Postgraduate Course: AI for Care in the Digital Age (HEIN11083)
Course Outline
| School | Deanery of Molecular, Genetic and Population Health Sciences |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course is designed to equip students to think strategically about the benefits and limitations of AI to improve outcomes in providing care in the digital age. It will introduce core concepts in statistics and AI for understanding and interpreting data, and offer examples of AI and data analytics processes in practice, both in Scotland and beyond. |
| Course description |
Academic description:
Leaders of digital transformation in the health, care and housing sectors need to understand and master a diverse set of skills. These are necessary to successfully tackle increasingly complex problems when faced with real-world data, for example understanding what could be derived from data and the benefits this might yield. This course aims to equip students with the core knowledge and understanding to appreciate at a strategic level how to think about tackling problems that can be solved using AI and data analytics. The course will focus on indicative practical examples, from Scotland and beyond, to highlight some cases where data analytics processes work well and where they are not, including examples where the strategic interpretation of data is flawed.
Student Learning Experience:
Students will learn from experts who work in leading and managing data analytics, and AI tools. The course is delivered online and is divided into five sessions, each lasting a week. Teaching sessions will be composed of written materials and video presentations, accompanied by guided reading in the form of links to journal articles with problem-based learning questions. Discussion of the content and reading materials will be posted to an online forum, along with students' answers to the problem-based learning questions. Course tutors will moderate discussion boards. Students will be graded on discussion board postings. Students will further evidence their learning by writing a management plan for a case study from the health and social care sector by the end of the course. Formative peer and teacher-led feedback will be given throughout the course through the discussion boards, and summative assessment feedback will be provided at the end of the course.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2024/25, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Flexible |
| Course Start Date |
17/02/2025 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 5,
Seminar/Tutorial Hours 1,
Online Activities 35,
Feedback/Feedforward Hours 5,
Formative Assessment Hours 5,
Revision Session Hours 1,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
46 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Graded discussion posts
Essay submission |
| Feedback |
Feedback is information provided to the students about their learning relative to learning outcomes. The two main types of feedback are formative and summative. Formative feedback is generated to engage learners to constantly reflect on how they can approach, orient and evaluate learning, which leads to successful learning outcomes. Summative feedback provides an evaluation of how much a student has learned at the end of the course through a final assessment.
Formative feedback will be provided throughout the course, for example, during live question and answer sessions, quizzes, and discussion boards. A formative task will also be offered before the student submitting their summative assessed course work. All assignments will be marked, and feedback is provided within fifteen working days (where possible). |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Critically understand data quality requirements, in the context of care and living well in the digital age
- Demonstrate critical awareness of the insights that AI tools can offer in relation to opportunities in care in the digital age
- Appraise the use, limitations, tensions, and working models of statistical and AI tools at a strategic level
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Reading List
An introduction to statistical learning by G. James, D. Witten, T. Hastie, R. Tibshirani. It is freely available as a pdf from the authors website. I would encourage students to download it, consult it and use it as a reference. The book has some lab sessions in R, which nicely complements the presented material.
A. Tsanas, M.A. Little, P.E. McSharry: A methodology for the analysis of medical data, in Handbook of Systems and Complexity in Health, Eds. J.P. Sturmberg, and C.M. Martin, Springer, pp. 113-125 (chapter 7), 2013 |
Additional Information
| Graduate Attributes and Skills |
Personal and professional skills:
A willingness to engage with the material and learn is by far the most important element, some prior knowledge of statistics would be useful. Programming is not a requirement and will only be provided as further optional material. |
| Keywords | clinical decision support,data analysis,machine learning,statistical hypothesis testing |
Contacts
| Course organiser | Dr Thanasis Tsanas
Tel: (0131 6) 51 78 87
Email: Athanasios.Tsanas@ed.ac.uk |
Course secretary | Mr Matthew Newlands
Tel:
Email: Matt.Newlands@ed.ac.uk |
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