An integrated approach of multi-objective optimization model for evaluating new supporting program in Irish hospitals

Rashwan, Wael; Habib, Heba; Courtney, Garry; Arisha, Amr; Kennelly, Sean

doi:10.1109/wsc.2016.7822236

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…In Taboada, Cabrera, Iglesias, Epelde, & Luque (2011), an agent-based decision support system was developed for a hospital's ED. Rashwan et al (2016) applied a model that integrates three approaches: simulation, multivariate factor analysis, and multi-objective optimization to support management decisions on key parameters affecting the treatment journey of patients. A set of supervised ML predictive models was tested to select the best surrogate model for each response variable.…”

Section: Related Workmentioning

confidence: 99%

“…The emergency department (ED) is normally the initial point where healthcare services are accessed for many hospital visits, and in that process becomes the starting point of unplanned episodes of patient care. According to Rashwan, Habib, Arisha, Courtney & Kennelly (2016), the key functions of the ED include the provision of immediate attention to the acutely unstable patients and timely treatment of patients with non-life-threatening cases so patients do not remain more than 12 hrs. The ED is regarded as a complex service system where most patients arrive with little or no information about their health conditions or requirements.…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Framework for Length of Stay Minimization in Healthcare Emergency Department

Asuquo,

Umoren,

Osang

et al. 2023

SET

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The emergency departments (EDs) in most hospitals, especially in middle-and-low-income countries, need techniques for minimizing the waiting time of patients. The application and utilization of appropriate methods can enhance the number of patients treated, improve patients’ satisfaction, reduce healthcare costs, and lower morbidity and mortality rates which are often associated with poor healthcare facilities, overcrowding, and low availability of healthcare professionals. Modeling the length of stay (LOS) of patients in healthcare systems is a challenge that must be addressed for sound decision-making regarding capacity planning and resource allocation. This paper presents a machine learning (ML) framework for predicting a patient’s LOS within the ED. A study of the services in the ED of a tertiary healthcare facility in Uyo, Nigeria was conducted to gain insights into its operational procedures and evaluate the impact of certain parameters on LOS. Then, a computer simulation of the system was performed in R programming language using data obtained from records in the hospital. Finally, the performance of four ML classifiers involved in patients’ LOS prediction: Classification and Regression Tree (CART), Random Forest (RF), K-Nearest Neighbour (K-NN), and Support Vector Machine (SVM), were evaluated and results indicate that SVM outperforms others with the highest coefficient of determination (R2) score of 0.986984 and least mean square error (MSE) value of 0.358594. The result demonstrates the capability of ML techniques to effectively assess the performance of healthcare systems and accurately predict patients’ LOS to mitigate the low physician-patient ratio and improve throughput.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%