Classificação semiautomática de prioridade no atendimento a emergências médicas

• Ano de defesa: 2018
• Autor(a) principal: Couto, Daniel Costa do
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal Rural do Semi-Árido; Brasil; Centro de Ciências Exatas e Naturais - CCEN; UFERSA; Programa de Pós-Graduação em Ciência da Computação
• Palavras-chave em Português: Serviço de emergência médica; SAMU; Ontologia; Medical emergency service; Ontology; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufersa.edu.br/handle/prefix/877

Emergency Medical Services, also known as Ambulance Services or Paramedical Services (i.e., abbreviated in some countries as EMS, EMAS, EMARS or SAMU) are emergency services dedicated to medical care outside the hospital context, where time and space limitations in the patient care may lead, in the worst cases, to severe worsening of their health condition or death. The main Medical Emergency Service in Brazil is the SAMU, maintained with public resources of federal, state and municipal management. Medical care through SAMU is an inter-organizational process that articulates a patient population, a network of public and private hospitals, and a telephone service regulation service through which the anamnesis is performed. Considering the shortage of vehicles in SAMU, human resources and adequate medical equipment, the regulation process becomes not only a medical screening, but also a mechanism of prioritization of life. In addition, the risk of fraudulent or opportunistic calls to service, e.g. cases of hypochondria or trotting, associated to the loss of time commonly perceived in access to the service via telephone connection only, makes the regulation of SAMU a process that can be constantly optimized. Thus, the central research question in this work is how the SAMU medical emergency prioritization process could be optimized with the use of Artificial Intelligence technologies for semiautomatic classification of information. Therefore, the overall objective of this work is to build a Decision Support System designed to reduce overall patient care time by SAMU. The specific objectives of this research include: (1) to describe the state of the art in Decision Support Systems for Medical Emergencies in Multiple Victim Incidents; (2) modeling an ontology for prioritizing medical emergency cases served by SAMU; (3) to evaluate the usability, acceptance and usefulness of the classification produced by the ontology with the support of the Municipal Health Department of the city of Mossoró. The paradigm of scientific research adopted in this work is Design Science, which consists in the organization of scientific thinking by solving socially relevant problems