MSc/PGDip/PGCert Health Informatics (UCL/UoM Joint Award) / Course details
Year of entry: 2022
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Course unit details:
Principles of Health Data Analytics (UCL)
|Unit level||FHEQ level 7 – master's degree or fourth year of an integrated master's degree|
|Teaching period(s)||Semester 2|
|Offered by||Division of Informatics, Imaging and Data Sciences|
|Available as a free choice unit?||No|
This module is taught at UCL.
This module covers the key ideas and concepts of operational research as applied to healthcare. Students focus on a variety of mathematical techniques used to improve the efficiency, productivity and quality of healthcare processes and systems. Students will be introduced to mathematical techniques for analysing and evaluating the performance of organisations, including predicting demand, planning capacity and monitoring patient flow.
Students undertake a number of different activities including mapping processes and modelling flow through an A and E department and suggesting process improvements to increase efficiency.
The module introduces students to the range of mathematical techniques used by operational researchers to assess healthcare organisations and enable improvements. This include approaches to the analysis of variability designed to identify outliers or opportunities for process improvements. Students learn how measures of demand and capacity can be used to optimize service, eg by minimizing waiting times. Students study how analyses of the flows in a system can be used to identify inefficiencies.
· Subject-Specific Knowledge
- Discuss systems and technologies in relation to current and future thinking around health systems.
- Understand the range of health systems used to collect, produce and store data.
- Understand how data is collected and created to support clinical, research, direct care and commissioning decisions.
- Use measures of variance to identify outliers
- Use data to predict demand, plan capacity and monitor flow
· Intellectual, Academic and Research Skills
- Assess the quality and value of data
- Apply mathematical techniques to data in order to derive measures that inform decision-making
- Identify the barriers to improving organisations
· Practical and Transferrable Skills
- Prepare written reports
- Design an effective visualisation for complex data.
Teaching and learning methods
The module is delivered over nine weeks using Moodle as the Virtual Learning Environment. Students attend at UCL for an intensive block of three days of face to face teaching, usually in week four or five. Students on campus may attend two additional seminars, these are also live-streamed and recorded for other students. Formative and summative assignments are submitted via Moodle.
|Written assignment (inc essay)||100%|
Students complete two formative assessments. One is an outline of a piece of operational research outlining a measure of clinical performance or patient experience or outcome and the data that could be used to derive this measure, with details of how it could be obtained and used to assess improvement. One is the design of a simulation that could be used to test the impact of an intervention on a suitable metric.
|Independent study hours|