---
title: 'Mayo Clinic primary-care practices: EAGLE AI-ECG clinical decision support'
slug: mayo-eagle-ai-ecg-screening
stable_id: 304701f2a0cd4f80
company: Mayo Clinic primary-care practices
function_code: customer_service
pattern_codes:
  - continuous_decisioning
evidence_strength: verified
publication_tier: showcase
freshness: current
reviewed_at: '2026-08-23'
updated_at: '2026-08-22'
source_quality_summary: 1 peer reviewed; publication outcomes are verified.
caveat_summary: >-
  Pragmatic cluster-randomized result supports causality for diagnosis, not downstream
  morbidity or mortality benefit.
collections: []
bundle_version: 1.0.0
bundle_fingerprint: sha256:c23c6cc2b88153d008ea8fda928f632ce0011fc2d4c5036672a16e5d895bab93
canonical_url: https://brianletort.ai/transformations/mayo-eagle-ai-ecg-screening
---

# Mayo Clinic primary-care practices: EAGLE AI-ECG clinical decision support

A routine ECG can trigger evaluation for asymptomatic low ejection fraction before specialist suspicion.

Function: Customer service. Patterns: Continuous decisioning. Evidence: verified.

Freshness: current. Reviewed: 2026-08-23. Updated: 2026-08-22.


Source quality: 1 peer reviewed; publication outcomes are verified.

## Before

1. **Primary-care clinician** — Order ECGs for routine clinical reasons and evaluate heart failure using usual-care signals. (control: Usual care)
2. **Clinician** — Order echocardiography when symptoms or judgment indicate it. (control: Human suspicion)

## After

1. **AI-ECG model** — Analyzes routine ECGs and reports high likelihood of low ejection fraction. (control: Positive/negative decision support)
2. **Primary-care clinician** — Interprets the AI result and decides whether to order echocardiography and diagnose low EF. (control: Clinician retains diagnostic authority)

## Decision rights

The model flags risk; clinicians decide whether to order an echocardiogram and make the diagnosis.

## Exception path

A positive result does not diagnose low EF; clinicians can decline imaging, and usual care continues for negative results.

## Outcomes

- **New diagnosis of ejection fraction at or below 50% within 90 days** (verified): 1.6% in usual-care control practices → 2.1% with clinician access to AI-ECG results; odds ratio 1.32, P=0.007. Pragmatic cluster-randomized result supports causality for diagnosis, not downstream morbidity or mortality benefit.

## Executive lesson

A low-cost model created value because it changed which existing ECGs triggered confirmatory action, while the clinician retained the diagnosis.

## Anti-pattern

Calling the AI output a diagnosis or extrapolating increased case finding to mortality reduction.

## Questions for leaders

- What confirmatory action follows a positive score?
- Are we measuring patient outcomes beyond detection?

## Sources

- [Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial](https://pubmed.ncbi.nlm.nih.gov/33958795/) — Nature Medicine / PubMed