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

Christoffer Olling Back; Areti Manataki; Angelos Papanastasiou; Ewen Harrison

doi:10.1007/978-3-030-72379-8_28

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

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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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 language	English
Title of host publication	Biomedical Engineering Systems and Technologies
Subtitle of host publication	13th International Joint Conference, BIOSTEC 2020, Valletta, Malta, February 24–26, 2020, Revised Selected Papers
Editors	Xuesong Ye, Filipe Soares, Elisabetta De Maria, Pedro Gómez Vilda, Federico Cabitza, Ana Fred, Hugo Gamboa
Place of Publication	Cham
Publisher	Springer
Pages	565-591
ISBN (Electronic)	9783030723798
ISBN (Print)	9783030723781
DOIs	https://doi.org/10.1007/978-3-030-72379-8_28
Publication status	Published - 2021

Publication series

Name	Communications in Computer and Information Science
Publisher	Springer
Volume	1400

Keywords

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

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10.1007/978-3-030-72379-8_28

Stochastic_Workflow_Modeling_in_a_Surgical_Ward
Copyright © Springer Nature Switzerland AG 2021. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1007/978-3-030-72379-8_28.
Accepted author manuscript, 1.34 MB

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Olling Back, C., Manataki, A., Papanastasiou, A., & Harrison, E. (2021). Stochastic workflow modeling in a surgical ward: towards simulating and predicting patient flow. In X. Ye, F. Soares, E. De Maria, P. Gómez Vilda, F. Cabitza, A. Fred, & H. Gamboa (Eds.), Biomedical Engineering Systems and Technologies: 13th International Joint Conference, BIOSTEC 2020, Valletta, Malta, February 24–26, 2020, Revised Selected Papers (pp. 565-591). (Communications in Computer and Information Science; Vol. 1400). Springer. https://doi.org/10.1007/978-3-030-72379-8_28

Olling Back, Christoffer ; Manataki, Areti ; Papanastasiou, Angelos et al. / Stochastic workflow modeling in a surgical ward : towards simulating and predicting patient flow. Biomedical Engineering Systems and Technologies: 13th International Joint Conference, BIOSTEC 2020, Valletta, Malta, February 24–26, 2020, Revised Selected Papers. editor / Xuesong Ye ; Filipe Soares ; Elisabetta De Maria ; Pedro Gómez Vilda ; Federico Cabitza ; Ana Fred ; Hugo Gamboa. Cham : Springer, 2021. pp. 565-591 (Communications in Computer and Information Science).

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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.",

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Olling Back, C, Manataki, A, Papanastasiou, A & Harrison, E 2021, Stochastic workflow modeling in a surgical ward: towards simulating and predicting patient flow. in X Ye, F Soares, E De Maria, P Gómez Vilda, F Cabitza, A Fred & H Gamboa (eds), Biomedical Engineering Systems and Technologies: 13th International Joint Conference, BIOSTEC 2020, Valletta, Malta, February 24–26, 2020, Revised Selected Papers. Communications in Computer and Information Science, vol. 1400, Springer, Cham, pp. 565-591. https://doi.org/10.1007/978-3-030-72379-8_28

Stochastic workflow modeling in a surgical ward: towards simulating and predicting patient flow. / Olling Back, Christoffer; Manataki, Areti; Papanastasiou, Angelos et al.
Biomedical Engineering Systems and Technologies: 13th International Joint Conference, BIOSTEC 2020, Valletta, Malta, February 24–26, 2020, Revised Selected Papers. ed. / Xuesong Ye; Filipe Soares; Elisabetta De Maria; Pedro Gómez Vilda; Federico Cabitza; Ana Fred; Hugo Gamboa. Cham: Springer, 2021. p. 565-591 (Communications in Computer and Information Science; Vol. 1400).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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T1 - Stochastic workflow modeling in a surgical ward

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AU - Manataki, Areti

AU - Papanastasiou, Angelos

AU - Harrison, Ewen

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Surgery

KW - Surgical workflow

KW - Bayesian network

KW - Petri Nets

KW - Simulation

KW - Data mining

KW - Patient flow

KW - Process mining

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SP - 565

EP - 591

BT - Biomedical Engineering Systems and Technologies

A2 - Ye, Xuesong

A2 - Soares, Filipe

A2 - De Maria, Elisabetta

A2 - Gómez Vilda, Pedro

A2 - Cabitza, Federico

A2 - Fred, Ana

A2 - Gamboa, Hugo

PB - Springer

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Olling Back C, Manataki A, Papanastasiou A, Harrison E. Stochastic workflow modeling in a surgical ward: towards simulating and predicting patient flow. In Ye X, Soares F, De Maria E, Gómez Vilda P, Cabitza F, Fred A, Gamboa H, editors, Biomedical Engineering Systems and Technologies: 13th International Joint Conference, BIOSTEC 2020, Valletta, Malta, February 24–26, 2020, Revised Selected Papers. Cham: Springer. 2021. p. 565-591. (Communications in Computer and Information Science). Epub 2021 Mar 30. doi: 10.1007/978-3-030-72379-8_28

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

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