Deployment of a remote monitoring solution for the elderly: experiment with an elderly patient / Déploiement d'une solution de télésurveillance du sujet âgé : expérimentation auprès d'un patient âgé

Introduction: Telemedicine can help manage patients suffering from chronic pathologies, particularly elderly patients with numerous comorbidities. We experimented with the e-platform, MyPredi, dedicated to the automated, intelligent detection of situations where patients are at risk of decompensation from geriatric syndromes. We focused our experiment on one particular patient included in the GER-e-TEC study. Methods: The MyPredi platform uses on medical sensors that communicate and relay real-time feedback to an intelligent system of physiological information that analyzes medical ontology, ultimately leading to the generation of alerts. These alerts are linked to a deterioration in the patient's state of health due to a decompensation of chronic pathologies. We reported the results of this experiment for the patient who was participating. Results: The telemedicine solution made 6,073 measurements for the patient throughout his hospitalization, averaging 253 measurements per day. The telemedicine solution issued 110 alerts for the patient during his stay, with an average of 5 alerts per day. The patient had 15 mild alerts, 31 moderate alerts, and 64 severe alerts. In terms of sensitivity, the results are 100% for all geriatric risks and very satisfactory in terms of positive and negative predictive value. Conclusion: MyPredi telemedicine platform enables the generation of alerts in an automatic and non-intrusive way relating to the deterioration of a patient's state of health with regard to geriatric risks.

Introduction: La télémédecine est susceptible d'apporter une aide à la prise en charge des patients souffrant de pathologies chroniques, en particulier les sujets âgés porteurs de nombreuses comorbidités. Dans ce cadre, nous avons expérimenté la e-plateforme MyPredi dédiée à la détection automatisée et intelligente des situations à risque de décompensation des syndromes gériatriques, auprès d'un patient inclus pour l'étude GER-e-TEC (pour geriatrics and e-technology). Méthodes: La plateforme MyPredi repose sur des capteurs médicaux communicants permettant de remonter, en temps réel, à un système intelligent des informations physiologiques, et sur une analyse de l'ontologie médicale, ce qui aboutit in fine à la génération d'alertes. Ces dernières sont liées à une dégradation de l'état de santé des patients en rapport avec une décompensation des pathologies chroniques. Nous indiquons les résultats de cette expérimentation pour le patient inclus. Résultats: La solution de télémédecine a réalisé 6 073 mesures pour le patient durant tout le long de son hospitalisation, avec en moyenne 253 mesures par jour. La solution de télémédecine a émis 110 alertes pour le patient durant son séjour, avec en moyenne cinq alertes par jour. Le patient a eu 15 alertes légères, 31 modérées et 64 sévères. On note une sensibilité de 100 % pour l'ensemble des risques gériatriques, avec des résultats très satisfaisants en termes de valeurs prédictives positives et négatives. Conclusion: En pratique, le système de télémédecine MyPredi permet, de façon automatique et non intrusive, de générer des alertes en rapport avec la dégradation de l'état de santé du patient en ce qui concerne les risques gériatriques.

Glycemic Disorder Risk Remote Monitoring Program in the COVID-19 Very Elderly Patients: Preliminary Results.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has necessitated the use of new technologies and new processes to care for hospitalized patients, including diabetes patients. This was the basis for the "GER-e-TEC COVID study," an experiment involving the use of the smart MyPredi TM e-platform to automatically detect the exacerbation of glycemic disorder risk in COVID-19 older diabetic patients. Methods: The MyPredi TM platform is connected to a medical analysis system that receives physiological data from medical sensors in real time and analyzes this data to generate (when necessary) alerts. An experiment was conducted between December 14th, 2020 and February 25th, 2021 to test this alert system. During this time, the platform was used on COVID-19 patients being monitored in an internal medicine COVID-19 unit at the University Hospital of Strasbourg. The alerts were compiled and analyzed in terms of sensitivity, specificity, positive and negative predictive values with respect to clinical data. Results: 10 older diabetic COVID-19 patients in total were monitored remotely, six of whom were male. The mean age of the patients was 84.1 years. The patients used the telemedicine solution for an average of 14.5 days. 142 alerts were emitted for the glycemic disorder risk indicating hyperglycemia, with an average of 20.3 alerts per patient and a standard deviation of 26.6. In our study, we did not note any hypoglycemia, so the system emitted any alerts. For the sensitivity of alerts emitted, the results were extremely satisfactory, and also in terms of positive and negative predictive values. In terms of survival analysis, the number of alerts and gender played no role in the length of the hospital stay, regardless of the reason for the hospitalization (COVID-19 management). Conclusion: This work is a pilot study with preliminary results. To date, relatively few projects and trials in diabetic patients have been run within the "telemedicine 2.0" setting, particularly using AI, ICT and the Web 2.0 in the era of COVID-19 disease.

Results of the Second Phase of the GER-e-TEC Experiment concerning the Telemonitoring of Elderly Patients Affected by COVID-19 Disease to Detect the Exacerbation of Geriatric Syndromes.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has wreaked health and economic damage globally. This pandemic has created a difficult challenge for global public health. The coronavirus disease 2019 (COVID-19) pandemic has necessitated the use of new technologies and new processes to care for hospitalized patients, including elderly patients. Our team developed a telemonitoring program focused on the prevention of geriatric syndromes, the "GER-e-TEC COVID study". METHODS: This second phase took place during the 3rd wave of the epidemic in France, between 14 December 2020 and 25 February 2021, conducted in the University Hospital of Strasbourg. RESULTS: 30 elderly patients affected by COVID-19 disease were monitored remotely; the mean age was 85.9 years and a male/female ratio of 1.5 to 1.11 (36.7%) died during the experiment. The patients used the telemedicine solution for an average of 27.3 days. 140,260 measurements were taken while monitoring the geriatric syndromes of the entire patient group. 4675 measurements were recorded per patient for geriatric disorders and risks. 319 measurements were recorded per patient per day. The telemedicine solution emitted a total of 1245 alerts while monitoring the geriatric syndromes of the entire patient group. In terms of sensitivity, the results were 100% for all geriatric risks and extremely satisfactory in terms of positive and negative predictive values. Survival analyses showed that gender played no role in the length of the hospital stay, regardless of the reason for the hospitalization (decompensated heart failure (p = 0.45), deterioration of general condition (p = 0.12), but significant for death (p = 0.028)). The analyses revealed that the length of the hospital stay was not affected by the number of alerts. The results concerning the predictive nature of alerts are satisfactory. CONCLUSIONS: The MyPredi™ telemedicine system allows for the generation of automatic, non-intrusive alerts when the health of a COVID-19 elderly patient deteriorates due to risks associated with geriatric syndromes.

Results of the "GER-e-TEC" Experiment Involving the Use of an Automated Platform to Detect the Exacerbation of Geriatric Syndromes.

INTRODUCTION: Telemedicine is believed to be helpful in managing patients suffering from chronic diseases, in particular elderly patients with numerous accompanying conditions. This was the basis for the "GERIATRICS and e-Technology (GER-e-TEC) study", which was an experiment involving the use of the smart MyPredi™ e-platform to automatically detect the exacerbation of geriatric syndromes. METHODS: The MyPredi™ platform is connected to a medical analysis system that receives physiological data from medical sensors in real time and analyzes this data to generate (when necessary) alerts. These alerts are issued in the event that the health of a patient deteriorates due to an exacerbation of their chronic diseases. An experiment was conducted between 24 September 2019 and 24 November 2019 to test this alert system. During this time, the platform was used on patients being monitored in an internal medicine unit at the University Hospital of Strasbourg. The alerts were compiled and analyzed in terms of sensitivity, specificity, and positive and negative predictive values with respect to clinical data. The results of the experiment are provided below. RESULTS: A total of 36 patients were monitored remotely, 21 of whom were male. The mean age of the patients was 81.4 years. The patients used the telemedicine solution for an average of 22.1 days. The telemedicine solution took a total of 147,703 measurements while monitoring the geriatric risks of the entire patient group. An average of 226 measurements were taken per patient per day. The telemedicine solution generated a total of 1611 alerts while assessing the geriatric risks of the entire patient group. For each geriatric risk, an average of 45 alerts were emitted per patient, with 16 of these alerts classified as "low", 12 classified as "medium", and 20 classified as "critical". In terms of sensitivity, the results were 100% for all geriatric risks and extremely satisfactory in terms of positive and negative predictive values. In terms of survival analysis, the number of alerts had an impact on the duration of hospitalization due to decompensated heart failure, a deterioration in the general condition, and other reasons. CONCLUSION: The MyPredi™ telemedicine system allows the generation of automatic, non-intrusive alerts when the health of a patient deteriorates due to risks associated with geriatric syndromes.

e-Health: A Future Solution for Optimized Management of Elderly Patients. GER-e-TEC™ Project.

Background: Elderly residents in nursing homes have multiple comorbidities (including cognitive and psycho-behavioral pathologies, malnutrition, heart failure, diabetes, chronic obstructive pulmonary disease, and renal failure) and use multiple medications. Methods: The GER-e-TEC project aims to provide these fragile and complex patients with telemedicine tools, more specifically telemonitoring, backed by a well-defined and personalized protocol. Results: Medically, this implies the need for regular monitoring and a high level of medical and multidisciplinary expertise for the healthcare team. The tools use non-invasive communicating sensors and artificial intelligence techniques, allowing daily monitoring with the ability to detect any abnormal changes in the patient's condition early. Conclusions: The GER-e-TEC project specifically considers the challenges of aging residents and significant challenges in nursing homes, with the main geriatric syndromes (falls, malnutrition, cognitive-behavioral disorders, and iatrogenic conditions).

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.

Telemonitoring in diabetes: evolution of concepts and technologies, with a focus on results of the more recent studies.

This is a narrative review of telemonitoring (remote monitoring) projects and studies within the field of diabetes, with a focus on results of the more recent studies. Since the beginning of the 1990s, several telemedicine projects and studies focused on type 1 and type 2 diabetes. Over the last 5 years, numerous telemedicine projects based on connected objects and new information and communication technologies (ICT) (elements defining telemedicine 2.0) have emerged or are still under development. Two examples are the DIABETe and Telesage telemonitoring project which perfectly fits within the telemedicine 2.0 framework - the first to include artificial intelligence (AI) with MyPrediTM and DiabeoTM. Mainly, these projects and studies show that telemonitoring diabetic result in: improvements in control of blood glucose (BG) level and significant reduction in HbA1c (e.g., for Telescot et TELESAGE studies); positive impact on co-morbidities (arterial hypertension, weight, dyslipidemia) (e.g., for Telescot and DIABETe studies); better patient's quality of life (e.g., for DIABETe study); positive impact on appropriation of the disease by patients and/or greater adherence to therapeutic and hygiene-dietary measures (e.g., The Utah Remote Monitoring Project); and at least, good receptiveness by patients and their empowerment. To date, the magnitude of its effects remains debatable, especially with the variation in patients' characteristics (e.g., background, ability for self-management, medical condition), samples selection and approach for the treatment of control groups. All of the recent studies have been classified as "Moderate" to "High".

Current Research and New Perspectives of Telemedicine in Chronic Heart Failure: Narrative Review and Points of Interest for the Clinician.

BACKGROUND: This is a narrative review of both the literature and Internet pertaining to telemedicine projects within the field of heart failure, with special attention placed on remote monitoring of second-generation projects and trials, particularly in France. RESULTS: Since the beginning of the 2000's, several telemedicine projects and trials focused on chronic heart failure have been developed. The first telemedicine projects (e.g., TEN-HMS, BEAT-HF, Tele-HF, and TIM-HF) primarily investigated telemonitoring or for the older ones, telephone follow-up. Numerous second-generation telemedicine projects have emerged in Europe over the last ten years or are still under development for computer science heart failure, especially in Europe, such as SCAD, OSICAT, E-care, PRADO-INCADO, and TIM-HF2. The E-care telemonitoring project fits within the telemedicine 2.0 framework, based on connected objects, new information and communication technologies (ICT) and Web 2.0 technologies. E-care is the first telemedicine project including artificial intelligence (AI). TIM-HF2 is the first positive prospective randomized study with regards to EBM with positive significant clinical benefit, in terms of unplanned cardiovascular hospital admissions and all-cause deaths. The potential contribution of second-generation telemedicine projects in terms of mortality, morbidity, and number of hospitalizations avoided is currently under study. Their impact in terms of health economics is likewise being investigated, taking into account that the economic and social benefits brought up by telemedicine solutions were previously validated by the original telemedicine projects.

Telemedicine in elderly patients with heart failure.

Here, we carry out a review of the literature focused on telemedicine projects developed in the field of heart failure. We will particularly detail the remote monitoring project called E-care, dedicated to automated, intelligent detection of situations at risk of heart failure. Prospects for the development of the E-care system in the field of geriatry will also be discussed. Results: Numerous telemedicine projects, based on connected objects or technology sciences of information and communication, have emerged in the last five years or are under development in the field of computer science'heart failure. This is the case of the E-care telemonitoring project, which fits perfectly within the framework of telemedicine 2.0 projects. Their potential contribution in terms of mortality or morbidity, in number of hospitalizations avoided is currently under study or documentation. Their impact in terms of health economics is also being validated, knowing that the oldest telemedicine projects had already validated the economic and social benefits brought by telemedicine solutions.