Metadata management in clinical R&D is centered on the concept that each piece of data collected for a clinical trial, as defined by that trial’s protocol, can be managed independently. Each piece of metadata and logical groupings of many metadata items together can be governed and managed in the organization. This includes version control, data edit rules for that data item, and transformation rules for that data item as it changes to support the analysis process. In addition, it also supports the ability to trace that data element through the entire clinical trial lifecycle. This trace-ability extends from the time it’s initially captured during the clinical trial through to that data element’s submission to regulators. This trace covers all information on how that data element contributes to proving the efficacy and safety of a new therapy for regulatory approval.
As a quick example, blood pressure can be a metadata item for a clinical trial. One can attach edit rules to that blood pressure item to insure accuracy. When the user inputs the data, the rules assure that it is edit-checked properly and that transformation logic is defined to change the representation of the blood pressure data element during data entry to a completely different representation for an analysis dataset that will be written in SAS code to prove efficacy and safety of the therapy.
I can do all that and maintain version control of that blood pressure data element as I change its representation over time. Here’s another example. If I use that blood pressure data element consistently across all my clinical trials, then when I change that data element and produce a new version; I can query my metadata system on which clinical trials in my portfolio will be impacted by changing that blood pressure data element.
If I am running multiple clinical trials in which each has potentially hundreds of data items to be collected, then metadata management can help me manage those clinical trials operationally. Metadata management also helps me to insure that I maintain full regulatory compliance and trace-ability as data goes through its lifecycle from capture to submission.
As mentioned at the beginning, this industry trend has been a long time coming. The industry move over the last 10 years away from paper based clinical trials to electronic data capture based trials set the stage for this type of capability and for the operational savings from a successful metadata management solution deployment.
Unfortunately,expanding from the basic functionality described and scaling it up across a large scale clinical trial operation has proven to be very elusive to date for many organizations. The metadata management highway is littered with several organizations that have failed to y deploy it successfully in their complex clinical trial environments. The reasons for failure are complex, including the challenge of the activity and the resulting impact on the business processes of these large, complex, early- pioneering organizations.
Recently, there’s been a new wave of momentum! Oracle Health Sciences’ (OHS) powerful partner ecosystem around its clinical R&D applications is kicking in to drive the next set of attempts at metadata management. Specifically, OHS partner Accenture is leading the charge in close collaboration with two OHS top customers - GlaxoSmithKline (GSK) and Eli Lilly & Company – to tackle, once again, this very complex problem space.
Accenture is working to build a module called Metadata Registry (MDR). The Accenture, GSK, and Lilly team is working to build this module with the above mentioned capabilities. Progress is very promising to date! They do have a large number of the above mentioned capabilities implemented successfully and are going through testing with GSK and Lilly.
In addition, the team will ultimately integrate the MDR with OHS Central Designer and Data Management Workbench applications. This integration fulfills the promise of the enormous value of the MDR module. Clinical trial metadata can be managed, version controlled, and more, within the module. Then, those same metadata data can be pushed into our Central Designer and Data Management Workbench applications at study startup and for the management of changes to study(s) in progress.
This will reduce the amount of time it takes to manage clinical trials operationally in each, respective, company’s portfolio and will increase the trace-ability and audit-ability quality each company needs for regulatory compliance.
Greg Jones is an Enterprise Strategy Architect with Oracle Health Sciences.