NOrthwestern Tempus AI-enaBLed Electrocardiography (NOTABLE) Trial

There is a large burden of undiagnosed, treatable cardiovascular disease (CVD), encompassing various heart conditions such as arrhythmias (e.g., atrial fibrillation) and structural heart diseases (e.g., valvular disease). Early detection and accurate diagnosis can significantly improve patient outcomes by enabling timely, guideline-based interventions or therapies. The goal of this study is to leverage machine learning approaches to enhance the detection and diagnosis of CVD. By identifying patients at risk of undiagnosed CVD and referring them for further clinical evaluation, the study aims to improve health outcomes. Study Overview: The NOTABLE study will compare the rates of new disease diagnoses, therapeutic interventions, and cardiovascular outcomes between two groups of patients managed by clinicians at Northwestern Medicine: Patients whose clinicians use ECG predictive models. Patients whose clinicians do not use ECG predictive models. Intervention Details: This study utilizes Tempus AI algorithms (rECHOmmend and ECG-AF) to analyze 12-lead ECGs. Clinicians randomized to the intervention group will receive a "Risk-Based Assessment for Cardiac Dysfunction" when ordering a 12-lead ECG within EPIC. If a high-risk result is identified, clinicians receive an EHR inbox message suggesting a follow-up diagnostic test, such as echocardiography and/or ambulatory ECG monitoring. Outcome Tracking: Weekly summaries will inform clinicians in the intervention group of high-risk results identified by the AI algorithm. Clinicians in the usual care group will not receive any communication from the study investigators regarding AI predictions.