Management of Tensions in Emergency Services

  • Mouna BerquedichEmail author
  • Oualid Kamach
  • Malek Masmoudi
  • Laurent Deshayes
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 914)


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.


Hospital environment AIS Negative selection Clonal selection 


  1. 1.
    Kellermann, A.: Crisis in the emergency department. N. Engl. J. Med. 355(13), 1300–1303 (2006)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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. 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)CrossRefGoogle Scholar
  5. 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. 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. 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. 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)CrossRefGoogle Scholar
  9. 9.
    Virenque, C.: Large influx of injured people in hospital. Hôpital Purpan, TSA 40031, 31059 Toulouse cedex 09 (2016). Journal, 712–715Google Scholar
  10. 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. 11.
    Timmis, J., Hone, A., Stibor, T., Clark, E.: Theoretical advances in artificial immune systems. Theor. Comput. Sci. Rev. 403(1), 11–32 (2008)MathSciNetCrossRefGoogle Scholar
  12. 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. 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. 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)CrossRefGoogle Scholar
  15. 15.
    Jerne, N.K.: Towards a network theory of the immune system. Ann. Immunol. 125, 373–389 (1974)Google Scholar
  16. 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)CrossRefGoogle Scholar
  17. 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. 18.
    Burnet, M.: The Clonal Selection Theory of Acquired Immunity (1959)Google Scholar
  19. 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. 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. 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)CrossRefGoogle Scholar
  22. 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)CrossRefGoogle Scholar
  23. 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)CrossRefGoogle Scholar
  24. 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)CrossRefGoogle Scholar
  25. 25.
    Jones, S., Joy, M., Pearson, J.: Forecasting demand of emergency care. Health Care Manag. Sci. 5(4), 297–305 (2002)CrossRefGoogle Scholar
  26. 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)CrossRefGoogle Scholar
  27. 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)CrossRefGoogle Scholar
  28. 28.
    Cooke, M., Jinks, S.: Does the Manchester triage system detect the critically ill? J. Accid. Emerg. Med. 16(3), 179–181 (1999)CrossRefGoogle Scholar
  29. 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. 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)CrossRefGoogle Scholar
  31. 31.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2001)zbMATHGoogle Scholar
  32. 32.
    Noel, L., Bernardete, R.: On the impact of distance metrics in instance-based learning algorithms. UDI, Polytechnic of Guarda, Portugal (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mouna Berquedich
    • 1
    Email author
  • Oualid Kamach
    • 1
  • Malek Masmoudi
    • 2
  • Laurent Deshayes
    • 3
  1. 1.Laboratory of Innovative Technologies (LTI)Abdelmalek Saâdi UniversityTangierMorocco
  2. 2.Laboratory of Industrial EngineeringJean Monnet UniversityRoanneFrance
  3. 3.Polytechnic Mohammed VI UniversityBen-GuérirMorocco

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