Stochastic workflow modeling in a surgical ward: towards simulating and predicting patient flow

Christoffer Olling Back, Areti Manataki*, Angelos Papanastasiou, Ewen Harrison

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

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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.
Original languageEnglish
Title of host publicationBiomedical Engineering Systems and Technologies
Subtitle of host publication13th International Joint Conference, BIOSTEC 2020, Valletta, Malta, February 24–26, 2020, Revised Selected Papers
EditorsXuesong Ye, Filipe Soares, Elisabetta De Maria, Pedro Gómez Vilda, Federico Cabitza, Ana Fred, Hugo Gamboa
Place of PublicationCham
PublisherSpringer
Pages565-591
ISBN (Electronic)9783030723798
ISBN (Print)9783030723781
DOIs
Publication statusPublished - 2021

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer
Volume1400

Keywords

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

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