{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T16:17:09Z","timestamp":1793463429393,"version":"4.5.0"},"reference-count":23,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      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 (\u226565 years),\n                      <jats:italic>post-hoc<\/jats:italic>\n                      power assessment, and a Budget Impact Analysis (BIA) with sensitivity analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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\u2005min (70.0% reduction), while report turnaround time (TAT) was 3.79\u2009\u00b1\u20091.81\u2005min. No significant differences were observed between PHC institutions and the tertiary hospital in TAT (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.4384) or report review times (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.8102). Diagnostic accuracy at the PHC level increased from 82.30% to 98.11%. PHC institutions achieved a survival-to-discharge rate of 75.00% among acute MI cases. The BIA showed an average patient saving of 26 CNY per encounter and an annual net social benefit of 154,182.50 CNY for the regional network.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2026.1869716","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T15:26:15Z","timestamp":1782919575000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["AI-Enabled regional tele-ECG cloud platform and improving access to cardiovascular diagnosis: real-world evidence from southern China"],"prefix":"10.3389","volume":"13","author":[{"given":"Jia","family":"Xu","sequence":"first","affiliation":[{"name":"Department of IT & Data Management of West China Hospital, Sichuan University","place":["Chengdu, China"]},{"name":"Laboratory of Medical Artificial Intelligence, West China Hospital, Sichuan University","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Pan","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Sanya People's Hospital","place":["Sanya, China"]},{"name":"West China (Sanya) Hospital, Sichuan University","place":["Sanya, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Sanya People's Hospital","place":["Sanya, China"]},{"name":"West China (Sanya) Hospital, Sichuan University","place":["Sanya, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Fu","sequence":"additional","affiliation":[{"name":"West China (Sanya) Hospital, Sichuan University","place":["Sanya, China"]},{"name":"Department of Cardiocerebral Function, Sanya People's Hospital","place":["Sanya, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of IT & Data Management of West China Hospital, Sichuan University","place":["Chengdu, China"]},{"name":"Laboratory of Medical Artificial Intelligence, West China Hospital, Sichuan University","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of IT & Data Management of West China Hospital, Sichuan University","place":["Chengdu, China"]},{"name":"Laboratory of Medical Artificial Intelligence, West China Hospital, Sichuan University","place":["Chengdu, China"]},{"name":"West China (Sanya) Hospital, Sichuan University","place":["Sanya, China"]},{"name":"Information Center, Sanya People's Hospital","place":["Sanya, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2982","DOI":"10.1016\/j.jacc.2020.11.010.","article-title":"Global burden of cardiovascular diseases and risk factors, 1990\u20132019: update from the GBD 2019 study","volume":"76","author":"Roth","year":"2020","journal-title":"J Am Coll Cardiol"},{"key":"B2","first-page":"481","article-title":"Interpretation of the China cardiovascular health and disease report 2021","volume":"30","author":"Ma","year":"2022","journal-title":"Chin J Interv Cardiol"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1802","DOI":"10.1016\/S0140-6736(20)30122-7","article-title":"Quality of primary health care in China: challenges and recommendations","volume":"395","author":"Li","year":"2020","journal-title":"Lancet"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.1016\/S0140-6736(12)61228-8","article-title":"WHO European review of social determinants of health and the health divide","volume":"380","author":"Marmot","year":"2012","journal-title":"Lancet"},{"key":"B5","doi-asserted-by":"publisher","first-page":"100767","DOI":"10.1016\/j.ijcha.2021.100767","article-title":"Accuracy of OMI ECG findings versus STEMI criteria for diagnosis of acute coronary occlusion myocardial infarction","volume":"33","author":"Pendell Meyers","year":"2021","journal-title":"Int J Cardiol Heart Vasc"},{"key":"B6","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1097\/CNQ.0b013e318266bed1","article-title":"Building a telehealth network through collaboration: the story of the Nebraska statewide telehealth network","volume":"35","author":"Meyers","year":"2012","journal-title":"Crit Care Nurs Q"},{"key":"B7","doi-asserted-by":"publisher","first-page":"170","DOI":"10.4103\/ijcm.IJCM_368_16","article-title":"Feasibility of telecardiology solution to connect rural health clinics to a teaching hospital","volume":"42","author":"Shetty","year":"2017","journal-title":"Indian J Community Med"},{"key":"B8","doi-asserted-by":"publisher","first-page":"e90144","DOI":"10.2196\/90144","article-title":"Impact of a prehospital chest pain alert app-mediated prehospital-in-hospital coordination model on treatment delays and clinical outcomes in patients with ST-elevation myocardial infarction: protocol for a 4-year retrospective real-world cohort study","volume":"15","author":"Chen","year":"2026","journal-title":"JMIR Res Protoc"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.23736\/S0031-0808.24.05277-7","article-title":"Regional disparities in the management and outcomes of ST-elevation myocardial infarction: an Italian analysis focusing on time-dependent reperfusion networks and in-hospital logistics","volume":"67","author":"Colaiori","year":"2025","journal-title":"Panminerva Med"},{"key":"B10","doi-asserted-by":"publisher","first-page":"2648","DOI":"10.1016\/j.jaip.2024.06.040","article-title":"Concepts for the development of person-centered, digitally enabled, artificial intelligence-assisted ARIA care pathways (ARIA 2024)","volume":"12","author":"Bousquet","year":"2024","journal-title":"J Allergy Clin Immunol Pract"},{"key":"B11","doi-asserted-by":"publisher","first-page":"105114","DOI":"10.1016\/j.compbiomed.2021","article-title":"Self-supervised representation learning from 12-lead ECG data","volume":"141","author":"Mehari","year":"2022","journal-title":"Comput Biol Med"},{"key":"B12","doi-asserted-by":"publisher","first-page":"e67485","DOI":"10.2196\/67485","article-title":"AI For IMPACTS framework for evaluating the long-term real-world impacts of AI-powered clinician tools","volume":"27","author":"Jacob","year":"2025","journal-title":"J Med Internet Res"},{"key":"B13","doi-asserted-by":"publisher","first-page":"1596574","DOI":"10.3389\/fcvm.2025.1596574","article-title":"Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations","volume":"12","author":"Antoun","year":"2025","journal-title":"Front Cardiovasc Med"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1635","DOI":"10.3233\/SHTI190571","article-title":"Brazilian national service of telediagnosis in electrocardiography","volume":"264","author":"Alkmim","year":"2019","journal-title":"Stud Health Technol Inform"},{"key":"B15","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1089\/tmj.2020.0262","article-title":"Quality of telehealth services in primary care in the state of Minas Gerais","volume":"27","author":"Dramos","year":"2021","journal-title":"Telemed J E Health"},{"key":"B16","doi-asserted-by":"publisher","first-page":"2","DOI":"10.5334\/gh.1182","article-title":"Implementation of a regional STEMI network in north Cairo (Egypt): impact on the management and outcome of STEMI patients","volume":"18","author":"Shaheen","year":"2023","journal-title":"Glob Heart"},{"key":"B17","year":""},{"key":"B18","first-page":"5","article-title":"A novel approach to smart prevention and treatment of cardiovascular diseases at county-level areas\u2014\u2014based on the operational practice and effectiveness evaluation of \u201celectrocardiograph network\u201d in haian","volume":"40","author":"Zhang","year":"2026","journal-title":"Soft Sci Health"},{"key":"B19","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1186\/1472-6947-12-77","article-title":"A cloud computing based 12-lead ECG telemedicine service","volume":"12","author":"Hsieh","year":"2012","journal-title":"BMC Med Inform Decis Mak"},{"key":"B20","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.3390\/bioengineering11111109","article-title":"Preprocessing and denoising techniques for electrocardiography and magnetocardiography: a review","volume":"11","author":"Jia","year":"2024","journal-title":"Bioengineering (Basel)"},{"key":"B21","doi-asserted-by":"publisher","first-page":"08TR01","DOI":"10.1088\/1361-6579\/ac7fd9","article-title":"Application of artificial intelligence techniques for automated detection of myocardial infarction: a review","volume":"43","author":"Hassannataj Joloudari","year":"2022","journal-title":"Physiol Meas"},{"key":"B22","doi-asserted-by":"publisher","first-page":"044002","DOI":"10.1088\/1361-6579\/acc77f","article-title":"Integrating multimodal information in machine learning for classifying acute myocardial infarction","volume":"44","author":"Xiao","year":"2023","journal-title":"Physiol Meas"},{"key":"B23","doi-asserted-by":"publisher","first-page":"6691177","DOI":"10.1155\/2021\/6691177","article-title":"Automated atrial fibrillation detection based on feature fusion using discriminant canonical correlation analysis","volume":"2021","author":"Shi","year":"2021","journal-title":"Comput Math Methods Med"}],"updated-by":[{"DOI":"10.3389\/fcvm.2026.1927426","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000}}],"container-title":["Frontiers in Cardiovascular Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcvm.2026.1869716\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T10:16:22Z","timestamp":1784196982000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcvm.2026.1869716\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,1]]},"references-count":23,"alternative-id":["10.3389\/fcvm.2026.1869716"],"URL":"https:\/\/doi.org\/10.3389\/fcvm.2026.1869716","relation":{},"ISSN":["2297-055X"],"issn-type":[{"value":"2297-055X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,1]]},"article-number":"1869716"}}