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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wickramasinghe, Nilmini | - |
dc.contributor.other | Sako, Zaid | - |
dc.contributor.other | Karpathiou, Vass | - |
dc.contributor.other | Adibi, Sasan | - |
dc.date.accessioned | 2018-10-17T22:33:20Z | - |
dc.date.available | 2018-10-17T22:33:20Z | - |
dc.date.issued | 2017 | - |
dc.identifier.isbn | 9781522509202 | en_US |
dc.identifier.uri | http://hdl.handle.net/11434/1527 | - |
dc.description.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. | en_US |
dc.publisher | IGI Global | en_US |
dc.subject | mHealth | en_US |
dc.subject | Information Integrity | en_US |
dc.subject | Data Integrity | en_US |
dc.subject | Data Accuracy | en_US |
dc.subject | Chair of Health Informatics Management, Epworth HealthCare, Victoria, Australia | en_US |
dc.title | Data accuracy considerations with mHealth. | en_US |
dc.type | Chapter | en_US |
dc.identifier.doi | 10.4018/978-1-5225-0920-2.ch001 | en_US |
dc.description.affiliates | Health Informatics | en_US |
dc.description.affiliates | RMIT University, Melbourne, Victoria, Australia | en_US |
dc.description.affiliates | Deakin University, Melbourne, Victoria, Australia | en_US |
dc.type.contenttype | Text | en_US |
dc.title.book | Handbook of Research on Healthcare Administration and Management | en_US |
Appears in Collections: | Health Informatics |
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