Best Practices to Increase Efficiency of Data Review and Collaboration
Clinical trial sponsors often face challenges in efficiently reviewing and collaborating on increasingly diverse data, which can delay critical decision-making processes and the ability to execute clinical strategy effectively. Existing practices for time-relevant data review and collaboration often rely on manual steps and approvals that can delay access to emerging trial data needed to drive medical monitoring and site oversight activities and require collaboration by various clinical team members.
Spreadsheets are a popular methodology for data revitew but often require manual preparation, have limited analytics, and can have challenging version control, limiting their effectiveness. By adopting certain best practices, clinical team experts comfortable with spreadsheets can improve their data fluency while automating data access, standardization, and data and risk derivations. This will accelerate data review and enable integrated communication methods to facilitate collaboration.
Implementing best practices for early data cleaning, standardization, mapping, and aggregation can improve data oversight. Incorporating human and machine detection of scientifically and clinically significant events and trends can enable frequent refresh (e.g., daily) and review of merging data. Collaboration tools can also help manage approval and communication among team members and external partners, providing better auditing and tracking of decisions made during data review. Technologies supporting these types of best practices can drive the efficiency of clinical trial execution and improve the quality of data available for clinical readout at the conclusion of trials and trial cohorts.
In this webinar, we will address the following:
- Best practices for accessing, standardizing, and analyzing emerging data from safety, operational, and efficacy data sources.
- Automated tracking and auditing of collaboration and decisions made during data review to streamline communication between team members and external partners.
- Accelerating data review of clinical trials through early data cleaning, standardization, mapping, and aggregation.
- How implementing these best practices can improve emerging data review and support clinical decision-making.
In summary, adopting best practices for data review and collaboration can help clinical trial sponsors overcome challenges and efficiently review and collaborate on diverse data. These practices can drive the efficiency of clinical trial execution, resulting in improved data quality and better support for clinical decision-making.
- Brent Meyers / Vice President, Clinical & Translational Analytics. PerkinElmer Informatics
- Philip Ross / Clinical Trial Analytics Champion. PerkinElmer Informatics