Recent findings from Medidata Trial Assurance highlight the power of machine learning algorithms and their impact on pharmaceutical companies seeking greater capabilities to ensure data quality.
Life science companies are continuously looking for ways to advance clinical research while simultaneously improving the understanding of drugs they are developing.
Geeks Talk Clinical contributor—often exhorts his fellow “Medidatians” to not simply work hard, but most importantly to work smart. While I’m sure he fully expects that we will continue to work with the same high level of passion as before, what he really wants is for us to seek out opportunities to increase the impact of our collective efforts.
We hear it from our customers all the time: complexity is a problem. Too many steps in the clinical process. Too many procedures. Too many doctor’s visits. Too much data. So in a recent contribution to the Data Analytics blog on Applied Clinical Trials, we decided to dig into a Medidata Insights metric that addresses one piece of the complexity puzzle: eCRF design complexity, which is a score that reflects the relative estimated work effort associated with implementing a clinical study eCRF—including eCRF build, testing and deployment—using the Medidata Rave system for electronic data capture, management and reporting.