Using Machine Learning to address Data Accuracy and Information Integrity in Digital Health Delivery

Authors

Zaid Zekiria Sako, Deakin University, AustraliaFollow
Vass Karpathiou, RMIT University, AustraliaFollow
Sasan Adibi, Deakin University, AustraliaFollow
Nilmini Wickramasinghe, Deakin University and Epworth HealthCare, AustraliaFollow

Abstract

Today, much of healthcare delivery is digital. In particular, there exists a plethora of mHealth solutions being developed. This in turn necessitates the need for accurate data and information integrity if superior mHealth is to ensue. Lack of data accuracy and information integrity can cause serious harm to patients and limit the benefits of mHealth technology. The described exploratory case study serves to investigate data accuracy and information integrity in mHealth, with the aim of incorporating Machine Learning to detect sources of inaccurate data and deliver quality information.

Recommended Citation

Sako, Zaid Zekiria; Karpathiou, Vass; Adibi, Sasan; and Wickramasinghe, Nilmini, "Using Machine Learning to address Data Accuracy and Information Integrity in Digital Health Delivery" (2016). BLED 2016 Proceedings. 26.
https://aisel.aisnet.org/bled2016/26