First Test of an Automated Detection Platform to Identify Risk of Decompensation in Elderly Patients.

Zulfiqar, Abrar-Ahmad; Vaudelle, Orianne; Hajjam, Mohamed; Letourneau, Dominique; Hajjam, Jawad; Erve, Sylvie; Garate Escamilla, Anna Karen; Hajjam, Amir; Andres, Emmanuel

Introduction We tested the MyPrediâ„¢ e-platform which is dedicated to the automated, intelligent detection of situations posing a risk of decompensation in geriatric patients. Objective The goal was to validate the technological choices, to consolidate the system and to test the robustness of the MyPrediâ„¢ e-platform through daily use. Results The telemedicine solution took 3,552 measurements for a hospitalized patient during her stay, with an average of 237 measurements per day, and issued 32 alerts, with an average of 2 alerts per day. The main risk was heart failure which generated the most alerts (n=13). The platform had 100% sensitivity for all geriatric risks, and had very satisfactory positive and negative predictive values. Conclusion The present experiment validates the technological choices, the tools and the solutions developed. LEARNING POINTS Patients with chronic conditions can be monitored with telemedicine systems to optimise their management, particularly during the COVID-19 pandemic.The goal was to validate the technological choices, to consolidate the system and to test the robustness of the MyPrediâ„¢ e-platform, through daily use in an elderly patient.The present experiment demonstrates the relevance of the technological choices, the tools and the solutions developed.