Register Now - Beyond Recruitment: The Future of Patient Site Engagement in Oncology Clinical Trials
|Articles|December 1, 2016

Applied Clinical Trials

  • Applied Clinical Trials-12-01-2016
  • Volume 25
  • Issue 12

How Important is Big Data?

Survey collects thoughts from industry professionals on the current and future impact of big data in clinical trials.

In October, SCORR Marketing and Applied Clinical Trials surveyed our audience on big data. As results indicate at right, most respondents agree that big data is important to the clinical research enterprise. However, when asked if it was important in their own companies, those numbers were noticeably higher in the “slightly” to “not at all” categories.

Regardless, the survey did uncover some big data trends. For example, the majority of respondents are using medical records, biomarker and registry data as sources, in that order. Of those, the medical record data is rated the most important by 48% of the respondents.

As for the future uses and impact of big data in clinical trials, some areas are brighter than others. One-third of respondents believe the EHR will be the data of record for all new patient data, and an additional one-fourth believe that predictive modeling will be used to inform preclinical and translational research.

Conversely, only 4% of respondents think patient recruitment rates will be improved in five years if they used big data. Please download the free report here.

Articles in this issue


Related to this article

Closing the Community Access Gap: Q&A with Kent Thoelke, Paradigm Health
In this Q&A, Kent Thoelke, CEO of Paradigm Health, discusses why participation gaps in community and rural settings come down to both patient cost barriers and site infrastructure, how legacy workflows weigh on community clinics, and what policy and technology changes it would take to make clinical research a real care option for more patients.
© Grispb - © Grispb - stock.adobe.com
Mistral and OpenAI produced identical overall eligibility labels across 200 synthetic patient-study assessments, with criterion-level disagreements concentrated in inclusion criteria where models switch between “Uncertain” and “Not included” when records lack sufficient evidence, so model choice should be driven by deployment architecture, control, and cost rather than assumed interchangeability.
The Human Side of Clinical Trials: Before Burden Becomes Risk, Capturing Friction in Real Time
In Episode 4 of The Human Side of Clinical Trials, host Brian S. McGowan, PhD, FACEHP, chief learning officer and co-founder of ArcheMedX, Inc., speaks with Jason Riis, PhD, CEO and chief behavioral scientist at Behavioralize, about how capturing friction closer to the moment it happens can reveal unclear instructions, technology friction, and workarounds that retrospective surveys and debriefs tend to miss.