Using discrete event simulation to improve the patient care process in the emergency department of a rural Kentucky hospital.

Abstract

The patient care process of a rural Kentucky hospital is a complex process that must be flexible in order to deal with a large variety of patient needs and a fluctuating patient volume where all patients are unscheduled. A simulation model of an average month in the emergency department was built using the Arena Simulation package. Methods for creating a simulation using Arena are included in this work. Statistics were generated from a number of different sources to create an accurate representation of the model.The Hospital reporting shows a need to improve on two quality measures being tracked, the length of time a patient is in the emergency department from entry to completion of care, and the number of patients who leave without being seen by the physician (most often due to the length of their waiting room time prior to the initiation of care). Due to the complex nature of the emergency department and its impact by other departments of the Hospital as well as outside factors such as patient demand, the ability to quantify an expected gain from a change to the facility or to a process can be difficult to establish. A simulation model will allow for experiments on the system to be created and observed, thus enabling the Hospital to identify the best opportunities for improvement.Experiments included in this work show changes to the emergency department facility by adding an additional patient treatment bed, and changing a policy regarding transfer of a patient from the emergency department to inpatient care in the Hospital. Both experiments show improvement in quality measures, with reduced waiting room times, fewer patients who leave without being seen by the physician, and an overall reduction in the length of stay from entry to completion of care in the ED.In the creation of the simulation model, an objective was to develop a model that could be used to guide decision through its flexibility and statistical reliability. The model can be used to test a variety of physical or procedural changes to the emergency department, as well as to test to the impacts of increased patient volume.vi