A few months ago, I focused my post on a three-letter word fragment. Today, I turn my attention to a three-letter acronym.
RWE. Real World Evidence. For such a short “word” it carries a big definition. Or at least a lot of definitions. Or at least it should.
I like this description of RWE, offered by the Public Relations Society of America:
RWE is the medical equivalent of Big Data — the use of massive data sets to see how medicines perform outside the tightly corseted world of clinical trials. While clinical trials traditionally restrict participation to select patients, RWE looks at massive blocks of information from patients of every hue and stripe.
A recent report from SAS (Statistical Analysis System) echoes the emphasis on using RWE in pharmaceutical development:
Real-world evidence provides significant insight into how a drug or drug class performs or is used in real-world medical settings.
It is interesting to me that the lion share of what I read discusses RWE in the context of drugs. That seems so limited to me. Big data are BIG. So we need to think BIG.
I practiced as an internist for nearly a decade before transitioning to industry. I was taught by my instructors, as well as my colleagues, to be a data-driven practitioner. We used to argue whether we could truly make decisions about the patient in our exam room based on the study of hundreds of patients with similar issues, albeit not the SAME issues, and certainly not the same genetic make-up. At most, the studies – particularly in the field of cardiology – enrolled thousands of patients. So we are left with making our best guess when choosing a blood pressure medication, or selecting an antibiotic. The art of medicine, we call it.
That is of course changing with the advent of precision medicine, and RWE.
But we are just getting started. There is so much more we can do with RWE.
Vencore Health Analytics is tackling many new ways to use RWE (this is our corporate blog after all; I have to get a little plug in)! We are asking – and answering – many new questions.
Why don’t we use big data analytics to investigate natural history of a rare condition? Or to determine the true gender distribution in an illness? Or the geographic location of diagnosed patients? Or the biases inherent to medical practice based on proximity to a key opinion leader? Or the prevalence of a disease?
In the field of medicine, we have traditionally relied on epidemiologists to help us with these types of queries. They have used math and good guesswork to understand large trends based on small numbers. Now, they are considering how data might transform the field (Am J Epidemiol. 2015.182(12):977-979.)
Thank goodness! I would like to propose a new three-letter acronym:
This time it stands for Real World Epidemiology.
Tara Grabowsky, MD
Chief Medical Officer, Vencore