Systems science in healthcare
This unit provides you with a new way of looking at how health systems work together. Health care systems are complex consisting of many interacting components, people and perspectives. You will learn about systems theories (for example, resilience, consumer participation, and sociotechnical perspectives) and apply them to examine policy and practice underpinning public health problems. The unit is delivered by experts in health systems, including digital health informatics, safe systems and systems evaluation methods. You will learn through guest lectures, tutorials, online activities and assessments requiring you to analyse real-life case studies and evaluate health interventions.
Learning Outcomes –
On successful completion of this unit, you will be able to:
- Demonstrate an understanding of the current discipline of systems thinking and how it underpins healthcare
- Discuss the rationale for adopting a systems approach to address population health issues
- Examine the role of digital health and health informatics in systems thinking
- Analyse and apply a range of theoretical concepts related to systems thinking including resilience, sociotechnical systems, safe systems, consumer participation and systems evaluation methods to public health issues
- Examine the challenges of adopting a systems approach to public health
clinical bioinformatics and biostatistics
Using real-world problems that span the cutting-edge research fields of genomics, proteomics and metabolomics, we will show you how to apply computer programming and biostatistical skills to understand and investigate the underlying processes of human diseases and develop or improve treatments.
Targeted at students who have a basic understanding of, but more importantly, a keen interest in computing, programming, and statistics, this unit will allow you to explore coding languages to handle large datasets used in the health and medical research setting, interpret and communicate data generated from clinical research, and gain practical knowledge and training to tackle common biostatistical problems faced by medical professionals.
Learning Outcomes –
- Apply fundamental knowledge of coding functions and statistical terminology and their relevance in biostatistics and bioinformatics.
- Appraise statistical approaches underpinning study design for research in genomics, proteomic and metabolomics.
- Organise and manage datasets generated from next generation sequencing technologies.
- Implement common statistical approaches to analyse genomic, proteomic and metabolomics data.
- Interpret statistical and graphical outputs to communicate biological processes that underlie human diseases and treatment response.
health economics
This unit examines the economics behind the growing health sector and other advanced economies. It helps students to understand issues such as the unique characteristics of the health care market, pricing by doctors and hospitals, and the mix of public and private funding and insurance. Topics can include the economics of health care (demand and supply, market imperfections including market failure and resulting resource allocation), the behaviour of agents (hospitals, physicians and private health insurers), health care systems globally, equity and ethics in health resource allocation.
Learning Outcomes –
- Apply theoretical microeconomics to health economics.
- Identify and analyse the role of health economics in understanding health systems and how different solutions affect different stakeholders.
- Analyse the unusual features of markets for health care compared with markets for other goods and services.
- Effectively communicate, individually and as a group, knowledge of health economics in technical and non-technical language.
Health Data Intelligence & Predictive Systems: AI at the Frontline of Modern Healthcare
This course explores how artificial intelligence (AI), data analytics, and predictive intelligence are transforming the healthcare ecosystem. Learners will gain hands-on understanding of data-driven models, clinical decision support, and population-level health forecasting — with real-world applications in hospital management, patient care, genomics, and digital epidemiology.
By bridging clinical knowledge with computational techniques, this course empowers professionals to design intelligent systems that drive evidence-based healthcare, improve diagnostics, and enhance patient outcomes.
Learning Outcomes
- By the end of this course, learners will be able to:
- Understand real-world case studies of AI implementation in global health systems.
- Explain the foundations of health data intelligence and predictive modeling.
- Design AI workflows tailored to healthcare challenges.
- Apply machine learning and deep learning methods to medical data.
- Interpret clinical and genomic datasets for decision-making.
- Build ethical, explainable, and patient-centered AI systems.
- Evaluate predictive models for population health and personalized medicine.