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Annotated BPMN Models for Optimised Healthcare Resource Planning

  • Juliana Bowles
  • Ricardo M. Czekster
  • Thais Webber
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11176)

Abstract

There is an unquestionable need to improve healthcare processes across all levels of care in order to optimise the use of resources whilst guaranteeing high quality care to patients. However, healthcare processes are generally very complex and have to be fully understood before enhancement suggestions can be made. Modelling with widely used notation such as BPMN (Business Process Modelling and Notation) can help gain a shared understanding of a process, but is not sufficient to understand the needs and demands of resources. We propose an approach to enrich BPMN models with structured annotations which enables us to attach further information to individual elements within the process model. We then use performance analysis (e.g., throughput and utilisation) to reason about resources across a model and propose optimisations. We show the usefulness of our approach for an A&E department of a sizeable hospital in the south of Brazil and how different stakeholders may profit from a richer annotated BPMN-based model.

Keywords

Process modelling BPMN Performance analysis Optimisation Healthcare 

References

  1. 1.
    Arena simulation. https://www.arenasimulation.com/. Accessed 06 June 2018
  2. 2.
    Antonacci, G., Calabrese, A., D’Ambrogio, A., Giglio, A., Intrigila, B., Ghiron, N.L.: A BPMN-based automated approach for the analysis of healthcare processes. In: Proceedings of the 25th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), pp. 124–129. IEEE Computer Society (2016)Google Scholar
  3. 3.
    Baril, C., Gascon, V., Miller, J., Côté, N.: Use of a discrete-event simulation in a Kaizen event: a case study in healthcare. Eur. J. Oper. Res. 249, 327–339 (2016)CrossRefGoogle Scholar
  4. 4.
    Bertoli, M., Casale, G., Serazzi, G.: JMT: performance engineering tools for system modeling. ACM SIGMETRICS Perform. Eval. Rev. 36, 10–15 (2009)CrossRefGoogle Scholar
  5. 5.
    Bisogno, S., Calabrese, A., Gastaldi, M., Ghiron, N.L.: Combining modelling and simulation approaches: how to measure performance of business processes. Bus. Process Manag. J. 22, 56–74 (2016)CrossRefGoogle Scholar
  6. 6.
    Bocciarelli, P., D’Ambrogio, A., Giglio, A., Paglia, E., Gianni, D.A.: Transformation approach to enact the design-time simulation of BPMN models. In: IEEE 23rd International WETICE Conference, pp. 199–204. IEEE Computer Society (2014)Google Scholar
  7. 7.
    Bocciarelli, P., D’Ambrogio, A., Paglia, E.: A language for enabling model-driven analysis of business processes. In: 2nd International Conference on Model-Driven Engineering and Software Development (MODELSWARD), pp. 325–332. IEEE Computer Society (2014)Google Scholar
  8. 8.
    Bowles, J., Caminati, M., Cha, S.: An integrated framework for verifying multiple care pathways. In: Eleventh International Symposium on Theoretical Aspects of Software Engineering (TASE). IEEE Computer Society (2017)Google Scholar
  9. 9.
    Costa, L.T., Czekster, R., de Oliveira, F.M., de M. Rodrigues, E., da Silveira, M.B., Zorzo, A.F.: Generating performance test scripts and scenarios based on abstract intermediate models. In: Proceedings of the 24th International Conference on Software Engineering and Knowledge Engineering (SEKE 2012), pp. 112–117 (2012)Google Scholar
  10. 10.
    Doniec, K., Dall’Alba, R., King, L.: Brazil’s health catastrophe in the making. Lancet 392, 731–732 (2018)CrossRefGoogle Scholar
  11. 11.
    Forsberg, H.H., Aronsson, H., Keller, C., Lindblad, S.: Managing health care decisions and improvement through simulation modeling. Qual. Manag. Health Care 20, 15–29 (2011)CrossRefGoogle Scholar
  12. 12.
    Günal, M., Pidd, M.: Discrete event simulation for performance modelling in health care: a review of the literature. J. Simul. 4, 42–51 (2010)CrossRefGoogle Scholar
  13. 13.
    Harper, P.R., Pitt, M.A.: On the challenges of healthcare modelling and a proposed project life cycle for successful implementation. J. Oper. Res. Soc. 55, 657–661 (2004)CrossRefGoogle Scholar
  14. 14.
    Ioan, B., Nestian, A.S., Tita, S.M.: Relevance of key performance indicators (KPIs) in a hospital performance management model. J. East. Eur. Res. Bus. Econ. 2012, 1–15 (2012)Google Scholar
  15. 15.
    Khalifa, M., Khalid, P.: Developing strategic health care key performance indicators: a case study on a tertiary care hospital. Proc. Comput. Sci. 63, 459–466 (2015)CrossRefGoogle Scholar
  16. 16.
    Mandahawi, N.: Reducing waiting time at an emergency department using design for six sigma and discrete event simulation. Int. J. Six Sigma Competitive Adv. 6(1/2), 91–104 (2010)CrossRefGoogle Scholar
  17. 17.
    Marzolla, M.: The qnetworks toolbox: a software package for queueing networks analysis. In: Al-Begain, K., Fiems, D., Knottenbelt, W.J. (eds.) ASMTA 2010. LNCS, vol. 6148, pp. 102–116. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-13568-2_8CrossRefGoogle Scholar
  18. 18.
    OMG: Business Process Model & Notation. v2.0. OMG (2011). http://www.omg.org. Doc. id: formal/2011-01-03
  19. 19.
    Rossetti, M.D.: Simulation Modeling and Arena, 2nd edn. Wiley Press, Hoboken (2010)zbMATHGoogle Scholar
  20. 20.
    Shim, S.J., Kumar, A.: Simulation for emergency care process reengineering in hospitals. Bus. Process Manag. J. 16, 795–805 (2010)CrossRefGoogle Scholar
  21. 21.
    Shitkova, M., Taratukhin, V., Becker, J.: Towards a methodology and a tool for modeling clinical pathways. Proc. Comput. Sci. 63, 205–212 (2015)CrossRefGoogle Scholar
  22. 22.
    Sokolowski, J.A., Banks, C.M.: Principles of Modeling and Simulation: A Multidisciplinary Approach. Wiley, Hoboken (2011)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Juliana Bowles
    • 1
  • Ricardo M. Czekster
    • 2
  • Thais Webber
    • 2
  1. 1.School of Computer ScienceUniversity of St AndrewsSt AndrewsUK
  2. 2.UNISC - University of Santa Cruz do SulSanta Cruz do Sul/RSBrazil

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