Every step in the clinical trial process, including the planning, execution, monitoring, management, analysis, and closing phases, creates huge amount of information that are mostly not used to their fullest extent to optimize and accelerate the conduction of the study.
Exom introduces the use of data analytics, Genius Wizards™, to address Boost your Clinical Trial with Advanced Analytics Providing Deeper Insights for Study Teams and Sponsors the challenges companies face in their clinical trial execution. Genius Wizards™, powered by SAS, is a machine learning platform that analyses in real time data from many study data sources like the eCRF, eTMF, CTMS, eTraining and Time Recording.
Genius Wizards™ is an interactive, visual tool for the early detection of signals and ongoing monitoring of data collected in Exom managed clinical trials. It is available for running trials, meaning that we don’t have to wait for the study to finish, to view all the data.
For example, it can be used to help fully understand the toxicity of the immunotherapy and target therapy combinations and moreover it will help identify the most efficacious combinations quickly. The tool enables more informed reasoning, informed decision making and an earlier understanding of the patient benefit-risk trajectory will support oncology studies more widely and ultimately positively impact a higher number of patients.
The machine-learning algorithm of Genius Wizards allows a Machine Learning-Driven Risk-Based Monitoring & Management MLD-RBMM) by accessing, harmonizing, and analyzing data from diversesources to identify patterns and trends and pinpoint anomalies and potential points of failure.
With the MLD-RBMM, we move away from the traditional approach of frequent and pre-scheduled on-site visits and 100 percent source data verification (SDV) toward a combination of activities, including centralized data analytics and monitoring.
The type and number of monitoring activities, either on-site, off-site or central, as well as the tasks to be performed, will not be defined a priori but adapted and conducted according to the results of the continuous.
By using our MLD-RBMM digital solution, we can bridge real-time information
gaps between centralized analysis and in-the-field monitoring teams, reducing risk, ensuring documentation, and increasing quality.
Impact on Monitoring & Management
- The right insights and analysis-ready data are made available faster
- Real-time knowledge of any study metric and advanced analytics allow for ongoing, actionable insights throughout trial execution
- Using dashboards to visualize on-demand subject, site, and monitor performance data, the study manager could keep any study metric under control by prioritizing the right actions at the right time
- The medical Monitor can efficiently help to ensure subject data are medically congruent and sound within and across subjects leading to
an improved patient safety
- Performing trend review and data analytics, comparing day-to-day safety and operational triggers across sites and countries, helps medical monitors and centralized data monitors to optimize trial performance and support the monitoring strategy
- The centralized data monitoring team helps resolve queries faster and supports project teams globally for improved clinical execution and site communications. The on-site Monitors can then focus on strategic issues, such as patient recruitment
The Values of AI in Clinical Trials
Identification of bottlenecks and inefficiencies. Optimization of the different phases.
Site Selection & Patient Recruitment Simulation
Machine learing techniques to identify the sites that more likely will have a better performance during the clinical study.
Predicts which patients have a higher chance of dropping out or not following protocols and the study timelines.
Faster Study Conduction
Early detection of risks allows immediate preventive actions and accelerate study conclusion.
Detection of any kind of data anomaly
- Combines clinical data with data from the clinical trial management system optimizes the conduct of the trial, such as patient recruitment and trial resource utilization
- Creates a historical warehouse and enables the execution of cross-trial safety queries
- Increases data quality and document completeness
- Enables faster FDA/EMA data review cycles
- Enables traceability between SDTM and ADaM
- Visualizes clinical data before submission using integrated safety and summary analysis