One of the major challenges in clinical trials begins before the trial can even start. Identifying and screening patients who might be eligible study participants requires intensive, in-person effort that can disrupt standard-of-care clinical workflows. Most Electronic Medical Records (EMR) systems are not equipped to effectively search their databases based on current, or past medical histories, closing off an effective method for narrowing the funnel. This is especially acute for the vast amount of un-funded, unsponsored, investigator-initiated research.
Another common problem in clinical trials is the burden of manual data entry on research staff which can take a disproportionate amount of time in their day-to-day activities. That’s costly not only in terms of lost time for recruitment or patient follow-up, but also because clinical trial sponsors don’t fully compensate the site for data entry. Complex drug and medical device clinical trials require researchers with significant clinical experience to even be able to review the data; having them spend more than half their time on manual data entry is obviously not optimal.
Part of the reason so much time is wasted on data entry is a result of duplication of effort. Once data is collected from clinical trials, they’re entered and stored in separate, redundant systems and, once the trial is concluded, are inaccessible for further research.
And depending on the patient population of the study, the technology itself could pose a challenge, for example in an older demographic, or with individuals who require legal authorized representatives to help them enroll.
Newer technologies involving connected health devices, AI, and machine learning can facilitate the decentralization of clinical trials, integrate with EMRs for pre-screening of potential study participants (regardless of their location), and eliminate the costly and wasteful duplication of effort involved in manual data entry.
Take for example a research coordinator responsible for three heart failure clinical trials. The coordinator must enter the same data in three different trial databases, as well as into the EMR system. A platform that bridges the gaps between those trial databases and the EMR system, enabling the extraction and downstream use of the data, for any use case, would drastically lower the data entry burden on that coordinator, freeing her to spend more time recruiting new patients and on follow-up care for existing patients.
As another example, consider a large registry clinical trial with thousands or even tens of thousands of patients. Enabling remote “e-enrollment” would open the pool of potential participants to anyone, even those who aren’t local to the clinical site. This is important not only to broaden the reach of the research, but to increase the diversity represented in the patient pool.
Finally, it’s incumbent on the providers of these technologies to learn in detail how data is collected in the normal course of clinical care, and then customize the way data can be ingested and transformed for use in clinical research.