AI and telemedicine: total remote guidance of AMI management. (25th November 2020) - Mehta, S; Gibson, M; Niklitschek, S; Fernandez, F; Villagran, C; Escobar, E; Vera, F; Frauenfelder, A; Vieira, D; Quintero, S; Merchant, S; Tamayo, C.J; et al.
Abstract: Background: For a decade, Latin American Telemedicine Infarct Network (LATIN) Telemedicine has transformed AMI management in Brazil, Colombia, Mexico, Chile, and Argentina. With a hub and spoke strategy, AMI coverage was expanded to 100 million population and 877, 177 telemedicine encounters were performed. Cost savings from avoiding unnecessary transfer of patients was 291 million. We are now rapidly escalating on a path to making the telemedicine process "physician-free" by utilizing Artificial Intelligence (AI) protocols. Purpose: To demonstrate that AI can replace a cardiologist for remote AMI telemedicine guidance. Methods: The process of total AI guidance focused on both aspects of our telemedicine strategy – accurate AMI diagnosis and tele-guidance of the entire STEMI process. We developed our innovative approach by initially creating AI algorithms for computer-aided diagnosis. Next, we incorporated logistic variables (duration of chest pain, transfer times to LATIN hub, etc) to the algorithm for physician-free triage into thrombolysis, primary PCI and pharmaco-invasive management. The intent of creating AI algorithms was early STEMI detection and triage. After the patient was efficiently transferred to the hub, a final treatment decision was made by the hub cardiologists. Results: Three crucial areas of telemedicine efficiency are being monitored – Time-to-Telemedicine Diagnosis (TTD), Door-In-Door-Out (DIDO) and Transfer Times (TT). All are showing improvements. Detailed results will be available at the time of presentation. Conclusions: We are encouraged with the possibility of making the entire telemedicine guidance of AMI management "physician-free". Next-Gen improvements are being contemplated by including a Single Lead EKG for AMI detection that will impact symptom-to-balloon times. Funding Acknowledgement: Type of funding source: None