BU275 Simulation Case Assignment
Rama Sai Sreehita Turaga
169026604
March 24th, 2024
, Introduction
The challenge of the Emergency Room wait times in Canada has been an ongoing and
multifaceted issue that significantly impacts patient care and healthcare system efficiency. To
develop a better understanding of this problem, it was crucial to take time to review scholarly
articles that provided valuable insights into the causes, consequences and solutions for this
problem. Often, patients are used to waiting for an appointment to see a physician, however, are
also further required to wait at their actual appointment. This causes numerous consequences
such as delayed care, financial implications and poor patient satisfaction. Through an analysis of
over 20 million visits to the ER across the span of a few years, researchers figured out that ER
crowding worsens the likelihood of patient death and readmission to the hospital escalates.
Reducing the average wait time to access the ER to under one hour has the potential to save up to
150 lives annually in Ontario. Often, one can use the queuing theory to explain the delays caused
by the difference between the demand for a service and the capacity to meet the particular
demand. With the use of queuing models, one can better manage the flow of unscheduled patient
arrivals. The Health Quality Ontario website states that on average the wait time to first get
assessed by a doctor in the ER is 2 hours and around 89% of patients finish their visit in a target
time of 8 hours. Thus with the given information, for this simulation, I assumed my arrival and
service rate of 14 customers/hour for arrival and 12 customers/hour for service. With these
assumptions and the implementation of an M/M/1 queuing model, one can better understand the
approximate waiting times of patients and come up with solutions to reduce these times, overall
increasing patient satisfaction and care.
Rama Sai Sreehita Turaga
169026604
March 24th, 2024
, Introduction
The challenge of the Emergency Room wait times in Canada has been an ongoing and
multifaceted issue that significantly impacts patient care and healthcare system efficiency. To
develop a better understanding of this problem, it was crucial to take time to review scholarly
articles that provided valuable insights into the causes, consequences and solutions for this
problem. Often, patients are used to waiting for an appointment to see a physician, however, are
also further required to wait at their actual appointment. This causes numerous consequences
such as delayed care, financial implications and poor patient satisfaction. Through an analysis of
over 20 million visits to the ER across the span of a few years, researchers figured out that ER
crowding worsens the likelihood of patient death and readmission to the hospital escalates.
Reducing the average wait time to access the ER to under one hour has the potential to save up to
150 lives annually in Ontario. Often, one can use the queuing theory to explain the delays caused
by the difference between the demand for a service and the capacity to meet the particular
demand. With the use of queuing models, one can better manage the flow of unscheduled patient
arrivals. The Health Quality Ontario website states that on average the wait time to first get
assessed by a doctor in the ER is 2 hours and around 89% of patients finish their visit in a target
time of 8 hours. Thus with the given information, for this simulation, I assumed my arrival and
service rate of 14 customers/hour for arrival and 12 customers/hour for service. With these
assumptions and the implementation of an M/M/1 queuing model, one can better understand the
approximate waiting times of patients and come up with solutions to reduce these times, overall
increasing patient satisfaction and care.
