A Multi-criteria Optimization Approach to Health Care Tasks Scheduling Under Resources Constraints

Othman, Sarah Ben; Hammadi, Slim

doi:10.2991/ijcis.2017.10.1.29

Cited by 3 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tsai, Chiang, Ksentini, and Chen ( 2016) provide a brief survey on metaheuristics and emphasized the essence of big-data analytics framework for healthcare systems. Othman and Hammadi (2017) formulated a fitness function using an evolutionary algorithm and fuzzy logic suitable for building a decision support tool for healthcare task scheduling in Pediatric ED. They were able to predict specific limits for the optimal values of the criteria to solve the problem of peaks of activity and overcrowding as well as improve system performance and patient satisfaction.…”

Section: Related Workmentioning

confidence: 99%

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

Asuquo,

Umoren,

Osang

et al. 2023

SET

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

Asuquo,

Umoren,

Osang

et al. 2023

SET

View full text Add to dashboard Cite

show abstract

“…Other examples in the application of integrated OR methods when dealing with lengthy ED waits are provided in Daldoul et al [168], He et al [109], Lau et al [175], Othman et al [178], Sir et al [122], and Umble and Umble [182]. Other combinations aiming at facing the extended waiting times are simplified in Mazzocato et al [177], Othman and Hammadi [179], Perry [180], and Stephens and Broome [181].…”

Section: Papers Focusing On Reducing the Waiting Timementioning

confidence: 99%

Methodological Approaches to Support Process Improvement in Emergency Departments: A Systematic Review

Ortíz‐Barrios

Alfaro-Saiz

2020

IJERPH

View full text Add to dashboard Cite

The most commonly used techniques for addressing each Emergency Department (ED) problem (overcrowding, prolonged waiting time, extended length of stay, excessive patient flow time, and high left-without-being-seen (LWBS) rates) were specified to provide healthcare managers and researchers with a useful framework for effectively solving these operational deficiencies. Finally, we identified the existing research tendencies and highlighted opportunities for future work. We implemented the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to undertake a review including scholarly articles published between April 1993 and October 2019. The selected papers were categorized considering the leading ED problems and publication year. Two hundred and three (203) papers distributed in 120 journals were found to meet the inclusion criteria. Furthermore, computer simulation and lean manufacturing were concluded to be the most prominent approaches for addressing the leading operational problems in EDs. In future interventions, ED administrators and researchers are widely advised to combine Operations Research (OR) methods, quality-based techniques, and data-driven approaches for upgrading the performance of EDs. On a different tack, more interventions are required for tackling overcrowding and high left-without-being-seen rates.

show abstract

“…Moreover, some researchers focused on minimising the total patient tardiness or waiting time through the timelines of the system [19], [20]. Fuzzy logic and an evolutionary algorithm were proposed to solve a stochastic optimisation problem with multiple objectives, such as minimising the total patient waiting time and the makespan [21]. The metamodelling optimisation approach was suggested to investigate and optimise the effective resources in the ED by reducing the total average waiting time for patients in the ED [22].…”

Section: Introductionmentioning

confidence: 99%

Solving the Emergency Care Patient Pathway by a New Integrated Simulation-Optimisation Approach

et al. 2021

View full text Add to dashboard Cite

One of the most critical objectives in the healthcare system is maximising patient flows in the emergency care patient pathway. Patient emergency flow analysis indicates that the timetabling of a patient's movement from one activity to another through the Emergency Department (ED) is critical for treating patients. The ED deals with the patient's arrival, triage, physician assessment, imaging and laboratory studies, treatment planning, nursing procedures, and decisions to admit or discharge the patient. Any delayed activities in patient flow reduce the service level of healthcare. To address these challenges, this paper develops a stochastic ED Simulation-Optimisation approach by considering stochastic variables, such as patient interarrival times and treatment times, using statistical distributions. This type of distribution depends on two main elements: day shifts and patient categories. A hybrid evolutionary algorithm is integrated with the simulation to find a satisfactory solution for this stochastic optimisation problem in real time. Computational experiments show that the proposed approach can serve more patients in specific time windows or provide the same quality of the service with the use of fewer medical resources.

show abstract

A Multi-criteria Optimization Approach to Health Care Tasks Scheduling Under Resources Constraints

Cited by 3 publications

References 26 publications

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

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

Methodological Approaches to Support Process Improvement in Emergency Departments: A Systematic Review

Solving the Emergency Care Patient Pathway by a New Integrated Simulation-Optimisation Approach

Contact Info

Product

Resources

About