Outpatient Appointment Scheduling Using Discrete Event Simulation Modeling

Problem:

Indiana University Health Arnett Hospital, consisting of a full-service acute care hospital and a multispecialty clinic, faced poor statistics because the number of no-show patients (those who don’t show up for their scheduled appointments) rose dramatically to 30%. This was primarily connected to the fact that clinic schedules were driven by individual preferences of the medical staff, which led to increased variations in scheduling rules. To eliminate the problem, the client wanted to develop a scheduling methodology that would benefit the clinic, doctors, and patients. The contractors from Texas A&M University were asked to make a predictive scheduling system to optimize doctors’ schedules and decrease the amount of no-shows. They also aimed for:

Solution:

To address the challenge in appointment scheduling, the contractors developed a discrete event simulation model using AnyLogic software.

Hospital Scheduling Simulation

The model’s input screen was used to insert parameters, including clinic capacity, no-show rates, patient mix, and more. The following data on the clinic was entered on this screen:

The user could change these capacity parameters to see what changes would help increase medical staff utilization and reduce working time for physicians and waiting time for patients.

The discrete event model showed the following sequence of operations:

Discrete Event Simulation Modeling

Output screen showed the model results and performance measures for a simulation run. Data included:

Discrete Event Simulation Modeling

After the users got the results, they could adjust physicians’ schedule in the model and run it again to see how these changes could impact performance measures, including:

Why AnyLogic?

The developers chose AnyLogic for several reasons. First, the AnyLogic software has multimethod modeling capabilities that allowed the developers to extend the model in the future if needed. In addition, user-friendly interface, and its engaging options, made it easy for other users to experiment with the model and change the input parameters without additional training.

Outcome:

The AnyLogic simulation model offered various ways of improving the clinic’s operational efficiency and patient satisfaction. The model did not require special skills to use and provided detailed output statistics that included:

The obtained data allowed users to see how the schedule affects the clinic’s working process and provided insight to choose better staff management policies.

AnyLogic presented a method to test theories before implementing them in the clinic and gave different forecasts. For example, with the model the users could predict when the clinic would reach its maximum capacity. In addition, the existing model could be expanded with agent-based and system dynamic approaches if needed, making the model more adjustable to design a predictive appointment scheduling system in other outpatient clinics with similar settings.

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