Abstract
A recent study carried out an empirical investigation of the quality of healthcare delivered to adults and found out that only 54.9±0.6% adult received recommended care. Huge variation in the quality of care depends on patient's condition. In fact, the literature on healthcare is laden with articles like these that emphasize on the importance of the systems view of healthcare problems. Healthcare is a very vast and complex system where different departments interact with each other in order to deliver a certain service to arriving patients. Emergency departments (EDs) are the busiest units of healthcare. Existing problems and their cascading effect will be highlighted by a literature review of a bunch of researches. The purpose of this work is to study, in specific, the emergency department of a hospital with the existing problems and how simulation modeling can interfere in order to solve these problems, increase patient satisfaction and reduce cost. Simulation has emerged as a popular decision support in the domains of manufacturing and services industries.
TopIntroduction
The medical sector has been growing largely over the last decade and healthcare services became more complex and costly, amplified by a poor healthcare delivery system. Healthcare is a highly interconnected dynamic environment where individuals and teams contribute in order to serve patients’ demand. The main focus of this study is to discuss this revolution and take care of the whole medical community not only illness, but also improving patient safety, quality, and effectiveness of the healthcare system. This can be achieved by developing new methodologies to improve the health care systems available nowadays.
Many methodologies were presented over the literature in order to study healthcare problems. Some of them are listed as follows (Ceglowski, 2006):
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Patients are grouped by clinicians under several cases; where similar cases should be treated alike and should share the same type of resources every time the same case arises (see Palmer, 1996). This approach can be valuable only in case of few available cases such as in clinics not in large complex systems like ED.
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Time and motion studies were used by industrial engineering analysts in order to introduce enhancement to healthcare (see Hoffenberg et al., 2001).
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Prevention of high patient waiting times and ambulance diversions were discussed over the years and simulation was introduced in order to alleviate this risk (see Jun et al., 1999; Preater, 2002).
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The flow of data in the ED was studied by information science analysts in order to design a computer system that supports the doctors and nurses in their roles (see Nelson et al., 2004).
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ED data inspection for better knowledge of information retrieved.
As a result of the above, the first area to focus on in order to develop an efficient and effective healthcare system is developing systems perspective, where simulation modeling can be generated and a review can be achieved. Simulation modeling can be a solution to tackle this complexity and valuable in providing predictions to forecast the outcome of a change in strategies or policies. The computer simulation is a decision making technique that allows management to conduct experiments with models representing the real system of interest. Busy and complex healthcare systems provide big challenges to managers and decision makers who should be able to serve the high demands constrained by limited budget and high costs of healthcare services. The highest number of patients should be cared of within a limited period of time in order to insure patient satisfaction (reduce waiting time) and increase hospital’s revenue (reduce cost).
The delivery of healthcare quality can vary depending on patient’s conditions, affecting the recommended care and leading sometimes to urgent and critical health conditions. This huge variation opens the eye on the importance of reviewing the healthcare systems’ problems and improving them.
Key Terms in this Chapter
Arena: A discrete event simulation software which helps the modeler in building an experiment model that is similar to the real system and perform experimentation where improvements can be suggested without any interruption of the currently working system.
Queuing Analysis: A method used in order to improve patient throughput.
Patient Flow: The process that a patient follows, from the time he enters the system until he is discharged. Patient flow includes both medical and administrative processes.
Simulation Modeling: A model designed using a simulation software for a process or system over a period of time.
Data Mining: The fact of dealing with big data where new information can be generated from pre-existing databases.
Waste: A non-added value activity that a certain process may encounter. Customers usually are not willing to pay for wasted activities.
What-If-Analysis: The simulation of several scenarios by applying some changes to the inputs and analyzing the outcome of the outputs.
Overcrowding: The fact of having excessive numbers of patients needing or receiving care.
Experimentation: The process of improving the system by applying new rules and suggesting new operations.
Patient LoS: The length of stay of a patient spends in the system.