Our centralized and proprietary algorithms automatically
identify discrepancies and inconsistencies in data: true risk prediction.
Centralized Data Monitoring Algorithm
In the face of massively growing data volumes, Cronos places great emphasis on automated controls. We have implemented sophisticated, instrument-specific algorithms to detect potential data inconsistencies in real time so that they can be addressed immediately - not discovered after database closure.
Automated controls however are not a complete substitute for expert human review which still has an important place in our quality control environment. For example, there is no real substitute for the expert review of subtle relationships that exist between the data and investigator conduct.
Although typical compliance and error detection controls are designed specifically within the boundaries of process controls – those typically implemented within an EDC system – Cronos’ risk-based data monitoring algorithms actually detect inconsistences and discrepancies within the dataset.
The algorithms are a powerful component of Cronos’ quality control process. It allows us to monitor 100% of the data for unexpected changes quickly – something that would be practically impossible based on manual review.
“Cronos was a critical component to the success of our study. Without them, we would have never been able to detect and correct the quality issues encountered in our study." - VP Clinical Management
Ask us about our three modes of centralized risk-based data monitoring: Active, Passive, and SnapShot.
Active: real-time monitoring where issues are identified and then corrected directly with the site.
Passive: trends and tendencies of study conduct are identified and we advise you and make recommendations for further corrective action.
SnapShot: post-hoc analysis is peformed and issues are identified that allow you to allocate resources to areas that present the greatest risk signals.