Medical Expert Systems: A Study of Trust and Acceptance by Healthcare Stakeholders

Ioannis Vourgidis; Shadreck Joseph Mafuma; Paul Wilson; Jenny Carter; Georgina Cosma

doi:10.1007/978-3-319-97982-3_9

Medical Expert Systems: A Study of Trust and Acceptance by Healthcare Stakeholders

Ioannis Vourgidis, Shadreck Joseph Mafuma, Paul Wilson, Jenny Carter, Georgina Cosma

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Citations (Scopus)

Abstract

The increasing prevalence of complex technology in the form of medical expert systems in the healthcare sector is presenting challenging opportunities to clinicians in their quest to improve patients’ health outcomes. Medical expert systems have brought measurable improvements to the healthcare outcomes for some patients. This paper highlights the importance of trust and acceptance in the healthcare industry amongst receivers of the care as well as other stakeholders and between large healthcare organizations. Studies show that current conceptual trust models, which are being used to measure the degree of trust relationships in different healthcare settings, cannot be easily evaluated because of the resistance of organizational and social changes which are to be implemented. Research findings also suggest that the use of medical expert systems do not automatically guarantee improved patient healthcare outcomes. Furthermore, during the building of predictive and diagnostic expert medical systems, studies recommend the use of algorithms which can deal with noisy and imprecise data which is typical in healthcare data. Such algorithms include fuzzy rule based systems.

Original language	English
Title of host publication	Advances in Computational Intelligence Systems
Subtitle of host publication	Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK
Editors	Ahmad Lotfi, Hamid Bouchachia, Alexander Gegov, Caroline Langensiepen, Martin McGinnity
Publisher	Springer Verlag
Pages	108-119
Number of pages	12
ISBN (Electronic)	9783319979823
ISBN (Print)	9783319979816
DOIs	https://doi.org/10.1007/978-3-319-97982-3_9
Publication status	Published - 11 Aug 2018
Event	18th UK Workshop on Computational Intelligence - Nottingham Trent University, Nottingham, United Kingdom Duration: 5 Sep 2018 → 7 Sep 2018 Conference number: 18 http://ukci2018.uk/ (Link to Workshop Website)

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	840
ISSN (Print)	2194-5357

Workshop

Workshop	18th UK Workshop on Computational Intelligence
Abbreviated title	UKCI 2018
Country/Territory	United Kingdom
City	Nottingham
Period	5/09/18 → 7/09/18
Internet address	http://ukci2018.uk/ (Link to Workshop Website)

UN SDGs

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

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10.1007/978-3-319-97982-3_9Licence: Unspecified

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Vourgidis, I., Mafuma, S. J., Wilson, P., Carter, J., & Cosma, G. (2018). Medical Expert Systems: A Study of Trust and Acceptance by Healthcare Stakeholders. In A. Lotfi, H. Bouchachia, A. Gegov, C. Langensiepen, & M. McGinnity (Eds.), Advances in Computational Intelligence Systems : Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK (pp. 108-119). (Advances in Intelligent Systems and Computing; Vol. 840). Springer Verlag. https://doi.org/10.1007/978-3-319-97982-3_9

Vourgidis, Ioannis ; Mafuma, Shadreck Joseph ; Wilson, Paul et al. / Medical Expert Systems : A Study of Trust and Acceptance by Healthcare Stakeholders. Advances in Computational Intelligence Systems : Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK. editor / Ahmad Lotfi ; Hamid Bouchachia ; Alexander Gegov ; Caroline Langensiepen ; Martin McGinnity. Springer Verlag, 2018. pp. 108-119 (Advances in Intelligent Systems and Computing).

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title = "Medical Expert Systems: A Study of Trust and Acceptance by Healthcare Stakeholders",

abstract = "The increasing prevalence of complex technology in the form of medical expert systems in the healthcare sector is presenting challenging opportunities to clinicians in their quest to improve patients{\textquoteright} health outcomes. Medical expert systems have brought measurable improvements to the healthcare outcomes for some patients. This paper highlights the importance of trust and acceptance in the healthcare industry amongst receivers of the care as well as other stakeholders and between large healthcare organizations. Studies show that current conceptual trust models, which are being used to measure the degree of trust relationships in different healthcare settings, cannot be easily evaluated because of the resistance of organizational and social changes which are to be implemented. Research findings also suggest that the use of medical expert systems do not automatically guarantee improved patient healthcare outcomes. Furthermore, during the building of predictive and diagnostic expert medical systems, studies recommend the use of algorithms which can deal with noisy and imprecise data which is typical in healthcare data. Such algorithms include fuzzy rule based systems.",

keywords = "Acceptance, Artificial intelligence, Healthcare NHS, Medical expert systems, Systematic literature review, Trust",

author = "Ioannis Vourgidis and Mafuma, {Shadreck Joseph} and Paul Wilson and Jenny Carter and Georgina Cosma",

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Vourgidis, I, Mafuma, SJ, Wilson, P, Carter, J & Cosma, G 2018, Medical Expert Systems: A Study of Trust and Acceptance by Healthcare Stakeholders. in A Lotfi, H Bouchachia, A Gegov, C Langensiepen & M McGinnity (eds), Advances in Computational Intelligence Systems : Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK. Advances in Intelligent Systems and Computing, vol. 840, Springer Verlag, pp. 108-119, 18th UK Workshop on Computational Intelligence, Nottingham, United Kingdom, 5/09/18. https://doi.org/10.1007/978-3-319-97982-3_9

Medical Expert Systems : A Study of Trust and Acceptance by Healthcare Stakeholders. / Vourgidis, Ioannis; Mafuma, Shadreck Joseph; Wilson, Paul et al.

Advances in Computational Intelligence Systems : Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK. ed. / Ahmad Lotfi; Hamid Bouchachia; Alexander Gegov; Caroline Langensiepen; Martin McGinnity. Springer Verlag, 2018. p. 108-119 (Advances in Intelligent Systems and Computing; Vol. 840).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Medical Expert Systems

T2 - 18th UK Workshop on Computational Intelligence

AU - Vourgidis, Ioannis

AU - Mafuma, Shadreck Joseph

AU - Wilson, Paul

AU - Carter, Jenny

AU - Cosma, Georgina

N1 - Conference code: 18

PY - 2018/8/11

Y1 - 2018/8/11

N2 - The increasing prevalence of complex technology in the form of medical expert systems in the healthcare sector is presenting challenging opportunities to clinicians in their quest to improve patients’ health outcomes. Medical expert systems have brought measurable improvements to the healthcare outcomes for some patients. This paper highlights the importance of trust and acceptance in the healthcare industry amongst receivers of the care as well as other stakeholders and between large healthcare organizations. Studies show that current conceptual trust models, which are being used to measure the degree of trust relationships in different healthcare settings, cannot be easily evaluated because of the resistance of organizational and social changes which are to be implemented. Research findings also suggest that the use of medical expert systems do not automatically guarantee improved patient healthcare outcomes. Furthermore, during the building of predictive and diagnostic expert medical systems, studies recommend the use of algorithms which can deal with noisy and imprecise data which is typical in healthcare data. Such algorithms include fuzzy rule based systems.

AB - The increasing prevalence of complex technology in the form of medical expert systems in the healthcare sector is presenting challenging opportunities to clinicians in their quest to improve patients’ health outcomes. Medical expert systems have brought measurable improvements to the healthcare outcomes for some patients. This paper highlights the importance of trust and acceptance in the healthcare industry amongst receivers of the care as well as other stakeholders and between large healthcare organizations. Studies show that current conceptual trust models, which are being used to measure the degree of trust relationships in different healthcare settings, cannot be easily evaluated because of the resistance of organizational and social changes which are to be implemented. Research findings also suggest that the use of medical expert systems do not automatically guarantee improved patient healthcare outcomes. Furthermore, during the building of predictive and diagnostic expert medical systems, studies recommend the use of algorithms which can deal with noisy and imprecise data which is typical in healthcare data. Such algorithms include fuzzy rule based systems.

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KW - Healthcare NHS

KW - Medical expert systems

KW - Systematic literature review

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T3 - Advances in Intelligent Systems and Computing

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BT - Advances in Computational Intelligence Systems

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A2 - Bouchachia, Hamid

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A2 - McGinnity, Martin

PB - Springer Verlag

Y2 - 5 September 2018 through 7 September 2018

ER -

Vourgidis I, Mafuma SJ, Wilson P, Carter J, Cosma G. Medical Expert Systems: A Study of Trust and Acceptance by Healthcare Stakeholders. In Lotfi A, Bouchachia H, Gegov A, Langensiepen C, McGinnity M, editors, Advances in Computational Intelligence Systems : Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK. Springer Verlag. 2018. p. 108-119. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-97982-3_9

Medical Expert Systems: A Study of Trust and Acceptance by Healthcare Stakeholders

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