Ask Indegene (Beta)
Online
🧠Building on our previous conversation...
Hello, how can I help you today?
You may type your question or choose from the options below:
Explore Solutions
Browse Insights
View Case Studies
Read Latest News
Explore Careers
Connect with an Expert
Thank you!
We'll be in touch. In the meantime, feel free to keep exploring!
#PractitionerLevelConfidence
Patient selection is a cornerstone of clinical trial success, but it is also one of the most resource-intensive and time-consuming steps. Large and diverse patient datasets, complex eligibility criteria, and manual screening of thousands of records often make the process cumbersome and error-prone. These challenges contribute to significant delays; 80% of clinical trials do not finish on time and nearly half of trial sites miss their enrollment goals1. Such setbacks increase operational costs, delay product launches, and put competitive advantage at risk. To address these issues, pharma companies are now turning to AI-based patient selection for clinical trials to bring greater efficiency and precision into the process.
By applying AI and machine learning in clinical trials, vast volumes of patient data from EHRs and claims to registries, lab results, and medical literature can be analyzed quickly and accurately. AI/ML models automate screening, predict eligibility, and continuously refine outputs to improve accuracy over time. This approach accelerates recruitment, ensures more diverse and representative patient pools, and increases the likelihood of trials finishing on schedule. For life sciences companies, AI-based patient selection for clinical trials not only reduces time and costs but also strengthens the overall reliability of clinical research.
Explore this infographic to discover how.
References
1.
Clinical Trials AReNA Clinical Trial Delays: America's Patient Recruitment Dilemma. 2012
