In my last blog post, I presented some thoughts on showing data changed since last review. Today, we will concentrate on the level of scrutiny and what happens when recording the user actions in the review.
Not all data may require the same level of scrutiny during a review. Additional focus may need to be placed on certain critical measures. For these critical measures, it may be necessary to establish additional value added data or visualizations/aids to support the desired level of scrutiny.
This guide analyzes how artificial intelligence – including machine learning – can be used by pharmaceutical and medical device companies to improve the clinical data review and cleansing process.
The post entitled 4 Ways You Can Improve The Clinical Data Review Process provides a few examples of visualizations that can help pinpoint outliers.
If you are interested in learning how Perficient can help improve your clinical data review process, please send us an email or fill out the “contact us” form in the footer of this page.