Modelo de apoio à decisão no acesso aos serviços de fisioterapia para reabilitação de pacientes com acidente vascular encefálico

• Ano de defesa: 2015
• Autor(a) principal: Mendes, Luciana Moura
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Ciências Exatas e da Saúde; Programa de Pós-Graduação em Modelos de Decisão e Saúde; UFPB
• Palavras-chave em Português: Acesso aos serviços de saúde; Access to health services; Redes neurais (Computação); Tomada de decisões; Acidente cerebrovascular; Saúde pública; Neural networks (Computing); Decision-making; Cerebrovascular accident; CIENCIAS DA SAUDE::SAUDE COLETIVA
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/tede/7551

Cerebrovascular Accident (CVA) is a disease characterized by an interruption of blood flow to the encephalon, which represents the leading cause of long-term disability and functional impairment in adult population. Therefore, the individual who had suffered CVA needs to access health services that offer rehabilitation assistance as they promote a better physical, functional, and mental capacity, helping the reinsertion and reintegration of this individual into society. Thus, this study aims to develop a decision-making model to determine the access to physiotherapy services for rehabilitation of patients who had suffered acute CVA in the cities of João Pessoa and Cabedelo. This is an observational-longitudinal study among man and women who were admitted at a public hospital in João Pessoa and live in its metro area, who had presented CVA as primary cause of hospitalization. A questionnaire was used containing items related to socioeconomic, demographic, and clinical data from this person, such as general health conditions, risk factors, functionality evaluation, and access to physiotherapy services. Interviews were conducted between 15 and 21 days after hospital discharge (T0) and between 90 and 105 days after the first interview (T1). There was a homogeneous distribution of sexes, group age over 60 years (mean age= 61.6 years; standard deviation= 15.7 years). Most of the subjects (69.2%) have had a ischemic CVA, which the right side was more affected (46.2%) and 89.7% have had up to two CVA episodes. From interviewed patients, 69.2% have not had access to physiotherapy services after three months from the first interview. For utilization of decision model, 16 variables were selected helped by WEKA software, generating a feedfoward Artificial Neural Network model composed by 16 neurons in the input layer, followed by two hidden layers with two hidden neurons in each layer and an output layer with 2 neurons with backpropagation learning. This decision model allowed classifying correctly almost all subjects that accessed or not the physiotherapy services, achieving 97.4% of successes, representing a greater reliability. Therefore, this model is constituted as an important tool in the visibility of the problem, helping in the decision-making process, planning, and reorganization of public health system and its several attention levels.