1 min read

How to Manage Machine Learning Algorithms in Clinical Trials

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…

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1 min read

Six Considerations When Implementing Machine Learning Algorithms in Clinical Research

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

1 min read

Age of the Machine: Using Algorithms to Advance Outcomes

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.

1 min read

Swimming in Data

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

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How machine learning and artificial intelligence can drive clinical innovation

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

4 min read

COVID-19: A Wakeup Call for Clinical Trial Monitoring

Clinical trials have two key goals: to prove the safety and efficacy of new therapeutics. Their success rests on the accurate collection, monitoring, and analysis of data. While today’s data collection is increasingly electronic, monitoring itself still largely occurs onsite, with monitors verifying source data by physically comparing the electronic data capture with the data

4 min read

What’s Needed: An Alternative Approach To Clinical Trial Management

Risk-Based Monitoring. Risk-Based Execution. Risk-Based Quality Management. The monitoring and management of clinical trials are called many things—yet, they are, in fact, different. FDA Director of the Office of Scientific Investigations, David Burrow, notes that “Risk-based quality management is not just risk-based monitoring.” RBQM is a more substantial undertaking, requiring quality, reliability, and interpretability. It

1 min read

Alternative Monitoring: Clinical Trial Management In The Era Of COVID-19

Effective Trial Continuity In the Face of a Pandemic While social distancing and isolation are paramount, clinical trials must adapt to remain viable. However, many clinical trial sponsors still have questions about how to effectively gather collective insights and apply them to improve their clinical trials. The result is a call for risk-based quality management

2 min read

Sharp Thinking A New Angle on Clinical Trial Management Systems

New technologies are inevitably heralded as making life simpler: streamlining tasks, eliminating steps, managing things so you don’t have to manage them. Yet the practical application of a new technology often seems to make things more complex. Clinical trial management is a case in point. Contract Research Organizations (CROs) and sponsors are juggling increased data