A Simulation Optimization Approach for Resource Allocation in an
                        Emergency Department Healthcare Unit

Bahari, Arman; Asadi, Farzaneh

doi:10.5334/gh.528

Cited by 15 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed framework for optimizing triage structures and resource allocation is designed with scalability and adaptability in mind, acknowledging the specific challenges confronted with the aid of diverse emergency care centers. By presenting a bendy version grounded in statistics-pushed insights, this research objectives to empower healthcare institutions globally to tailor and enforce these techniques to their particular contexts (Bahari & Asadi, 2020) [5] . The overarching intention is to set up a benchmark for great practices in emergency care, fostering collaboration and information alternatives among healthcare specialists and policymakers.…”

Section: Introductionmentioning

confidence: 99%

Optimizing triage systems in emergency care: A comprehensive analysis of patient outcomes and resource allocation

Alotaibi,

Alahmari

et al. 2024

Pharma Innovation

View full text Add to dashboard Cite

This study affords a complete analysis of optimizing triage systems in emergency care with a focal point on improving affected person outcomes and aid allocation. In the quick-paced and dynamic surroundings of emergency departments, efficient triage is paramount to ensure timely and appropriate care. Through a radical exam of affected person information and operational metrics, our studies identify key factors influencing triage effectiveness. By using superior facts analytics and device learning algorithms, we advise a sophisticated triage device that complements the accuracy of affected person prioritization, leading to advanced results. The observation also investigates the impact of optimized useful resource allocation on typical emergency branch performance and price effectiveness. Results display a vast discount in patient wait times, multiplied workforce pleasure, and more sensible use of sources. Moreover, our findings underscore the ability to mitigate overcrowding and enhance affected person reviews via strategic allocation of personnel, equipment, and centers. This research contributes to the continued discourse on healthcare optimization, supplying realistic insights that can be applied to decorate emergency care shipping, in the end benefitting each patient and healthcare institution. The proposed version offers a scalable and adaptable framework for other healthcare settings looking to enhance triage systems and aid utilization in emergency conditions.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimizing triage systems in emergency care: A comprehensive analysis of patient outcomes and resource allocation

Alotaibi,

Alahmari

et al. 2024

Pharma Innovation

View full text Add to dashboard Cite

show abstract

“…• Hybrid studies in healthcare modelling, i.e. Cappanera et al (2014), Ghanes et al (2015), Saadouli et al (2015), Uriarte et al (2017), Ordu et al (2019b), Bahari and Asadi (2020) and Sasanfar et al (2020). • Other topics (i.e.…”

Section: Introduction and Related Workmentioning

confidence: 99%

A hybrid analytical model for an entire hospital resource optimisation

2021

View full text Add to dashboard Cite

Given the escalating healthcare costs around the world (more than 10% of the world's GDP) and increasing demand hospitals are under constant scrutiny in terms of managing services with limited resources and tighter budgets. Hospitals endeavour to find sustainable solutions for a variety of challenges ranging from productivity enhancements to resource allocation. For instance, in the UK, evidence suggests that hospitals are struggling due to increased delayed transfers of care, bed-occupancy rates well above the recommended levels of 85% and unmet A&E performance targets. In this paper, we present a hybrid forecasting-simulation-optimisation model for an NHS Foundation Trust in the UK. Using the Hospital Episode Statistics dataset for A&E, outpatient and inpatient services, we estimate the future patient demands for each speciality and model how it behaves with the forecasted activity in the future. Discrete event simulation is used to capture the entire hospital within a simulation environment, where the outputs is used as inputs into a multi-period integer linear programming (MILP) model to predict three vital resource requirements (on a monthly basis over a 1-year period), namely beds, physicians and nurses. We further carry out a sensitivity analysis to establish the robustness of solutions to changes in parameters, such as nurse-to-bed ratio. This type of modelling framework is developed for the first time to better plan the needs of hospitals now and into the future.

show abstract

“…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]. By considering the budget, a patient's wait time was improved by 49.6%, and the cost of resource usage was reduced by 51%.…”

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 Simulation Optimization Approach for Resource Allocation in an Emergency Department Healthcare Unit

Cited by 15 publications

References 6 publications

Optimizing triage systems in emergency care: A comprehensive analysis of patient outcomes and resource allocation

Optimizing triage systems in emergency care: A comprehensive analysis of patient outcomes and resource allocation

A hybrid analytical model for an entire hospital resource optimisation

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

Contact Info

Product

Resources

About