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Online course

Data Science for Healthcare: Using Real World Evidence

Discover the importance of real world evidence (RWE) and learn how it can be used in healthcare.

Data Science for Healthcare: Using Real World Evidence

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.

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

When would you like to start?

  • Available now

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.

Who is the course for?

This course is for anyone with an interest in the relationship between ICT and healthcare, especially those interesting in data analysis. You might be an undergraduate student in data science, an analyst or commercial manager working in life sciences pharmaceuticals, healthcare regulation, biotech and medical devices. This course focuses on UK health data, however the course is relevant to learners in other countries.

What software or tools do you need?

To take part in this course, you will need a computer with high-speed access to the internet.

Who will you learn with?

Edward Meinert

I am a chartered engineer and my research interests are centred on software engineering, digital health and applying public health to global challenges.

Hassan Chaudhury

I am a founding director at Health iQ - a UK-based, award-winning data science firm.
My background spans public health and NHS informatics and I'm an Honorary Researcher at Imperial College London.

Yusuf Ermak

I am a healthcare data strategy specialist and I've supported a number of NHS organisations in the development of their data architecture, capability and tools.

Who developed the course?

EIT InnoEnergy is the European company promoting innovation, entrepreneurship and education in sustainable energy.

EIT Health promotes healthy living, active ageing and improvements in healthcare.

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Join this course

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  • Access to this course for 7 weeks
  • Includes any articles, videos, peer reviews and quizzes

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  • Includes any articles, videos, peer reviews and quizzes
  • Certificate of Achievement to prove your success when you're eligible
  • Download and print your Certificate of Achievement anytime
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