Brief Synopsis:

Today's clinical research technology landscape has evolved into the next generation of data acquisition systems and processes. What was once a fairly simple process of paper collection and entry into a single database has now transformed into a complex environment of multiple vehicles for faster, better acquisition methods. Ultimately, these next generation technologies can be leveraged to provide more immediate access to higher quality data for analysis that may reduce the overall time from protocol development through data submission. With these technical improvements has emerged the ability to make available new data that was not previously useful for analysis. Patient diaries are one such example where the paper predecessor was not as evaluable as the contemporaneous electronic solutions of today. Furthermore, data requirements to support the safety and efficacy of advanced therapies have become increasingly complex by incorporating image, genomic, pharmacokinetic, and other supporting data. These factors rooted in technical and scientific progress have increased study complexity and exacerbated some facets of study conduct that were once easily handled via manual management or weekly project team communications. Some areas in study conduct that have become more strained as a result include:

• Harmonization and reconciliation of data from multiple vendors (i.e., EDC, ePRO, Central Labs, Image Labs, and/or IVRS/IWR.)
• Management of multiple vendors "study startups", each with different deliverables, timelines, and managers.
• Technical support across different platforms and terminologies to a larger, changing user base (i.e., investigator sites).
• Understanding if data is available as expected, if delays in data acquisition Impact the study timeline, or if problems with data quality will surface once data are integrated.

In the current clinical research technology landscape, complexities surrounding the effective management of a clinical trial introduced by the use of multiple data sources and vendors make it more difficult to determine the holistic study status. To tackle this challenge, a methodology must be adopted that incorporates both technology and process. The optimal solution includes automation of operational data integration based on data expectedness, and appropriate management of these data once they become "ready". In this White Paper, Mr. Neri will describe these new process gaps in applied study scenarios, and explore new concepts and methods for combining operational data integration with configuration of data expectedness and protocol events to provide one unified view of study progress. To download the complete white paper please click here>>