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A Next Available Appointment (NAA) Tool to Better Manage Patient Delay Risk and Patient Scheduling Expectations in Specialist Clinics

Author

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  • Vahid Riahi
  • David Rolls
  • Ibrahima Diouf
  • Sankalp Khanna
  • Kim O'Sullivan
  • Rajiv Jayasena

Abstract

Every year there are approximately 3 million new outpatient specialist clinic appointments at local hospital networks in Victoria, Australia. Growing daily demand for these services leads to high‐volume waiting lists and therefore long appointment delays for patients. This phenomenon emphasises the importance of providing analytics and tools to assist with waiting list management in outpatient specialist clinics. In this paper, we developed a novel Next Available Appointment (NAA) tool, to assist clinicians to manage delayed‐appointment risk and improve the patient experience by aligning the expected and actual day of the appointment. The NAA uses simulation to determine the earliest available week for a patient appointment on or after the timeframe requested by the clinician, considering the current waiting list and future planned clinician availability. It was validated using 3 years of historical waiting list information across several scenarios chosen to capture operational diversity. As a practical example, a scenario chosen for implementation within the clinic's operational setting achieved a simulated reduction in overdue appointments from 41% to 25% (i.e., a reduction of 47,000 overdue appointments over 3 years). We also provided early details on the implementation of the tool currently underway.

Suggested Citation

  • Vahid Riahi & David Rolls & Ibrahima Diouf & Sankalp Khanna & Kim O'Sullivan & Rajiv Jayasena, 2025. "A Next Available Appointment (NAA) Tool to Better Manage Patient Delay Risk and Patient Scheduling Expectations in Specialist Clinics," International Journal of Health Planning and Management, Wiley Blackwell, vol. 40(3), pages 607-619, May.
    • Handle: RePEc:bla:ijhplm:v:40:y:2025:i:3:p:607-619
    DOI: 10.1002/hpm.3904
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    References listed on IDEAS

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    1. Jin Qi, 2017. "Mitigating Delays and Unfairness in Appointment Systems," Management Science, INFORMS, vol. 63(2), pages 566-583, February.
    2. Jacob Feldman & Nan Liu & Huseyin Topaloglu & Serhan Ziya, 2014. "Appointment Scheduling Under Patient Preference and No-Show Behavior," Operations Research, INFORMS, vol. 62(4), pages 794-811, August.
    3. M M Günal & M Pidd, 2010. "Discrete event simulation for performance modelling in health care: a review of the literature," Journal of Simulation, Taylor & Francis Journals, vol. 4(1), pages 42-51, March.
    4. Golmohammadi, Davood & Zhao, Lingyu & Dreyfus, David, 2023. "Using machine learning techniques to reduce uncertainty for outpatient appointment scheduling practices in outpatient clinics," Omega, Elsevier, vol. 120(C).
    5. Jonathan Patrick & Martin L. Puterman & Maurice Queyranne, 2008. "Dynamic Multipriority Patient Scheduling for a Diagnostic Resource," Operations Research, INFORMS, vol. 56(6), pages 1507-1525, December.
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