The Challenges of Big Data Governance in Healthcare

Tse, Daniel; Chow, Chung-kin; Ly, Ting-pong; Tong, Chung-yan; Tam, Kwok-wah

doi:10.1109/trustcom/bigdatase.2018.00240

Cited by 24 publications

(12 citation statements)

References 16 publications

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“…The Taiwan government may pledge AI funding schemes, such as the strategies used in the US and Germany [10,11], for promoting joint efforts in creating breakthroughs in AI. As Taiwan embraces complete big data in national health insurance, it is essential for the government to formulate regulations of data governance [12] and release authorization for the development of the AI industry.…”

Section: The Governmentmentioning

confidence: 99%

The Critical Factors Affecting the Deployment and Scaling of Healthcare AI: Viewpoint from an Experienced Medical Center

et al. 2021

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Healthcare Artificial Intelligence (AI) has the greatest opportunity for development. Since healthcare and technology are two of Taiwan’s most competitive industries, the development of healthcare AI is an excellent chance for Taiwan to improve its health-related services. From the perspective of economic development, promoting healthcare AI must be a top priority. However, despite having many breakthroughs in research and pilot projects, healthcare AI is still considered rare and is broadly used in the healthcare setting. Based on a medical center in Taiwan that has introduced a variety of healthcare AI into practice, this study discussed and analyzed the issues and concerns in the development and scaling of medical AIs from the perspective of various stakeholders in the healthcare setting, including the government, healthcare institutions, users (healthcare workers), and AI providers. The present study also identified critical influential factors for the deployment and scaling of healthcare AI. It is hoped that this paper can serve as an important reference for the advancement of healthcare AI not only in Taiwan but also in other countries.

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Section: The Governmentmentioning

confidence: 99%

The Critical Factors Affecting the Deployment and Scaling of Healthcare AI: Viewpoint from an Experienced Medical Center

et al. 2021

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“…The immeasurable mass of data available today will continue to grow exponentially as time goes by. Seeking to address data security and privacy together and not apart is inevitable in the Big Data era (Kshetri 2014;Thuraisingham 2015).…”

Section: ) Data Security and Privacymentioning

confidence: 99%

“…(1) Adapting and applying Big Data technologies to existing application areas and the IT landscape, including the prediction of their effects, remains a daunting challenge (Gong and Janssen 2017). For example, through analytical techniques and tools, Big Data has increased the scope of personally identifiable information or information that can be used alone or combined with other information to identify an individual (Crawford and Schultz 2014;Sampson 2014;Tene and Polonetsky 2011;Thuraisingham 2015). As a result, organisations are not yet capable of utilising Big Data to its fullest potential.…”

Section: Mrq6: Can We Identify Any Topics Within These Areas That Could Be Subjects For Further Research?mentioning

confidence: 99%

“…It would, however, be a wrong assumption that the same set of data security and privacy protection measures could be uniformly applicable. Hence, prior to any consideration, it is necessary to analyse them from different organisational perspectives (Holm and Ploug 2017;Kunz et al 2014;Li and Guo 2014;Patil and Seshadri 2014;Tse et al 2018). (4) The increasing strategic role of Big Data in competitive environments requires the use of a thorough legal model to understand the legal rights and responsibilities (Kemp 2014;Tene and Polonetsky 2012) and thus cope with legal uncertainties that surround its management (Spiekermann et al 2015).…”

Section: Mrq6: Can We Identify Any Topics Within These Areas That Could Be Subjects For Further Research?mentioning

confidence: 99%

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Enterprise architecture management as a solution for addressing general data protection regulation requirements in a big data context: a systematic mapping study

Georgiadis

Poels

2021

Inf Syst E-Bus Manage

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Context Big Data Analytics is a rapidly emerging IT practice whose applications offer benefits for a wide variety of business areas across an organisation. Given the wide scope of applications, the many types of processing involved, including those for purposes not yet foreseen, and the inherent privacy concerns resulting from collecting and storing personal data, the newly introduced General Data Protection Regulation (GDPR) poses specific challenges for safeguarding the security and protection of big data. These challenges are not limited to the IT function but extend across the entire organisation. This raises the question whether Enterprise Architecture Management (EAM), as an approach for ensuring the coherence, strategic alignment and focus on value creation of all organisational resources, offers guidance for addressing those challenges in a holistic manner, and thus provides a fruitful ground for developing an approach for complying to GDPR requirements in a Big Data context. Objective This study surveys the state-of-the-art in research on security, privacy, and protection of big data. The focus is on investigating which specific issues and challenges have been identified and whether these have been linked to GDPR requirements. Further, it examines whether previous research has investigated the potential of EAM in addressing those challenges and what the main findings of those studies are. Method We used Systematic Mapping Review (SMR), which is a methodology for literature review aimed at surveying the state-of-the-art in a research field as it is documented in the scientific literature. Further, we used Template Analysis, which is a thematic analysis technique, for coding the texts of the selected papers, classifying the research studies, and interpreting the different themes addressed in the literature.

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“…Inaccurate data can lead to incorrect decisions and long term strategic planning. Because health-care data often comes from various sources, robust authentication systems are needed to ensure that health-care data are submitted from actual registered clinics, hospitals, and medical institutions (Tse et al, 2018). Collecting data that is clean, formatted, thorough, and precise in a health-care system is challenging (Anagnostopoulos et al, 2016).…”

Section: Collection and Management Of Datamentioning

confidence: 99%

Smart healthcare

Zeadally

Siddiqui

Baig

et al. 2019

PRR

109

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Purpose -The aim of this paper is to identify some of the challenges that need to be addressed to accelerate the deployment and adoption of smart health technologies for ubiquitous healthcare access. The paper also explores how internet of things (IoT) and big data technologies can be combined with smart health to provide better healthcare solutions.Design/methodology/approach -The authors reviewed the literature to identify the challenges which have slowed down the deployment and adoption of smart health.Findings -The authors discussed how IoT and big data technologies can be integrated with smart health to address some of the challenges to improve health-care availability, access and costs.Originality/value -The results of this paper will help health-care designers, professionals and researchers design better health-care information systems.

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The Challenges of Big Data Governance in Healthcare

Cited by 24 publications

References 16 publications

The Critical Factors Affecting the Deployment and Scaling of Healthcare AI: Viewpoint from an Experienced Medical Center

The Critical Factors Affecting the Deployment and Scaling of Healthcare AI: Viewpoint from an Experienced Medical Center

Enterprise architecture management as a solution for addressing general data protection regulation requirements in a big data context: a systematic mapping study

Smart healthcare

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