Data and AI scientists must strive to eliminate bias from anything that touches patient care.

Clinical trial recruitment is notoriously difficult. Finding, vetting, qualifying, and managing trial participants is time-consuming and expensive, with numerous obstacles hindering every step of the arduous process. From precise patient identification to compelling behavioral motivation, traditional approaches often fall short and cost pharma companies millions of dollars and months if not years of delays in drug development. 

The World Health Organization defines health technology as “the application of organized knowledge and skills in the form of devices, medicines, vaccines, procedures, and systems developed to solve a health problem and improve quality of lives.” While that’s true, it’s a pretty broad statement. In an era when we’re seeing rapid technological shifts in the way people measure, monitor, and think about their health, it’s worth focusing any discussion of health technology to a few key areas that best highlight some of the more exciting – and challenging – aspects of health technology development at this point in 2021.

It’s a word that’s been popping up more and more frequently in the pharma context, but not everyone understands what it is and what it can do. We asked the experts to explain blockchain.

As large pharmaceutical companies increasingly look to outsource aspects of drug discovery and development to external collaborators, the ability to identify the right partnership candidates before competitors has become critical.

The healthcare industry is increasingly focusing on niche patient populations. However, finding hard-to-reach rare disease patients is difficult and keeping them engaged over time even more so. Could machine learning platforms that deliver personalized experiences for patients and caregivers be part of the answer?

Everyone knows the terms “machine learning” and “artificial intelligence.” Few can define them, much less explain their inestimable value to clinical trials. So, it’s not surprising that, despite their ability to minimize risk, improve safety, condense timelines, and save costs, these technology tools are not widely used by the clinical trial industry.