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.

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Skip to 0 minutes and 20 secondsIn the past decisions about patient care were based upon the results of randomized controlled trials. However these studies are highly controlled and may not necessarily represent the conditions in the real world. Due to the increase in the use of technology, We now have access to vast amounts of health related data, often collected within the context of routine clinical care.

Skip to 0 minutes and 45 secondsIt makes such a difference when you're able to use data that actually comes from real life experience and it reflects what's actually happening, rather than something experimental and I guess a lot of whats happened in recent years where we've tried to know what's going on and healthcare has been based on modelling and synthetic data. It's a breath of fresh air to know that we can use what's actually there. Also we get to look outcomes we can follow patients over time and see what happens to them.Do they get better? Do they get worse? Is it something we need to treat them more? Treat them less? Change the treatment? How much they cost?

Skip to 1 minute and 20 secondsThose are the kind of things we can get from real world.

Skip to 1 minute and 27 secondsI think there's a wonderful opportunity here to learn from a combination of academic rigour and real life practice. Some of the people who are involved in developing this course have got wonderful theory and on top of that we're adding the chance to see what's actually happening for example with industry with major drug companies and medical device companies and how they're using the evidence. So that will all together inform the course and the opportunity isn't just about theory. Learners will get a chance to do the practical examples, projects, activities. Hands on hands on with the data and I think that's going to be a great opportunity. A great course.

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
    This course started 12 November 2018

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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