Semester 1

Introduction to Digital Health Transformation: to realise the potential of digital health ecosystem solutions requires innovators to possess skills to examine frameworks to design, develop, implement and evaluate digital technologies and innovations, and appreciate that a digital health intervention should adapt and evolve based on understanding user needs and data informed outcomes. This module aims to address these requirements.

eHealth Systems, Standards, and Digital Services: introduces leaders to the underlying principles and concepts of healthcare information systems, electronic health records, personal health records, and other digital services so that they are in a better position to understand and evaluate the opportunities and risks of different approaches to small and large scale digital solutions in health and social care.

Health Information Modelling and Governance: introduces students to the principles of information modelling and information processing with significant emphasis placed on the governance of information from the dimensions of people, processes, policies, standards and technologies.

Digital Health Research Design: provides students with the knowledge and skills to identify, understand, find and appraise scientifically valid evidence; an appreciation of the scientific discipline and rigour involved in creating and applying evidence; and the value of scientific research.

Health Service Digital Transformation: considers how integrated technology can help healthcare providers to work differently. This module explores the cultural, structural, and technology dynamics of transformation within organisations and provides participants with a critical understanding of the major benefits and challenges associated with the management and execution of digital transformation programmes.

Semester 2

Digital Health and Wellbeing in the Community: examines the potential of sustainable intelligent health and wellbeing systems and the associated enabling technologies; as well as considerations for the design, architecture, governance, interoperability, management, adaptability, personalization, evaluation and maintenance of such systems.

Digital Health Change Management: presents the processes and concepts of project management from the perspective of healthcare information system (HIS) projects. It examines the processes by which the development of HISs are managed, and the considerations needed for the successful implementation of such systems.

Clinical Decision Making and Knowledge Discovery: introduces students to the functions, features, roles and limitations of clinical decision support systems. Students will apply quantitative and qualitative approaches to decision making by applying decision theory and learn to analyse data from various data sources using data mining techniques

Data Science and Artificial Intelligence in Healthcare: examines the application of big data analytics techniques and artificial intelligence (AI)/machine learning techniques in healthcare. Students will learn of the ethical, social and political challenges of using AI in healthcare; the difficulties of integrating intelligent applications to workflows; and the requirement to demonstrate clinical value.

Medical Devices: innovations driven by the medical technologies sector that contribute to the value-based paradigm are leading to the creation of the internet of medical things. This module details the status of the medical device industry, responsibilities of manufacturers and software developers and how emerging trends in this sector will impact the patient experience, pharma, the medical device industry and the medical devices ecosystem.

Medical Diagnostics: examines the range of diagnostic solutions available to clinicians and how digital aspects of such technologies have led to improvements in the care process. The module will also look at the evolving nature of diagnostics in areas such as imaging, laboratory testing, patient wearable physiological measurement devices and how technologies such as artificial intelligence and machine learning are assisting in the diagnostic processes and staffing constraints.

Digital Health and Clinical Trials: explores the adoption of transformative technologies in end-to-end data management in clinical trial activities in order to effectively enhance engagement with the myriad of stakeholders, rationalise processes, drive efficiencies, and reduce cycle times and costs.