Clinical research organizations (CROs) are often required to adhere to a variety of sponsor-specific protocol and/or reporting templates, often presenting the same information in various places or in different ways, depending on sponsor-specific requests. Maintaining multiple templates requires a significant amount of time and resources.
The appallingly low rates of patient participation in cancer clinical trials, especially in the community setting, is no secret. The fact is that 98 percent% of our patient population isn’t aware of available trials, doesn’t know how to apply, or is under too much financial and emotional strain, etc. to even consider joining a trial. The blame for this rightfully falls on the proverbial shoulders of the main stakeholders: the biopharmaceutical industry, healthcare institutions, government, and — yes — patient support groups.
Part 1 of this two-part article examined the implications the transition to value-based, patient-centric, precision medicine has for drug discovery, development, and deployment, particularly for clinical trials. Part 2 explores precision medicine in more depth regarding the tailored and targeted treatments that aim to match patients to medicines according to particular clinical, demographic, and lifestyle factors.
Many biotechnology/pharmaceutical organizations are in the early stages of developing their risk management process. For the most part, we tend to see this process developing at the clinical trial level. The struggle with implementing these practices solely at the trial level is that it quickly becomes inconsistent and obsolete. Study teams are taking the time to assess risk as it applies to their trial, but the information gained is seldom shared cross-functionally or upward through an organization. It is more of a “going through the motions” type practice — which is then filed away, never to be utilized again.
This is the first part of a two-part article that examines the implications the transition to value-based, patient-centric, precision medicine has for drug discovery, development, and deployment, particularly for clinical trials.
While the industry is taking baby steps into using machine learning in clinical trials and for other data-generating objectives, the value of this data will depend on the transparency of algorithms, robustness of data sources, and extrapolation to real-world outcomes.
Advancements in technology create new avenues for optimizing clinical trials and the lessons that can be learned from them. With the gap between advancements in technology and regulatory uptake in clinical trials, it is important to rigorously examine technology applications. While the promise of new technology can lead to overenthusiastic adoption, mirroring the pace of other industries may not always lead to the best outcomes for patients. To increase regulatory buy-in to the use of these technologies, the clinical trial world needs a thorough understanding of the functionalities of each technical innovation, its potential utility and limitations, and how to build trust among patients, regulators, and other healthcare professionals.
Clinical research professionals often turn to healthcare professionals to critically assess the complex prioritization and planning of clinical research and to act as surrogates for the patient perspective.
Patient-facing digital technologies — also referred to as patient technology (PT) — have the potential to make a significant impact on the clinical research environment. PT includes any digital technology patients interact with to participate in clinical trial activities. When implemented effectively, these technologies can enhance patients’ experiences and engagement, streamline clinical trial processes, and enable better, more robust data collection in clinical trials.
Electronic consent to clinical research — “eConsent” — is a hot topic within the clinical research world as more sponsors and sites explore what solutions are available and appropriate for them. Yet many clinical trials are stuck in paper-based consent, either underutilizing the technology tools at their disposal or not using them at all. Understanding the reasons for a transition to eConsent and the hesitancies to make these changes gives insight into one of the last bastions of the clinical research industry to embrace the digital revolution.
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