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High-quality clinical trial data serves as the foundation for analysis, submission, approval, labeling and marketing of a compound under study. Widely used throughout the industry, data cleaning ensures that the process deployed to collect data is consistent and accurate.
Challenges in collection include data errors during manual data entry, i.e., spelling and transcription mishaps, range, and text errors, which impact coding. Automated edit checks can prevent the entry of inaccurate information, but they cannot detect all potential data entry issues.
Numerous manually generated queries put pressure on time and cost. Applying AI (artificial intelligence) techniques to understand the context of these queries may improve automated edit checks and offer opportunities to add checks or processes to identify issues earlier in the studies. Additionall…