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
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.
