Tackling Complexity in E-health with Actor-Network Theory

Troshani, Indrit; Wickramasinghe, Nilmini

doi:10.1109/hicss.2014.372

Cited by 11 publications

(5 citation statements)

References 76 publications

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“…Actor‐Network theory is a sociological approach to understand humans and their interaction with technology in specific settings. Its main characteristic is symmetry, which treats equally human and nonhuman objects 32 . It is a framework based on the following concepts 19,33 : (a) actors, the participants in the network which are human and nonhuman objects; (b) heterogeneity, each actor importance is given by the web of relations; (c) quasi‐objects, the successful outcomes which pass from actor to actor within the network; (d) punctualization, a similar concept to abstraction in object‐oriented programming, referred here as encapsulation; (e) obligatory passage point, situations that have to occur for all actors to satisfy the interests of the network; and (f) irreversibility, wherein healthcare is not likely to occur due do the importance of developing robust and effective studies to maintain patient safety.…”

Section: Methodsmentioning

confidence: 99%

On the clinical acceptance of black‐box systems for EEG seizure prediction

et al. 2022

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Seizure prediction may be the solution for epileptic patients whose drugs and surgery do not control seizures. Despite 46 years of research, few devices/systems underwent clinical trials and/or are commercialized, where the most recent state‐of‐the‐art approaches, as neural networks models, are not used to their full potential. The latter demonstrates the existence of social barriers to new methodologies due to data bias, patient safety, and legislation compliance. In the form of literature review, we performed a qualitative study to analyze the seizure prediction ecosystem to find these social barriers. With the Grounded Theory, we draw hypotheses from data, while with the Actor‐Network Theory we considered that technology shapes social configurations and interests, being fundamental in healthcare. We obtained a social network that describes the ecosystem and propose research guidelines aiming at clinical acceptance. Our most relevant conclusion is the need for model explainability, but not necessarily intrinsically interpretable models, for the case of seizure prediction. Accordingly, we argue that it is possible to develop robust prediction models, including black‐box systems to some extent, while avoiding data bias, ensuring patient safety, and still complying with legislation, if they can deliver human‐ comprehensible explanations. Due to skepticism and patient safety reasons, many authors advocate the use of transparent models which may limit their performance and potential. Our study highlights a possible path, by using model explainability, on how to overcome these barriers while allowing the use of more computationally robust models.

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Section: Methodsmentioning

confidence: 99%

On the clinical acceptance of black‐box systems for EEG seizure prediction

et al. 2022

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“…ANT is a sociological approach to understand humans and their interaction with technology in specific settings. Its main characteristic is symmetry, which treats equally human and non-human objects [54]. It is a framework based on the following concepts [19,55]: i) actors, the participants in the network which are human and non-human objects; ii) heterogeneity, each actor importance is given by the web of relations; iii) quasi-objects, the successful outcomes which pass from actor to actor within the network; iv) punctualization, a similar concept to abstraction in object-oriented programming, referred here as encapsulation; v) obligatory passage point, situations that have to occur for all actors to satisfy the interests of the network; and vi) irreversibility, wherein healthcare is not likely to occur due do the importance of developing robust and effective studies to maintain patient safety.…”

Section: Network Creationmentioning

confidence: 99%

On the clinical acceptance of black-box systems for EEG seizure prediction

Pinto¹,

Leal²,

Lopes³

et al. 2021

Preprint

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Despite 46 years of seizure prediction research, few devices/systems underwent clinical trials and/or are commercialised, where the most recent state-of-the-art approaches are not used to their full potential. This demonstrates the existence of social barriers to new methodologies. Based on the literature, we performed a qualitative study to analyse the seizure prediction ecosystem to find these barriers. With Grounded Theory and Actor-Network Theory, we draw hypothesis from data and considered that technology shapes social configurations and interests. For seizure prediction, as long as an algorithm proves to be useful to the patient, we conclude that we may only need to explain the model’s decisions, and not to necessarily obtain intrinsically interpretable models. Accordingly, we argue that it is possible to develop robust prediction models, including black-box systems to some extent, while avoiding data bias, ensuring patient safety, and still complying with legislation, as long as they can deliver human-comprehensible explanations.

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“…Various measures can be taken to investigate the mechanisms of online technology. The use of Actor-Network Theory (ANT) is common in past studies that involved online technology usage, especially those related to e-government and ehealth (Greenhalgh & Stones, 2010;Ranerup, 2012;Troshani & Wickramasinghe, 2014 on 1980s, ANT is a study of the sociology of translation that emerged in social and technology research (Callon, 1986). It uses the interpretative actor-network lens to integrate technology into social processes (Shim and Shin, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Impediment Factors to Successful Usage of Online Halal Certification

Arif

Bakar

Sidek

2019

HSSR

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Purpose of the study: The purpose of this paper is to identify the impediment factors that hinder the successful usage of online halal certification. Methodology: Semi-structured interviews were conducted with 14 representatives from 14 halal food companies in Melaka, Malaysia. The respondents had used the system either to apply for new halal certificates or to renew the existing ones. The identification of the impediment factor was a part of the fulfillment of the translation process by Actor-Network Theory (ANT) during the process of problematization, interessement, enrollment, and mobilization. Identifying the impediment factors was conducted during the first translation stage of problematization. Main Findings: The findings indicate that there are three impediment factors that hinder the successful usage of MYeHalal system. The factors were categorized into three categories, namely, applicants' problem, technical problem, and operational problem. The applicants' problems were caused by the applicants themselves, while the technical problems were caused by the non-performing dysfunctions of the technology, and lastly, the operational problems were caused by the inefficiency of the certifier in fulfilling the tasks and providing the services to applicants. Applications of this study: The identification of these impediment factors will help halal applicants to be aware of the important elements before submitting their application via MYeHalal system. Further, this study will help to enlighten the ways to improve the performance of the system and make it convenient to be used and managed by both halal applicants and JAKIM as the certifier body. Originality: This paper is the first to discuss the inefficiency issues in the usage of online halal certification, since it was first introduced in Malaysia.

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Tackling Complexity in E-health with Actor-Network Theory

Cited by 11 publications

References 76 publications

On the clinical acceptance of black‐box systems for EEG seizure prediction

On the clinical acceptance of black‐box systems for EEG seizure prediction

On the clinical acceptance of black-box systems for EEG seizure prediction

Impediment Factors to Successful Usage of Online Halal Certification

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