Simulation and Improvement of Patients’ Workflow in Heart Clinics during COVID-19 Pandemic Using Timed Coloured Petri Nets

Zeinalnezhad, Masoomeh; Chofreh, Abdoulmohammad Gholamzadeh; Goni, Feybi Ariani; Klemeš, Jiří Jaromír; Sari, Emelia

doi:10.3390/ijerph17228577

Cited by 24 publications

(24 citation statements)

References 32 publications

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“… [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] .…”

Section: Uncited Referencesunclassified

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods

et al. 2021

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The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.

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Section: Uncited Referencesunclassified

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods

et al. 2021

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“…A recent work [ 41 ] presented the use of a colored Petri net to model and analyze medical resource allocation. One of the most recent works [ 42 ] adopted a timed colored Petri net modeling approach for the simulation and improvement of patients’ workflow in heart clinics during the Covid-19 pandemic. These works showed the significant adoption of Petri net variants in the modelling and analysis of healthcare services in real-world hospitals or medical centers.…”

Section: Related Workmentioning

confidence: 99%

Performability Evaluation of Load Balancing and Fail-over Strategies for Medical Information Systems with Edge/Fog Computing Using Stochastic Reward Nets

Nguyen

Fé

Brito

et al. 2021

Sensors

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The aggressive waves of ongoing world-wide virus pandemics urge us to conduct further studies on the performability of local computing infrastructures at hospitals/medical centers to provide a high level of assurance and trustworthiness of medical services and treatment to patients, and to help diminish the burden and chaos of medical management and operations. Previous studies contributed tremendous progress on the dependability quantification of existing computing paradigms (e.g., cloud, grid computing) at remote data centers, while a few works investigated the performance of provided medical services under the constraints of operational availability of devices and systems at local medical centers. Therefore, it is critical to rapidly develop appropriate models to quantify the operational metrics of medical services provided and sustained by medical information systems (MIS) even before practical implementation. In this paper, we propose a comprehensive performability SRN model of an edge/fog based MIS for the performability quantification of medical data transaction and services in local hospitals or medical centers. The model elaborates different failure modes of fog nodes and their VMs under the implementation of fail-over mechanisms. Sophisticated behaviors and dependencies between the performance and availability of data transactions are elaborated in a comprehensive manner when adopting three main load-balancing techniques including: (i) probability-based, (ii) random-based and (iii) shortest queue-based approaches for medical data distribution from edge to fog layers along with/without fail-over mechanisms in the cases of component failures at two levels of fog nodes and fog virtual machines (VMs). Different performability metrics of interest are analyzed including (i) recover token rate, (ii) mean response time, (iii) drop probability, (iv) throughput, (v) queue utilization of network devices and fog nodes to assimilate the impact of load-balancing techniques and fail-over mechanisms. Discrete-event simulation results highlight the effectiveness of the combination of these for enhancing the performability of medical services provided by an MIS. Particularly, performability metrics of medical service continuity and quality are improved with fail-over mechanisms in the MIS while load balancing techniques help to enhance system performance metrics. The implementation of both load balancing techniques along with fail-over mechanisms provide better performability metrics compared to the separate cases. The harmony of the integrated strategies eventually provides the trustworthiness of medical services at a high level of performability. This study can help improve the design of MIS systems integrated with different load-balancing techniques and fail-over mechanisms to maintain continuous performance under the availability constraints of medical services with heavy computing workloads in local hospitals/medical centers, to combat with new waves of virus pandemics.

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“…Single Tang et al [93] Discrete event simulation Nepomuceno et al [81] Data envelopment analysis (DEA) Mehrotra et al [77] Stochastic optimization AbdelAziz et al [40] Multi-objective pareto optimization Peng et al [85]; Moss et al [80] Simulation Aggarwal et al [41] Additive utility assumption Araz et al [45] System dynamics Hybrid Garbey et al [62] Markov chains, stochastic optimization Albahri et al [42] Entropy, TOPSIS De Nardo et al [58] Potentially all pairwise ranking of all possible alternatives (PAPRIKA), multi-criteria decision making (MCDM) Parker et al [84] Linear programming, mixed-integer programming Zeinalnezhad et al [98] Colored petri nets, discrete event simulation Zhang & Cheng. [100] Logistic regression, Markov chains Abadi et al [39] Hybrid salp swarm algorithm and genetic algorithm (HSSAGA) Haddad et al [67] Simulation, optimization…”

Section: Authors Technique Typementioning

confidence: 99%

“…On a different tack, different interventions employed OR techniques in a paired way. For instance, Zeinalnezhad et al [98] implemented colored petri nets and discrete event simulation to prepare hospitals for a virus outbreak. The initial simulation of current cardiac clinic processes first identified the ED bottlenecks.…”

Section: Authors Technique Typementioning

confidence: 99%

Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review

Ortíz‐Barrios

Coba-Blanco

Alfaro-Saiz

et al. 2021

IJERPH

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The COVID-19 pandemic has strongly affected the dynamics of Emergency Departments (EDs) worldwide and has accentuated the need for tackling different operational inefficiencies that decrease the quality of care provided to infected patients. The EDs continue to struggle against this outbreak by implementing strategies maximizing their performance within an uncertain healthcare environment. The efforts, however, have remained insufficient in view of the growing number of admissions and increased severity of the coronavirus disease. Therefore, the primary aim of this paper is to review the literature on process improvement interventions focused on increasing the ED response to the current COVID-19 outbreak to delineate future research lines based on the gaps detected in the practical scenario. Therefore, we applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to perform a review containing the research papers published between December 2019 and April 2021 using ISI Web of Science, Scopus, PubMed, IEEE, Google Scholar, and Science Direct databases. The articles were further classified taking into account the research domain, primary aim, journal, and publication year. A total of 65 papers disseminated in 51 journals were concluded to satisfy the inclusion criteria. Our review found that most applications have been directed towards predicting the health outcomes in COVID-19 patients through machine learning and data analytics techniques. In the overarching pandemic, healthcare decision makers are strongly recommended to integrate artificial intelligence techniques with approaches from the operations research (OR) and quality management domains to upgrade the ED performance under social-economic restrictions.

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Simulation and Improvement of Patients’ Workflow in Heart Clinics during COVID-19 Pandemic Using Timed Coloured Petri Nets

Cited by 24 publications

References 32 publications

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods

Performability Evaluation of Load Balancing and Fail-over Strategies for Medical Information Systems with Edge/Fog Computing Using Stochastic Reward Nets

Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review

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