Analysing data derived from healthcare forms the cornerstone of evidence-based practice. Understanding the processes in which health data are generated and acknowledging what these data can (and cannot!) tell us is a vital skill for a health informatician. In Principles of Health Data Analytics, you will explore the key principles and concepts of statistics, operational research and machine learning as applied to healthcare. The module will guide you through the variety of mathematical and statistical techniques commonly used in research to improve the efficiency, productivity and quality of healthcare processes and systems. Additionally, the module will introduce you to techniques for analysing and evaluating the performance of organisations, including predicting demand, planning capacity and monitoring patient flow through a healthcare system (e.g. by minimizing waiting times).
Module code
CHME0001
UCL credits
15
Course Length
9 Weeks
Module Start Date
The CHME0001: Principles of Health Data Analytics 2020/21 module will become visible to students in the VLE on 22nd February 2021. This is to allow time before the formal start of the module for you to verify that you are ready and able to begin the module on the start date (e.g. can access the VLE without problems). It is important that you access the VLE within 24 hours of the start of the module so that you can read the online instructions for the activities of the first topic.
SYNCH Days
Wk 5: (Wed-Fri) 24 – 26 March 2021
Assessment Dates
04 May 2021
Module organiser
Prof Martin Utley Please direct queries to courses-IHI@ucl.ac.uk
Content
Theme | Description | Week |
---|---|---|
Introduction to the module | This week will explore the different roles and scope of analysis in health care, the types of decisions involved in the design and delivery of health services and the types of questions that arise in quality assurance and quality improvement. You will gain exposure to what good (and bad) health data analytics looks like. | 1 |
The challenges of analysing systems that have variability | In this week you will understand the difficulty in interpreting outcome data through peer-to-peer discussion and through finding and critically reviewing news stories that report on findings from health data analytics. You will be introduced to the concept of synthetic data and to a simulated data set that will be used later in the course. | 2 |
Data visualisation: the good, the bad and the ugly. | Here, you will:
| 3 |
Introduction to Machine Learning for analysis of health care data | You will learn what we mean by ‘machine learning’ and be introduced to some initial algorithms. Using the simulated data set introduced in week 2, you will begin to analyse these data using machine learning approaches. | 4 |
Applied Health Data Analytics | The face-to-face week will be used to consolidate the teachings from weeks 1-4 and form the foundations for the topics covered in weeks 6-8. This will include, but is not limited to:
| 5 (face to face) |
From decision trees to Algogeddon | You will learn the uses (and abuses) of different machine learning algorithms (i.e. what machine learning can and cannot do). We will cover best practices of how to develop a clinical prediction models (and what should be avoided). Through this, you will learn how to critically review published prediction models to assess their reliability/ usability in practice. | 6 |
Demand, Capacity and Flow | Here, you will begin to:
| 7 |
Barriers and Challenges in Using Models and Simulations | This week will unpick the non-analytical skills essential to effective analysis of health data. For example, how do we translate the results from statistical models, operational research or machine learning to wider audiences (e.g. policy makers). | 8 |
Preparing for assignment | Students will complete their assignments in this week. | 9 |
Teaching and learning methods
This 15 credit module lasts for 9 weeks and comprises roughly 150 hours of learning time, but with a break over the holiday period.. The module comprises in sequence:
- A 4-week introductory phase that includes a short introduction giving the background and learning objectives of the module and a period of individual study, with structured learning activities accessed through the VLE.
- A 2-week period including a period of 3 consecutive days of intensely interactive face-to-face learning that take place at UCL. The face-to-face sessions will provide an opportunity to engage further in the topics introduced in the first phase as well as introducing new material which will be developed further in weeks 5-6.
- A further 3-week period of study, development and consolidation using resources accessed through the VLE. The main focus of this period will be undertaking activities related to the assessment and writing up your final report.
- Final assessment.
For timetabling reasons, the face-to-face week is scheduled differently in different modules. In this module face-to-face teaching takes place in week 5.
Assessment
This module has a single summative assessment component, consisting of an individual written report. The assessment and marking criteria are outlined below.