Cambridge, MA – CluePoints, a leading provider of Risk-Based Monitoring solutions for clinical trials, today announced that it has won a Clinical Informatics News Best Practices Award for “creating the ultimate Risk-Based Monitoring and Data Quality Oversight Solution.” The Clinical Informatics News Best Practices awards recognize outstanding examples of applied strategic innovation-partnerships, deployments, and collaborations that manifestly improve the clinical trial process. CluePoints is delighted to receive another accolade recognising the considerable efforts of all staff members. We are committed to driving better quality into clinical trials and creating a company that is fun to work for and with.
Since conception, CluePoints has been known for breaking new ground as an enabling technology for Risk-Based Monitoring (RBM), CluePoints’ Central Statistical Monitoring (CSM) software utilizes statistical algorithms to determine the quality, accuracy and integrity of clinical trial data both during and after study conduct. Deployed to support traditional monitoring and data management as well as improved “risk-based approaches,” CluePoints’ CSM software can be implemented as the ultimate engine to drive Risk-Based Monitoring. The value in the solution lies in its ability to identify anomalies in data earlier, offering the opportunity to eradicate issues as they are uncovered, increasing patient safety and reducing risks of data quality and integrity issues when submitting regulatory approval.
In addition, CluePoints recently announced that they signed an agreement with the US FDA to explore a data-driven approach to quality oversight in clinical trials. This Cooperative Research and Development Agreement (CRADA) explores a data driven approach to selecting sites which exhibit data anomalies indicative of fraud, misconduct or sloppiness. Anticipated benefits to the FDA of the CRADA’s data driven approach include the detection of anomalous sites which may have escaped detection previously, rapid turnaround of results, the ability to determine the nature and extent of data anomalies, the ability to explore the interaction of various factors with data quality.