Discover the importance of real world evidence (RWE) and learn how it can be used in healthcare. Understand real world evidence (RWE) and learn how to use it. Real world data (RWD) is the huge quantity of data that falls outside the boundaries of controlled clinical trials, data that is increasingly used to inform decisions in healthcare. Real world evidence (RWE), is the conclusions drawn from this data.
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On this course you will learn how real world evidence can be used in healthcare, exploring current trends and existing methodologies for using it. You will consider ethics, design thinking, commercial applications, and the limitations of RWE.
You will also practice using RWE, applying a user-friendly business intelligence tool to examine RWD.
What topics will you cover?
Week 1 - Principles of Real World Evidence, Health and How they Align
Week 2 - Information Governance and Data Results Deployment
Week 3 - Design Thinking, Methodology and Framework
Week 4 - Analysing Real World Data using Business Intelligence
Week 5 - Developing Real World Evidence from Real World Data
What will you achieve?
By the end of the course, you'll be able to...
- Apply knowledge in fundamentals of Real World Data (RWD) and Real World Evidence (RWE) to include definitions, scope, pros and cons, and potential use.
- Apply knowledge of information governance requirements and policy with regard to patient data as well as knowledge of key datasets that RWE can exploit across primary and secondary care (HES/CPRD)
- Identify RWD and RWE studies and understand the difference what is RWE and what is not.
- Classify the essential theory of using RWE with data science, and key differences between using RWE with and without data science.
- Classify different data investigation tasks and the most appropriate algorithms for selecting/addressing them.
- Apply appropriate data analytic techniques to a problem using an RWE framework (decision tree) further to practical group sessions thereby demonstrating an understanding of knowledge gained.
- Calculate experiments using exploratory analysis of RWD (structured data).
- Evaluate RWD, models or algorithms for accuracy in order to make an informed decision with regard to their use.
- Compare current RWD trends and formulate ideas on how to improve data literacy
- Interpet datasets and identify which meet open data criteria
- Develop a RWD project proposal to be positioned to your organisation lead, identifying a problem and the key aims and objectives to solve the issue.
