Increasing Safety and Security Through Cyberspace by an Adaptive Network Model for AI-Assisted Risk Management of Neonatal Respiratory Distress

Nisrine Mokadem; Fakhra Jabeen; Jan Treur; H. Rob Taal; Peter H.M.P. Roelofsma

doi:10.1007/978-3-031-72075-8_5

Increasing Safety and Security Through Cyberspace by an Adaptive Network Model for AI-Assisted Risk Management of Neonatal Respiratory Distress

Nisrine Mokadem
, Fakhra Jabeen
, Jan Treur^*
, H. Rob Taal
, Peter H.M.P. Roelofsma

^*Corresponding author for this work

Neonatal and Pediatric Intensive Care

Vrije Universiteit Amsterdam
Delft University of Technology
The Hague University of Applied Sciences

Research output: Chapter/Conference proceeding › Chapter › Academic

3 Citations (Scopus)

27 Downloads (Pure)

Abstract

This chapter presents the use of second-order adaptive network models of hospital teams consisting of doctors and nurses, interacting together. A variety of scenarios are modelled and simulated, in relation with respiratory distress of a neonate, along with the integration of an AI-Coach for monitoring and support of such teams and of organizational learning. The research highlights the benefits of introducing a virtual AI-Coach in a hospital setting. The practical application setting revolves around a medical team responsible for managing neonates with respiratory distress. In this setting an AI-Coach act as an additional team member, to ensure correct execution of medical procedure. Through simulation experiments, the adaptive network models demonstrate that the AI-Coach not only aids in maintaining correct medical procedure execution but also facilitates organizational learning, leading to significant improvements in procedure adherence and error reduction during neonatal care.

Original language	English
Title of host publication	Using Shared Mental Models and Organisational Learning to Support Safety and Security Through Cyberspace
Subtitle of host publication	A Computational Analysis Approach
Editors	Peter H.M.P. Roelofsma, Fakhra Jabeen, H. Rob Taal, Jan Treur
Publisher	Springer Science+Business Media
Pages	123-166
Number of pages	44
Edition	1
ISBN (Electronic)	978-3-031-72075-8
ISBN (Print)	978-3-031-72074-1, 978-3-031-72077-2
DOIs	https://doi.org/10.1007/978-3-031-72075-8_5
Publication status	Published - 3 Jan 2024

Publication series

Series	Studies in Systems, Decision and Control
Volume	570
ISSN	2198-4182

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1007/978-3-031-72075-8_5

Increasing Safety and Security ThroughFinal published version, 2.09 MBLicence: Article 25fa Dutch Copyright Act

Cite this

Mokadem, N., Jabeen, F., Treur, J., Rob Taal, H., & Roelofsma, P. H. M. P. (2024). Increasing Safety and Security Through Cyberspace by an Adaptive Network Model for AI-Assisted Risk Management of Neonatal Respiratory Distress. In P. H. M. P. Roelofsma, F. Jabeen, H. R. Taal, & J. Treur (Eds.), Using Shared Mental Models and Organisational Learning to Support Safety and Security Through Cyberspace: A Computational Analysis Approach (1 ed., pp. 123-166). Springer Science+Business Media. https://doi.org/10.1007/978-3-031-72075-8_5

Mokadem, Nisrine ; Jabeen, Fakhra ; Treur, Jan et al. / Increasing Safety and Security Through Cyberspace by an Adaptive Network Model for AI-Assisted Risk Management of Neonatal Respiratory Distress. Using Shared Mental Models and Organisational Learning to Support Safety and Security Through Cyberspace: A Computational Analysis Approach. editor / Peter H.M.P. Roelofsma ; Fakhra Jabeen ; H. Rob Taal ; Jan Treur. 1. ed. Springer Science+Business Media, 2024. pp. 123-166 (Studies in Systems, Decision and Control, Vol. 570).

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abstract = "This chapter presents the use of second-order adaptive network models of hospital teams consisting of doctors and nurses, interacting together. A variety of scenarios are modelled and simulated, in relation with respiratory distress of a neonate, along with the integration of an AI-Coach for monitoring and support of such teams and of organizational learning. The research highlights the benefits of introducing a virtual AI-Coach in a hospital setting. The practical application setting revolves around a medical team responsible for managing neonates with respiratory distress. In this setting an AI-Coach act as an additional team member, to ensure correct execution of medical procedure. Through simulation experiments, the adaptive network models demonstrate that the AI-Coach not only aids in maintaining correct medical procedure execution but also facilitates organizational learning, leading to significant improvements in procedure adherence and error reduction during neonatal care.",

author = "Nisrine Mokadem and Fakhra Jabeen and Jan Treur and \{Rob Taal\}, H. and Roelofsma, \{Peter H.M.P.\}",

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Mokadem, N, Jabeen, F, Treur, J, Rob Taal, H & Roelofsma, PHMP 2024, Increasing Safety and Security Through Cyberspace by an Adaptive Network Model for AI-Assisted Risk Management of Neonatal Respiratory Distress. in PHMP Roelofsma, F Jabeen, HR Taal & J Treur (eds), Using Shared Mental Models and Organisational Learning to Support Safety and Security Through Cyberspace: A Computational Analysis Approach. 1 edn, Springer Science+Business Media, Studies in Systems, Decision and Control, vol. 570, pp. 123-166. https://doi.org/10.1007/978-3-031-72075-8_5

Increasing Safety and Security Through Cyberspace by an Adaptive Network Model for AI-Assisted Risk Management of Neonatal Respiratory Distress. / Mokadem, Nisrine; Jabeen, Fakhra; Treur, Jan et al.
Using Shared Mental Models and Organisational Learning to Support Safety and Security Through Cyberspace: A Computational Analysis Approach. ed. / Peter H.M.P. Roelofsma; Fakhra Jabeen; H. Rob Taal; Jan Treur. 1. ed. Springer Science+Business Media, 2024. p. 123-166 (Studies in Systems, Decision and Control, Vol. 570).

Research output: Chapter/Conference proceeding › Chapter › Academic

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AB - This chapter presents the use of second-order adaptive network models of hospital teams consisting of doctors and nurses, interacting together. A variety of scenarios are modelled and simulated, in relation with respiratory distress of a neonate, along with the integration of an AI-Coach for monitoring and support of such teams and of organizational learning. The research highlights the benefits of introducing a virtual AI-Coach in a hospital setting. The practical application setting revolves around a medical team responsible for managing neonates with respiratory distress. In this setting an AI-Coach act as an additional team member, to ensure correct execution of medical procedure. Through simulation experiments, the adaptive network models demonstrate that the AI-Coach not only aids in maintaining correct medical procedure execution but also facilitates organizational learning, leading to significant improvements in procedure adherence and error reduction during neonatal care.

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Mokadem N, Jabeen F, Treur J, Rob Taal H, Roelofsma PHMP. Increasing Safety and Security Through Cyberspace by an Adaptive Network Model for AI-Assisted Risk Management of Neonatal Respiratory Distress. In Roelofsma PHMP, Jabeen F, Taal HR, Treur J, editors, Using Shared Mental Models and Organisational Learning to Support Safety and Security Through Cyberspace: A Computational Analysis Approach. 1 ed. Springer Science+Business Media. 2024. p. 123-166. (Studies in Systems, Decision and Control, Vol. 570). doi: 10.1007/978-3-031-72075-8_5

Increasing Safety and Security Through Cyberspace by an Adaptive Network Model for AI-Assisted Risk Management of Neonatal Respiratory Distress

Abstract

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UN SDGs

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