Evidence map›Paper›PMID 42459195›Full record

ArticleFrontiers in cardiovascular medicine2026

AI-Enabled regional tele-ECG cloud platform and improving access to cardiovascular diagnosis: real-world evidence from southern China.

Jia Xu, Min Pan, Lin Chen, Juan Fu, Rui Shi, Jun Xie

Erratum issuedAbstract read
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Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

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No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Jia XuDepartment of IT & Data Management of West China Hospital, Sichuan University, Chengdu, China.
Min PanDepartment of Cardiovascular Medicine, Sanya People's Hospital, Sanya, China.
Lin ChenDepartment of Cardiovascular Medicine, Sanya People's Hospital, Sanya, China.
Juan FuWest China (Sanya) Hospital, Sichuan University, Sanya, China.
Rui ShiDepartment of IT & Data Management of West China Hospital, Sichuan University, Chengdu, China.
Jun XieDepartment of IT & Data Management of West China Hospital, Sichuan University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Significant disparities in cardiovascular disease outcomes persist between urban centers and resource-constrained primary healthcare (PHC) settings due to geographic barriers and uneven access to specialist expertise. Digital health networks offer a potential strategy to improve diagnostic accessibility. This study evaluated the real-world implementation of an AI-enabled regional tele-ECG cloud platform within an integrated urban medical group. Methods: A longitudinal real-world evaluation was conducted using 1,998 tele-ECG transmissions, including 53 confirmed myocardial infarction (MI) cases at the PHC level within a network of 1,166 MI patients. The platform integrated AI-assisted ECG interpretation with centralized specialist review through a cloud-based B/S architecture. Analyses included inter-tier comparisons, subgroup analyses by geographic location and age (≥65 years), Results: Platform implementation was associated with reduced delays in emergency cardiovascular care. Among patients presenting with chest pain at PHC institutions, median clinical decision-making time decreased from 10 to 3 min (70.0% reduction), while report turnaround time (TAT) was 3.79 ± 1.81 min. No significant differences were observed between PHC institutions and the tertiary hospital in TAT ( Conclusions: The AI-enabled regional collaborative model was associated with improved access to cardiovascular diagnosis across geographically diverse settings. Despite limited statistical power in the PHC subgroup (mean power: 32.4%) and potential confounding from seasonal population migration, the platform may provide a scalable approach for strengthening cardiovascular diagnostic capacity in resource-constrained regions.

Indexed as

artificial intelligencebudget impact analysisdigital health infrastructurehealthcare accessibilityreal-world evidencetele-cardiologyworkflow reengineering

Identifiers

PMID42459195
PMCPMC13369231

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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.