Close Icon
Article Blog March 15, 2017 3 minute read

Medical Data Review: 3 Process Improvement Takeaways

In a recent monthly demo, one question from a data manager who joined the event struck me as particularly telling:

“Is there a way to automatically aggregate and refresh data sources from multiple vendors – for example, one study from Oracle EDC and one from Medidata Rave?”

This isn’t a unique question. In fact, I’d be surprised if most people reading this article haven’t asked a similar question themselves. Rather, it sticks out because it is such a pervasive problem in the industry. The success of the push to capture new data sources has indirectly led to a situation where data is a liability rather than an asset. How can we evolve to a point where we flip this equation?

It is this core question that drove much of the discussion last week, which, in turn, led to three key takeaways worth sharing – each of which relates to a different problem that currently plagues the medical data review process.

1. Automate data aggregation so we can make sense of the data we capture

As mentioned above, many systems have been developed to handle this all-important data capture step. When this data capture is not paired with a system capable of making sense of it, however, this data becomes a problem rather than a solution. One solution to this problem is to leverage an agnostic platform, like Clinical Intelligence, that aggregates and normalizes datathrough common data model and pre-integrates with the data capture sources, regardless of type. By having data aggregation automated, both the clinical and data management teams have real-time access to all the information they need for medical data review in one location.

2. It is time to integrate workflow with the data being reviewed

The days of reviewing data offline in paper format and using email or phone calls for follow up are over. Between operations, data, clinical and the various vendors and CROs involved in trials, the data review process quickly grows complex. And the medical data review process itself usually involves dozens of steps – from data aggregation to issuing queries to clinical assessment – without access to accurate patient level data. By integrating task management and data review workflow tools alongside data on adverse events, protocol compliance, and lab values, Clinical Intelligence streamlines this process in a single system.

3. Big clinical decisions should be informed with as many insights as possible

Making the right clinical decisions at the right times is fundamental to achieving regulatory approval for a new drug. Yet manual data compilation often results in delayed access to incomplete data that could help teams make these critical decisions. Automating data aggregation is a step in this direction, but it should be coupled with useful insights that provide context – whether from labs, medical history, dosing schedule, demographics, adverse events or concomitant medications – for any clinical assessment that leads to a significant clinical decision. Built into the clinical intelligence platform are tools like graphical patient profiles and a customizable reporting studio that enable in depth data analysis and in turn provides the insights needed for the core team’s informed decision making.

During last week’s demo, we learned that, despite the high stakes, there are still a few stumbling blocks to running a successful clinical trial. By taking a few simple steps and investing in new types of software like Clinical Intelligence, we can make significant strides toward addressing them.

For greater details on these takeaways, the recording of the demo and discussion can be found here.

Saama can put you on the fast track to clinical trial process innovation.