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Data Accuracy Considerations with mHealth
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Author(s): Zaid Zekiria Sako (Deakin University, Australia), Vass Karpathiou (RMIT, Australia), Sasan Adibi (Deakin University, Australia)and Nilmini Wickramasinghe (Epworth HealthCare, Australia & Deakin University, Australia)
Copyright: 2020
Pages: 16
Source title:
Cognitive Analytics: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-2460-2.ch084
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Abstract
With the plethora of mHealth solutions developed being digital, this necessitates the need for accurate data and information integrity. Lack of data accuracy and information integrity in mHealth can cause serious harm to patients and limit the benefits of such promising technology. Thus, this exploratory study investigates data accuracy and information integrity in mHealth by examining a mobile health solution for diabetes, with the aim of incorporating Machine Learning to detect sources of inaccurate data and deliver quality information.
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