Data Governance in the Health Industry: Investigating Data Quality Dimensions within a Big Data Context

Juddoo, Suraj; George, Carlisle; Duquenoy, Penny; Windridge, David

doi:10.3390/asi1040043

Cited by 24 publications

(11 citation statements)

References 51 publications

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“…The integration and governance of big data technologies in healthcare has local and global implications in terms of challenges and opportunities [6,8,12,23]. Challenges in healthcare include "issues of data structure, security, data standardisation, storage and transfers, and managerial skills such as data governance" [24].…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Big Data Analytics and Processing Platform in Czech Republic Healthcare

2020

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Big data analytics (BDA) in healthcare has made a positive difference in the integration of Artificial Intelligence (AI) in advancements of analytical capabilities, while lowering the costs of medical care. The aim of this study is to improve the existing healthcare eSystem by implementing a Big Data Analytics (BDA) platform and to meet the requirements of the Czech Republic National Health Service (Tender-Id. VZ0036628, No. Z2017-035520). In addition to providing analytical capabilities on Linux platforms supporting current and near-future AI with machine-learning and data-mining algorithms, there is the need for ethical considerations mandating new ways to preserve privacy, all of which are preconditioned by the growing body of regulations and expectations. The presented BDA platform, has met all requirements (N > 100), including the healthcare industry-standard Transaction Processing Performance Council (TPC-H) decision support benchmark in compliance with the European Union (EU) and the Czech Republic legislations. Currently, the presented Proof of Concept (PoC) that has been upgraded to a production environment has unified isolated parts of Czech Republic healthcare over the past seven months. The reported PoC BDA platform, artefacts, and concepts are transferrable to healthcare systems in other countries interested in developing or upgrading their own national healthcare infrastructure in a cost-effective, secure, scalable and high-performance manner.

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Section: Challenges and Opportunitiesmentioning

confidence: 99%

Big Data Analytics and Processing Platform in Czech Republic Healthcare

2020

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“…As delineated previously, data quality and its value can be defined by relevance, sufficiency, and veracity [32] (p. 5). Particularly, data must be timely, accurate, and of high quality for promoting DBDM [4,13,15,20,24,30,54]. In the case of the KEIDS, the main data-related problem is the deficiency of the KEIDS in maintaining and disclosing the latest data [2,20,23,26].…”

Section: Data Qualitymentioning

confidence: 99%

“…The Schoolinfo data system does not provide high-quality information on how school programs for gifted children and children with learning disabilities are implemented, so the KEIDS does not provide educators with data on how students' learning, aptitude, and talent have been improved and developed. (Teacher A3) These problems are created by both the timeliness of information [2,4,13,15,23,29] and the absence of relevant and sufficient data [22,30,32] on student progress and development, including gifted children and children with learning disabilities. In addition, as reported in Park and Hong [54] (p. 386), data within the Schoolinfo data system is updated very slowly, and the KEIDS does not update the data as an ongoing process [54] (p. 386).…”

Section: Data Qualitymentioning

confidence: 99%

Promoting Sustainable Data-Based Decision-Making in the Korean Educational Information Disclosure System

Joo

2020

Sustainability

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This study investigates the Korean Educational Information Disclosure System (KEIDS) and suggests sustainable development policies for KEIDS to improve school-level data-based decision-making (DBDM) from the educational administration’s perspective. It also raises the following questions: What are the barriers impeding effective data use by the KEIDS? How do school teachers, who are directly involved in using data, effectively prepare for DBDM using the KEIDS? How can the KEIDS be improved for DBDM concerning quality data, school context, and institutional support? To answer these questions, the study reviewed KEIDS-related documents and interviewed 24 school teachers through an interpretive case study approach while using a research framework of data quality, school contexts, and institutional support. Its results highlight important issues with the KEIDS and sustainable DBDM, in other words, teachers and administrators are not always conscious of the need for using data; the lack of data use understanding creates issues among principal leadership and teachers’ involvement and cooperation; the quality of the student data in the Schoolinfo system is questionable; and the central education authority focuses on simply disclosing student data rather than pursuing the goal of the KEIDS. The study suggests facilitating DBDM through the KEIDS in terms of data quality, school context, and institutional support.

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“…However, big data has low veracity, which implies that its accuracy cannot be easily ascertained or validated, given the fact that accumulates fast and from different sources. 10 Variability implies that the nature and value of big data easily vary from one stage of its processing to the other. While the term value implies that big data is an important source of valuable information.…”

Section: Literature Review On Big Data and Big Data Analytics In Healthcarementioning

confidence: 99%

Usability of Big Data Analytics Within Clinical Decision Support Systems

Christopher¹,

Tsinale²

2019

IJEAST

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The adoption of electronic health record systems and other digital technologies such as Magnetic Resonance Imaging (MRI) techniques, automated laboratory tests, and body sensors have brought the era of big data technology into the healthcare industry. The use of big data technologies has the potential to provide medical organizations with powerful tools to gather and analyze large data volumes and to use this information to their advantage. However, special skills, systems, and capabilities are required to be able to analyze and extract useful information from big data. The objective of this paper was to explore the literature regarding the usability of big data analytics in supporting medical decision making. This information will guide healthcare organizations in understanding how they can adopt the utilization of big data to enhance decision making. A systematic review of evidence-based research articles from within the past five years was used to gather information in regards to this topic. The articles were derived from scientific databases. Based on the literature review, big data and big data analytics has the capability to improve decision making in the healthcare sector, predict disease outbreaks as well as the trends and patterns of the spread of such diseases, predict occurrence of medical phenomenon's such as hospital readmission, reoccurrence of diseases, and risk of infection among others. Moreover, big data analytics has the capability to help healthcare organizations to streamline processes within the healthcare setting. However, the process of integrating big data analytics in the healthcare setting follows distinct phases. Healthcare organizations also have to consider the challenges associated with adopting big data analytics. Nevertheless, based on the literature, big data analytics has the capability to improve delivery systems and outcomes within the healthcare sector.

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Data Governance in the Health Industry: Investigating Data Quality Dimensions within a Big Data Context

Cited by 24 publications

References 51 publications

Big Data Analytics and Processing Platform in Czech Republic Healthcare

Big Data Analytics and Processing Platform in Czech Republic Healthcare

Promoting Sustainable Data-Based Decision-Making in the Korean Educational Information Disclosure System

Usability of Big Data Analytics Within Clinical Decision Support Systems

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