Accelerating tomorrow's cures:
Faster Enrollments for faster trial completions
Challenges in Patient Recruitment
- Missed Deadlines: Approximately 80% of clinical trials fail to meet their enrollment deadlines. Delays in patient recruitment can have a cascading effect, postponing the introduction of potentially life-saving treatments.
- Terminated Trials: In a distressing statistic, 42% of clinical trials are prematurely terminated due to low enrollments. This not only wastes valuable resources but also squanders the research and development efforts invested in these trials.
- Patient Dropouts: An alarming 30% of patients drop out of clinical trials before study completion. This attrition undermines the validity of the trial results and can be attributed to various factors, including stringent inclusion and exclusion criteria, distance to trial sites, and patient dissatisfaction.
AI-Powered Patient Recruitment: A Game Changer
- Enhanced Access: AI proactively identifies eligible patients for trials by analyzing vast datasets and patient records, broadening the pool of potential participants, and making trials more accessible to those who can benefit.
- Reduced Timelines: AI improves the success rate of trial enrollments, swiftly identifying eligible patients and reducing recruitment time, ultimately lowering costs.
- Empowered Physicians: AI equips physicians with a searchable repository of ongoing trials, enabling faster and more accurate patient recommendations, enhancing their role in the recruitment process. This innovation accelerates treatment development, making healthcare more accessible and efficient.
How AI-Powered Patient Recruitment Works
- Data Collection: Aggregate clinical data for patients from multiple sources and clinical trials related information, creating a comprehensive patient and clinical trial database.
- Natural Language Processing (NLP): An NLP Engine extracts key information on inclusion and exclusion criteria from clinical trial protocols and extract patient related data from hospital or healthcare data sources.
- Fuzzy Matching: AI-based fuzzy matching engine matches patients to clinical trials based on inclusion and exclusion criteria and patient related information, enhancing precision and speed.
- Recommendations: The system suggests clinical trial sites with target patient pools based on matching scores, streamlining recruitment.
- Assessment: The AI assesses clinical trial designs to estimate their potential for finding matching patients. This innovation accelerates treatment development and elevates the well-being of patients.