Date of Graduation

5-2017

Document Type

Thesis

Degree Name

Bachelor of Science in Industrial Engineering

Degree Level

Undergraduate

Department

Industrial Engineering

Advisor/Mentor

Zhang, Shengfan

Committee Member/Reader

Rossetti, Manuel D.

Abstract

Many primary care clinics are using overbooking as a strategy to mitigate the negative impacts on operations and performance caused by patient nonattendance of appointments, also known as “no-shows”. However, overbooking tends to increase patient waiting time and worker overtime. It is also acknowledged that patient waiting time is associated with no-show behavior, yet there is a lack of observational study to quantify the relationship. The overall goal of this research is to explore the relationships between patient waiting time, no-show behavior and overbooking strategy in terms of clinic performance. Arena® simulation software is used to create a discrete-event simulation model that represents daily processes of a standard primary care clinic. The model is used to test the three variables by varying (1) the amount increase in no-show probability by tolerance group, (2) waiting time tolerance threshold, and (3) overbooking strategy. We observe from the results that the three features (waiting time, no-show behavior and overbooking strategy) are interrelated because higher no-show probability leads to higher number of no-shows, which suggests overbooking more patients, and eventually leads to longer waiting time, resulting in an increase in the patient’s no show probability. However, as limited by the size of the clinic case, we were not able to see a clear cut-off of average waiting tolerance for making overbooking decisions that are not only based on the prediction of patient no-shows, but also consider the impact on patient waiting time and its association with no-show behavior. Nevertheless, by having the waiting time as one of the constraint variables, we were able to see the trade-off of choosing a certain overbooking decision and its impact on no-shows. To fully understand the impact of the relationship between the three variables, we recommend that more observational studies should be conducted as pertaining to the desired clinic environment.

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