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The three hurdles facing your RWE efforts and how to get over them

December 02, 2019
By Matthew Michela

There are three big hurdles in the way of effective use of Real World Evidence (RWE). Each of them is more complex, harder to deal with and takes longer to implement than most people think.

Fortunately, solutions for each one of these issues are also closer to being available than most people realize.

The three problems are:

• The vast size, scope and heterogeneity of the data required
• The difficulty in incorporating the results of machine learning (ML) and artificial intelligence (AI) algorithms into real-life clinical workflows
• The seductive trap of adopting proprietary technology stacks

Without an adequate approach to manage each topic, many RWE efforts will fail to achieve their promise of supporting innovation and improving safety in order to provide an understanding of outcomes in real-life clinical practice and risk exploding budgets and timelines.

Real World Imaging (RWI), or medical imaging, will be an increasingly important part of RWE. Radiologists must be aware of the ways their clinical practice can create RWI to mature evidence arguments, and how the results will affect their work.

The importance of heterogenous data
Healthcare is moving its focus from procedures to outcomes. RWE will be the way real-life clinical practice demonstrates these outcomes. Randomized clinical trials (RCTs) will continue to be the basis of clinical practice, but RWE will increasingly help show which directions are likely to create the most value and minimize harm when applied to large, non-controlled populations.

In order to provide the best long-term statistical validity, RWE should be based on a wide range of data sources.

First, it really needs to be a large enough volume of the right kind of data. The trick here is to realize that more is not necessarily better – unless it is more of exactly the right data. For RWE to start becoming usable, specific information about procedures on each patient is needed, as are enough patients within different clinical contexts. Given the technical and legal barriers in accessing and sharing medical data from even one setting, let alone many settings, it is easy to underestimate the cost and time delays experienced in acquiring enough data from different sources.

Second, each type of data must be diverse. Sometimes large health systems or hospitals will assume that since they provide a large number of a type of service in a year, and therefore have such large volumes of a type of diagnostic data, for example, they can themselves independently generate valuable RWE.

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