Meet MSR: Why Data Accuracy Matters in Clinical Trials
Accurate data is the backbone of successful clinical trials. Every piece of patient data, from electronic health records (EHRs) to lab results, holds critical value
10 Steps for Practical RBQM Implementation for Your Business
Many clinical trial Sponsors and Contract Research Organizations (CROs) realize the great benefits of risk-based quality management (RBQM), and leading regulatory bodies such as the
The Bold Future of RBQM for CROs
In a world where complacency is the enemy of progress, Contract Research Organizations (CROs) stand at a crossroads. Traditional approaches have their merits, but they’re
Meet SPOT: Transforming Site Monitoring Practices with Adaptive Intelligence
Site monitoring is one of the most expensive components of clinical trials; on-site monitoring alone accounts for around 25 to 30% of the total trial
A New Era of Automation: Improving Efficiency & Outcomes with Intelligent Medical Coding
In clinical trials, patient data such as adverse events and concomitant medications is coded against standard medical dictionaries. However, this process comes with challenges. A
Centralized Monitoring In Clinical Trials: Everything You Should Know
What Is Centralized Monitoring? Centralized monitoring is a component of risk-based quality management (RBQM) that aims to detect emerging quality-related risks (either pre-identified or unanticipated
What Is Trending In Data Management And AI?
With the advent of AI promising to transform the way we process and interpret data, there has never been a more exciting time to be
A Year In Review: 2023 As A Watershed Moment In The Evolution Of Clinical Data Management
From risk-based quality management’s (RBQM) inclusion in ICH E6 (R3) to the roll-out of numerous AI-driven solutions, 2023 has been a year of huge advance
How To Leverage Machine Learning & Deep Learning For Natural Language Processing In Clinical Trials
Machine learning (ML) and deep learning (DL) are transforming natural language processing (NLP). A key use case is medical coding. ML models can significantly reduce
Enhancing RBQM With Artificial Intelligence: Your Questions Answered
With traditional approaches to clinical trial data management being manual, cumbersome, and prone to human error, streamlining processes offers huge efficiency- and quality-boosting potential. Risk-based
