A Data-Driven Model of an Appointment-Generated Arrival Process at an Outpatient Clinic
A Data-Driven Model of an Appointment-Generated Arrival Process at an Outpatient Clinic
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DOI：
10.1287/ijoc.2017.0773
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发表时间：
2018-02
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影响因子：
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通讯作者：
Song-Hee Kim;W. Whitt;W. Cha
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文献类型：
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作者：
Song-Hee Kim;W. Whitt;W. Cha
我们开发了一个高保真度的模拟模型的病人到达过程中的内分泌诊所仔细检查预约和到达数据，从该诊所。数据包括最初进行预约的时间以及患者实际到达的时间，以及除了预定的预约时间之外，患者是否根本没有到达。我们采用基于数据的方法，通过前一天结束时的值指定每天的日程。这种基于数据的方法表明，给定日期的时间表随时间随机演变。事实上，除了三个公认的可变性来源-（一）不显示，（二）额外的计划外到达，（三）实际到达时间从预定时间的偏差，我们发现，在到达过程中的可变性的主要来源是在日常日程本身的可变性。尽管预约到达的服务系统可以在许多方面有所不同，但我们认为，我们基于数据的方法可以更好地解决问题。
We develop a high-fidelity simulation model of the patient arrival process to an endocrinology clinic by carefully examining appointment and arrival data from that clinic. The data include the time that the appointment was originally made as well as the time that the patient actually arrived, as well as if the patient did not arrive at all, in addition to the scheduled appointment time. We take a data-based approach, specifying the schedule for each day by its value at the end of the previous day. This data-based approach shows that the schedule for a given day evolves randomly over time. Indeed, in addition to three recognized sources of variability—(i) no-shows, (ii) extra unscheduled arrivals, and (iii) deviations in the actual arrival times from the scheduled times—we find that the primary source of variability in the arrival process is variability in the daily schedule itself. Even though service systems with arrivals by appointment can differ in many ways, we think that our data-based approach to mo...