CONTEXT
A healthcare client conducting a clinical trial for a new diabetes medication was facing major inefficiencies during patient recruitment. Manual screening of electronic health records (EHRs) for eligibility criteria such as age, HbA1c levels, medication history, and comorbidities was time-consuming, error-prone, and delaying trial timelines.

RESOLUTION
Our data science team developed a lightweight AI-based screening tool using Python and scikit-learn. The model was trained on historical patient data to predict trial eligibility with high accuracy. It was seamlessly integrated into the client’s EHR system, allowing real-time identification of qualified participants. Custom logic and explainability features ensured the tool remained compliant and clinically interpretable.

RESULT
The AI solution reduced screening time by 50%, minimized manual errors, and accelerated participant enrollment. It improved operational efficiency and allowed the clinical team to focus more on patient engagement and care delivery, ensuring a timely and credible trial launch.