Maximum artificial intelligence and complete reconstruct of population-based AMI care. (25th November 2020) - Mehta, S; Gibson, M; Niklitschek, S; Fernandez, F; Villagran, C; Escobar, E; Vera, F; Frauenfelder, A; Vieira, D; Vijayan, Y; Quintero, S; Vallenilla, I; et al.
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