Managing Site and Study Performance in Clinical Trials with AI

Clinical trials are extremely expensive and are often fraught with delays. With the increasing complexity of clinical trials, the cost and delays tend to increase and add to the potential risk-factors as well. The pharmaceutical industry is leveraging AI technology to streamline clinical study processes and make them more efficient. It has already made a significant difference in site and study performance management.

Here are a few ways in which an AI-powered platform can manage the performance of the clinical study across sites more effectively than any other tool:

  1. Collecting and sharing clinical data in real-time: The users involved with the clinical study can access and use the clinical data recorded at sites in almost real-time. With built-in data standardization procedures, the platform immediately harmonizes all data that it receives and makes it ready for analysis and quantification. Such access to clinical data allows users to keep track of study milestones and checkpoints and immediately address any potential red flags.
  2. Improving site and patient outcomes: One of the reasons for a rise in cost and increase in delays is patient dropout. By using machine learning capabilities to integrate patient inclusion/exclusion criteria into its processes, it is possible to choose the sites with access to the most relevant patient pools. The solution can also flag a particular site’s performance in advance if a potential reason for any future delay is identified.
  3. Using advanced features like virtual assistants: As AI technology advances, it is able to offer more ways of leveraging data. One such capability is the use of Virtual Assistants (VAs). A VA that is context and domain aware, and is able to leverage Natural Language Processing (NLP), makes it possible for the user to interact with their data in new and dynamic ways. This capability allows more flexibility to the business users, in making the most advantageous strategic decisions.

With time, the application of AI technologies in clinical trials is bound to grow even more. It is critical for pharmaceutical companies to choose the right AI solution that is developed to deliver outcomes specific to the industry and has the most experienced data and AI experts designing it.

Saama’s Life Science Analytics Cloud (LSAC) makes it possible to address site and study performance challenges by using its Machine Learning and Deep Learning capabilities.

Facebook
LinkedIn
Twitter
YouTube

About Leon Surgeon

mmLeon Surgeon has over 20 years of experience in the Software and Telecommunications industries. Results oriented product marketing/management leader skilled in profitably driving key areas of corporate business operations including the conception, strategy and implementation of a wide array of technology products and services.


Related Posts