Outpatient Appointment Scheduling Using Discrete Event Simulation Modeling

Problem:

Indiana University Health Arnett Hospital, 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 because clinic schedules were driven by the 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. Contractors from Texas A&M University were asked to make a predictive scheduling system to optimize doctors’ schedules and decrease the number of no-shows. They also aimed to:

Solution:

To address the challenge in appointment scheduling, the contractors developed a discrete event simulation model using AnyLogic software. The model simulated the patients’ appointment process and further checkup. To better represent patients in the model, they were attributed to one of five groups:

High priority patients had insurance, as opposed to those of low priority.

Hospital Scheduling Simulation

The interface showed how patients mix, depending on treatment time for patient types (it is assumed that new patients have a longer appointment time than re-check patients) and seasonal factors. The model’s input screen was used to insert the following parameters:

The user could change these capacity parameters to see what changes would help optimize working time for physicians and waiting time for patients. The discrete event model showed the following sequence of operations:

Discrete Event Simulation Modeling

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

The model was also helped doctors test different theories about their working schedule. They could adjust the schedule in the model and see how utilization and overtime changed.

Discrete Event Simulation Modeling

Why AnyLogic?

The developers chose AnyLogic for several reasons. First, the AnyLogic software allowed them to easily capture discrete event metrics, such as utilization rates, time patients are in the clinic, and wait time. With AnyLogic, it would be possible to expand the primarily discrete event model using agent-based and system dynamic approaches. In addition, AnyLogic capabilities in creating the model’s user-friendly and engaging interface 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 affected 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. In addition, the discrete event model could be expanded with other simulation approaches if needed. This feature made the model more adjustable to design a predictive appointment scheduling system in other outpatient clinics with similar settings.

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