Module overview
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- User-focused development, meaningful evaluation and successful implementation of digital health tools;
- Key qualitative and quantitative research methods used in the study of digital health care;
- The drivers, enablers, barriers and challenges to digital health innovation
- How those theories and techniques impact the quality of the data and how it should be used
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Critique and compare data science pipelines;
- Identify and utilise key types of health data.
- Explain and justify how data is collected, stored and responsibly shared in and between healthcare settings;
- Explain basic processes relating to data analytics and modeling;
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Present and interpret data to/for a non-technical audience.
Syllabus
The course will introduce students to the standards, theories and technologies in digital health. This module will cover how international healthcare Interoperability data standards can aid textual, image, and sensor analytics pipelines from data preparation to data processing, analysis and visualisation.
The course will cover the following:
Introduction to digital health
* Understanding the healthcare ecosystem, including ethics data protection & privacy
* Digital health drivers: data standards
* Digital health enablers, barriers and challenges
Overview of data standards in use for health data
* How do data standards work?
* How data standards are used in healthcare?
Data management
* Data types and formats
* Documentation and metadata
* Data storage and sharing
* Data principles & security
* Data management architectures
Data collection& Data sharing
* An introduction to mixed methods in health sciences
* Passive data collection: sensors for remote health monitoring
* Clinical unstructured data versus Clinical structured data
Data analytics essentials
* Overview of data-driven pipelines
* Practical Statistics
* Data visualisation
Application scenarios and state-of-the-art
* Using data for improvement: how to test a change that is implemented?
Learning and Teaching
Teaching and learning methods
Lectures and Tutorials
Type | Hours |
---|---|
Lecture | 24 |
Independent Study | 114 |
Tutorial | 12 |
Total study time | 150 |
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
Method | Percentage contribution |
---|---|
Exam | 60% |
Coursework | 40% |