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Managing access to primary care clinics using scheduling templates

Author

Listed:
  • Sina Faridimehr

    (WarnerMedia)

  • Saravanan Venkatachalam

    (Wayne State University)

  • Ratna Babu Chinnam

    (Wayne State University)

Abstract

An important challenge confronting healthcare is the effective management of access to primary care. Appointment scheduling policies/templates can help strike an effective balance between the lead-time to an appointment (a.k.a. indirect waiting time, measuring the difference between a patient’s desired and actual appointment dates) and waiting times at the clinic on the day of the appointment (a.k.a. direct waiting time). We propose methods for identifying effective appointment scheduling templates using a two-stage stochastic mixed-integer linear program model. The model embeds simulation for accurate evaluation of direct waiting times and uses sample average approximation method for computational efficiency. The model accounts for patients’ no-show behaviors, provider availability, overbooking, demand uncertainty, and overtime constraints. The model allows the scheduling templates to be potentially updated at regular intervals while minimizing the patient expected waiting times and balancing provider utilization. Proposed methods are validated using data from the U.S. Department of Veterans Affairs (VA) primary care clinics.

Suggested Citation

  • Sina Faridimehr & Saravanan Venkatachalam & Ratna Babu Chinnam, 2021. "Managing access to primary care clinics using scheduling templates," Health Care Management Science, Springer, vol. 24(3), pages 482-498, September.
    • Handle: RePEc:kap:hcarem:v:24:y:2021:i:3:d:10.1007_s10729-020-09535-z
    DOI: 10.1007/s10729-020-09535-z
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    References listed on IDEAS

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    1. Wen-Ya Wang & Diwakar Gupta, 2011. "Adaptive Appointment Systems with Patient Preferences," Manufacturing & Service Operations Management, INFORMS, vol. 13(3), pages 373-389, July.
    2. Linda V. Green & Sergei Savin, 2008. "Reducing Delays for Medical Appointments: A Queueing Approach," Operations Research, INFORMS, vol. 56(6), pages 1526-1538, December.
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    5. Renata Kopach & Po-Ching DeLaurentis & Mark Lawley & Kumar Muthuraman & Leyla Ozsen & Ron Rardin & Hong Wan & Paul Intrevado & Xiuli Qu & Deanna Willis, 2007. "Effects of clinical characteristics on successful open access scheduling," Health Care Management Science, Springer, vol. 10(2), pages 111-124, June.
    6. Diwakar Gupta & Lei Wang, 2008. "Revenue Management for a Primary-Care Clinic in the Presence of Patient Choice," Operations Research, INFORMS, vol. 56(3), pages 576-592, June.
    7. Nan Liu & Serhan Ziya & Vidyadhar G. Kulkarni, 2010. "Dynamic Scheduling of Outpatient Appointments Under Patient No-Shows and Cancellations," Manufacturing & Service Operations Management, INFORMS, vol. 12(2), pages 347-364, September.
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    Cited by:

    1. David D. Cho & Kurt M. Bretthauer & Jan Schoenfelder, 2023. "Patient-to-nurse ratios: Balancing quality, nurse turnover, and cost," Health Care Management Science, Springer, vol. 26(4), pages 807-826, December.
    2. Miao Bai & Bjorn Berg & Esra Sisikoglu Sir & Mustafa Y. Sir, 2023. "Partially partitioned templating strategies for outpatient specialty practices," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 301-318, January.

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