Business Problem – A major healthcare provider required an optimized solution to better identify patients eligible for clinical trials. Their existing systems limited the ability to analyze key factors influencing patient randomization and participation forecasts. The organization needed to enhance its data processing and analytics capabilities to streamline trial recruitment, forecast patient participation, and improve decision-making efficiency.
Approach
- Built sophisticated Power BI data models that integrated EHR data, demographics, and treatment history to analyze patient eligibility patterns for clinical trials.
- Used SQL and DAX for automated data transformation, creating calculated measures to identify patients with higher chances of being randomized based on historical data.
- Developed an ETL process that integrated unstructured data like clinical notes for a comprehensive view of health and study suitability.
- Created scalable workflows that allowed the incorporation of additional data sources and analytics in the future.
Impact
- Provided insights into key factors influencing patient randomization for clinical trials, enabling more informed decision-making.
- Enhanced the recruitment strategy by clarifying eligibility criteria based on historical data analysis, reducing the time to identify eligible patients by 40%.
- Improved communication and engagement with potential trial participants through real-time insights, resulting in a 30% increase in participant response rates.
- Aided strategic planning for trials by understanding factors impacting patient eligibility.
- Fostered a culture of continuous improvement in predictive modeling for better recruitment outcomes.
