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This post originally appeared on Medidata's blog Geeks Talk Clinical and is re-printed with permission.
As clinical researchers, it’s natural for us to think of patients first and foremost as research participants (potential or current).
But that’s not how patients think of themselves.
For most patients, clinical trials seem rather disconnected from their broader health (or human) interests. And until we meaningfully connect clinical trials to these larger interests, clinical trial recruitment will likely remain a bottleneck.
Drawing this connection is a challenge not just because of our natural tendency to classify patients as research participants. It’s also an issue of access.
Historically, our access to patient data has been mostly confined to the scope of clinical trials. And without access to a more holistic view of the patient, it’s difficult to connect clinical trials to larger patient interests.
Thanks to technology advancements, new access is opening up.
The Potential (and Pitfall) of EHRs
Electronic health record (EHR) data, for instance, can provide us with a better understanding of the patient. As a result of this understanding, we have new ability to strengthen the connection between clinical trials and the patient’s broader health interests.
But even EHR data has limitations.
EHRs provide information about patient interaction with the healthcare system, which is far from a complete patient picture. Furthermore, this incomplete picture tends to be painted primarily from one perspective, that of the healthcare provider.
Meanwhile, the provider perspective is losing the significance it once had.
Though physicians and providers remain an important source of health information, they are no longer the only source. Patients are shifting from passive participants operating at the periphery of their care to active participants operating at the center of their care.
The Rise of “Quantified Self”
In essence, quantified selfers gather and monitor data about their daily functioning in an effort to improve it. Nike, for example, recently announced that over 18 million people use its quantified self technology to track their exercise.
A common misconception about quantified self is that only the fitness fanatics, gadget geeks, and worried well practice it.
Though mainstream consumers may not be familiar with the term “quantified self,” most are participants in the movement. According to Pew Internet, 7 in 10 US adults track a health indicator for themselves or a loved one. Metrics and tools may range from simple and nontechnical (e.g. recording weight in a notebook) to complex and highly technical.
As the technology to capture health data evolves, it will become more sophisticated, unobtrusive, and inexpensive. And in many cases, quantified self technology will allow for nearly effortless passive data collection.
Writing weight in a notebook, for example, will become an unappealing health tracking option for most people. As a result, mainstream consumers will embrace what are by today’s standards quite advanced quantified self devices. The data enabled by this technology, of course, can be stored and moved around.
And that’s where quantified self gets quite interesting for clinical trials.
Bridging Quantified Self and Clinical Trial Data
Perhaps most notably, quantified self data could be pulled into EDC systems, providing us with unprecedented rich data about our patients. I just used the word “could,” but actually, it can.
Brock Heinz from Spaulding Clinical and Joe Dustin from Medidata recently collaborated on a proof-of-concept to do exactly what I’ve described. Heinz created an app that serves as a bridge between Medidata Rave and popular quantified self devices like the Fitbit or Nike Fuelband.
The big challenge, which is something we often struggle with in clinical trials, is how do we use this new ability?
In Part 2 of this blog, we’ll talk about that question.