At PAREXEL, we frequently get asked questions about protocol optimization – what it is, how it works, and trends in…
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Data-Driven Monitoring, as its name implies, brings together data and converts that into information that’s meaningful to monitoring teams to drive monitoring action. In effect, it brings together four major components which are: targeted monitoring, where we break down traditional monitoring activities into those that are data driven; Risk Based Monitoring, where we use not the quality issues like in lag measures but actually lead measures indicators of potential quality problems and use that information then to drive monitoring action; Remote Monitoring, where we use a decentralized structure to relate monitors to on a more frequent basis contact the sites and address the site’s needs, and then lastly; Risk Based Source Data Verification, where we use critical endpoint strategies to focus on the data that really matters and focus our monitoring review of that data, specifically at that to get the greatest value to drive quality and site performance.
Data-Driven Monitoring is critical to the monitoring performance. The key aspect of monitoring is to ensure that the sites have the highest quality and performance possible. With Data-Driven Monitoring, we’re now using actual real-time data to influence our resourcing to that need. In effect, we’re changing how we focus our resources to meet those goals. Risk Based Monitoring looks to the quality related issues but we’ve tied in the workload and cost side, so now we can make sound business choices in rolling out and releasing our resources to those challenges.
Data-Driven Monitoring improves clinical processes by giving critical insights to the monitors as well as line management. Those insights drive better clinical monitoring decisions directly affecting the site’s quality and performance. In addition, our own line managers are able to see the effectiveness of those interventions. That critical insight helps to drive our own resourcing requirements, modeling, as well as identification, and promotion of best practices.
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Global Drug Development
Data Analytics and Insights
Drew Garty, on the importance of Data-Driven Monitoring