An investigation into usability of big data analytics in the management of Type 2 Diabetes Mellitus

Abstract

<p>The global prevalence of Type 2 Diabetes Mellitus (T2DM) has been on the rise over the last four decades and is expected to rise further in the future. Big Data applications such as Artificial Intelligence (AI) and Machine learning (ML) are increasingly being used in the healthcare industry to manage various aspects of patient care. Researchers have so far studied the adoption of technologies including AI and ML in various contexts using technology adoption frameworks in the information systems (IS) domain, where the usability of technology is just viewed as one factor. Although, researches on technology adoption models in the IS domain has indicated that usability has a significant influence on the adoption of a technology, it appears that there are limited attempts made to study the factors influencing the usability of big data applications such as AI and ML for the management of T2DM. Since usability not only a factor that impacts the adoption of a technology, but also determines the outcomes of the management process, there is a need to understand the factors that influence the usability of a big data analytics application for the management of T2DM, this research aims to identify and analyse the factors influencing the usability of big data applications such as AI and ML in management of T2DM. The research is designed as mixed method research with qualitative research undertaken first to confirm the conceptualised research model followed by quantitative research to genaralise the model. This research would contribute to the academic literature in the areas of Information Systems Quality, Human-Computer Interaction (HCI), design and development big data applications, usability engineering, user experience (UX), and usability measurement model. The contributions from this research would also benefit the healthcare industry, predominantly that part of an industry that is directly involved in the management of T2DM and indirectly involved in the management of comorbidities on T2DM. The learnings from this research can also be extended to the management of many other chronic conditions and many other contexts.</p>