PU5532: HEALTH INFORMATICS (2026-2027)

The aim of this course is to enable students to develop a critical and applied understanding of Health Informatics as a discipline that underpins contemporary health research, service planning, and policy evaluation. Students will explore how routinely collected digital health data can be harnessed to improve health outcomes, reduce inequalities, and inform evidence-based decision-making across healthcare systems. Emphasis is placed on cultivating both conceptual and practical competencies — from understanding complex data infrastructures to designing ethically robust, methodologically sound studies that make responsible use of linked health data.

The course provides a detailed introduction to the principles and practices that define health informatics research. Students will examine key data sources within the UK and internationally, with a particular focus on administrative and clinical datasets used in population health research, such as cancer registries, hospital episode statistics, and prescribing databases. Through guided exploration of real-world examples, students will develop the ability to evaluate the strengths and limitations of these data sources, including issues of completeness, representativeness, and data quality.

A core theme running throughout the course is data linkage — the process of combining records from different datasets to provide a more comprehensive view of patient pathways, disease trajectories, and treatment outcomes. Students will gain an applied understanding of linkage principles.

Another central focus of the course is research governance and ethics. Students will critically evaluate the frameworks that regulate the use of health data in research, including UK GDPR, the Common Law Duty of Confidentiality, and the operation of Trusted Research Environments (Safe Havens).

Methodologically, the course introduces students to the full data lifecycle, from study conceptualisation to data specification. Students will learn how to translate a public health or clinical problem into a measurable research question, develop a PICO framework, and apply causal reasoning tools such as Directed Acyclic Graphs (DAGs) to visualise relationships between exposures, outcomes, and confounders. They will then apply these concepts to the design of a realistic data linkage study, including the preparation of a data specification sheet detailing the variables, coding frameworks, and linkage fields required for analysis.

Teaching will be supported by a combination of lectures, interactive workshops, and applied assessment tasks that mirror real-world data research processes. For example, students will practise constructing research questions that can be answered using linked administrative data, designing causal frameworks to guide analytic strategies, and preparing mock applications for access to health data through a Safe Haven. These exercises will allow students to integrate methodological, ethical, and governance considerations into a coherent research design — preparing them for further study or professional roles in health informatics, epidemiology, and population data science.

There are no assessments for this course.