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

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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.

Original languageEnglish
Title of host publicationBiomedical Engineering Systems and Technologies - 13th International Joint Conference, BIOSTEC 2020, Revised Selected Papers
EditorsXuesong Ye, Filipe Soares, Elisabetta De Maria, Pedro Gómez Vilda, Federico Cabitza, Ana Fred, Hugo Gamboa
Publication date2021
ISBN (Print)9783030723781
Publication statusPublished - 2021
Event13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020 - Valetta, Malta
Duration: 24 Feb 202026 Feb 2020


Conference13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020
SeriesCommunications in Computer and Information Science
Volume1400 CCIS

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

    Research areas

  • Bayesian network, Data mining, Patient flow, Petri nets, Process mining, Simulation, Surgery, Surgical workflow

ID: 283134749