Evaluating Emergency Department Resource Capacity Using Simulation

Ahmad, Norazura; Ghani, Noraida Abdul; Kamil, Anton Abdulbasah; Tahar, Razman Mat; Howe, Teo Aik

doi:10.5539/mas.v6n11p9

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…Other studies exploring efficient resource allocation included a simulation model used to provide insight into better resource utilisation (e.g. personnel and physical resources) in an emergency department in Malaysia [ 100 ], and an optimisation model used to explore the optimal allocation of resources in a region in Tanzania given different health objectives (e.g. minimise number of deaths, minimise disease incidence, minimise loss of quality of life, etc.)…”

Section: Resultsmentioning

confidence: 99%

Operations research in global health: a scoping review with a focus on the themes of health equity and impact

Bradley

Jung

Tandon-Verma

et al. 2017

Health Res Policy Sys

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BackgroundOperations research (OR) is a discipline that uses advanced analytical methods (e.g. simulation, optimisation, decision analysis) to better understand complex systems and aid in decision-making.SummaryHerein, we present a scoping review of the use of OR to analyse issues in global health, with an emphasis on health equity and research impact. A systematic search of five databases was designed to identify relevant published literature. A global overview of 1099 studies highlights the geographic distribution of OR and common OR methods used. From this collection of literature, a narrative description of the use of OR across four main application areas of global health – health systems and operations, clinical medicine, public health and health innovation – is also presented. The theme of health equity is then explored in detail through a subset of 44 studies. Health equity is a critical element of global health that cuts across all four application areas, and is an issue particularly amenable to analysis through OR. Finally, we present seven select cases of OR analyses that have been implemented or have influenced decision-making in global health policy or practice. Based on these cases, we identify three key drivers for success in bridging the gap between OR and global health policy, namely international collaboration with stakeholders, use of contextually appropriate data, and varied communication outlets for research findings. Such cases, however, represent a very small proportion of the literature found.ConclusionPoor availability of representative and quality data, and a lack of collaboration between those who develop OR models and stakeholders in the contexts where OR analyses are intended to serve, were found to be common challenges for effective OR modelling in global health.Electronic supplementary materialThe online version of this article (doi:10.1186/s12961-017-0187-7) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Operations research in global health: a scoping review with a focus on the themes of health equity and impact

Bradley

Jung

Tandon-Verma

et al. 2017

Health Res Policy Sys

View full text Add to dashboard Cite

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“…A number of studies in the literature have developed systems of A&Es by means of scenarios. Alternative scenarios are generated and compared by measuring the performances of A&Es, for example Komashie and Mousavi (2005), Duguay and Chetouane (2007), Meng and Spedding (2008), Gul, Celik, Guneri, and Taskin Gumus (2012), Wang, Li, Tussey, and Ross (2012), Ahmad, Ghani, Kamil, Tahar, and Teo (2012), , Al-Refaie, Fouad, Li, and Shurrab (2014), and Oh et al (2016).…”

Section: Discrete Event Simulation Modellingmentioning

confidence: 99%

A decision support system for demand and capacity modelling of an accident and emergency department

2019

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Accident and emergency (A&E) departments in England have been struggling against severe capacity constraints. In addition, A&E demands have been increasing year on year. In this study, our aim was to develop a decision support system combining discrete event simulation and comparative forecasting techniques for the better management of the Princess Alexandra Hospital in England. We used the national hospital episodes statistics data-set including period April, 2009 -January, 2013. Two demand conditions are considered: the expected demand condition is based on A&E demands estimated by comparing forecasting methods, and the unexpected demand is based on the closure of a nearby A&E department due to budgeting constraints. We developed a discrete event simulation model to measure a number of key performance metrics. This paper presents a crucial study which will enable service managers and directors of hospitals to foresee their activities in future and form a strategic plan well in advance.

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“…Hence, the increasing pressure exerted on health practitioners to deliver swift, efficient, and precise diagnosis, has forced them to find alternatives to streamline the delivery of high-quality medical care to their patients (Agrawal, & Kosowsky, 2009). Many issues at ED, such as long waiting time and the uncertainty conditions create an extremely high-pressure department that are required to perform speedy treatments and produce highly accurate diagnosis (Sinclair & Croskerry, 2010;Ahmad, Ghani, Anton, Tahar & Teo, 2012). Asthma exacerbation severity determination is an important procedure during the triage process in ED for the immediate treatment to reduce the possibility for the disease to worsen, and the delay for treatment may lead to death (Broaddus et al, 2016).…”

Section: Related Work Asthma Diagnosis In Emergency Departmentmentioning

confidence: 99%

A Fuzzy Rule-Based Expert System for Asthma Severity Identification in Emergency Department

Sharif

Ahmad

et al. 2019

Journal of Information and Communication Technology

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The emergency department (ED) of a hospital is an important unit that deals with time-sensitive and life-threatening medical cases. Rapid treatment and accuracy in diagnosis are considered the main characteristics of excellent operational processes in ED. However, in reality, long waiting time and uncertainty in the diagnosis has affected the quality of ED services. Nonetheless, these problems can be improved by utilising computing technologies that assist medical professionals to make fast and accurate decisions. This paper investigates the issues of under-treatment and uncertainty condition of acute asthma cases in ED. A novel approach, known as the fuzzy logic principle is employed to determine the severity of acute asthma. The fuzzy set theory, known as Fuzzy Rule-based Expert System for Asthma Severity (FRESAS) determination is embedded into the expert system (ES) to assess the severity of asthma among patients in ED. The proposed fuzzy methodology effectively 416 manages the fuzziness of the patient's information data, and determines the subjective judgment of medical practitioners' level on eight criteria assessed in severity determination. Knowledge acquisition and representation, fuzzification, fuzzy inference engine, and defuzzification are the processes tested by the FRESAS development that incorporates expert advice. The system evaluation is performed by using datasets that were extracted from the ED clerking notes from one of the hospitals in Northern Peninsular Malaysia. System evaluation demonstrates that the proposed system performs efficiently in determining the severity of acute asthma. Furthermore, the proposed system offers opportunities for further research on other types of diseases in ED, and improves other hybridisation approaches.

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Evaluating Emergency Department Resource Capacity Using Simulation

Cited by 10 publications

References 26 publications

Operations research in global health: a scoping review with a focus on the themes of health equity and impact

Operations research in global health: a scoping review with a focus on the themes of health equity and impact

A decision support system for demand and capacity modelling of an accident and emergency department

A Fuzzy Rule-Based Expert System for Asthma Severity Identification in Emergency Department

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