Proper quality management requires advanced data analysis and interpretation techniques involving both computer data science and clinical operations expertise. Without this technique, monitoring analyses can omit critical observations in data quality.
1. Align Monitoring Directives
Convert study risks identified in RBM plans into specific analytical measures that can be evaluated through computer data science.
2. Analyze and Visualize Data using Advanced Computer Modeling
Access advanced computer data science algorithms, state-of-the-art visualizations, and more than a decade of clinical trial experience to explore and interpret study risks.
3. Analytical Monitoring Reports
Discern critical findings with comprehensive monitoring reports containing interactive visualizations. Drive proactive risk mitigation using improved data visibility through timely reports that enhance communication and collaboration capabilities. Consolidate and standardize all report data to provide a fully integrated performance management approach that productively improves study quality.
4. Rapidly Eradicate Poor Quality
Take advantage of strategic clinical operational expertise to guide on-site monitors and data managers with actionable risk mitigation directives. Move beyond classic one-dimensional operational metrics by use of a comprehensive oversight program that focuses on the actual clinical trial management activities and performance metrics. Document corrective actions, perform periodic root cause analyses of poor quality indicators in both internal and external sources and track performance to eradicate poor quality quickly and efficiently.