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.


Policy makers were motivated by the overall ranking of healthcare in Canada. A study from 2010 showed low rankings in quality of care, effective care, coordinated care, patient centered care, timeliness of care, efficiency, and equity, in comparison to Australia, Germany, the Netherlands, New Zealand, and the United Kingdom.

In order to test and visualize policy changes and other possible solutions, the CRHE planned to build a decision support tool to observe behavior and select appropriate policy changes that could ultimately increase life expectancy by increasing access to healthcare, increasing patient satisfaction, and changing the perception of healthiness.

Healthcare System Model Structure

Healthcare System Model Structure


AnyLogic simulation modeling was chosen to build the decision support tool due to its mulimethod modeling capabilities. System dynamics modeling was used to study aggregate behaviors (interactions between major groups), and agent-based modeling was used for adaptive behavior.

The model included descriptive data from the Irish healthcare system codified through content analysis, and quantitative data from the Central Statistics office of Ireland, OECD health statistics, and Eurostat health databases, which were codified through statistical analysis. Data from Ireland was used because the country experienced substantial strategic shifts, external economic shock, and could provide extensive, transparent documentation.

The documentation from the healthcare systems was compiled, run through a content analysis process, moved through a UML for structure, and plugged into AnyLogic as a platform to build the model and run scenarios.

The model structure of the healthcare system included elements, goals, and strategies. Elements included patients, physicians, other clinicians, hospitals, clinics, and corporations, including insurers and regulators. Goals were outcomes such as life expectancy and mortality, access to healthcare, and other determinants of health, as well as subjective outcomes such as satisfaction and perception.

Strategies and policies that were considered included:

  • Insurance- equity in accessibility
  • Sectoral- primary care and elder care
  • Capital investment- facilities and technology
  • Regulations- gatekeeping and models of care
  • Governance- public/private care and mergers


The ability to visualize the large amount of data and derive patterns was the greatest benefit achieved from this project. Variables such as life expectancy, expenditures, consumption, etc., could be plotted and observations could be made dependent upon country. In future research, the CRHE will test policy change scenarios and work to improve the healthcare system of Canada and other low ranking countries.

Watch the presentation of the project by Neil McEvoy from CRHE:

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.
  • 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.
  • 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.
  • 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.
  • Modeling of a Pharmaceutical Product Launch
    One of the huge pharmaceutical companies employed Bayser Consulting for development of product launch strategy. Simulation modeling was applied for reconstruction of interactions between the company, doctors and patients.
  • A Pharmaceutical Company Decides on a Marketing Strategy Using Agent-Based Modeling
    Sterling Simulation consulting company was chosen to provide an agent-based marketing model for a pharmaceutical firm. The company owned two competing non-generic drugs on the same market. One drug was well established and tended to be the industry leader, and the other one was recently introduced. There were several concerns about how to obtain a useful market share for the newer drug, while maintaining or increasing the market share for the company’s drugs as a whole.