Evaluating the Dispatching Policies for a Regional Network of Emergency Departments Exploiting Health Care Big Data

  • Roberto AringhieriEmail author
  • Davide Dell’Anna
  • Davide Duma
  • Michele Sonnessa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10710)


The Emergency Department (ED) is responsible to provide medical and surgical care to patients arriving at the hospital in need of immediate care. At the regional level, the EDs system can be seen as a network of EDs cooperating to maximise the outputs (number of patients served, average waiting time, ...) and outcomes in terms of the provided care quality. In this paper we discuss how quantitative analysis based on health care big data can provide a tool to evaluate the dispatching policies for the network of emergency departments operating in Piedmont, Italy: the basic idea is to exploit clusters of EDs in such a way to fairly distribute the workload. Further, we discuss how big data can enable a novel methodological approach to the health system analysis.


Emergency care pathway Health systems Big data 


  1. 1.
    Aringhieri, R., Addis, B., Tànfani, E., Testi, A.: Clinical pathways: insights from a multidisciplinary literature survey. In: Proceedings ORAHS 2012 (2012), ISBN: 978-90-365-3396-6Google Scholar
  2. 2.
    Aringhieri, R., Bruni, M., Khodaparasti, S., van Essen, J.: Emergency medical services and beyond: addressing new challenges through a wide literature review. Comput. Oper. Res. 78, 349–368 (2017)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Aringhieri, R., Carello, G., Morale, D.: Supporting decision making to improve the performance of an Italian emergency medical service. Ann. Oper. Res. 236, 131–148 (2016)CrossRefGoogle Scholar
  4. 4.
    Aringhieri, R., Duma, D.: The optimization of a surgical clinical pathway. In: Obaidat, M.S., Ören, T., Kacprzyk, J., Filipe, J. (eds.) Simulation and Modeling Methodologies, Technologies and Applications. AISC, vol. 402, pp. 313–331. Springer, Cham (2015). Scholar
  5. 5.
    Borshchev, A.: The Big Book of Simulation Modeling. Multimethod Modeling with AnyLogic, vol. 6 (2013), ISBN: 978-0-9895731-7-7Google Scholar
  6. 6.
    Brailsford, S., Lattimer, V., Tarnaras, P., Turnbull, J.: Emergency and on-demand health care: modelling a large complex system. J. Oper. Res. Soc. 55(1), 34–42 (2004)CrossRefGoogle Scholar
  7. 7.
    Cardoen, B., Demeulemeester, E.: Capacity of clinical pathways - a strategic multi-level evaluation tool. J. Med. Syst. 32(6), 443–452 (2008)CrossRefGoogle Scholar
  8. 8.
    De Bleser, L., Depreitere, R., De Waele, K., Vanhaecht, K., Vlayen, J., Sermeus, W.: Defining pathways. J. Nurs. Manag. 14, 553–563 (2006)CrossRefGoogle Scholar
  9. 9.
    Hoot, N., Aronsky, D.: Systematic review of emergency department crowding: causes, effects, and solutions. Ann. Emerg. Med. 52(2), 126–136 (2008)CrossRefGoogle Scholar
  10. 10.
    Hwang, U., Concato, J.: Care in the emergency department: how crowded is overcrowded? Acad. Emerg. Med. 11(10), 1097–1101 (2004)CrossRefGoogle Scholar
  11. 11.
    Ozcan, Y., Tànfani, E., Testi, A.: Improving the performance of surgery-based clinical pathways: a simulation-optimization approach. Health Care Manag. Sci. 20, 1–15 (2017)CrossRefGoogle Scholar
  12. 12.
    Panella, M., Marchisio, S., Stanislao, F.: Reducing clinical variations with clinical pathways: Do pathways work? Int. J. Qual. Health Care 15, 509–521 (2003)CrossRefGoogle Scholar
  13. 13.
    Proudlove, N., Black, S., Fletcher, A.: Or and the challenge to improve the NHS: modelling for insight and improvement in in-patient flows. J. Oper. Res. Soc. 58(2), 145–158 (2007)CrossRefGoogle Scholar
  14. 14.
    Rotter, T., Kinsman, L., James, E., Machotta, A., Gothe, H., Willis, J., Snow, P., Kugler, J.: Clinical pathways: effects on professional practice, patient outcomes, length of stay and hospital costs (review). The Cochrane Library, vol. 7 (2010)Google Scholar
  15. 15.
    Setzler, H., Saydam, C., Park, S.: EMS call volume predictions: a comparative study. Comput. Oper. Res. 36(6), 1843–1851 (2009)CrossRefGoogle Scholar
  16. 16.
    Vanderby, S., Carter, M.: An evaluation of the applicability of system dynamics to patient flow modelling. J. Oper. Res. Soc. 61(11), 1572–1581 (2010)CrossRefGoogle Scholar
  17. 17.
    Wolstenholme, E.: A patient flow perspective of U.K. health services: exploring the case for new “intermediate care” initiatives. Syst. Dyn. Rev. 15(3), 253–271 (1999)CrossRefGoogle Scholar
  18. 18.
    Wolstenholme, E., Monk, D., McKelvie, D., Arnold, S.: Coping but not coping in health and social care: masking the reality of running organisations beyond safe design capacity. Syst. Dyn. Rev. 23(4), 371–389 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Roberto Aringhieri
    • 1
    Email author
  • Davide Dell’Anna
    • 2
  • Davide Duma
    • 1
  • Michele Sonnessa
    • 3
  1. 1.Computer Science DepartmentUniversità degli Studi di TorinoTorinoItaly
  2. 2.Department of Information and Computing SciencesUtrecht UniversityUtrechtThe Netherlands
  3. 3.Department of Economics and Business StudiesUniversità degli Studi di GenovaGenovaItaly

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