Whitepapers

This white paper will explore the key challenges impacting clinical trial medical reviews from the perspective of Dr. Gareth Tomlinson, PhD, Director, Clinical Research Scientist at Taiho Oncology, Inc.

AI can be applied to patient data in real time, allowing medical reviewer teams to actively monitor patient safety, with the AI alerting medical review teams to identify potential adverse events (AEs), data anomalies, critical issues for patients on treatment, and more.

Over the last two decades, clinical trials have increased in complexity by approximately 61%. Additionally, data volume has grown to be too complex and resource consuming to be processed manually. As a result, patient data is dispersed over so many various systems, that it’s impossible to get a clear picture of the patient, making it difficult to understand how they’re responding to treatment – thereby impeding a patient’s time to recovery.

By leveraging real-time data that offers unprecedented insights into physician behavior and patient outcomes, companies can gain a competitive advantage with prescribers. Alerts, powered by PurpleLab translates complex information into actionable insights, empowering companies to identify the right physicians to target, determine the most effective marketing strategies and ultimately improve patient care.

Healthcare today is evolving into precision medicine, or what the FDA refers to as personalized medicine. To stay competitive in the constantly shifting healthcare landscape, organizations across the healthcare ecosystem must embrace this targeted approach and leverage the tools needed to make safe decisions for their patients or populations. By unlocking insights provided from advanced claims data, precision medicine can assist clinicians, thereby reducing their educated guesses, improving patient outcomes, and making more informed decisions.

As medical facilities become more equipped to handle different telemedicine platforms and procedures, more clinical trials are back up and running. This also means more electronic data is being generated than ever. This Ebook will explain how to reach patients and facilities, especially in this decentralized clinical trial format.

Although biologics and biosimilar drugs only represent 2% of U.S. prescription volume, these naturally sourced medications have recently become the focus of much discussion across healthcare channels. Biologics – often considered specialty drugs – have accounted for as much as 37% of drug spending in a single year. Read more about how Biologics and Biosimilars help fight chronic disease as well as the difference between Generics and Biosimilars.

Patient selection can be a nuanced process in clinical trials, particularly those involving therapeutic areas with more complex or subjective inclusion/exclusion criteria. Determining patient eligibility may often depend on factors that require clinical expertise in such areas as disease progression, comorbidities, genetic mutations, and disease severity. Discover the role eligibility adjudication committees play in ensuring trial success.

EACs/CECs evaluate potential adverse events, participants’ safety, whether a patient meets complex eligibility criteria, and much more. Thus, the need to select the right EAC vendor is critical – selecting the wrong partner risks time, money, and success. With so many EAC vendors, it can be challenging to find the ideal partner. Fortunately, WCG has outlined the top five benefits of utilizing an independent EAC.

Is your eCOA provider built to deliver clinical trial success? Our eCOA checklist provides key questions to ask when evaluating potential vendors.

Learn how to develop and market products and interventions that actually meet individuals' needs by incorporating patient input exist throughout the trial lifecycle

Determining the most effective locations and formats to get your clinical trial in front of willing, eligible participants can seem like a daunting task.. What is the most efficient way to introduce more targeted recruitment approaches?

In March 2024, the US FDA issued its guidance, “Artificial Intelligence & Medical Products: How CBER, CDER, CDRH, and OCP are Working Together,” outlining the agency’s objectives for protecting public health as artificial intelligence (AI) and other technologies become more prevalent in healthcare.

YPrime announces a new report unveiling the top challenges that clinical trial executives face when implementing eCOA, and what to look for when evaluating new solutions.

Digital biomarkers provide a pathway to the future where clinical trial sponsors can potentially benefit from reduced trial duration and sample size. Learn more about these benefits from a simulated study.

Digital biomarkers, smartphones, and wearables are transforming clinical trials by revolutionizing remote data collection and providing insights into treatment efficacy. Learn more about these exploratory endpoints and how they provide greater confidence in a clinical trial.

Explore the five key advantages that underscore the significance of digital measurements in the context of clinical trials

Learn how your team can use EHR-to-EDC technology to reduce data monitoring costs, speed up data cleaning timelines, and hit study milestones faster.

Looking for an EDC solution but overwhelmed by the process? This free guide highlights five steps to help you navigate the EDC vendor landscape and choose the right system for your study.

Inside this eBook, readers can expect to learn how to effectively navigate the various challenges associated with clinical trial data collection and reporting in the age of digitalization and at a time in which site staff are experiencing turnover and seeking more efficiency.

In this playbook, learn how a virtual site solution expands access to infectious disease clinical trials by allowing patients to enroll and participate from anywhere.

The rapid evolvement of precision oncology requires real-world data generated at a scale that unlocks insights that can not be provided from clinical trial evidence. Machine learning and large language models can be used to quickly identify information, supporting custom integrated real-world evidence solutions to make informed decisions.