An emergency department (ED) is considered the most important part of any hospital. It is responsible for providing care to patients who need immediate but unscheduled healthcare services, 24 hours a day, 7 days a week. However, because of an ED’s important role in providing urgent care for ill or injured patients, EDs face a constantly large demand that often results in long wait time. Due to many factors, such as insufficient staffing, budget constraints, poor inpatient bed turnover, unscheduled arrivals, and growing and aging populations, ED services are seriously affected and patient wait time has reached a critical level in many hospitals, which in turn causes serious health consequences and adds an economic cost for both patients and societies. In this context, many healthcare organizations and research centers are wondering whether the analysis results of ED simulation models can help reduce patient wait time.
Background on Patient Wait Time
Wait time is usually known as the difference between the time of arrival in the ED and the time the patient has contact with a physician for the first time. Others define it as the time a patient has spent waiting for diagnostic tests (e.g., X-ray or blood test) or waiting after returning from external testing to get therapy (Chin & Fleisher, 1998). According to the Canadian Institute for Health Information (2012), four relevant measures can contribute to patient wait time in the ED:
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ED Length of Stay: Time from patient registration to discharge or admission;
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Time Waiting for Initial Physician Assessment: Time from patient registration to the moment a physician first assesses the patient;
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Time to Disposition: Time from patient registration to the moment the decision is taken to either discharge or admit the patient to a hospital bed; and
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Time Waiting for Inpatient Bed: Time from patient admission to the moment the patient leaves the ED to go to the inpatient unit (inside the hospital).
Different organizations have defined targets that give a maximum time a patient should spend in the ED. For instance, in Ontario (Canada), provincial targets for the ED length of stay are eight and four hours for the high acuity and low acuity patients, respectively (Ontario Ministry of Health and Long-Term Care, 2015). In Québec (Canada), the targeted provincial average wait time for ED length of stay is 12 hours (Ministère de la Santé et des Services Sociaux du Québec, 2011). In the UK, the target wait time is set to four hours from arrival to admission, transfer, or discharge (NHS Choices, 2015).
Unfortunately, in many cases, hospitals cannot meet their targets and patients wait longer than expected. Such long time causes negative effects on the patients and the service quality. Patients may experience delays in the treatment of pain or suffering, higher dissatisfaction, and higher risks of stronger or more permanent damage. Some patients even decide to leave without receiving treatment. On the other hand, the efficiency and stress level of physicians and nurses can also be affected negatively by such long waits (Waldrop, 2009).
Since long patient wait time is one of the most important issues in ED, and due to its direct impact on the quality of healthcare services and the satisfaction level of patients, it has attracted much attention lately. A variety of solutions have been considered toward shortening ED wait time, such as better resource allocation strategies (Day, Al-Roubaie, & Goldlust, 2013; Xu, Roger, Rohleder, & Cooke, 2008), improved staff working systems (Kuo, 2014; Kuo, Leung, & Graham, 2015; Wang, McKay, Jewer, & Sharma, 2013), and separate care programs for minor injuries (Khadem, Bashir, Al-Lawati, & Al-Azri, 2008; Maulla, Smarta, Harrisb, & Karasnehc, 2009; Rasheed, Lee, Kim, & Park, 2012). However, because ED is a dynamic system with complex interactions among different components and processes, the challenge with most of the suggested solutions is that, in addition to the possibility of failure, such solutions cost much money and time to be implemented. In this context, hospital decision makers need effective techniques to help them test proposed scenarios and predict results before the actual implementation. Simulation, which is used to imitate in an abstract way the operation of a real-world process or system over time, is a candidate technique that can likely help here.