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

Introduction: We tested the MyPredi^™ 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 MyPredi^™ 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 MyPredi^™ 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.