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Small Changes in Patient Arrival and Consultation Times Have Large Effects on Patients’ Waiting Times: Simulation Analyses for Primary Care

Author

Listed:
  • Matthias Grot

    (Institute of Management, Operations Research, Ruhr University Bochum, Universitätsstr. 150, 44801 Bochum, Germany)

  • Simon Kugai

    (Institute for General Practice and Family Medicine, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany)

  • Lukas Degen

    (Institute for General Practice and Family Medicine, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany)

  • Isabel Wiemer

    (Institute of Management, Operations Research, Ruhr University Bochum, Universitätsstr. 150, 44801 Bochum, Germany)

  • Brigitte Werners

    (Institute of Management, Operations Research, Ruhr University Bochum, Universitätsstr. 150, 44801 Bochum, Germany)

  • Birgitta M. Weltermann

    (Institute for General Practice and Family Medicine, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany)

Abstract

(1) Background: Workflows are a daily challenge in general practices. The desired smooth work processes and patient flows are not easy to achieve. This study uses an operational research approach to illustrate the general effects of patient arrival and consultation times on waiting times. (2) Methods: Stochastic simulations were used to model complex daily workflows of general practice. Following classical queuing models, patient arrivals, queuing discipline, and physician consultation times are three key factors influencing work processes. (3) Results: In the first scenario, with patients arriving every 7.6 min and random consultation times, the individual patients’ maximum waiting time increased to more than 200 min. The second scenario with random patient arrivals and random consultation times increased the average waiting time by up to 30 min compared to patients arriving on schedule. A busy morning session based on the second scenario was investigated to compare two alternative intervention strategies to reduce subsequent waiting times. Both could reduce waiting times by a multiple for each minute of reduced consultation time. (4) Conclusions: Aiming to improve family physicians’ awareness of strategies for improving workflows, this simulation study illustrates the effects of strategies that address consultation times and patient arrivals.

Suggested Citation

  • Matthias Grot & Simon Kugai & Lukas Degen & Isabel Wiemer & Brigitte Werners & Birgitta M. Weltermann, 2023. "Small Changes in Patient Arrival and Consultation Times Have Large Effects on Patients’ Waiting Times: Simulation Analyses for Primary Care," IJERPH, MDPI, vol. 20(3), pages 1-11, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:3:p:1767-:d:1039920
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    References listed on IDEAS

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    1. Alexopoulos, Christos & Goldsman, David & Fontanesi, John & Kopald, David & Wilson, James R., 2008. "Modeling patient arrivals in community clinics," Omega, Elsevier, vol. 36(1), pages 33-43, February.
    2. Guido Kaandorp & Ger Koole, 2007. "Optimal outpatient appointment scheduling," Health Care Management Science, Springer, vol. 10(3), pages 217-229, September.
    3. Song-Hee Kim & Ward Whitt & Won Chul Cha, 2018. "A Data-Driven Model of an Appointment-Generated Arrival Process at an Outpatient Clinic," INFORMS Journal on Computing, INFORMS, vol. 30(1), pages 181-199, February.
    4. Kuiper, Alex & de Mast, Jeroen & Mandjes, Michel, 2021. "The problem of appointment scheduling in outpatient clinics: A multiple case study of clinical practice," Omega, Elsevier, vol. 98(C).
    5. Tugba Cayirli & Kum Khiong Yang & Ser Aik Quek, 2012. "A Universal Appointment Rule in the Presence of No‐Shows and Walk‐Ins," Production and Operations Management, Production and Operations Management Society, vol. 21(4), pages 682-697, July.
    6. Anja Viehmann & Christine Kersting & Anika Thielmann & Birgitta Weltermann, 2017. "Prevalence of chronic stress in general practitioners and practice assistants: Personal, practice and regional characteristics," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-13, May.
    7. Lara Wiesche & Matthias Schacht & Brigitte Werners, 2017. "Strategies for interday appointment scheduling in primary care," Health Care Management Science, Springer, vol. 20(3), pages 403-418, September.
    8. Ahmadi-Javid, Amir & Jalali, Zahra & Klassen, Kenneth J, 2017. "Outpatient appointment systems in healthcare: A review of optimization studies," European Journal of Operational Research, Elsevier, vol. 258(1), pages 3-34.
    9. Kong, Qingxia & Lee, Chung-Yee & Teo, Chung-Piaw & Zheng, Zhichao, 2016. "Appointment sequencing: Why the Smallest-Variance-First rule may not be optimal," European Journal of Operational Research, Elsevier, vol. 255(3), pages 809-821.
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