Riahi Vahid, Rolls David, Diouf Ibrahima, Khanna Sankalp, O'Sullivan Kim, Jayasena Rajiv
The Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Melbourne, Australia.
The Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia.
Int J Health Plann Manage. 2025 May;40(3):607-619. doi: 10.1002/hpm.3904. Epub 2025 Jan 23.
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.
在澳大利亚维多利亚州的地方医院网络中,每年约有300万例新的门诊专科诊所预约。对这些服务日益增长的日常需求导致了大量的候诊名单,从而使患者的预约延迟时间很长。这种现象凸显了提供分析方法和工具以协助门诊专科诊所管理候诊名单的重要性。在本文中,我们开发了一种新颖的“下一个可用预约”(NAA)工具,以帮助临床医生管理延迟预约风险,并通过使预约的预期日期和实际日期一致来改善患者体验。NAA使用模拟方法来确定在临床医生要求的时间范围内或之后患者最早可预约的周数,同时考虑当前的候诊名单和未来计划的临床医生可用时间。我们使用了3年的历史候诊名单信息,通过选择多种场景来反映操作的多样性,从而对该工具进行了验证。作为一个实际例子,在诊所运营环境中选择实施的一个场景实现了将逾期预约模拟减少,从41%降至25%(即3年内减少了47000例逾期预约)。我们还提供了目前正在实施的该工具的早期详细信息。