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International Joint Conference on Biomedical Engineering Systems and Technologies

BIOSTEC 2020: Biomedical Engineering Systems and Technologies pp 565–591Cite as

Stochastic Workflow Modeling in a Surgical Ward: Towards Simulating and Predicting Patient Flow

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1400))

Abstract

Intelligent systems play an increasingly central role in healthcare systems worldwide. Nonetheless, operational friction represents an obstacle to full utilization of scarce resources and improvement of service standards. In this paper we address the challenge of developing data-driven models of complex workflow systems - a prerequisite for harnessing intelligent technologies for workflow improvement. We present a proof-of-concept model parametrized using real-world data and constructed based on domain knowledge from the Royal Infirmary of Edinburgh, demonstrating how off-the-shelf process mining, machine learning and stochastic process modeling tools can be combined to build predictive models that capture complex control flow, constraints, policies and guidelines.

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Notes

  1. 1.

    NCEPOD Classification of Intervention [30].

  2. 2.

    http://www.github.com/apapan08.

  3. 3.

    Linear Temporal Logic, Metric Interval Temporal Logic.

  4. 4.

    \(E[X] = \sum ~x~ p(X\,=\,x)\).

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Acknowledgements

We would like to thank Cameron Fairfield and Stephen Knight for their generous feedback regarding policies and on-the-ground practices at the Royal Infirmary of Edinburgh surgical ward.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Copenhagen, Copenhagen, Denmark

    Christoffer O. Back

  2. Usher Institute, University of Edinburgh, Edinburgh, U.K.

    Ewen Harrison

  3. Department of Mathematics and Statistics, University of Cyprus, Nicosia, Cyprus

    Angelos Papanastasiou

  4. School of Computer Science, University of St Andrews, St Andrews, U.K.

    Areti Manataki

Authors
  1. Christoffer O. Back
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  2. Areti Manataki
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  3. Angelos Papanastasiou
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  4. Ewen Harrison
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Corresponding author

Correspondence to Areti Manataki .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Xuesong Ye

  2. Fraunhofer Portugal Research Center for Assistive Information and Communication Solutions, Porto, Portugal

    Filipe Soares

  3. Nice Sophia Antipolis University, Nice Cedex 2, France

    Elisabetta De Maria

  4. Universidad Politécnica de Madrid, Madrid, Spain

    Pedro Gómez Vilda

  5. University of Milano-Bicocca, Milan, Italy

    Federico Cabitza

  6. Instituto de Telecomunicações, University of Lisbon, Lisbon, Portugal

    Ana Fred

  7. Universidade Nova de Lisboa, Caparica, Portugal

    Hugo Gamboa

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Back, C.O., Manataki, A., Papanastasiou, A., Harrison, E. (2021). Stochastic Workflow Modeling in a Surgical Ward: Towards Simulating and Predicting Patient Flow. In: Ye, X., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2020. Communications in Computer and Information Science, vol 1400. Springer, Cham. https://doi.org/10.1007/978-3-030-72379-8_28

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  • DOI: https://doi.org/10.1007/978-3-030-72379-8_28

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  • Print ISBN: 978-3-030-72378-1

  • Online ISBN: 978-3-030-72379-8

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