The development of ‘artificial intelligence’(AI)-driven tools for data management has accelerated in the past few years. The promise of these systems is to provide a more intuitive experience for data management and sponsor teams, making it easier to track trends, spot anomalies, and generate near-real time data reports. These features, combined with AI’s ability to learn from data, make these tools a modern essential for improving data quality and accelerating clinical trials.
Currently available tools provide advanced techniques for completing the many complex tasks faced by data managers. A pre-study task of data managers is the database build. One development company has created an AI tool that integrates with its eClinical platform and will digitize the clinical trial protocol into structured elements such as visit schedules, cohorts, and eCRFs. For the data manager, this means faster review of the protocol, automated generation of visit schedules, automated prediction of eCRF accumulation with over 80% accuracy, and a database build up to 40% faster than using traditional methods.
Following the database build, user acceptance testing (UAT) helps data managers find glitches in the programming and correct these prior to activating the database. This step is also used to coordinate database features among similar studies in a sponsor’s clinical program. A new tool utilizes real-time, risk-based UAT, which accelerates data managers’ comparisons of study differences with high accuracy.
Once data has begun to accumulate in the database, data managers are continually reviewing entries for completeness and accuracy, and evaluating records to ensure participant safety. Medidata’s latest module provides seamless integration of data from Medidata and non-Medidata sources. Clinical Data Studio leverages AI to streamline workflows, allowing multiple users to review data in real time. This tool automatically detects data inconsistencies, flags complex database and external data issues, and generates standardized queries. These functions reduce timelines for data manager and third-party reviews.
As the industry continues to evolve, it’s clear that AI will play a vital role in enhancing data quality, accuracy, and efficiency. Of course, careful planning will address data security and integrity in AI-driven systems. PROMETRIKA’s team recognizes that responsible AI adoption and transparency are crucial for success. To review specific technologies, PROMETRIKA can meet with your research team to help you determine which technologies and innovations are best to drive excellence in data management for your projects.
