Indiana University Health Arnett Hospital, a full-service acute care hospital and a multispecialty outpatient 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 this outpatient 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:
- Increase physician efficiency.
- Increase facility utilization.
- Keep down physician overtime.
- Decrease waiting time for patients.
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 patient flow in the model, they were attributed to one of five groups:
- Patients requesting same-day appointments
- New patients of high priority
- Re-check of high priority
- New patients of low priority
- Re-check of low priority.
High priority patients had insurance, as opposed to those of low priority.
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:
- Number of appointment requests per hour the outpatient clinic can have at each day stage.
- No-show rate.
- Doctors’ working schedule including time of availability and number of patients they were able to help per day. It was also possible to limit the number of sick and new patients a doctor could see each day.
- Sick patients’ field showing the share of patients diverted to a different doctor or nurse.
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:
- The appearing patients are divided into the five groups. Same-day sick patients are treated in the same day, while others schedule their time of visit and wait at home.
- When they need to arrive at the clinic on the day of the appointment, the model calculates the no-show rate based on probability specified by the input data.
- If the patients come, they are seen by doctors or nurses, and after that they leave the hospital.
The output screen showed the patient flow simulation model results and performance measures for a simulation run. Data included:
- Number of treated patients for patient visit type.
- Number of no-show patients for patient visit type.
- Appointment lead time for patient visit type.
- Proportions of discharged patients per doctor, nurse, or peer/urgent care.
- Maximum daily clinic capacity.
The model also helped doctors test different theories about their working schedules. They could adjust the schedule in the patient flow simulation model and see how utilization and overtime changed.
Why Create Patient Flow Simulation Model in 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 outpatient 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’s capabilities for creating user-friendly and engaging interfaces made it easy for other users to experiment with the patient flow simulation model and change the input parameters without additional training.
The AnyLogic patient flow simulation model offered various ways to improve the outpatient clinic’s operational efficiency and patient satisfaction. The patient flow simulation model did not require special skills to use and provided detailed output statistics that included:
- Staff time utilization and overtime amount.
- Patient distribution among medical staff.
- Patient waiting time, and more.
The obtained data allowed users to see how the schedule affected the clinic’s work processes and provided insight for improving patient flow at the outpatient clinic and choosing 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.