Beaconcure, a leader in AI-enabled clinical data validation and automation, has launched its new Verify capability to automate validation of statistical analysis outputs against Analysis Data Model (ADaM) datasets. Verify is a secure online workspace that allows clinical research teams to collaborate on clinical study analysis validation and workflows, and it has been used to validate hundreds of clinical studies to date. ADaM standards play a key role in the clinical research process, by providing a structured and consistent framework for organizing and analyzing data, making it easier to generate accurate findings.
Validating outputs against ADaM allows biostatisticians and clinical programmers to confirm that analyses accurately reflect the intended version of core data. This validation is generally performed manually, using double programming or visual review. By using the new automated Verify processes to validate outputs against ADaM datasets, programmers can quickly identify and remediate discrepancies that might not have been found through traditional methods. Irving Dark, biometrics expert and Beaconcure Strategic Advisory Board member, stated that “The ability to use AI to validate outputs against ADaM provides biopharmaceutical companies an enhanced layer of reliability and accuracy in clinical analysis. The addition of this Verify feature further sets Beaconcure apart as an innovator in addressing the industry’s current and future challenges.”
Using Verify as a collaborative workspace allows global users to work together to identify, assign, and resolve issues, with an automatically generated audit trail. The new capability automates data extraction and calculations across multiple ADaM datasets and correlates values to the corresponding outputs. Automated validation workflows can help sponsors increase the volume of studies analyzed and improve the accuracy and traceability of statistical analysis outputs. Beaconcure COO and co-founder Ilan Carmeli comments on the expanded Verify validation capabilities, highlighting the significance to pharmaceutical innovation: “Automated validation of outputs against ADaM datasets enables statistical programmers and biostatisticians to focus on higher-value activities, such as new data visualizations and summaries, changing requirements, and complex data analyses. This is also an important step towards our vision of replacing double programming.”