The IND application process can be complex, especially when data is collected at multiple locations that involve many investigators. However, data science initiatives can help organizations break down data silos and leverage that data to design more optimized clinical trials. For example, data scientists can use an AI software solution
to analyze patient profiles along with their medical histories to pick patients who will respond best to a drug being tested. The result is valuable time saved when trying to find suitable patients for clinical trials.
Through predictive analytics, pharmaceutical companies can use their past trials or a database of trial data from other companies to find and establish best practices for upcoming clinical trials. Having information about procedures, clinical trial operations, and the trial’s relative success rate will help researchers plan future trials with ideal conditions and avoid past mistakes.