As part of our ongoing series about best-in-class approaches for rapidly assessing, prototyping, and introducing digital and other clinical trial technologies, we focus our attention in this article on the “human” component of technology-enabled transformation.
As part of our ongoing series detailing an approach for the rapid assessment and prototyping of digital and other clinical trial technologies, we introduce here the use of artificial intelligence (AI) and machine learning (ML) to optimize clinical study design and execution.
Last month we introduced an approach for the rapid assessment and prototyping of digital and other clinical trial technologies. Advances in technologies and digital innovations targeting the clinical research environment are evolving at dizzying rates, and the need to efficiently assess and implement opportunities is greater than ever.
It is generally accepted that a significant and unacceptable percentage of IT projects fail. According to one 2017 report, 14 percent of all IT projects were total failures; a remaining 31 percent didn’t meet their goals, while 43 percent were over budget and 49 percent exceeded timelines. .
The disconnect between increased investment/activity and output points to continuing significant challenges in the global clinical trials market. Here we examine three of the leading issues facing the biopharmaceutical industry today.
The challenge to recruit and retain numbers of patients for Phase 3 industry-sponsored trials is not new to the industry. In fact, 48 percent of sites miss their enrollment targets and 80 percent of trials are delayed due to recruitment, but there are new opportunities to achieve recruitment goals while also minimizing dropout.