Abstract
The Emergency Department (ED) represents the first stage of the path for some patients in a hospital. The JUNEAU project aims to propose a Digital Twin (DT) for the ED to both visualise the service behaviour in quasi real-time, forecast and anticipate its behaviour to control the “Emergency throughput time” indicator. This DT will be centred on the “patient pathway” view, which needs to first precise necessary data and information and decision support. The project will tackle this issue with a particular attention on exploring the DT architecture (with the help or holonic architecture), on coupling centralised and decentralised approaches to obtain a model as close as possible to the system dynamics and on managing DT evolution in a complex environment.
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Notes
- 1.
In French, JUNEAU stands for “JUmeau Numérique pour un sErvice d’Accueil des Urgences”, i.e., DT for an ED.
References
Ajmi, F., Zgaya, H., Othman, S.B., Hammadi, S.: Agent-based dynamic optimization for managing the workflow of the patient’s pathway. Simul. Model. Pract. Theory 96, 101935 (2019)
Angulo, C., Ortega, J.A., Gonzalez-Abril, L.: Towards a healthcare digital twin. In: Artificial Intelligence Research and Development, pp. 312–315. IOS Press (2019)
Augusto, V., Xie, X.: A modeling and simulation framework for health care systems. IEEE Trans. Syst. Man, Cybern. Syst. 44(1), 30–46 (2013)
Ben-Tovim, D., Filar, J., Hakendorf, P., Qin, S., Thompson, C., Ward, D.: Hospital event simulation model: arrivals to discharge-design, development and application. Simul. Model. Pract. Theory 68, 80–94 (2016)
Boyle, L.M., Marshall, A.H., Mackay, M.: A framework for developing generalisable discrete event simulation models of hospital emergency departments. Eur. J. Oper. Res. 302(1), 337–347 (2022)
Cardin, O., Trentesaux, D., Thomas, A., Castagna, P., Berger, T., Bril El-Haouzi, H.: Coupling predictive scheduling and reactive control in manufacturing hybrid control architectures: state of the art and future challenges. J. Intell. Manuf. 28(7), 1503–1517 (2017)
Chase, J.G., et al.: Digital twins in critical care: what, when, how, where, why? IFAC-PapersOnLine 54(15), 310–315 (2021)
Croatti, A., Gabellini, M., Montagna, S., Ricci, A.: On the integration of agents and digital twins in healthcare. J. Med. Syst. 44(9), 1–8 (2020)
Curtis, C., Liu, C., Bollerman, T.J., Pianykh, O.S.: Machine learning for predicting patient wait times and appointment delays. J. Am. Coll. Radiol. 15(9), 1310–1316 (2018)
Darema, F.: Dynamic data driven applications systems: a new paradigm for application simulations and measurements. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3038, pp. 662–669. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24688-6_86
Frazzon, E.M., Kück, M., Freitag, M.: Data-driven production control for complex and dynamic manufacturing systems. CIRP Ann. 67(1), 515–518 (2018)
Goodall, P., Sharpe, R., West, A.: A data-driven simulation to support remanufacturing operations. Comput. Ind. 105, 48–60 (2019)
Huet, J.C.: Proposition d’une méthodologie de réingénierie pour le contrôle par le produit de systèmes manufacturiers: application au circuit du médicament d’un hôpital. Ph.D. thesis, Université Blaise Pascal-Clermont-Ferrand II (2011)
Itmi, M., Cardon, A.: Model for self-adaptive SoS and the control problem. In: 2010 5th International Conference on System of Systems Engineering, pp. 1–6. IEEE (2010)
Karakra, A., Fontanili, F., Lamine, E., Lamothe, J., Taweel, A.: Pervasive computing integrated discrete event simulation for a hospital digital twin. In: 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA) (2018). https://doi.org/10.1109/AICCSA.2018.8612796
Kaushal, A., et al.: Evaluation of fast track strategies using agent-based simulation modeling to reduce waiting time in a hospital emergency department. Socioecon. Plann. Sci. 50, 18–31 (2015)
Koestler, A.: The Ghost in the Machine. Hutchinson, London (1967)
Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W.: Digital twin in manufacturing: a categorical literature review and classification. IFAC-PapersOnLine 51(11), 1016–1022 (2018)
Lachtar, D.: Contribution des systèmes multi-agent à l’analyse de la performance organisationnelle d’une cellule de crise communale. Ph.D. thesis, Ecole Nationale Supérieure des Mines de Paris (2012)
Liu, Z., Rexachs, D., Epelde, F., Luque, E.: An agent-based model for quantitatively analyzing and predicting the complex behavior of emergency departments. J. Comput. Sci. 21, 11–23 (2017)
Markdahl, J.: A geometric obstruction to almost global synchronization on riemannian manifolds. arXiv preprint arXiv:1808.00862 (2018)
Melesse, T.Y., Di Pasquale, V., Riemma, S.: Digital twin models in industrial operations: a systematic literature review. Procedia Manuf. 42, 267–272 (2020)
Negri, E., Fumagalli, L., Macchi, M.: A review of the roles of digital twin in CPS-based production systems. Procedia Manuf. 11, 939–948 (2017)
Noyel, M., Thomas, P., Thomas, A., Charpentier, P.: Reconfiguration process for neuronal classification models: application to a quality monitoring problem. Comput. Ind. 83, 78–91 (2016)
Patrone, C., Galli, G., Revetria, R.: A state of the art of digital twin and simulation supported by data mining in the healthcare sector. In: Advancing Technology Industrialization Through Intelligent Software Methodologies, Tools and Techniques, pp. 605–615. IOS Press (2019)
Pujo, P., Broissin, N., Ounnar, F.: PROSIS: An isoarchic structure for HMS control. Eng. Appl. Artif. Intell. 22(7), 1034–1045 (2009)
Salmon, A., Rachuba, S., Briscoe, S., Pitt, M.: A structured literature review of simulation modelling applied to emergency departments: current patterns and emerging trends. Oper. Res. Health Care 19, 1–13 (2018)
Semeraro, C., Lezoche, M., Panetto, H., Dassisti, M.: Digital twin paradigm: a systematic literature review. Comput. Ind. 130, 103469 (2021)
Acknowledgement
The JUNEAU Project is going to be supported by the Agence Nationale de la Recherche of the French government between October 2022 and October 2026.
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Bouleux, G. et al. (2023). Requirements for a Digital Twin for an Emergency Department. In: Borangiu, T., Trentesaux, D., Leitão, P. (eds) Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. SOHOMA 2022. Studies in Computational Intelligence, vol 1083. Springer, Cham. https://doi.org/10.1007/978-3-031-24291-5_11
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