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Management of Tensions in Emergency Services

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 914))

Abstract

The study of emergency services management within hospitals typically requires an effective manipulation and capitalizing of the knowledge. To manipulate and capitalize management strategies, an agile approach of decision making to address massive crowding in emergency department considering constraints such as human resources, costs, patient cases prioritization, capacity and logistics. We inspired from biological immune defense system to design piloting emergency system, basically, the artificial immune system (SIA). The system provides an intelligent assistance to hospital decision-makers to adjust their supplying strategies, and provide relevant traces from previous gathering information assisting hospital staff, facing the massive patient flow, to execute an efficient solution, excellently. In fact, we made a mixture of two related SIA techniques; the negative selection and the clonal selection. The system agility form is gained throughout adopting the approach of components. This paper will focus on the patient overcrowdings dilemmas, raising the reception capacities articulating on coordination networks amid regional hospitals, and simultaneously conserving the safety of the hospitalizing people in every hospital. The main purpose is to decreasing the tension within the emergency department and supplying hospital chiefs working under stress.

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References

  1. Kellermann, A.: Crisis in the emergency department. N. Engl. J. Med. 355(13), 1300–1303 (2006)

    Article  Google Scholar 

  2. Harrou, F., Kadri, F., Chaabane, S., Tahon, C., Sun, Y.: Improved principal component analysis for anomaly detection: application to an emergency department. Comput. Ind. Eng. 88, 63–77 (2015)

    Article  Google Scholar 

  3. Kadri, F., Pach, C., Chaabane, S., Berger, T., Trentesaux, D., Tahon, C., Sallez, Y.: Modelling and management of strain situations in hospital systems using an orca approach. In: Proceedings of 2013 International Conference on Industrial Engineering and Systems Management, IESM, pp. 1–9. IEEE (2013)

    Google Scholar 

  4. Kadri, F., Harrou, F., Chaabane, S., Tahon, C.: Time series modelling and forecasting of emergency department overcrowding. J. Med. Syst. 38(9), 1–20 (2014)

    Article  Google Scholar 

  5. Harrou, F., Sun, Y., Kadri, F., Chaabane, S., Tahon, C.: Early detection of abnormal patient arrivals at hospital emergency department. In: 6th IESM Conference, Seville, Spain (2015)

    Google Scholar 

  6. Kadri, F., Chaabane, S., Tahon, C.: A simulation-based decision support system to prevent and predict strain situations in emergency department systems. Journal 42, 32–52 (2014)

    Google Scholar 

  7. Carey, P., Cuthbert, G., Dang, R., Greystoke, B., McGregor, A., Oakes, R., Wallis, J.: A more devolved and inclusive approach to integrated reporting facilited by an IT system (Haemosys) networked to local information management systems (LIMS) in all participating regional hospitals. Br. J. Haematol. 173(1), 43 (2016)

    Google Scholar 

  8. Carvalho, D., Joao, V., Rocha, A., Vasconcelos, J.: Towards an encompassing maturity model for the management of hospital information systems. J. Med. Syst. 39(9), 99 (2015)

    Article  Google Scholar 

  9. Virenque, C.: Large influx of injured people in hospital. Hôpital Purpan, TSA 40031, 31059 Toulouse cedex 09 (2016). Journal, 712–715

    Google Scholar 

  10. De Castro, L.N., Timmis, J.: Artificial immune systems: a novel paradigm to pattern recognition. In: Corchado, J.M., Alonso, L., Fyfe, C. (eds.) Artificial Neural Networks in Pattern Recognition, pp. 67–84. Springer, Berlin (2002)

    Google Scholar 

  11. Timmis, J., Hone, A., Stibor, T., Clark, E.: Theoretical advances in artificial immune systems. Theor. Comput. Sci. Rev. 403(1), 11–32 (2008)

    Article  MathSciNet  Google Scholar 

  12. Aickelin, U., Dasgupta, D.: Artificial immune systems. In: Burke, E.K., Kendall, G. (eds.) Research Methodologies, pp. 375–399. Springer, New York (2005)

    Google Scholar 

  13. De Castro, L.N., Von Zuben, F.J.: The clonal selection algorithm with engineering applications. Paper presented at The Workshop on Artificial Immune Systems and Their Applications, Las Vegas, USA (2000)

    Google Scholar 

  14. De Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Trans. Evol. Comput. 6(3), 239–251 (2002)

    Article  Google Scholar 

  15. Jerne, N.K.: Towards a network theory of the immune system. Ann. Immunol. 125, 373–389 (1974)

    Google Scholar 

  16. Greensmith, J., Aickelin, U., Tedesco, G.: Information fusion for anomaly detection with the dendritic cell algorithm. Inf. Fusion J. 11(1), 21–34 (2010)

    Article  Google Scholar 

  17. Greensmith, J., Aickelin, U., Cayzer, S.: Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) Proceedings of the 4th International Conference on Artificial Immune Systems, pp. 153–167. Springer, Heidelberg (2005)

    Google Scholar 

  18. Burnet, M.: The Clonal Selection Theory of Acquired Immunity (1959)

    Google Scholar 

  19. Schmidt, B., Ala, A., Ajay, G., Dionysios, K.: Optimizing an artificial immune system algorithm in support of flow-Based internet traffic classification (2017)

    Google Scholar 

  20. Forrest, S., Perelson, A., Allen, L., Cherukuri, R.: Self-nonself discrimination in a computer. In: Proceedings of the 1994 IEEE Computer Society Symposium on Research in Security and Privacy, pp. 202–212. IEEE (1994)

    Google Scholar 

  21. De Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Trans. Evol. Comput. 6(3), 239–251 (2000)

    Article  Google Scholar 

  22. Tandberg, D., Qualls, C.: Time series forecasts of emergency department patient volume, length of stay, and acuity. Ann. Emerg. Med. 23(2), 299–306 (1994)

    Article  Google Scholar 

  23. Rotstein, Z., Wilf-Miron, R., Lavi, B., Shahar, A., Gabbay, U., Noy, S.: The dynamics of patient visits to a public hospital: a statistical model. Am. J. Emerg. Med. 15(6), 596–599 (1997)

    Article  Google Scholar 

  24. Abdel-Aal, R., Mangoud, A.: Modeling and forecasting monthly patient volume at a primary health care clinic using univariate time-series analysis. Comput. Methods Programs Biomed. 56(3), 235–247 (1998)

    Article  Google Scholar 

  25. Jones, S., Joy, M., Pearson, J.: Forecasting demand of emergency care. Health Care Manag. Sci. 5(4), 297–305 (2002)

    Article  Google Scholar 

  26. Eitel, D., Travers, D., Rosenau, A., Gilboy, N., Wuerz, R.: The emergency severity index triage algorithm version 2 is reliable and valid. Acad. Emerg. Med. 10(10), 1070–1080 (2003)

    Article  Google Scholar 

  27. Tanabe, P., Gimbel, R., Yarnold, P., Adams, J.: The emergency severity index (version 3) 5-level triage system scores predict ED resource consumption. J. Emerg. Nurs. 30(1), 22–29 (2004)

    Article  Google Scholar 

  28. Cooke, M., Jinks, S.: Does the Manchester triage system detect the critically ill? J. Accid. Emerg. Med. 16(3), 179–181 (1999)

    Article  Google Scholar 

  29. Jimenez, J., Murray, M., Beveridge, R., Pons, J., Cortes, E., Garrigos, J., et al.: Implementation of the Canadian Emergency Department Triage and Acuity Scale (CTAS) in the Principality of Andorra: can triage parameters serve as emergency department quality indicators? CJEM 5(5), 315–322 (2007)

    Google Scholar 

  30. Bullard, M., Unger, B., Spence, J., Grafstein, E.: Group CNW. Revisions to the Canadian Emergency Department Triage and Acuity Scale (CTAS) adult guidelines. CJEM 10(2), 136–151 (2008)

    Article  Google Scholar 

  31. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2001)

    MATH  Google Scholar 

  32. Noel, L., Bernardete, R.: On the impact of distance metrics in instance-based learning algorithms. UDI, Polytechnic of Guarda, Portugal (2017)

    Google Scholar 

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Correspondence to Mouna Berquedich .

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Berquedich, M., Kamach, O., Masmoudi, M., Deshayes, L. (2019). Management of Tensions in Emergency Services. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2018). AI2SD 2018. Advances in Intelligent Systems and Computing, vol 914. Springer, Cham. https://doi.org/10.1007/978-3-030-11884-6_9

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