Case Study: Identifying Potential High-Risk Factor Arrhythmia Patients Using Predictive Modeling

The Situation

For its flagship product, the Zio monitor, iRhythm was interested in knowing the populations that would best benefit from early monitoring of arrythmia to build a value story for payers. EVERSANA collaborated with iRhythm to harness real-world data (RWD) and predictive modeling techniques for enhancing arrhythmia patient management. This project sought to leverage EVERSANA’s robust data infrastructure and predictive capabilities to address critical healthcare questions, such as “What are the key risk factors for arrhythmia?” and “Who are the most suitable patients for monitoring?”

The Solution

The primary objective of this project was to develop a predictive model to accurately identify patients at risk of arrhythmias and to tailor treatment strategies effectively. By utilizing extensive patient data, including clinical, demographic and social determinants of health (SDOH), the model aimed to provide actionable insights into patient care and improve the efficiency of healthcare providers (HCPs) in managing high-risk patients. The ultimate goal was to enhance the quality of life for patients while ensuring cost-effective healthcare delivery.

To address these challenges, EVERSANA employed a multipronged strategy. A comprehensive data aggregation platform was set up, integrating various sources of healthcare data and allowing models to analyze features extracted from healthcare claims data, such as treatment histories, healthcare provider characteristics, patient demographics and social determinants of health. Then, advanced machine learning techniques, including SHAP analysis and clustering algorithms, were utilized to dissect and predict risk factors associated with arrhythmias, as well as understand different patient phenotypes.

The Results

The project successfully demonstrated the capacity of predictive modeling to revolutionize arrhythmia patient care. Key findings revealed significant risk factors that can aid in crafting targeted, personalized treatment plans. These results could lead to not only improved patient outcomes, but also optimized resource allocation, proving the immense value of predictive modeling in healthcare. For potential clients, these insights and capabilities highlight EVERSANA’s commitment to advancing healthcare analytics and its continued impact on global health systems.