#FutureReadyHealthcare
Harness the power of AI/ML algorithms to enhance patient selection in clinical trials
Patient selection is one of the cornerstones for the success of clinical trials in the life sciences industry, ensuring that trials run smoothly and yield reliable results. However, the process can be resource-intensive and time-consuming due to multiple challenges—managing large and diverse patient datasets, navigating complex eligibility criteria, screening large volume of patient records, and many more. These challenges inadvertently result in trials delays that can lead to increased operations costs and risks, delayed product launches, and the risk of losing competitive advantage. Given that 80% trials do not finish on time, traditional patient selection and recruitment methods are slow and prone to error1.
So, how can pharma companies leverage technology to effectively address these challenges and streamline the entire process?
The answer lies in AI/ML (artificial intelligence/machine learning) models. AI/ML models can help trial organizers and clinicians automate, expedite, and optimize the patient selection process. Explore this infographic to discover how!
Reference
1. Clinical Trials AReNA Clinical Trial Delays: America's Patient Recruitment Dilemma. 2012