Postgraduate Course: Data Standards and Core Technologies in Health and Social Care (on campus) (HEIN11079)
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) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Records from health, social and care services are increasingly becoming digitised. As some service users move through the health, social and care services system, their service records must be accessible, linked, shared and understandable across disparate systems. This course aims to provide students with a theoretical and practical understanding of the existing data standards and technologies used in data standarisation activities to design, evaluate and promote integration across health, social and care service information systems. |
| Course description |
1) Academic description
The requirement for interoperable systems is evident in every component of health, social and care services delivery. Interoperability is the capacity of different information systems and software applications to communicate, exchange data accurately, effectively and consistently, and use the exchanged information. However, to date, most health, social and care services data lack interoperability, as it is hidden in isolated databases or incompatible systems and is difficult to exchange, analyse and interpret. Lack of interoperability results in service users receiving suboptimal services, impairs the management of services within and across health, social and care service systems, and limits data access for research and service development.
An important technology that has increased interoperability is the emergence of health and social services application programming interfaces (APIs). An API is a set of data standards that govern how software applications communicate so that data in one component of the health and social services system is accessible and meaningful across a wide range of health and social services information systems. Data standards are the rules that govern the way service user and administrative data is electronically stored within and exchanged across information systems. Data scientists need to undertake data standards, as they will have to make key decisions about how and when data standards should be implemented; so that health, social and care service professionals can seamlessly digest service user and administration records.
2) Course outline
The course will provide the foundations of data standards and technologies used to manage and exchange service user and administrative data within and across health and social services institutions. It will give an overview of interoperability, data standards, health, social and care services information and information exchange. The course will cover standards development organisations, health, social and care services, interoperability resources, terminology standards and application programming interfaces.
Experts in the field will explore case examples from various health, social and care service settings to demonstrate how organisations are making progress toward interoperability.
3) Student Learning Experience
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 and students' answers to the problem-based learning questions. Students will be graded on discussion board postings. Students will further evidence their learning by completing a group project and writing a reflective essay by the end of the course.
Learning will be reinforced by in-person seminars.
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.
Learning will be reinforced by in-person seminars. During these sessions, the students will have an opportunity to discuss specific case studies of how data standards affect the practice of data scientists, and will also receive formative feedback on their assessment ideas.
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Course Delivery Information
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| Academic year 2024/25, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Flexible |
| Course Start Date |
06/01/2025 |
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 %
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| Additional Information (Assessment) |
Written Exam 0%, Coursework 100%, Practical Exam 0%
Formative feedback will be provided throughout the course through discussion board postings.
Assessment summary:
LO1 and LO3: Reflective Blog (20%)
LO1 and LO3: Self-reflective critical report (65%)
LO2: Problem-based learning example (15%) |
| 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 submits their 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:
- Demonstrate a critical understanding of data standards and technologies in the health, social and care services sector.
- Apply logical, analytical and problem-solving skills to recognise the need for interoperability and decide how and when data standards and technologies should be implemented within and across health, social and care service systems.
- Effectively communicate about interpretability, data standards and technologies with peers, more senior colleagues and specialists within the local and broader health, social and care service sectors.
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Reading List
Books:
P.Aspden, J.M. Corrigan and J. Wolcott (2004) 'Health care data standards' in Patient safety: achieving a new standard for care, pp. 127-168.
T.Benson and G.Grieve (2016) Principles of health interoperability: SNOMED CT, HL7 and FHIR.
(Electronic copies of Aspden, Corrigan and Wolcott (2004) and Benson and Grieve (2016) are available to download from the University of Edinburgh Library.)
Policy papers:
Department of Health and Social Care (2018) The future of healthcare: our vision for digital, data and technology in health and care.
Local Government Association (2019) Local government social care data standards and interoperability.
Articles:
C.Sunil Kumar, C.V.Guru Rao, and A.Govardhan (2010) A framework for interoperable healthcare information systems. Computer Information Systems and Industrial Management Applications. (CISIM): 604-608.
M.Lehne, J.Sass, A.Essenwanger, J.Schepers and Sylvia Thun (2019) Why digital medicine depends on interoperability. NPJ Digital Medicine. 2: 79.
(Specific journal articles will also be selected nearer the time of course delivery.)
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Additional Information
| Graduate Attributes and Skills |
1) Mindsets:
Enquiry and lifelong learning
Students on this course will be encouraged to seek out ways to develop their expertise in data standards and technologies. They will also be encouraged to strive for excellence in their professional practice and to use established and developed approaches to resolve ethical challenges and data ownership issues as they arise in health and social care systems.
Aspiration and personal development
Students will be encouraged to draw on the quality, depth and breadth of their experiences to expand their potential and identify areas in which they wish to develop and grow. Students will also be encouraged to understand their responsibility within and contribute positively, ethically and respectfully to the health and social care community while acknowledging that different students and community members will have other priorities and goals.
Outlook and engagement
Students will be expected to take responsibility for their own learning. Students will be asked to use their initiative and experience, often explicitly relating to their professional, educational, geographical or cultural context, to engage with and enhance the learning of students from the diverse communities on the programme. Students will also be asked to reflect on their peers' experience and identify opportunities to enhance their learning.
2) Skills:
Research and enquiry
Students will use self-reflection to seek out learning opportunities. Students will also use the newly acquired knowledge and critical assessment to identify and creatively tackle problems and assimilate the findings of primary research and peer knowledge in their arguments, discussions and assessments.
Personal and intellectual autonomy
Students will be encouraged to use their personal and intellectual autonomy to critically evaluate learning materials and exercises. Students will also be supported through self-directed learning, discussion boards and collaborative activities to critically evaluate concepts, evidence and experiences of peers and superiors from an open-minded and reasoned perspective.
Personal effectiveness
Students will need to be effective and proactive learners that can articulate what they have learned, and have an awareness of their strengths and limitations, and a commitment to learning and reflection to complete this course successfully.
Communication
Effective data scientists' practitioners in the health and social care sector require excellent oral and written communication, presentation and interpersonal skills. The structure of the interactive (problem-based learning examples, discussion boards and collaborative activities) and assessment elements incorporate constant reinforcement and development of these skills.
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| Keywords | Interoperability,data standards,information exchange,technologies,application programming interface |
Contacts
| Course organiser | Miss Michelle Evans
Tel: (0131 6)51 5440.
Email: |
Course secretary | Ms Rebecca Sewell
Tel: (0131 6)51 7112
Email: |
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