At the 2017 SCDM conference, held Sep 24-26 in Orlando, much was covered regarding the state of data management today. New approaches in risk monitoring, latest in automation in areas such as eSource and eConsent, and new perspectives in optimizing data processes were the focus. As these areas continue to evolve, a progression to “Clinical Development Enlightenment” becomes evident. In a speech to a crowd of more than 200, Comprehend’s CEO Rick Morrison outlined this progression, from 100% SDV and monolithic data warehousing to the goal of a “fully engaged” clinical data environment.
This progression takes five steps:
Traditional
The initial stage where data managers rely on siloed data sources, and apply data warehousing and horizontal Business Intelligence tools to derive insights in a custom fashion. Most information is captured in Excel spreadsheets and 100% SDV is used for data verification.
Experimental
Organizations depend on their CROs to manage study data. Data is captured in “study planning” Excel spreadsheets, which still need manual collation, and are often delivered later than required for critical insights. Medical review processes are 100% manual.
Operational
Organizations begin to adopt inhouse transactional systems such as CTMS and EDC to capture data. They begin to study risk and explore RBM initiatives. They graduate to “portfolio planning” Excel spreadsheets, but still are working with highly siloed data and are challenged by difficult reporting requirements.
Progressive
This stage is where organizations begin to adopt a more streamlined manner of managing data. They engage a data-system agnostic approach to centralized data, work well with their CROs for full data transparency and have a good understanding of their monthly portfolio status. However, many of their workflows and processes are still manual.
Fully Engaged
In this most advanced stage, organizations have fully embraced and adopted risk-based monitoring. With a unified, real-time command center they are able to take advantage of automated outlier detection and data-driven decisions. Their insights are guided by industry benchmarked data. Workflows are automated and push-based. These advances acknowledge the true power of automation in the clinical data environment.
As companies traverse this landscape, a couple of things become apparent:
1) No longer should clinical data managers rely on manual data management
2) Information becomes ubiquitous, no more “siloed” data
3) Data accuracy is a given, using best practices and automation in a data-driven environment
In order to reach the top level, systems must be in place and integration among systems needs to be automated. The goal of this design is to collect, correlate, harmonize data and then quickly present the insights to users in a personalized fashion to be able to speed processes and decision-making with a minimum of errors. Once an organization reaches this level, it is able to provide clinical data in a fast, proactive and fact-based way.
Where are you in this progression?
For more information about this presentation and how Clinical Intelligence solves the data divide, please contact us at info@comprehend.com.