Hospital Emergency Response Simulation
Infectious Disease and Pandemic Preparedness Using Simulation
The Ebola epidemic in western Africa and recent outbreaks in the USA and Europe have caused hospitals, healthcare organizations and emergency response teams to take a hard look at their preparedness and ability to deal with an outbreak. Unfortunately, recent cases in the USA have demonstrated that paper plans don’t always play out as intended.
A major challenge in developing these plans is fully understanding the complexities and interdependencies that exist in a hospital, emergency department or emergency response event. The number of resources interacting with patients (and each other) like admissions personnel, EMT’s, nurses, doctors, lab technicians, volunteers, other patients, families and support staff can be complicated. Add to this the uncertainty and variability surrounding arrival of new cases and it is no wonder that even the most prestigious of hospitals could find their plans insufficient to deal with an outbreak.
How does an organization validate their plans? How do they know if these plans are sufficient to address the potential outbreak? How do they know if their staffing plans are going to be able to efficiently handle anticipated patient flow? How do they get a handle on the complexities and interdependencies within their organizations?
Discrete Event Simulation (DES) has been proven to be a very valuable tool for healthcare organizations, government outbreak control organizations, airports and other travel-related entities such as airlines, cruise ships, trains, etc… to validate their preparedness plans before putting them to the test in a real-world situation.
Discrete Event Simulation enables an organization to build a model of their plan, including the interactions, interdependencies and variability in the system as well as all resources required. Once developed, the model can be run to simulate extended horizons to understand the performance of the plan and the resources required to implement it. In addition, modifications or alternatives to the plan can be evaluated to improve overall responsiveness.
A number of organizations and researchers have published articles and papers documenting the value of Discrete Event Simulation in these sorts of applications. A few are summarized here.
North Carolina State Fair E. Coli Outbreak
In 2004, a petting zoo at the North Carolina State Fair was responsible for infecting 108 fairgoers with E. coli. Of those cases, 15 were diagnosed with Hemolytic Uremic Syndrome (HUS), a severe, life-threatening complication of E. coli. The event was also significant because it drew visitors from across the state and represented a statewide public health threat. The widespread impact of this outbreak, required communication and coordination among jurisdictions and organizations across North Carolina.
Researchers at North Carolina State University used this outbreak to develop a Discrete Event Simulation model of public health emergency response to a public health crisis using Arena® simulation software. (Wynter & Ivy, 2009) The model simulated the role of key organizations, resources and IT support systems throughout the entire response to a communicable disease related health threat. The model included seven major steps:
- Notification and Mass Dissemination of Information
- Implementation of Control Measures
Their work demonstrated the impact of communication flow and efficiency between organizations. In other words, getting the right information to the right people at the right time. They were able to model the impact of getting information to various organizations and the public via different state supported IT systems.
The NC State researchers also modeled resource levels in hospitals and clinics to detect cases and treat patients diagnosed with the infection. They found that lab capacity was the most significant bottleneck to responsiveness to future outbreaks in North Carolina.
Sadly, this sort of outbreak is not uncommon. Since 2000, petting zoos and organized farm visits around the U.S. have caused at least 32 publicized outbreaks of E. coli, Salmonella and Cryptosporidium. (Andrews, 2012)
When properly applied, Discrete Event Simulation is an outstanding tool for evaluating and improving emergency preparedness and response to outbreaks of infectious diseases.
Pandemic Influenza Outbreak Modeling
Montgomery County, Maryland’s Advanced Practice Center for Public Health Emergency Preparedness and Response and the Institute for Systems Research at the University of Maryland published an article entitled “Embracing Computer Modeling to Address Pandemic Influenza in the 21st Century” (Aaby, et al., 2006, 12(4)). In this article, they describe how Arena simulation software was utilized to develop a tool to evaluate and modify Points of Dispensing (PODs) plans in advance of an influenza outbreak. According to the authors, “An important question for public health emergency planners—those persons within local public health agencies dedicated to creating POD plans—is how to allocate a limited number of staff to different workstations in a POD to maximize capacity.”
The team built a model designed to evaluate and improve patient flow patterns through the PODs. From this model, they were able to determine the number of days needed to treat patients, the number of personnel needed to staff the PODs (including administrative, support, security and logistics staff), the patient queue length and the total time in the PODs. The model was able to identify where congestion would occur as well as the maximum number of patients that could be treated in the shortest amount of time. With the model they were also able to modify and simplify the PODs to reduce the number of stations to minimize patient time in the PODs and reduce unnecessary exposure of arriving patients that were not symptomatic or unable to receive the vaccine.
The Department of Public Health at Weill Cornell Medical College have demonstrated the value of Discrete Event Simulation in improving local, state and national preparedness against bioterrorism.
Dr. Nathaniel Hupert, Assistant Professor of Public Health and Medicine and the study’s lead author, said, “In the event of a widespread bioterrorist attack on a major metropolitan area, a successful public health response requires carefully planned triage and drug-dispensing centers with specific staffing requirements. While the department of Health and Human Services mandates that every state provide a detailed plan, the states have been delayed by lack of research on how the centers should be designed and operated. The use of computer simulation modeling may remedy this situation.”
Using Arena simulation software, the researchers identified the optimal number of staff to place at patient triage and drug delivery stations in order to avoid long lines and delay in treatment for a variety of hypothetical bioterrorist attack scenarios. (Weil, 2002)
Healthcare Applications of Discrete Event Simulation
As the previous examples demonstrate, Discrete Event Simulation can be utilized to model many aspects of emergency preparedness and response. It is also widely used throughout the healthcare industry to simulate a broad range operations, including the following:
- Patient Flow
- Doctor/Nurse Schedules
- Patient Schedules
- Treatment plans and protocols
- Drug/vaccine distribution
- Emergency Departments
- Surgical Centers
- Urgent Care Facilities
- Physician Practices
- Cancer Research Institutes
- Pharmacy Departments
- Facility Layout Planning
Arena simulation software is an accepted and proven tool that enables healthcare organizations to address critical issues like the following:
- Optimal staffing levels to meet demand
- Improving allocation of equipment, resources and staff
- Evaluating and enhancing patient admission processes
- Reducing patient cost and time in system
- Improving patient satisfaction
- Increasing doctor/nurse retention rates
- Evaluating and improving Disaster/Emergency Response Planning
Making critical decisions about emergency preparedness and response is fraught with risk. The implication of making the wrong decision can mean the difference between life and death. Arena is a proven tool that enables healthcare organizations and emergency responders to test out the validity of their plans, to evaluate alternatives and to optimize their response to possible scenarios in a quick and cost effective manner.
Aaby, K., Abby, R. L., Herrmann, J. W., Treadwell, M., Jordan, C. S., & Wood, K. (2006, 12(4)). Embracing Computer Modeling to Address Pandemic Influenza in the 21st Century. Journal of Public Health Management and Practice, 365-372.
Andrews, J. (2012, November 16). The Petting Zoo Problem. Retrieved from Food Safety News: www.foodsafetynews.com
Weil, J. (2002, November 22). Weill Cornell Computer Simulation Model Helps Remedy Possible National Gap in Bioterrorism Preparedness. Retrieved from Weill Cornell Medical College Newsroom: http://weill.cornell.edu/news/pr/2002/11/weill-cornell-computer-simulation-model-helps-remedy-possible-national-gap-in-bioterrorism-preparedn.html
Wynter, S. A., & Ivy, J. E. (2009). Simulating Public Health Emergency Response: A Case Study of the 2004 North Carolina State Fair E.coli Outbreak. Proceedings of the 2009 Winter Simulation Conference (pp. 1957-1968). Austin, TX: WSC.