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The emergence of powerful software in healthcare has created conditions and approaches for large datasets to be collected and analyzed which has led to informed decision-making towards tackling health issues. Big Data Analytics (BDA) in Healthcare, otherwise Health Analytics, express the analysis methods of the wide amount of electronic data related to patient healthcare and well-being that are very diverse and difficult to be measured by traditional software or hardware. This PhD Thesis includes two parts. The first part presents a systematic review using PRISMA of the research activity in Big Data Analytics (BDA) in the field of health and demonstrates the existing knowledge. The objective of this profiling study is to discuss this scientific field through related examples and to inform researchers about the nature and magnitude of the technological innovations in health information analysis tools, its influence, and where and how further material could be searched. With reference to the resource-based view theory this Doctoral Thesis has focused on how big data resources are utilised to create organization and social values, discussing the classification of big data types related to healthcare, the associate analysis techniques, the platforms and tools for handling big health data and the future aspects in the field. In recent years a large number of mobile health applications (mHealth) have been developed for medical practitioners and students that use apps and other digital technologies as part of their practice training and education. These trends have created a new social context in clinical diagnosis process based on technology innovation in the field. Inspired by this, the Second Part of the Doctoral Thesis aims to review available mHealth apps addressed to medical professionals and students designed to assist in the diagnosis process and explore the multiple dimensions of the research subject. Based on three conceptual frameworks, different approaches have been taken intending to investigate the social dimension of the intention of integration of mHealth innovation in the diagnosis process, explore the ethical challenges related to their data governance and reliability and explain how the specific consumers’ behaviour is affected by certain app characteristics and attributes. A special emphasis is placed on mHealth apps that use artificial intelligence and a future agenda is provided for the development of new apps for medical professionals with the use of responsible innovative methods. To investigate the relationships between app quality, downloads, features and users’ ratings multiple linear regression statistical analysis was used. The Thesis contributes to the information 5 systems and operations management research, while empowers mobile health literature providing a better understanding of the matter. This study also provides a multi-layered analysis and aims to assist health professionals and health policy makers with a better understanding of how the development of an innovative data driven strategy can improve public health and the functioning of healthcare organizations but also how such a strategy creates challenges that need to be addressed in the near future to avoid societal malfunctions.