Research Cohort Builder

This Research Cohort Builder page demonstrates how AI can transform clinical research recruitment by allowing researchers to:

1. Define cohorts using natural language

   • Instead of building complex SQL queries or filling out numerous form fields, researchers can simply describe the patients they're looking for in plain English. The AI understands medical terminology, conditions, lab values, and temporal relationships.

2. See AI's understanding in real-time

   • The system extracts and displays the key inclusion/exclusion criteria it identified from the natural language query, showing researchers exactly how it interpreted their request and allowing them to refine if needed.

3. Process vast amounts of data quickly

   • The mockup shows the system processing over 15,000 patient records in just 4.2 seconds to identify eligible patients, a task that would take researchers hours or days manually.

4. Discover meaningful patterns

   • The AI doesn't just find matching patients but provides research insights by identifying patterns across the cohort, such as treatment gaps, social determinants of health factors, and educational needs.

5. Generate visualizations automatically

   • The system creates relevant charts and graphs showing the characteristics of the identified cohort, making it easier to understand the patient population.

6. Build protocol from identified patients

   • Researchers can use the identified cohort to automatically generate a research protocol, streamlining the study startup process.

7. De-identify patient data for research

   • The system can prepare anonymized datasets suitable for research while maintaining compliance with privacy regulations.

8. Save and track cohorts over time

   • Researchers can save their query results and track how the patient cohort changes over time as new data enters the system.

This approach dramatically reduces the time and expertise needed to identify patients for clinical trials, potentially accelerating research timelines and improving recruitment for clinical studies.