Monitoring and Measuring of Clinical Trials: A Comprehensive Review

Abstract

Clinical trial monitoring and measurement are essential components of pharmaceutical research that ensure data quality, patient safety, and regulatory compliance. This study examines the evolution of monitoring approaches from traditional on-site, SDV-based methods to modern technology-driven systems, including Risk-Based Monitoring (RBM), hybrid models, and centralized monitoring. It also highlights the growing role of digital technologies such as Electronic Data Capture (EDC), Clinical Trial Management Systems (CTMS), Artificial Intelligence (AI), and wearable devices in improving data accuracy, real-time access, and decision-making efficiency. In addition, the study discusses advanced measurement parameters such as patient-reported outcomes (PROs), biomarkers, and realtime physiological data, which have enhanced the scientific validity and patient-centric nature of clinical trials. Despite these advancements, challenges such as regulatory complexity, data privacy concerns, high implementation costs, and skill shortages persist. Overall, the findings indicate a significant transformation toward more efficient, technology-enabled, and risk-focused clinical trial systems that improve transparency, reliability, and global research outcomes.