(Award available for year: Master of Science)
LO1 – Find, critically evaluate and analyse current research evidence from published sources, for example journal articles, which drive decisions in the health domain, demonstrating a sophisticated understanding of evidence synthesis
LO2 – Demonstrate a sophisticated understanding of the provenance and quality of health data for identifying care quality improvement opportunities, for example for process and pathway innovation
LO3 – Safely and legally interrogate health data within a framework of health data governance and apply appropriate statistical and machine learning approaches using software tools such as Python and R to support quality improvement in health care
LO4 – Describe the key legal, ethical and governance frameworks applicable to health data science in the United Kingdom, and evaluate their implication for the safe storage and processing of data
LO5 – Critically evaluate and apply appropriate frameworks to health care systems and demonstrate a sophisticated understanding of the societal context in which health professionals and patients engage with digital technologies
LO6 – Critically evaluate and interpret data analysis methods and results within the context of a formulated health research question, and share with varied audiences using appropriate dissemination techniques
LO7 – Proactively formulate a health research question, develop and implement a methodologically appropriate research study
Skills Learning Outcomes
SLO1 – Use appropriate online resources and digital tools to support work, find detailed relevant information about health datasets, and to plan and deliver robust approaches to literature searching (information and digital literacy)
SLO2 – Critically appraise sources of information, health data, approaches to analysing data, and ethical and legal frameworks to address their suitability for addressing a specific research question, considering the advantages and disadvantages of each approach (critical thinking)
SLO3 – Plan a solution to address a research question using data and consider how it may fit into a clinical workflow, appreciating any potential benefits and limitations of these changes (integrated problem solving)
SLO4 – Critically assess how lack of diversity can be an ethical problem, particularly for AI methods in data science, and apply ethical frameworks to data science scenarios including assessment of the ethical consequences of proposed projects (ethics and diversity awareness)
SLO5 – Effectively communicate ideas and key findings from health data sets to others through written and graphical representation, including demonstrating imaginative thinking and creative ideas through the use of digital technology to create accessible digital tables and charts that help to answer research questions (digital creation and communication)
SLO6 – Demonstrate understanding of the impact of various working practices in the healthcare system that change the way that data is captured and recorded for research, and explain clearly how parts of a complex system interact in relation to health care delivery (systems thinking)
SLO7 – Develop and refine a research question; plan and implement a small research study using a systematic approach to search for, evaluate, and use appropriate, relevant sources of information (research skills)
Summative:
The modules are assessed summatively through written work, which measures competence around finding, identifying, analysing and/or interpreting aspects of key topics within health informatics and data science, then communicating these to a wide ranging audience. These assessments are authentic to real-world working environments. They also meet a range of skills learning outcomes, which are essential for a student to pursue a career in this field.
The written work differs across modules in approach and content. Students develop their knowledge and skills through authentic assessment approaches, such as critical appraisal, evaluation of real-world scenarios, reviewing data sources for research, statistical analysis, data coding and visualisations of data, machine learning and a review of digital tools used in clinical practice. The Research Project ensures students use their developed expertise to produce a comprehensive and detailed written report. The diversity in assessment approach ensures students are assessed in an inclusive manner.
Each module has one summative assessment (retaining presentations as formative assessment. Formative and summative assessment deadlines have been set taking into account delivery and assessment of all other modules (including optional modules).
Progression of assessment and skills:
The initial modules focus on the key foundational aspects of health informatics and data science, which includes developing a critical understanding of health data and methods, learning and applying data science techniques, and engaging with legal, ethical and governance frameworks. Assessments allow students to demonstrate their understanding and competence in these topic areas. This knowledge and understanding is built on in the PGD modules through in-depth engagement with health care systems and a further development of data science skills through advanced statistical techniques and cutting-edge applications. The optional modules allow specialisation in areas such as data visualisation, public health or health research. The research project module for the MSc requires students to synthesise their knowledge, skills and understanding. Students have autonomy around how they plan and approach their research, and are required to demonstrate the appropriate application of relevant skills.
Formative:
Students will receive support and guidance on the assessments from the module leader. Lectures and seminars are interactive, including group discussion and relevant exercises, and students receive immediate formative feedback during sessions. The majority of modules offer a formative presentation opportunity (group or individual; poster or oral), where students receive verbal feedback from the module lead and from peers. This allows students to fully explore a range of ideas and concepts before selecting their approach to the summative assessment. All modules offer students the opportunity to submit a written piece of work for detailed individual written formative feedback. Formative feedback includes guidance on writing skills, for example academic writing, structure, citations and references. The Research Project module offers two opportunities for formative feedback: an early outline of the proposed work which receives verbal feedback; and a mid-point protocol of the planned work which students have developed with their supervisor(s), which receives detailed individual written feedback, including feedback on the presentation of ideas in a longer format.
Additional scaffolding and activities:
Additional scaffolding and activities are in place on the programme to help students to develop their writing skills. Students are also expected to read a substantial amount of academic literature throughout the programme, which is identified through module reading lists and guidance from module leads. This familiarises students with the style of academic writing used within the subject discipline.
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