Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-25 @ 3:21 AM
Ignite Modification Date:
2025-12-25 @ 3:21 AM
NCT ID:
NCT06511505
Brief Summary:
The goal of this clinical trial is to determine if a machine learning/artificial intelligence (AI)-based electrocardiogram (ECG) algorithm (Tempus Next software) can identify undiagnosed cardiovascular disease in patients. It will also examine the safety and effectiveness of using this AI-based tool in a clinical setting. The main questions it aims to answer are:
1. Can the AI-based ECG algorithm improve the detection of atrial fibrillation and structural heart disease?
2. How does the use of this algorithm affect clinical decision-making and patient outcomes? Researchers will compare the outcomes of healthcare providers who receive the AI-based ECG results to those who do not.
Participants (healthcare providers) will:
Be randomized into two groups: one that receives AI-based ECG results and one that does not.
In the intervention group, receive an assessment of their patient's risk of atrial fibrillation or structural heart disease with each ordered ECG.
Decide whether to perform further clinical evaluation based on the AI-generated risk assessment as part of routine clinical care.
Detailed Description:
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, we aim 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 the Tempus Next software, which includes AI algorithms for analyzing 12-lead ECGs. Clinicians randomized to the intervention group will automatically receive an ECG with "Risk-Based Assessment for Cardiac Dysfunction" when ordering a 12-lead ECG within EPIC during the study period. If a high-risk result is identified, clinicians will receive an EHR inbox message recommending a follow-up diagnostic test, such as echocardiography and/or ambulatory ECG monitoring.
Outcome Tracking:
A monthly report will track and provide data on:
The proportion of patients with a high-risk result. The proportion of patients receiving the follow-up diagnostic test. The proportion of patients receiving guideline-recommended therapies. This report will be sent to the study participants and clinicians randomized to the intervention group. Clinicians in the usual care group will not receive any communication from the study investigators.
Study:
NCT06511505
Study Brief:
Protocol Section:
NCT06511505