Leveraging Electronic Health Records to improve Patient Appointment Scheduling: A design-oriented Approach

As demand for healthcare services continues to increase, hospitals are under constant economic pressure to better manage patient appointments. It is common practice in clinical routine to schedule appointments based on average service times, resulting in overtime and waiting times for clinicians and patients. To address this problem, we propose a data-driven decision support system for scheduling patient appointments that accounts for variable service times. We take advantage of the growing amount of patient- and treatment-specific data collected in hospitals. Using a simulation study, we evaluate the decision support system on the practical example of a Gastroenterology facility. Our results demonstrate improved appointment scheduling efficiency compared to the approach currently in use

Exploring the Association Between Patient Waiting Time, No-Shows and Overbooking Strategy to Improve Efficiency in Health Care

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