Outpatient Appointment Scheduling Using Discrete Event Simulation Modeling


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:

  • Increasing physician efficiency
  • Increasing facility utilization
  • Keeping down physician overtime
  • Decreasing waiting time for patients


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:

  • Number of appointment requests per hour the clinic can have at each day stage.
  • No-show rate, taking variability into account.
  • Patient characteristics, including five types of patients. High priority ones were supposed to have insurance, as opposed to those of low priority.
  • Decreasing waiting time for patients:

    • Same-day sick patients (people who do not have an appointment and need to be urgently seen)
    • New patients of high priority
    • Re-check of high priority
    • New patients of low priority
    • Re-check 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.

    • Sick patients’ field showed the share of patients diverted to a different doctor or nurse.
    • Doctors’ working schedule included time of availability and number of patients they were able to help per day. It was also possible to limit the amount of sick and new patients a doctor could see each day.

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:

  • The appearing patients are divided into the four types.
  • Once the type is preassigned, the patients waited at home for their scheduled time of visit.
  • When they arrived at the clinic on the day of the appointment, the model calculated the no-show rate based on probability specified by the input data.
  • Patients were seen by doctors or nurses, and after that they left the hospital.

Discrete Event Simulation Modeling

Output screen showed the 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.
  • Doctors’ overall utilization rates in the facility and the overtime they spent in the clinic each day.
  • Proportions of discharged patients per doctor, nurse, or peer/urgent care.
  • Maximum daily clinic capacity.

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:

  • Average patient request-to-appointment time
  • Clinic utilization
  • Physician overtime for up to two years

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.


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:

  • Clinic’s daily capacity
  • Staff time utilization and overtime amount
  • Patients’ distribution among medical staff
  • Patient waiting time, and more

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.


More Case Studies

  • Modeling Operations at Pharmaceutical Distribution Warehouses
    Cardinal Health, a billion dollar pharmaceutical distribution and logistics firm, manages multiple products from brand name pharmaceuticals and generic drugs to over the counter drugs, health & beauty items and their own private label. They face a multitude of typical distribution warehouse challenges that are further complicated by the nature of pharmaceutical products. Brian Heath, Director of Advanced Analytics at Cardinal Health, and an experienced user of AnyLogic software, employed agent based modeling to solve various business problems, saving Cardinal Health over $3 Million annually.
  • Production Planning in Marine Industry
    The managers of one of the most important Italian yacht manufacturers needed a new, intelligent approach that would make the planning process simpler. The objective was to give the real production planner exceptionally rich planning information, which would allow the person to test and refine a plan before its implementation.
  • Modeling of Banca d'Italia Back Office System
    Banca d'Italia processes a certain amount of manual credit transfers every year. These transfers cannot be processed automatically and require two divisions of employees in the back office of the bank. The bank wanted to determine if merging these two divisions would be beneficial.
  • Simulation of Maternity Ward Operations
    This model simulates the maternity ward in a hospital currently under construction. The purpose of the model is to support discussions related to which resources, capacity, and work methods are required on the new ward. The project was carried out for Karolinska University Hospital in the Stockholm County, Sweden.
  • Evaluating Hospital Inpatient Care Capacity
    Stockholm County, Sweden was in the process of building a new, highly specialized hospital. The Health Administration of the county questioned whether they would get an acceptable level of care production with the current investments and reasoning concerning various operational and strategic issues. To find the answers, they used simulation modeling in AnyLogic.
  • Handling Total Care Need for Dialysis Patients
    The County of Stockholm (Sweden), like any country or region, experiences a continuous need to handle the healthcare necessities of various patient groups. Each group can be seen as a subpopulation, with its own distinctive traits, characteristics, and challenges. The discussed simulation project focused on the dialysis patients, a group who needs to visit caregiving facilities frequently.
  • Disaster Response Applications Using Agent-Based Modeling
    In an effort to find practical operational solutions for response to an unexpected crisis or natural disaster, Battelle, world’s largest, non-profit, independent R&D organization, needed to test the effectiveness of a 48 hour shelter-in-place order for an Improvised Nuclear Device scenario. The goal was to reduce radiation dosages received during an uncoordinated mass evacuation, by comparing immediate evacuation and shelter-in-place order.
  • Evaluating Healthcare Policies to Reduce Rates of Cesarean Delivery
    The challenge of reducing the cesarean delivery rate has been recognized by numerous researchers for years. For the first time, in research conducted for the Washington State, Alan Mills, FSA MAAA ND, a research actuary, and his colleagues reproduced this part of the United States healthcare system in a simulation model to allow the stakeholders, including health agencies, insurers, clinicians, and legislators, to test their assumptions on the model to find the right solutions.
  • Shaping Healthcare Policy Using Simulation
    An initiative by the Department of Mechanical and Industrial Engineering at the University of Toronto, the Centre for Research in Healthcare Engineering (CRHE), was in response to the immediate and compelling desire for efficiency and quality improvements in the Canadian healthcare system.
  • An Agent-Based Explanation for SPMI Living Situation Changes
    Over the past 60 years, the number of Severely and Persistently Mentally Ill (SPMI) patients in the US living in the community increased. Yet a growing minority of people with severe illness are worse off because they are homeless or incarcerated. In this case study, IBM Global Research and Otsuka Pharmaceuticals used an agent-based approach to model these remarkable swings.