A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods

Abstract

Background

Artificial intelligence (AI) enabled algorithms can detect or predict cardiovascular conditions using electrocardiogram (ECG) data. Clinical studies have evaluated ECG-AI algorithms, including a recent single-center study which evaluated outcomes when clinicians were provided with ECG-AI results. A Multicenter Pragmatic IMplementation Study of ECG-AI-Based Clinical Decision Support Software to Identify Low LVEF (AIM ECG-AI) will evaluate clinical impacts of clinical decision support software (CDSS) integrated within the electronic health record (EHR) to provide point-of-care ECG-AI results to clinicians during routine outpatient care.

Methods

AIM ECG-AI is a multicenter, cluster-randomized trial recruiting and randomizing clinicians to receive access to the CDSS (intervention) or provide usual care. Clinicians are recruited from 5 geographically distinct health systems and clustered at the care team level. AIM ECG-AI will evaluate clinical care provided during >32,000 eligible clinical encounters with adult patients with no history of low LVEF and who have a digital ECG documented within the health system's EHR, with 90 day follow up.

Results

Study data includes clinician surveys, study software metrics, and EHR data as a read-out for clinician decision-making. AIM ECG-AI will evaluate detection of left ventricular ejection fraction ≤40 % by echocardiography, with exploratory endpoints. Subgroup analyses will evaluate the health system, clinician, and patient-level characteristics associated with outcomes (NCT05867407).

Conclusion

AIM ECG-AI is the first multisite clinical evaluation of an EHR-integrated, point-of-care CDSS to provide ECG-AI results in the clinical workflow. The findings will provide valuable insights for clinically focused software design to bring AI into routine clinical practice.