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Commentary|Videos|October 23, 2024
Prioritizing Data Quality Amidst Complexity in Clinical Trials
Author(s)Andy Studna, Senior Editor
In this video interview with ACT editor Andy, Studna, Silvio Galea, chief data & analytics officer, WCG, highlights the need for relevant, quality data and the challenges with needing to use multiple technologies.
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In a recent video interview with Applied Clinical Trials, Silvio Galea, chief data & analytics officer, WCG, discussed the challenges in the clinical trial industry, emphasizing low awareness among both patients and providers. He highlighted operational issues, including the difficulty in identifying and engaging patients, data interoperability, and ensuring data quality. Galea stressed the importance of user experience in electronic data capture tools and the need for industry standards. Prioritizing data collection should focus on safety, efficacy, and efficiency, ensuring data quality, and avoiding workflow disruptions.
A transcript of Galea’s conversation with ACT can be found below.
ACT: On the clinical operations side, how should stakeholders be prioritizing the data they receive considering how many new solutions and data collecting mediums have entered the market recently?
Galea: Ideally we wouldn't have to prioritize the data we're collecting, because it should all be equally valuable, right? The challenge with that is that it's not coming in as a single stream, tying back to the lack of interoperability and the sheer number of technical solutions that are out there. I keep hearing metrics like 13-15 different technologies that CRC needs to work with to make sure that we're operating in accordance to the protocol. I mean, that's crazy. Do we use 13 different technologies on a particular day? Then you've got to do this across multiple trials. That’s one issue that's leading the need to prioritize data as you rightfully ask, but if we have to do a straight up prioritization of data, it has to be the data hat ties to the key facets of our industry; safety, efficacy, and efficiency. Are we collecting the right data? Is it the data that's needed to meet the goals. Is it being collected safely? Is there adequate quality checks in place to make sure that we're not putting any part of the process at risk and is it being processed in an effective, simple, issue-averse way? [It’s about] making sure that we're not introducing issues into the workflow that is going to put the entire integrity of everything that's happened at risk and we have to go back and redo any parts.
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