations

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    Cited by:

    1. Sharan Srinivas, 2020. "A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers," IJERPH, MDPI, vol. 17(10), pages 1-15, May.
    2. Sharan Srinivas & A. Ravi Ravindran, 2020. "Designing schedule configuration of a hybrid appointment system for a two-stage outpatient clinic with multiple servers," Health Care Management Science, Springer, vol. 23(3), pages 360-386, September.

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