Simulation-Based Analysis of Appointment Scheduling System in Healthcare Services: A Critical Review

Ala, Ali; Šimić, Vladimir; Deveci, Muhammet; Pamučar, Dragan

doi:10.1007/s11831-022-09855-z

Cited by 16 publications

(3 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The appointment system in the healthcare system plays a critical role for many vital reasons [1]. Generally, appointment systems belonging to sub-units of health institutions regulate and organize the functioning of hospitals and clinics [2].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Price Application to Prevent Financial Losses to Hospitals Based on Machine Learning Algorithms

Atalan,

Dönmez

2024

Healthcare

View full text Add to dashboard Cite

Hospitals that are considered non-profit take into consideration not to make any losses other than seeking profit. A model that ensures that hospital price policies are variable due to hospital revenues depending on patients with appointments is presented in this study. A dynamic pricing approach is presented to prevent patients who have an appointment but do not show up to the hospital from causing financial loss to the hospital. The research leverages three distinct machine learning (ML) algorithms, namely Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB), to analyze the appointment status of 1073 patients across nine different departments in a hospital. A mathematical formula has been developed to apply the penalty fee to evaluate the reappointment situations of the same patients in the first 100 days and the gaps in the appointment system, considering the estimated patient appointment statuses. Average penalty cost rates were calculated based on the ML algorithms used to determine the penalty costs patients will face if they do not show up, such as 22.87% for RF, 19.47% for GB, and 14.28% for AB. As a result, this study provides essential criteria that can help hospital management better understand the potential financial impact of patients missing appointments and can be considered when choosing between these algorithms.

show abstract

Section: Introductionmentioning

confidence: 99%

Dynamic Price Application to Prevent Financial Losses to Hospitals Based on Machine Learning Algorithms

Atalan,

Dönmez

2024

Healthcare

View full text Add to dashboard Cite

show abstract

“…These technologies provide the capacity to partially change the level of supply chains, production environment and the overall organizational level of firms. Thus, the manufacturing firms' dependence on the application of Industry 4.0 technologies will lead to the emergence of innovations (AL-Khatib and Ramayah, 2023; Ala et al. , 2023a) that enhance sustainable performance and improve the work environment, which leads to maximizing the economic value of the business and sustainable aspects.…”

Section: Introductionmentioning

confidence: 99%

Enabling the circular economy in the digital transformation era: evidence from an emerging country

Al-Khatib

2023

View full text Add to dashboard Cite

PurposeRecently, the concept of the circular economy (CE) has witnessed significant momentum in academic and professional circles. However, there is a dearth of research that studies the enabling factors of the CE in the era of digital transformation. The existing research aimed to identify the impact of Industry 4.0 readiness on the CE in manufacturing firms operating in Jordan, as well as to identify the mediating role of the industrial Internet of things and big data analytics.Design/methodology/approachFor this work objectives, 380 questionnaires were analyzed. Convergent validity and discriminant validity tests were performed through partial least squares-structural equation modelling (PLS-SEM) in the Smart-PLS programme. Data reliability was confirmed. A bootstrapping technique was used to analyze the data and then hypothesis testing was performed.FindingsThe results indicate that Industry 4.0 readiness, industrial Internet of things (IIoT) and big data analytics positively enable CE, also the IIoT and big data analytics positively mediate the nexus between Industry 4.0 readiness and CE.Practical implicationsThis study promotes the idea of focusing on Industry 4.0 readiness to enhance CE in the Jordanian manufacturing sector and knowing the effect of IIoT and big data analytics in this relationship.Originality/valueThis research developed a theoretical model to understand how Industry 4.0 readiness might enhance the CE in manufacturing firms by invoking the IIoT and big data analytics as mediating constructs in the relationship between Industry 4.0 readiness and CE. This paper offers new theoretical and practical contributions that add value to industry 4.0 and CE literature by testing these constructs' mediation models in the manufacturing sector.

show abstract

“…Ali et al [21] published a review for the complexity of the appointment scheduling problem. Concomitantly, they presented a critical review of the simulation appointment scheduling system (SASS) research topic, and highlighted critical challenges and outlined potential research topics [22].…”

Section: Introductionmentioning

confidence: 99%

A Review of Optimization Studies for System Appointment Scheduling

Niu,

Lei,

Guo

et al. 2023

Axioms

View full text Add to dashboard Cite

In the face of an increasingly high-demand environment for outpatients, achieving a balance between allocation of limited medical resources and patient satisfaction has considerable social and economic benefits. Therefore, appointment scheduling (AS) system operation is used in clinics and hospitals, and its operation optimization research is of great significance. This study reviews the research progress on appointment scheduling system optimization. Firstly, we classify and conclude the existing appointment scheduling system structures and decision-making frameworks. Subsequently, we summarize the system reliability optimization framework from three aspects: appointment scheduling system optimization objectives, decision variables and constraints. Following that, we methodically review the most applied system optimization algorithms in different appointment scheduling systems. Lastly, a literature bibliometric analysis is provided. During our review of the literature, we observe that (1) optimization methods in ASs predominantly involve the application of genetic algorithms and simulation optimization algorithms; (2) neural networks and deep learning methods are core technologies in health management optimization; (3) a bibliometric analysis reveals a heightened interest in the optimization technology of ASs within China compared to other nations; and (4) further advancements are essential in the comprehensive optimization of the system, exploration of practical usage scenarios, and the application of advanced simulation and modeling techniques in this research.

show abstract

Simulation-Based Analysis of Appointment Scheduling System in Healthcare Services: A Critical Review

Cited by 16 publications

References 75 publications

Dynamic Price Application to Prevent Financial Losses to Hospitals Based on Machine Learning Algorithms

Dynamic Price Application to Prevent Financial Losses to Hospitals Based on Machine Learning Algorithms

Enabling the circular economy in the digital transformation era: evidence from an emerging country

A Review of Optimization Studies for System Appointment Scheduling

Contact Info

Product

Resources

About