Reducing 2D echo turnaround time with AI-assisted triage

Why 2D echo turnaround is a bottleneck

A 2D echocardiogram — also called a transthoracic echo or TTE — is one of the most common cardiac imaging studies in the hospital. It is non-invasive, relatively quick to acquire, and rich with diagnostic information. Yet in many rural and critical-access hospitals, the time from acquisition to signed report is measured in days, not hours.

The delay is rarely clinical. Sonographers know how to capture the views. The holdup is logistical: the study sits in a queue, the on-call cardiologist is paged, voicemail is exchanged, images are faxed or emailed piecemeal, and a formal read is scheduled when the reader has bandwidth. Patients with potentially urgent findings wait alongside routine cases because the workflow cannot distinguish them quickly.

What AI echo triage actually does

Echo artificial intelligence in this context is a structured-triage layer. After a sonographer uploads a 2D echo, the model returns a preliminary impression organized by chamber, valve, and severity — for example, suspected severe aortic stenosis, reduced left-ventricular ejection fraction, or a moderate pericardial effusion. Each impression carries a confidence score.

The key word is preliminary. The AI does not sign the report. It simply sorts the queue so that higher-acuity or higher-confidence abnormal studies reach the cardiologist first. A normal, high-confidence study can be reviewed in batch; a severe, high-confidence finding gets immediate attention.

Turnaround comparison: fax-and-phone vs. AI triage

Subjectivity is not erased — it is made visible

Echocardiography is interpretive. Measurements like ejection fraction depend on view quality, endocardial definition, and reader technique. A well-designed echo AI workflow does not hide that subjectivity; it surfaces it. Low-confidence impressions are flagged for closer review, and every cardiologist override is logged for concordance analysis.

In the EchoReview AI research preview, the signed report always belongs to the licensed cardiologist. The AI impression is starting material that the reader can accept, edit, or discard. This preserves clinical accountability while still compressing the time before the reader touches the case.

Safety guardrails for rural pilots

For a hospital to adopt this workflow safely, the pilot is run in shadow mode against the existing clinical read. The AI impression is not inserted into the EHR or used for patient care during the quality-improvement period. A signed Business Associate Agreement and IRB approval are prerequisites when any PHI is involved.

De-identified uploads are also supported. DICOM files are parsed in the browser, and patient name, MRN, DOB, and accession number can be stripped before any byte crosses the hospital firewall. This lets sites evaluate turnaround impact without exposing PHI.

How to measure success

The north-star metric is time-to-read: median hours from upload to signed report, stratified by severity tier. Secondary metrics include concordance between AI triage and final cardiologist impression, the rate at which severe cases are correctly escalated, and sonographer satisfaction with the upload process.

These metrics align with the operational goal of rural QI programs: not to remove the expert, but to make the expert's time go further.