Maximum artificial intelligence and complete reconstruct of population-based AMI care

• Published in: European heart journal Vol. 41; no. Supplement_2
• Main Authors: Mehta, S, Gibson, M, Niklitschek, S, Fernandez, F, Villagran, C, Escobar, E, Vera, F, Frauenfelder, A, Vieira, D, Vijayan, Y, Quintero, S, Vallenilla, I, Pinto, G, Cardenas, J, Merchant, S
• Format: Journal Article
• Language: English
• Published: 01.11.2020
• ISSN: 0195-668X; 1522-9645
• DOI: 10.1093/ehjci/ehaa946.3520

Abstract Background After creating a behemoth hub and spoke AMI network that encompasses more than 100 million patients in 5 countries, we have begun to incorporate Artificial Intelligence (AI) algorithms into our telemedicine strategy with the goal of creating comprehensive, very early AMI diagnosis and physician-free triage. In doing so, we have replaced door-to-balloon times (d2b) with symptom-to-balloon times (s2b) as an immutable objective. Purpose To incorporate AI attributes for very early AMI detection, triage, and management. Methods We expanded our effective telemedicine strategy (100 million population; 877,178 telemedicine encounters; 55% overall mortality reduction; $291 million cost savings) with a logistic reset to impact s2b. To do this, we incorporated our Single Lead 1.0 (lead I) and Single Lead 2.0 (lead V2) technology for self-administered AMI detection with our physician-free STEMI diagnosis and triage AI algorithms. Single Lead algorithms and physician-free protocols were generated by utilizing Machine Learning from our mammoth annotated EKG repository. Results In addition to three logistic markers of efficiency Time-to-Telemedicine Diagnosis (TTD), Door-In-Door-Out (DIDO) and Transfer Times (TT); we are monitoring s2b. A gradual release of the algorithms and single lead is occurring at the telemedicine spokes. Detailed results will be available at the time of presentation. Conclusions Impacting s2b, the Achilles Heel of Primary PCI, may be achieved with the use of patient-administered AMI detection tools. Incorporation of these technologies into AI algorithms will add to telemedicine efficiencies for population-based AMI care. Funding Acknowledgement Type of funding source: None