Avaliação de uma ferramenta computacional na tomada de decisão quanto aos encaminhamentos realizados para serviço de nefrologia

• Ano de defesa: 2023
• Autor(a) principal: Litchteneker, Karina
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Toledo; Brasil; Programa de Pós-Graduação em Tecnologias em Biociências; UTFPR
• Palavras-chave em Português: Nefrologia; Rins - Doenças; Medicina preventiva; Nephrology; Kidneys - Diseases; Medicine, Preventive; CNPQ::CIENCIAS DA SAUDE::MEDICINA; Biotecnologia
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/32291

Chronic kidney disease (CKD) is a pathology with exponentially increasing prevalence worldwide. This is primarily due to population aging and the growth of chronic conditions (such as diabetes and hypertension). In this context, prevention is a priority topic in public health, and active search for early detection in high-risk patients for CKD development is a valid and effective strategy, along with the teams Primary Health Care (PHC). This justifies the development of a computational proposal (rulebased expert system) to assist healthcare professionals in times when computational systems have been used as support tools in various fields of knowledge, such as medicine and nephrology. The general objective of the present study contemplates the implementation of a computational tool and analysis of its influence on the referrals of patients made to the medical specialty of nephrologyTo achieve this, an observational, cross-sectional, and retrospective study was conducted, through data collection and analysis of electronic medical records from a sample of 119 patients. The majority of selected patients were feminine (56.30%) with a median age of 64 years. Overall, the waiting time for patients was 1.68 years, and the prevalent comorbidity was systemic arterial hypertension (SAH) with a frequency of 68.91%. It was observed that only 52.10% (62 out of 119) of the referrals made by healthcare professionals were actually necessary, while the application of the proposed computational tool ensured a 100% accuracy rate in such indication. Furthermore, the quantitative and qualitative analyses also allowed the identification of the main factors that influenced improper referrals, contributing to the epidemiological study of a fraction of referrals to the 20th health region and promoting improvements in the flow of the Unified Health System (SUS).