Data Accuracy Considerations with mHealth

Data Accuracy Considerations with mHealth

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: © 2017 |Pages: 15
DOI: 10.4018/978-1-5225-0920-2.ch001
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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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Background

This section explores mHealth technology and its role in connecting people to healthcare services. As well as what defines accurate data, effect of data inaccuracy on Information Integrity and the emergence of Machine Learning in the development of healthcare solutions and how it can be used to detect medical errors to enhance quality of mHealth.

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