Objective:

To evaluate the real-world implementation of an AI-enabled regional tele-ECG cloud platform and its impact on cardiovascular diagnostic accessibility.

Approach:

• Study Design: A longitudinal real-world evaluation using 1,998 tele-ECG transmissions, including 53 confirmed myocardial infarction cases.
• Data Analysis: Inter-tier comparisons, subgroup analyses by geographic location and age, post-hoc power assessment, and Budget Impact Analysis.

Key Findings:

• Median clinical decision-making time decreased from 10 to 3 minutes (70.0% reduction).
• Report turnaround time was 3.79 ± 1.81 minutes.
• Diagnostic accuracy at the PHC level increased from 82.30% to 98.11%.
• Survival-to-discharge rate among acute MI cases at PHC institutions was 75.00%.
• Average patient saving was 26 CNY per encounter with an annual net social benefit of 154,182.50 CNY.

Interpretation:

The AI-enabled regional collaborative model improved access to cardiovascular diagnosis across diverse settings.

Limitations:

• Limited statistical power in the PHC subgroup (mean power: 32.4%).
• Potential confounding from seasonal population migration.

Conclusion:

The platform provides a scalable approach for strengthening cardiovascular diagnostic capacity in resource-constrained regions.