System design and improvement of an emergency department using Simulation-Based Multi-Objective Optimization

Uriarte, Ainhoa Goienetxea; Zúñiga, Enrique Ruiz; Moris, Matías Urenda; Ng, Amos H. C.

doi:10.1088/1742-6596/616/1/012015

Cited by 12 publications

(9 citation statements)

References 9 publications

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“…This enabled paramedics and other healthcare professionals prepare and plan to deliver appropriate care to the affected patients. The findings are in agreement to an empirical research study by Uriarte and colleagues [4], which found data analytics to be useful in implementation of various healthcare processes in emergency department, which resulted to improvement in efficiency and patient outcomes. Application of data analytics in the study by Kuo [5] and others was found to enhance decision making, reduce the time and cost of care in addition to the other resources in the emergency care department.…”

Section: Resultssupporting

confidence: 90%

Application of Big Data in Decision Making for Emergency Healthcare Management

Leung

Stevenson

2018

IJRE

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Application of big data in healthcare has enhanced efficiency and decision making. This is of critical benefit to patients, healthcare professionals and the healthcare institution. Although various research studies have examined the application of big data analytics in healthcare, few studies have explored its application in emergency medicine. This research study explored the application of big data in emergency medicine in facilitating decision making among paramedics and other healthcare practitioners. Appropriate research studies were identified and reviewed systematically to explore the theme of the study. The study found that big data promoted decision making in emergency medicine through the predictor models, which enabled the healthcare practitioners make informed judgments concerning patient care.

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Section: Resultssupporting

confidence: 90%

Application of Big Data in Decision Making for Emergency Healthcare Management

Leung

Stevenson

2018

IJRE

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“…To solve the optimisation problem, different algorithms have been used including genetic algorithm. Sometimes, DES model is combined with other methods to get better result such as data mining or any meta-model to use as DSS tool (Ceglowski et al 2007;Ghanes et al 2014;Memari et al 2016;Uriarte et al 2015;Zeinali et al 2015). On the other hand, ABMS is mainly used for developing a DSS tool by optimising the human resources.…”

Section: Discussionmentioning

confidence: 99%

“…As EDs are designed to serve the patients who need immediate care and treatment, the failure may mean to leave the patients' lives in danger. Therefore, to improve the patient flow as well as to minimise the overcrowding and delays in the EDs, researchers from different areas have been working for long and some researchers recommend optimisation of the resources as a good option to solve this problem (Uriarte et al 2015). Optimised utilisation of necessary resources is vital to achieve the desired service level.…”

Section: Introductionmentioning

confidence: 99%

Emergency department resource optimisation for improved performance: a review

et al. 2019

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Emergency departments (EDs) have been becoming increasingly congested due to the combined impacts of growing demand, access block and increased clinical capability of the EDs. This congestion has known to have adverse impacts on the performance of the healthcare services. Attempts to overcome with this challenge have focussed largely on the demand management and the application of system wide process targets such as the "four-hour rule" intended to deal with access blocks. In addition, EDs have introduced various strategies such as "fast tracking", "enhanced triage" and new models of care such as introducing nurse practitioners aimed at improving throughput. However, most of these practices require additional resources. Some researchers attempted to optimise the resources using various optimisation models to ensure best utilisation of resources to improve patient flow. However, not all modelling approaches are suitable for all situations and there is no critical review of optimisation models used in hospital EDs. The aim of this article is to review various analytical models utilised to optimise ED resources for improved patient flow and highlight benefits and limitations of these models. A range of modelling techniques including agent-based modelling and simulation, discrete-event simulation, queuing models, simulation optimisation and mathematical modelling have been reviewed. The analysis revealed that every modelling approach and optimisation technique has some advantages and disadvantages and their application is also guided by the objectives. The complexity, interrelationships and variability of ED-related variables make the application of standard modelling techniques difficult. However, these models can be used to identify sources of flow obstruction and to identify areas where investments in additional resources are likely to have most benefit.

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“…Begitu juga, SD mempunyai keupayaan untuk meramalkan keadaan atau keperluan pada masa hadapan (Alasad et al 2012;Yusoff et al 2018a Sehubungan dengan itu, langkah yang efektif perlu diambil untuk mengurangkan masa menunggu pesakit untuk mendapatkan rawatan, memperbaiki mutu perkhidmatan yang diberikan, mengurangkan masalah kesesakan yang dihadapi dan seterusnya mencapai Penunjuk Prestasi Utama (KPI) jabatan yang disasarkan bagi setiap zon. Penyelidik terdahulu telah menjalankan pelbagai kajian di jabatan kecemasan dengan menggunakan pendekatan simulasi bagi memodelkan keseluruhan zon dalam jabatan kecemasan bagi menambahbaik masa menunggu pesakit dan mengenal pasti bilangan sumber yang diperlukan dalam meningkatkan kecekapan aliran pesakit di jabatan kecemasan (Aminuddin et al 2016;Blasak et al 2003;Chahal & Eldabi 2008;Gunal & Pidd 2010;Komashie & Mousavi 2005;Rashwan et al 2015;Uriarte et al 2015). Kajian ini akan menggunakan pendekatan simulasi SD dalam menambahbaik aliran pesakit di Zon Kuning JKHUSM.…”

Section: Tatakaedah Kajian Pemodelan Sdunclassified

Model Sistem Dinamik untuk Meramalkan Bilangan Pesakit dan Keperluan Sumber Tenaga di Zon Kuning Jabatan Kecemasan

Yusoff

Liong²,

Noh

et al. 2019

JSM

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(JKHUSM) telah menunjukkan perubahan yang pesat dan menghadapi transformasi drastik berhubung dengan kepentingan dalam sistem penjagaan kesihatan. Peramalan dan perancangan sumber yang munasabah perlu dilakukan bagi memenuhi permintaan pesakit yang kian bertambah. Justeru, kajian ini memfokuskan kepada Zon Kuning di JKHUSM yang memerlukan perancangan sewajarnya atas keperluan sumber yang sepatutnya disediakan pada masa kini dan akan datang untuk membantu pihak pengurusan dalam perancangan strategik jabatan serta menambah baik aliran pesakit dan perkhidmatan di zon tersebut. Pemodelan Sistem Dinamik telah dibangunkan untuk meramalkan bilangan pesakit yang akan berkunjung serta jumlah sumber yang diperlukan untuk memenuhi permintaan perkhidmatan di Zon Kuning JKHUSM pada masa sekarang (2014) dan masa hadapan bagi tempoh lima (2019) dan sepuluh tahun akan datang (2024). Hasil kajian meramalkan sumber yang diperlukan bagi memenuhi permintaan pesakit yang berkunjung di Zon Kuning pada masa sekarang adalah seramai 11 orang doktor, 12 orang jururawat dan 18 buah katil berbanding dengan sembilan orang doktor, sembilan orang jururawat dan 16 buah katil sedia ada. Seterusnya penambahan dua buah katil diramalkan untuk memenuhi keperluan pesakit bagi tempoh lima dan sepuluh tahun akan datang. Manakala tiada penambahan doktor dan jururawat diperlukan bagi memenuhi permintaan pesakit bagi tempoh lima tahun akan datang. Namun begitu dijangkakan penambahan seorang doktor dan seorang jururawat diperlukan bagi memenuhi permintaan 10 tahun akan datang. Oleh itu, peramalan penambahan sumber ini adalah sangat penting untuk menambah baik aliran pesakit di Zon Kuning JKHUSM serta membantu dalam mencapai Penunjuk Prestasi Utama jabatan ini. Hasil kajian yang diperoleh akan membantu pihak pengurusan membuat keputusan yang wajar dengan belanjawan yang telah ditetapkan demi meningkatkan kualiti perkhidmatan yang ditawarkan di samping meningkatkan tahap prestasi Zon Kuning JKHUSM.

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System design and improvement of an emergency department using Simulation-Based Multi-Objective Optimization

Cited by 12 publications

References 9 publications

Application of Big Data in Decision Making for Emergency Healthcare Management

Application of Big Data in Decision Making for Emergency Healthcare Management

Emergency department resource optimisation for improved performance: a review

Model Sistem Dinamik untuk Meramalkan Bilangan Pesakit dan Keperluan Sumber Tenaga di Zon Kuning Jabatan Kecemasan

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