Simulation and Improvement of Patients' Workflow in Heart Clinics during COVID-19 Pandemic Using Timed Coloured Petri Nets
Jazyk angličtina Země Švýcarsko Médium electronic
Typ dokumentu časopisecké články, práce podpořená grantem
Grantová podpora
project No. CZ.02.1.01/0.0/0.0/15_003/0000456
EU "CZ Operational Programme Research, Development and Education", Priority 1: Strengthening capacity for quality research. - International
PubMed
33227940
PubMed Central
PMC7699255
DOI
10.3390/ijerph17228577
PII: ijerph17228577
Knihovny.cz E-zdroje
- Klíčová slova
- COVID-19, discrete-event simulation, healthcare systems, heart clinic, hospital, timed coloured Petri net, waiting time,
- MeSH
- Betacoronavirus MeSH
- COVID-19 MeSH
- kardiologie organizace a řízení MeSH
- koronavirové infekce * MeSH
- lidé MeSH
- pandemie * MeSH
- počítačová simulace MeSH
- průběh práce * MeSH
- SARS-CoV-2 MeSH
- virová pneumonie * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
The COVID-19 epidemic has spread across the world within months and creates multiple challenges for healthcare providers. Patients with cardiovascular disease represent a vulnerable population when suffering from COVID-19. Most hospitals have been facing difficulties in the treatment of COVID-19 patients, and there is a need to minimise patient flow time so that staff health is less endangered, and more patients can be treated. This article shows how to use simulation techniques to prepare hospitals for a virus outbreak. The initial simulation of the current processes of the heart clinic first identified the bottlenecks. It confirmed that the current workflow is not optimal for COVID-19 patients; therefore, to reduce waiting time, three optimisation scenarios are proposed. In the best situation, the discrete-event simulation of the second scenario led to a 62.3% reduction in patient waiting time. This is one of the few studies that show how hospitals can use workflow modelling using timed coloured Petri nets to manage healthcare systems in practice. This technique would be valuable in these challenging times as the health of staff, and other patients are at risk from the nosocomial transmission.
Zobrazit více v PubMed
Klemeš J.J., Fan X.Y., Tan R.R., Jiang P. Minimising the present and future plastic waste, energy and environmental footprints related to COVID-19. Renew. Sustain. Energy Rev. 2020;127:109883. doi: 10.1016/j.rser.2020.109883. PubMed DOI PMC
Bettinelli G., Delmastro E., Salvato D., Salini V., Placella G. Orthopaedic patient workflow in CoViD-19 pandemic in Italy. J. Orthop. 2020;22:158–159. doi: 10.1016/j.jor.2020.04.006. PubMed DOI PMC
Virani S.A., Clarke B., Ducharme A., Ezekowitz J.A., Heckman G.A., McDonald M., Mielniczuk L.M., Swiggum E., Van Spall H.G.C., Zieroth S. Optimizing Access to Heart Failure Care in Canada During the COVID-19 Pandemic. Can. J. Cardiol. 2020;36:1148–1151. doi: 10.1016/j.cjca.2020.05.009. PubMed DOI PMC
Chofreh A.G., Goni F.A., Shaharoun A.M., Ismail S. Review on Enterprise Resource Planning Implementation Roadmap: Project Management Perspective. J. Teknol. 2014;2:135–138.
Klemeš J.J., Van Fan Y., Jiang P. The Energy and Environmental Footprints of COVID-19 Fighting Measures–PPE, Disinfection, Supply Chains. Energy. 2020;211:118701. PubMed PMC
Shan Z.h., Lin C.H., Ren F., Wei Y. Modeling and Performance Analysis of a Multi-Server Multi-Queue System on the Grid; Proceedings of the Ninth IEEE Workshop on Future Trends of Distributed Computing Systems 12; San Juan, PR, USA. 30 May 2003.
Brenner S.Z.Z., Liu Y., Li J., Howard P.K. Modelling and analysis of the emergency department at University of Kentucky Chandler Hospital using simulations. J. Emerg. Nurs. 2010;36:303–310. doi: 10.1016/j.jen.2009.07.018. PubMed DOI
Kabir S., Papadopoulos Y. Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review. Saf. Sci. 2019;115:154–175. doi: 10.1016/j.ssci.2019.02.009. DOI
Aeenparast A., Tabibi S.J., Shahanagi K., Aryanejhad M. Estimating outpatient waiting time: A simulation approach. Payesh. 2009;8:315–320. PubMed PMC
Creemers S., Beliën J., Lambrecht M. The optimal allocation of server time slots over different classes of patients. Eur. J. Oper. Res. 2012;219:508–521. doi: 10.1016/j.ejor.2011.10.045. DOI
Bevilacqua M., Ciarapica F.E., Giovanni M. Timed colored Petri nets for modeling and managing processes and projects; Proceedings of the 11th CIRP Conference on Intelligent Computation in Manufacturing Engineering; Gulf of Naples, Italy. 19–21 July 2017.
Chofreh A.G., Goni F.A., Zeinalnezhad M., Vavidar S., Shayestehzadeh H., Klemeš J.J. Value chain mapping of the water and sewage treatment to contribute to sustainability. J. Environ. Manag. 2019;239:38–47. doi: 10.1016/j.jenvman.2019.03.023. PubMed DOI
Chofreh A., Goni F., Klemeš J.J. A Master Plan for the Implementation of Sustainable Enterprise Resource Planning Systems (Part III): Evaluation of a Roadmap. Chem. Eng. Trans. 2016;52:1105–1110.
Chofreh A., Goni F., Malik M.N., Khan H.H., Klemeš J.J. The imperative and research directions of sustainable project management. J. Clean. Prod. 2019;238:117810. doi: 10.1016/j.jclepro.2019.117810. DOI
Klemeš J.J., Van Fan Y., Jiang P. COVID-19 pandemic facilitating energy transition opportunities. Int. J. Energy Res. 2020 doi: 10.1002/er.6007. PubMed DOI PMC
Harjai K.J., Agarwal S., Bauch T., Bernardi M., Casale A.S., Green S., Harostock M., Ierovante N., Mascarenhas V., Matsumura M., et al. Coronary and structural heart disease interventions during COVID-19 pandemic: A road map for clinicians and health care delivery systems. Cardiovasc. Revasc. Med. 2020 doi: 10.1016/j.carrev.2020.06.013. PubMed DOI PMC
Asdaghpour E., Baghaei R., Jalilifar N., Radmehr H., Shirzad M., Mirzaaghayan M.R., Yousefnia M.A., Austin N., Alizadeh Ghavidel A., Hossein Ahmadi Z., et al. Iranian society of cardiac surgeons position statement for the treatment of patients in need of cardiac surgery in the COVID-19 pandemic period (Version I) Multidiscip. Cardiovasc. Ann. 2020;11:e104296. doi: 10.5812/mca.104296. DOI
Fersia O., Bryant S., Nicholson R., McMeeken K., Brown C., Donaldson B., Jardine A., Grierson V., Whalen V., Mackay A. The impact of the COVID-19 pandemic on cardiology services. Open Heart. 2020;7:e001359. doi: 10.1136/openhrt-2020-001359. PubMed DOI PMC
Wei W., Zheng D., Lei Y., Wu S., Verma V., Liu Y., Wei X., Bi J., Hu D., Han G. Radiotherapy workflow and protection procedures during the Coronavirus Disease 2019 (COVID-19) outbreak: Experience of the Hubei Cancer Hospital in Wuhan, China. Radiother. Oncol. 2020;148:203–210. doi: 10.1016/j.radonc.2020.03.029. PubMed DOI PMC
Diaz M.C.G., Dawson K. Use of simulation to develop a COVID-19 resuscitation process in a pediatric emergency department. Am. J. Infect. Control. 2020;48:1244–1247. doi: 10.1016/j.ajic.2020.07.032. PubMed DOI PMC
Das A. Impact of the COVID-19 pandemic on the workflow of an ambulatory endoscopy center: An assessment by discrete event simulation. Gastrointest. Endosc. 2020;92:914–924. doi: 10.1016/j.gie.2020.06.008. PubMed DOI PMC
Quraishi M.I., Rizvi A.A., Heidel R.E. Off-site radiology workflow changes due to the coronavirus disease 2019 (COVID-19) pandemic. J. Am. College Radiol. 2020;17:878–881. doi: 10.1016/j.jacr.2020.05.008. PubMed DOI PMC
Dexter F., Elhakim M., Loftus R.W., Seering M.S., Epstein R.H. Strategies for daily operating room management of ambulatory surgery centers following resolution of the acute phase of the COVID-19 pandemic. J. Clin. Anesth. 2020;64:109854. doi: 10.1016/j.jclinane.2020.109854. PubMed DOI PMC
Phua J., Weng L., Ling L., Egi M., Lim C.M., Divatia J.V., Shrestha B.R., Arabi M., Mel J.N.M., Gomersall C.D., et al. Intensive care management of coronavirus disease 2019 (COVID-19): Challenges and recommendations. Lancet Respir. Med. 2020;8:506–517. doi: 10.1016/S2213-2600(20)30161-2. PubMed DOI PMC
Tey J., Ho S., Choo B.A., Ho F., Yap S.P., Tuan J.K.L., Leong C.N., Cheo T., Sommat K., Wang M.L.C. Navigating the challenges of the COVID-19 outbreak: Perspectives from the radiation oncology service in Singapore. Radiother. Oncol. 2020;148:189–193. doi: 10.1016/j.radonc.2020.03.030. PubMed DOI PMC
Yan C., Lin J., Xu Y. Recommendations for coronavirus disease 2019 (COVID-19) prevention and infection control in the radiology department: Chinese experience. Clin. Imaging. 2020;69:33–36. doi: 10.1016/j.clinimag.2020.06.044. PubMed DOI PMC
Esmaili H., Matin A. Principles and Basics of Petri Networks with an Industrial Engineering and Management Approach. Dibagaran Publication; Tehran, Iran: 2018.
Najmuddin A., Ibrahim I., Ismail S. A simulation approach: Improving patient waiting time for multiphase patient flow of obstetrics and gynecology department (O and G Department) in local specialist centre. WSEAS Trans. Math. 2010;10:778–790.
Julia S., Oliveira F.F.D., Valette R. Real time scheduling of workflow management systems based on a p-time Petri net model with hybrid resources. Simul. Model. Pract. Theory. 2008;16:462–482. doi: 10.1016/j.simpat.2008.01.006. DOI
Chofreh A.G., Goni F.A., Klemeš J.J. Development of a Framework for the Implementation of Sustainable Enterprise Resource Planning. Chem. Eng. Trans. 2017;61:1543–1548. doi: 10.1016/j.jclepro.2018.07.096. DOI
Giua A., Silva M. Modeling, analysis and control of discrete event systems: A Petri net perspective. Int. Fed. Autom. Control. 2017;12:1772–1783. doi: 10.1016/j.ifacol.2017.08.156. DOI
Chofreh A., Goni F., Klemeš J.J. A Master Plan for the Implementation of Sustainable Enterprise Resource Planning Systems (Part II): Development of a roadmap. Chem. Eng. Trans. 2016;52:1099–1104.
Kontogiannis T. A Petri Net-based approach for ergonomic task analysis and modeling with emphasis on adaptation to system changes. Saf. Sci. 2003;41:803–835. doi: 10.1016/S0925-7535(02)00035-8. DOI
Zeinalnezhad M., Sepehri F., Molana M.H., Pourrostam T., Chofreh A.G., Klemeš J.J., Goni F.A. Identification of performance evaluation indicators for health, safety, environment, and ergonomics management systems. Chem. Eng. Trans. 2018;67:451–456.
Zeinalnezhad M., Mukhtar M., Sahran S. An investigation of lead benchmarking implementation: A comparison of small/medium enterprises and large companies. Benchmarking Int. J. 2014;21:121–145. doi: 10.1108/BIJ-09-2011-0074. DOI
Zeinalnezhad M., Chofreh A.G., Goni F.A., Klemeš J.J. Critical success factors of the reliability-centred maintenance implementation in the oil and gas industry. Symmetry. 2020;12:1585. doi: 10.3390/sym12101585. DOI
Zeinalnezhad M., Chofreh A.G., Goni F.A., Klemeš J.J. Air pollution prediction using semi-experimental regression model and Adaptive Neuro-Fuzzy Inference System. J. Clean. Prod. 2020;261:121218. doi: 10.1016/j.jclepro.2020.121218. DOI
Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods