Making sure that clinical data is of the highest quality is a primary responsibility of every clinical data manager. It’s quite remarkable how adept the people in this profession are at reconciling spreadsheets and making sure that data in a given EDC system is as clean as it can be. They are also quite good at finding out why certain data is nonexistent or missing and following up to make sure that all data is received.
If there’s a problem with the things data managers do, it doesn’t have to do with the outcome as much as it has to do with the tedious, time-consuming nature of standard operating procedures (SOPs) that have been in place for the past twenty years.
Manual work can be prone to error, especially if it’s put into inexperienced hands. But regardless of a particular data manager’s expertise, everybody knows with certainty that manual work takes more time than it should. In today’s clinical trial scenarios, things that take weeks should really take minutes.
So why haven’t clinical study data management SOPs evolved? Why has the if-it-ain’t-broke-don’t-fix-it mentality persisted? The simple answer is that data managers don’t see an all-encompassing system out there that will make their lives easier. The prevailing attitude is that the time-consuming old way is preferable to trying something new that won’t measure up to the hype.
EDC systems, for example, were supposed to make data managers super men and super women. The problem is that EDC systems don’t hold all the necessary patient data, so data managers must continue to carry the burden of manually reconciling what’s in the EDC with data coming from different labs.
In a similar way, business intelligence systems were supposed to make short work of generating reports. While this can be true for existing data points, anything that requires new types of data takes weeks to update. So once again, data managers are stuck doing a lot of manual work.
One of our main goals at Comprehend is to tackle these issues and create processes that make clinical study data management faster and smarter. Getting to data lock faster through automation is the business outcome everyone can rally behind. To meet this objective, we already offer the following process improvements:
- Data Unification
By unifying data from disparate systems, EDC and lab data can be integrated and updated in near real time.
- Data Review Process
The Data Review Tracking feature of the Comprehend platform makes it possible to track data that’s already been reviewed by data managers, medical monitors, and others, so team members can easily see new data and data that’s changed. Using this feature, data managers can automatically highlight changes to, and discrepancies in, patient data—and mark reviewed data at the individual or functional level—so issues can be identified as soon as they happen for faster resolution.
- Common Data Language and Collaboration
All too often, data is viewed across teams in different formats, and in some cases it may present different scenarios to different functional groups within the study team. With Comprehend’s Unified Study Data Model, everyone shares a common data language from day one. What’s more, a built-in task management system facilitates collaboration by enabling the assignment of tasks in a transparent and auditable way until closure is achieved.
- Robust Reporting
Using a software module called KPI Studio, data managers can build their own reports without any programming, making it easy to view data within a study and across an entire portfolio.
The way data moves throughout the course of a clinical trial is a limiting factor in completing trials on time, mitigating risk, and receiving approval. As things stand today, manual work takes more time than necessary, and things that fall through the cracks show up so late in the process that Herculean efforts are required to make things right.
At Comprehend, we’re dedicated to streamlining SOPs so data managers can do what they do best a lot faster. To learn more, request a demo.
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