Optimization of Waiting Time in the Emergency Health Care Services

Introduction

Emergency healthcare centers are the most sensitive healthcare places of all the hospital departments across the world (Patrick & Puterman, 2008). In the hospital emergency centers, cases of increased waiting time are gradually raising concerns in the healthcare centers across the world. Waiting time makes the emergency health care services seem challenging due to overcrowding. Recently, emergency departments have been inadequate across the world.

According to Patrick and Puterman (2008), overcrowding and excessive waiting time in the emergency care centers are not only health care challenges, but are also traumatizing issues for the emergency physicians. This literature review uses five credible journals to discuss different strategies of optimizing waiting time in the emergency care services.

Government Policy Interventions

Policies and regulations often manage operations in the healthcare centers. In a study, Willcox, Seddon, Dunn, Edwards, Pearse, and Tu (2007) compared strategies of managing surgical waiting times in five countries, namely, England, Australia, Wales, Canada, and New Zealand. To review the policies established and enacted in the healthcare departments of these nations, the researchers considered several healthcare factors that often determine waiting time in the emergency centers (Willcox et al., 2007). Most of these nations intensified their funding to promote waiting time control techniques. Canada used $3.8 billion, England used a hospital budget of $55 billion, Wales used about $59 million, and New Zealand used a budget of $11.8 million (Willcox et al., 2007).

From 2000-2005, the study investigated the most effective policy strategies that measure and control surgical waiting times in the five countries. The Australian government policy states that access to public health services depends on the prevailing clinical need (Willcox et al., 2007).

The Canadian government established surgical care benchmarks, developed numerous targets, executed techniques to improve health care access, and undertook public reporting about the waiting times (Willcox et al., 2007). England reduced waiting time for the inpatient services, emphasized on the patient preferences, and financed the emergency centers. Wales increased its financial support, practiced rigorous benchmarking, and developed waiting-time targets (Willcox et al., 2007). Methodologically, the research fails to provide a statistical analysis to substantiate its findings scientifically.

Input and Output Strategic Actions

Strategies of optimizing the waiting time in the emergency healthcare centers are diverse. Boyle, Beniuk, Higginson, and Atkinson (2012), investigated interventions and evaluated policies that the Canadian government employs to avert the adverse consequences of overcrowding in the emergency health care services. According to Boyle et al. (2012), the study examined the impact of several input, throughput, and output solutions that the Canadian healthcare system uses to control overcrowding in the emergency departments. The model of input, throughput and output involves understanding the factors that contribute to overcrowding of the emergency healthcare centers. Boyle et al. (2012) states that reducing emergency admissions is a problem, since the causes of overcrowding departments are often miscellaneous.

The input interventions include distribution of the primary care services and diversion of the ambulance patients to the unoccupied healthcare centers (Boyle et al., 2012). For the throughput interventions, the Canadian hospitals consider timely attendance, train nurses to handle x-rays at the triage stage, and ensure that hospital staffs are able to multitask. Output interventions according to Boyle et al. (2012) include optimizing capacity protocols, flexible scheduling of the hospitals, supporting capacity protocols, staffing the emergency rooms, and increasing the emergency beds.

The models seem practical in time management in the emergency centers. However, the research method fails to validate the efficiency of diverting ambulance patients, and how hospitals can achieve it, considering the vulnerability of the emergency cases.

Using Surge Capacity

Several factors contribute to increased waiting time in the emergency healthcare centers. In a recent study, Patrick and Puterman (2008) undertook a research and identified three major aspects that significantly contribute to overcrowding in the emergency centers. The researchers discovered that inadequate hospital capacity, poor capacity management, and the variability of the demand for emergency services are contributing factors.

Patrick and Puterman (2008) also examined the significant benefits that hospitals can achieve through using the operations research. Markov Decision Processes (MDP), Simulation, and Programming models were useful tactics in examining the use of multiple priorities in the scheduling of patients. In the healthcare case researched, the Markov Decision Process was important in determining the optimal contingency policies.

The MDP scheduling model gives an optimal policy that determines the best approaches to employ in any contingency (Patrick & Puterman, 2008). The MDP model produces an Optimal Scheduling (OS) policy, which involves processes of filling the unused emergency spaces with patients in the priority order, scheduling priority classes without exceeding the recommended limit for the waiting time, and allocating the insufficient emergency space, based on the urgency of care (Patrick & Puterman, 2008). The method has limitations because the sizes of the emergency rooms differ from one hospital to another and from one hospital level to another. The overall admittances per an emergency duration also differ (Patrick & Puterman, 2008). Patients are heterogeneous and an emergency care might need a quick attention.

The Four Hour Rule

The most effective way to reduce waiting time in the emergency centers is through minimizing the wasted time. In Australia, the Four-Hour Rule strategy has been debatable. Jones and Schimanski (2010) investigated the impact of the Four-Hour Rule in the Australian emergency centers through reviewing several medical databases. The researchers carried out a systematic search of the credible hospital databases and discovered that, “Cochrane (CDSR and Central), Embase, Medline, and CINAHL,” were strategic medical databases suitable for the analysis (Jones & Schimanski, 2010, p. 392). These databases contain journals and research documents with great empirical evidence in several medical issues. The articles also discuss the achievement of the Four-Hour Rule in the emergency centers.

One of the reviewed studies revealed that the number of emergency presentations in the Australian public hospitals increased by 37%, and the emergency admittances increased by 35% (Jones & Schimanski, 2010). However, some studies revealed that some hospitals experienced reduced emergency admissions by 4.77%. The median period for seeing a physician in less than an hour remained constant at 57%.

Most hospitals finally achieved the 98% annual target in the emergency centers. However, the mortality rates between the different studies of the Four-Hour Rule demonstrated some consistent seasonal variations (Jones & Schimanski, 2010). The Four-Hour Rule may deem ineffective because the mortality rates of the emergency cases somehow inflated, or remained unchanged, while the medical outcomes diminished.

Queuing Model (QM) and the Agent-Based Model (ABM)

Reducing waiting time in the hospital emergency departments has impelled the innovation of time-reducing models. In 2009, Laskowski, McLeod, Friesen, Podaima, and Alfa, investigated the Queuing Model (QM) and the Agent-Based Model (ABM) in the healthcare emergency departments. Laskowski et al. (2009) used the two models in the Canadian hospitals to evaluate the concepts of patient flow and patient access to medical services in the hospital emergency departments. “Queuing-based modeling represents a well established and vetted methodology in operations research, with extensive applications in the service industries” (Laskowski et al., 2009, p.2). The Queuing Model (QM) and the Agent-Based Model (ABM) are computer models that have spurred innovation and efficiency in the service industry.

The ABM and the QM models entail simple physical layouts and social connections. The researchers developed and implemented the models independently, with an intention of analyzing their competence in the emergency departments (Laskowski et al., 2009). Queuing Model (QM) showed an impact in the patient access and patient flow aspects, which are two important determinants of treatment outcomes. Queuing model is vital in hastening the admission process, and in identifying argent cases, while using the registration, triaging, and diagnosing approaches. The ABM model enhances patient care access by attending to the urgent health care cases swiftly, arranging the stable patients, and discharging the addressed patients rapidly (Laskowski et al., 2009). However, the models fail to consider the severity of the emergencies.

Conclusion

A pool of literature reveals that emergency healthcare centers have different approaches of reducing the waiting time in the emergency services. Enhancing effective government funding and policing, improving emergency handling, reducing the hours spent in the admissions using the Four-Hour Rule, and using the queuing and agent-based models, are some of the time-efficiency strategies.

However, each of the strategies has its own weaknesses that can affect the efficiency of health care services, or increase the mortality rate in the emergency centers. Critically, emergency centers lack an adequate human capacity and resource capacity to reduce waiting time in the emergency healthcare centers. In suggestion, increased government funding and government policing are effective approaches that can effectively reduce waiting time.

References

Boyle, A., Beniuk, K., Higginson, I., & Atkinson, P. (2012). Emergency Department Crowding: Time for Interventions and Policy Evaluations. Emergency Medicine International, 2012 (1), 1-8. Web.

Jones, P., & Schimanski, K. (2010). The four hour target to reduce emergency department ‘waiting time’: A systematic review of clinical outcomes. Emergency Medicine Australasia, 22 (1), 391-398.

Laskowski, M., McLeod, R., Friesen, M., Podaima, B., & Alfa, A. (2009). Models of Emergency Departments for Reducing Patient Waiting Times. PLoS ONE, 4(7), 1-12.

Patrick, J., & Puterman, M. (2008). Reducing Wait Times through Operations Research: Optimizing the Use of Surge Capacity. Healthcare Policy, 3(3), 75-88.

Willcox, S,, Seddon, M., Dunn, S., Edwards, R., Pearse, J., & Tu, J. (2007). Measuring and Reducing Waiting Times: A cross-National Comparison of Strategies. Health Affairs, 26(4), 1078-1087.