Biotelemetry system for the reduction of care costs and focus on the quality of life of elderly people with chronic conditions

• Grant number: 17/15513-4
• Support Opportunities: Research Grants - Innovative Research in Small Business - PIPE
• Start date: April 01, 2018
• End date: December 31, 2018
• Field of knowledge: Interdisciplinary Subjects
• Principal Investigator: Alessandra Gallo Petraroli Tateyama
• Grantee: Alessandra Gallo Petraroli Tateyama
• Company: Deepgreen's Lab Treinamentos Ltda
• CNAE: Atividades de apoio à gestão de saúde
• Associated researchers: Antonio Valerio Netto ; Francine da Silva Branco ; Gustavo Alves Andrade dos Santos
• Associated scholarship(s): 18/10050-9 - Biotelemetry system for the reduction of care costs and focus on the quality of life of elderly people with chronic conditions, BP.TT; 18/10025-4 - Biotelemetry system for the reduction of care costs and focus on the quality of life of elderly people with chronic conditions, BP.TT; 18/10078-0 - Biotelemetry system for the reduction of care costs and focus on the quality of life of elderly people with chronic conditions, BP.PIPE

Abstract

The proposal is to develop a platform of biotelemetry based on mHealth, wearable technology and algorithms of analysis (analytics) that is able to act in the detection of intervention points or levels of application of preventive measures of the health-disease process of the elderly in chronic condition To reduce the first aid and possible hospital admissions, and with that decrease the financial costs. It will also make it possible to issue structured reports that can be used to practice Evidence-Based Health (SBE), reports to the physician and interdisciplinary team, to the provider and to the National Supplemental Health Agency. The health-disease process is analyzed as an evolutionary system, in which there are moments of preventive action in the progression of the disease. The distance monitoring will introduce physiological measurements into the daily journey of the elderly. The meters will be selected according to individual needs and the generated data will be automatically sent to a 24-hour monitoring center. Up to 4 pathologies will be monitored: hypertension, heart disease, COPD (chronic obstructive pulmonary disease) and diabetes. Operational protocols will be created to guide measurement parameters to support self-care, decision support, increase the elderly's sense of security, and improve treatment outcomes. In the future, we intend to work with analytical algorithms of the predictive and prescriptive category so that the system is able to anticipate possible risk situations for the elderly. (AU)