Life Sciences

How AI Can Enhance the Clinical Data Review and Cleaning Process

Ensuring that clinical trial data is accurate and that clinical trials are safe and effective, is a time-consuming and a manually intensive process. Many life sciences companies have implemented home-grown or off-the-shelf clinical data review platforms and defined the reviewing and cleaning processes to be used with them.

Many of these systems have proven to be challenging to use, inflexible, and created frustration among users.

Life Sciences - How Artificial Intelligence Can Enhance the Clinical Data Review and Cleaning Process
How Artificial Intelligence Can Enhance the Clinical Data Review and Cleaning Process

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.

Get the Guide

This guide discusses how artificial intelligence (AI) – including machine learning (ML), including deep learning (DL) and natural language processing (NLP) – can be used by pharmaceutical and medical device companies to improve the clinical data review and cleaning process. These technologies enable drugs and devices to reach the market faster and more safely and effectively.

To learn more about the clinical trial data process, you can download the guide here, or you can submit the form below.

About the Author

Prabha Ranganathan is a delivery director at Perficient and is responsible for delivering data warehousing and analytics solutions for various life sciences and health care companies. Prabha works closely with customers providing strategic advice on clinical data flows, data reviewing and cleaning using latest technological tools and solutions. Prabha has experience in building and releasing products from concept to release at Oracle, in various roles as Product Manager, Architect and Lead Developer. During most of her career, she has worked on enterprise products dealing with large volumes of data from various sources that need to be reviewed, cleaned and analyzed. With a clear understanding of business and strong technical knowledge, she brings a unique skillset to solve complex problems. She received her MBA from Babson College and M.S in Computer Science from Illinois Institute of Technology.

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