Health and Biomedical Informatics MSc - 2024/5
Awarding body
University of Surrey
Teaching institute
University of Surrey
Framework
FHEQ Levels 6 and 7
Final award and programme/pathway title
MSc Health and Biomedical Informatics
Subsidiary award(s)
| Award | Title |
|---|---|
| PGDip | Health and Biomedical Informatics |
| PGCert | Health and Biomedical Informatics |
Modes of study
| Route code | Credits and ECTS Credits | |
| Full-time | PTA61118 | 180 credits and 90 ECTS credits |
| Part-time | PTA61119 | 180 credits and 90 ECTS credits |
JACs code
100994, 100869
QAA Subject benchmark statement (if applicable)
Other internal and / or external reference points
N/A
Faculty and Department / School
Faculty of Health and Medical Sciences - School of Health Sciences
Programme Leader
GEIFMAN Nophar (Health Sci.)
Date of production/revision of spec
06/06/2023
Educational aims of the programme
- The MSc programme has been designed to be suitable for a range of professionals including bioscientists, clinicians, computer scientists, informaticians and clinical scientists. Health and biomedical informatics is an important new growth area and the learning opportunities will prepare students for a range of different job opportunities in the pharma industry, tech industry, healthcare providers, start-up companies, digital health providers, NHS and social care providers. There is a high-level ambition within this programme to produce graduates who understand the challenges faced by the generation of large datasets in biology, healthcare and medicine, and how to manipulate these for the creation and further development of outcomes to support improved understanding of disease, health and wellness. The requirements for a successful education in this arena lie in digital capabilities inclusive of machine learning and artificial intelligence, added to statistical analytical awareness and complemented by abilities to effectively communicate data sciences outputs to a range of audiences. The course aims to provide students with a wide range of experiences, through incorporation of a variety of teaching and learning methods, such as lectures, workshops, hands-on practicals, small-group work, problem-based learning, presentation opportunities, and seminars; as well as a range of different assessment methods that incorporate coding abilities, critical thinking, scientific writing, presentation skills, and creativity. Importantly, all of these will be grounded in exposure to real-world health and biomedical data, as well as real-world issues, research questions, and case studies. Thus, the course gives a generic skills-base in data sciences, artificial intelligence, and statistics coupled to understanding biomedical and healthcare issues. The MSc in Health and Biomedical Informatics is committed to producing graduates with, confidence, self-assurance, and competencies that are highly attractive to future employers as well as further education. The specific aims of the programme are: To provide students with the passion, knowledge, and confidence to apply data sciences and informatics to real world problems To provide learners with the hard and soft skills to prepare them for a career in data sciences To train students in core informatic and AI skills to apply to healthcare, biomedical and medical challenges. To provide multi-disciplinary learning by combining expertise in informatics, AI, statistics and the fields of epidemiological and biomedical, health and veterinary research Provide hands-on experience that trains students using real-world data from large multidimensional studies such as UK Biobank Foster a cohort culture that encourages peer-to-peer learning, with team projects to develop problem-solving skills in a collaborative and multi-disciplinary setting Develop an ethical and responsible approach to technology development and encourage students to consider how they can enhance societal wellbeing
Programme learning outcomes
| Attributes Developed | Awards | Ref. | |
| Understand and appreciate the potential, advantages, challenges and issues related to data sciences and informatics in the domains of biomedicine and health | KC | PGDip | |
| To be equipped in analysing specific health or medical informatic and bioinformatic problems in terms of their data, information, and knowledge components | KCPT | PGDip, MSc | |
| Apply, analyse, and create data structures, algorithms, programming in health and biomedical informatics | KCPT | PGDip, MSc | |
| Be able to communicate key ideas and concepts in data sciences, data analytics, and health informatics to a range of audiences (such as non-specialist, basic sciences, and clinical) | KCPT | PGDip, MSc | |
| Innovate: Infographics, methods, typologies and processes to address biomedical and health informatics problems | KCPT | MSc | |
| Create hypothesis driven research questions contextualised into big data; understand the power and limitations of hypothesis free research | CT | MSc | |
| Work collaboratively with partners within and across disciplines | PT | MSc | |
| Ability to manipulate and merge complementary datasets | KPT | PGDip, MSc | |
| Critically appraise novel and emerging methodologies and applications of health and biomedical informatics | KCT | PGDip, MSc | |
| Acquired breadth of knowledge of the principles of health and biomedical informatics | KCPT | PGDip, MSc |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Programme structure
Full-time
This Master's Degree programme is studied full-time over one academic year, consisting of 180 credits at FHEQ level 7*. All modules are semester based and worth 15 credits with the exception of project, practice based and dissertation modules.
Possible exit awards include:
- Postgraduate Diploma (120 credits)
- Postgraduate Certificate (60 credits)
*some programmes may contain up to 30 credits at FHEQ level 6.
Part-time
This Master's Degree programme is studied part-time over two academic years, consisting of 180 credits at FHEQ level 7*. All modules are semester based and worth 15 credits with the exception of project, practice based and dissertation modules.
Possible exit awards include:
- Postgraduate Diploma (120 credits)
- Postgraduate Certificate (60 credits)
*some programmes may contain up to 30 credits at FHEQ level 6.
Programme Adjustments (if applicable)
N/A
Modules
Year 1 (full-time) - FHEQ Levels 6 and 7
Module Selection for Year 1 (full-time) - FHEQ Levels 6 and 7
Students will develop their knowledge, understanding and hands-on analytical skills in this new field of health and biomedical informatics, as well as soft skills such as scientific presentation, dissemination, individual and group work, and multidisciplinary, and thereby enhance their career, via a series of interdigitating modules shown below. We will provide background knowledge on biology and medicine as required to contextualise the informatics into real world situations. At the heart of the programme is the use of the world leading resource at UK Biobank. Anonymised healthcare records, genome data, questionnaire responses (e.g. food preferences), biochemical data, proteomic data and metabolomic data are all available for teaching on statistics, machine learning, stratified medicine, data visualisation, omics data generation and usage, digital health, epidemiology, health and social care. These will provide students with a comprehensive introduction to the type of data that are typically collected across health and biomedical research and clinical practice; thereby equipping them with transferable skills in real-world settings. The precepts of health informatics and digital health, statistics and modelling, and Big Data, will be taught first; these will provide a strong foundation for students upon to build their learning journey through the second semester and their dissertation work. Following from the first semester, the Machine Learning and AI, as well as Reporting and Data Visualisation, Tutorials in Health Data Sciences, and Stratified Medicine and Biomedical Data Analysis modules will enhance the students¿ learning and skills development, by introducing more advanced topics in health and biomedical informatics. Throughout the process UK Biobank data will be used as a teaching aide.
Module title Credits
Introduction to Health Informatics and Digital Health 15
Machine Learning and AI 30
Tutorials in Health Data Science 15
Stratified Medicine and Biomedical Data Analysis 15
Statistics and Modelling for Health Data 30
Reporting and Data Visualisation 15
Big Data in Biomedicine and Health 15
Dissertation 60
Year 1 (part-time) - FHEQ Levels 6 and 7
| Module code | Module title | Status | Credits | Semester |
|---|---|---|---|---|
| BMSM033 | STATISTICS AND MODELLING FOR HEALTH DATA | Compulsory | 30 | 1 |
| BMSM035 | MACHINE LEARNING AND AI | Compulsory | 30 | 2 |
| BMSM036 | TUTORIALS IN HEALTH DATA SCIENCE | Optional | 15 | 2 |
| BMSM037 | STRATIFIED MEDICINE AND BIOMEDICAL DATA ANALYSIS | Optional | 15 | 2 |
| BMSM038 | REPORTING AND DATA VISUALISATION | Optional | 15 | 2 |
Module Selection for Year 1 (part-time) - FHEQ Levels 6 and 7
Students will develop their knowledge, understanding and hands-on analytical skills in this new field of health and biomedical informatics, as well as soft skills such as scientific presentation, dissemination, individual and group work, and multidisciplinary, and thereby enhance their career, via a series of interdigitating modules shown below. We will provide background knowledge on biology and medicine as required to contextualise the informatics into real world situations. At the heart of the programme is the use of the world leading resource at UK Biobank. Anonymised healthcare records, genome data, questionnaire responses (e.g. food preferences), biochemical data, proteomic data and metabolomic data are all available for teaching on statistics, machine learning, stratified medicine, data visualisation, omics data generation and usage, digital health, epidemiology, health and social care. These will provide students with a comprehensive introduction to the type of data that are typically collected across health and biomedical research and clinical practice; thereby equipping them with transferable skills in real-world settings. The precepts of health informatics and digital health, statistics and modelling, and Big Data, will be taught first; these will provide a strong foundation for students upon to build their learning journey through the second semester and their dissertation work. Following from the first semester, the Machine Learning and AI, as well as Reporting and Data Visualisation, Tutorials in Health Data Sciences, and Stratified Medicine and Biomedical Data Analysis modules will enhance the students¿ learning and skills development, by introducing more advanced topics in health and biomedical informatics. Throughout the process UK Biobank data will be used as a teaching aide.
Module title Credits
Introduction to Health Informatics and Digital Health 15
Machine Learning and AI 30
Tutorials in Health Data Science 15
Stratified Medicine and Biomedical Data Analysis 15
Statistics and Modelling for Health Data 30
Reporting and Data Visualisation 15
Big Data in Biomedicine and Health 15
Dissertation 60
Year 2 (part-time) - FHEQ Levels 6 and 7
Opportunities for placements / work related learning / collaborative activity
| Associate Tutor(s) / Guest Speakers / Visiting Academics | N | |
| Professional Training Year (PTY) | N | |
| Placement(s) (study or work that are not part of PTY) | N | |
| Clinical Placement(s) (that are not part of the PTY scheme) | N | |
| Study exchange (Level 5) | N | |
| Dual degree | N |
Other information
Throughout the programme we have considered how the course addresses the University¿s Curriculum Framework themes.
Global and cultural capabilities
Digital capabilities
Employability
Resourcefulness and resilience
Sustainability
As is outlined in the self-evaluation narrative and the specific module descriptors this list has been a touchstone for course development.
Importantly, these themes are themselves embedded in good educational practice emanating from the recent pedagogical literature. All academics associated with the course have the necessary experience and training to adhere to the process and principle of appropriate education in this discipline and in this decade (1). In respect of the pillars, Digital Capabilities is, of course, systemic within our course. Sustainability associated with living in a common world emanates in this course through the strand of using data wisely so to decrease the energy and activity associated with repeated and unneeded repeat studies. Our incorporation of elements of home/remote learning reduces the carbon footprint. Global and cultural capabilities is widely addressed through the use of data from around the globe which feeds into the inclusivity agenda for this course; as well as our coverage of ethical, bias, and diversity issues that relate to health data, and application of methods in statistics, machine learning and AI. There is a global unmet need for skilled informaticians and data scientists, and this is even more so in the fields of healthcare, biology and medicine; our graduates will have high rates of employment.
Based as we are in a School of Health Sciences we are aware of the TIGER Initiative that brings together a diverse group of stakeholders to develop a shared vision, strategies, and specific actions for improving nursing practice, education, and patient-care delivery through the use of health information technology (2). This has also informed our developments.
Reference
1. Sobe, Noah W. "Reworking Four Pillars of Education to Sustain the Commons". UNESCO Futures of Education Ideas LAB. 10 February 2021, https://en.unesco.org/futuresofeducation/ideas-lab/sobe-reworking-four-pillars-education-sustain-commons
2. http://www.tigersummit.com/uploads/TIGERInitiative_Report2007_Color.pdf
Quality assurance
The Regulations and Codes of Practice for taught programmes can be found at:
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2024/5 academic year.