Life Sciences

Overview of AI in Clinical Data Review Platforms

Enhancing the clinical data review and cleaning process using available AI technologies, will enable pharma companies to release new drugs to the market following an effective process and ensuring the safety & efficacy of the drugs released.

During the past several years, many pharmaceutical companies implemented home-grown or off-the-shelf Clinical Data Review Platforms. They defined the processes followed by reviewers/monitors to review and clean data. Both options have their challenges with regards to ease of use, flexibility and complexity of the product. Clinical data review and cleaning is currently a time-consuming, manually-intensive process.

The Current Process

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

The current process is causing delays in meeting milestones and releasing of drugs with required validations and approvals. This process can be improved using well-trained machine learning (ML) and natural language processing (NLP) models. These models provide a user-friendly, self-documenting solution for various teams reviewing, cleaning and analyzing the clinical data. With the proposed initiatives to improve Clinical Data Review Platforms, pharma companies can analyze and understand clinical data with minimal effort and cost.

There are various functionalities that can help to search and analyze data, streamline tasks based on review plans, prioritize tasks based on milestone and provide current study status. For each, ML, NLP or a combination of ML and NLP models can be used.

I will be adding more posts with details on these functionalities. The use of AI technology will have a major strategic impact on focus and differentiation. Artificial intelligence will significantly reduce the cost and effort needed for reviewing data.

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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