Evidence map›Paper›PMID 41574034›Full record

ArticleEuropean heart journal. Digital health2026

Factors associated with physician modifications to automated ECG interpretations.

I Min Chiu, Yuki Sahashi, Sam S Torbati, Sumeet S Chugh, David Ouyang

Abstract read
In one paragraph

Article in European heart journal. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

I Min ChiuDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Yuki SahashiDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.ORCID https://orcid.org/0000-0003-2260-4166
Sam S TorbatiDepartment of Emergency Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Sumeet S ChughDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.ORCID https://orcid.org/0000-0002-4417-5326
David OuyangDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.ORCID https://orcid.org/0000-0002-3813-7518

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Accurate diagnoses contribute to the improvement of clinical workflows and the enhancement of patient care. Commercially available automated electrocardiogram (ECG) interpretation systems require manual review by physicians despite their widespread use. This study investigates the frequency and characteristics of the modifications from automated ECG reports in routine clinical practice. Methods and results: We retrospectively analysed 159 630 ECGs from 2011 to 2023 and compared automated preliminary ECG reports generated by the GE Marquette™ 12SL ECG analysis programme with finalized reports by physicians. A modification was defined as any textual difference between the initial and final reports. Our analysis revealed that 31.3% of all ECG reports underwent some forms of modification by physicians. We analysed the frequency of 69 pre-defined ECG-related terms before and after physician review, categorizing modifications as unchanged, deleted, or newly added. Modifications were more frequent for ECGs performed during off-hours, in patients with higher ventricular rates and longer QRS durations. At the term-level, diagnoses such as 'prolonged QT interval' (newly added from 5.6% of original reports) and 'electronic ventricular pacemaker' (newly added from 3.6% of original reports) were frequently added by physicians, while diagnoses like 'inferior infarct' and 'anterior infarct' were frequently deleted from automated ECG reports (32.0% and 44.6% automated reports with these terms required removals). Conclusion: This large-scale real-world study demonstrated the high frequency of physicians' modification in automated ECG interpretation. The identified patterns of modifications highlight the limitations of current rule-based systems in handling complex cases and nuanced ECG findings.

Indexed as

12-Lead electrophysiologyAutomated interpretationsPhysician modifications

Identifiers

PMID41574034
PMCPMC12821064

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.