Analysis of Accuracy in ECGs Data Captured by Patients Using At-Home AI-Assisted Device Compared to Qualified Clinician Recordings

Analysis of Accuracy in ECGs Data Captured by Patients Using At-Home AI-Assisted Device Compared to Qualified Clinician Recordings

Alvin Sherman Library

Cardiovascular Diseases (CVDs) are a leading cause of death worldwide, accounting for 17.3 million deaths per year. Recent advancements in artificial intelligence (AI) introduce promising opportunities in the field of cardiology to support healthcare services outside traditional clinical settings. At-home care generally promotes patient independence and improved health outcomes, while staying cost-effective due to decreased hospital visits. This study will analyze deidentified patient pilot data to assess the feasibility and performance of AI-assisted at-home electrocardiogram (ECG) monitoring compared with standard clinician-obtained ECGs during routine clinical visits, focusing on signal quality and accuracy. Additionally, the data consistency will be examined to identify common user-related errors for the at-home recordings. Study findings will be used to improve AI-based clinical technology usage and patient training. Optimizing the effectiveness of at-home ECG monitoring may support earlier detection of cardiac rhythm abnormalities with real-time analysis and contribute to improved quality of life and reduced healthcare costs.