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Delayed data delivery is a growing problem It’s 2021, and the digital revolution is well on its way. Yet, for clinical trials worldwide, real-time access to data is not the norm — and that lack can create a cascading set of issues affecting every aspect of a study. With critical decisions too often being made…
Data literacy is a simple concept. Looking at data related to your area of specialty, you can deduce insights, ask appropriate questions, and make clear data-based decisions. Within the clinical trial ecosphere, each role requires a different degree of skill both vis-à-vis data in general and the trial data specifically.
The ICH E6 guidelines has set the clinical trial industry standard since it was first published in 1996. As clinical trials and technologies, however, continue to rapidly evolve, there is a need for these guidelines to shift as well. The industry has called for a revision of the ICH E6 (R2) guidelines to spotlight risk-based…
As today’s clinical trials grow increasingly complex, risk-based monitoring has become the watchword of the day. It is integral to any quality management program; it is mandated by regulatory agencies; and, critically, it is a potent tool for forestalling costly failures…
Machine learning provides a crucial opportunity to shape and direct clinical trial research, helping harmonize and analyze the massive amounts of data generated in the course of a study, providing the means needed by researchers to make faster, more informed decisions. Yet, the machines are only as effective as their algorithms and their interaction with…
Starting with trial design and site selection, machine learning algorithms can be used at every stage of drug development in order to increase efficiency and data quality. It offers endless opportunities for medical imaging, trial recruitment, and other aspects of the life-sciences industry.
By: Kristin Mauri and Rhonda Roberts at Remarque Systems The amount of data collected during clinical trials has increased dramatically. In order to keep up with trends, machine learning offers the opportunity to keep studies on the right trajectory using real-time data to identify anomalies and missing data. This is important not only during the…
As the global pandemic has created a greater need for remote monitoring, the adoption of machine learning and artificial intelligence (AI) into clinical trials can help to generate new efficiencies to enhancing patient safety and trial outcomes, while not replacing the need for clinical trial monitors or other research personnel. This Byline explores the links…