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An ad hoc process mining approach to discover patient paths of an Emergency Department

Author

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  • Davide Duma

    (Università degli Studi di Torino)

  • Roberto Aringhieri

    (Università degli Studi di Torino)

Abstract

The Emergency Department (ED) management presents a really high complexity due to the admissions of patients with a wide variety of diseases and different urgency, which require the execution of different activities involving human and medical resources. This can have an impact on ED overcrowding that may affect the quality and access of health care. In this paper we propose an ad hoc process mining approach to discover the paths of the patients served by an ED. Our aim is to obtain a process model capable (1) to replicate properly the possible patient paths, and (2) to predict the next activities in the view of a possible application to online optimisation. To prove its effectiveness, we apply our ad hoc approach to a real case study.

Suggested Citation

  • Davide Duma & Roberto Aringhieri, 2020. "An ad hoc process mining approach to discover patient paths of an Emergency Department," Flexible Services and Manufacturing Journal, Springer, vol. 32(1), pages 6-34, March.
    • Handle: RePEc:spr:flsman:v:32:y:2020:i:1:d:10.1007_s10696-018-9330-1
    DOI: 10.1007/s10696-018-9330-1
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    References listed on IDEAS

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    1. Luscombe, Ruth & Kozan, Erhan, 2016. "Dynamic resource allocation to improve emergency department efficiency in real time," European Journal of Operational Research, Elsevier, vol. 255(2), pages 593-603.
    2. Zied Jemai & L. Aboueljinane & E. Sahin, 2013. "A review on simulation models applied to emergency medical service operations," Post-Print hal-01672393, HAL.
    3. Yen-Yi Feng & I-Chin Wu & Tzu-Li Chen, 2017. "Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm," Health Care Management Science, Springer, vol. 20(1), pages 55-75, March.
    4. David Sinreich & Ola Jabali & Nico Dellaert, 2012. "Reducing emergency department waiting times by adjusting work shifts considering patient visits to multiple care providers," IISE Transactions, Taylor & Francis Journals, vol. 44(3), pages 163-180.
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    Cited by:

    1. Duma, Davide & Aringhieri, Roberto, 2023. "Real-time resource allocation in the emergency department: A case study," Omega, Elsevier, vol. 117(C).
    2. C. Potts & R. R. Bond & J-A. Jordan & M. D. Mulvenna & K. Dyer & A. Moorhead & A. Elliott, 2023. "Process mining to discover patterns in patient outcomes in a Psychological Therapies Service," Health Care Management Science, Springer, vol. 26(3), pages 461-476, September.
    3. Steven Mertens & Frederik Gailly & Diederik Sassenbroeck & Geert Poels, 2022. "Integrated Declarative Process and Decision Discovery of the Emergency Care Process," Information Systems Frontiers, Springer, vol. 24(1), pages 305-327, February.
    4. Paola Cappanera & Jingshan Li & Evren Sahin & Nico J. Vandaele & Filippo Visintin, 2020. "Editorial for the special issue on “Modelling, simulation, and optimization in health care”," Flexible Services and Manufacturing Journal, Springer, vol. 32(1), pages 1-5, March.
    5. Minsu Cho & Minseok Song & Junhyun Park & Seok-Ran Yeom & Il-Jae Wang & Byung-Kwan Choi, 2020. "Process Mining-Supported Emergency Room Process Performance Indicators," IJERPH, MDPI, vol. 17(17), pages 1-20, August.

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