Method To Optimize Non-Medical Care Using Natural Language Processing And Transformer Modeling For Patients At A University Medical Center

Resumen

This paper presents a method to optimize non-medical care in a university health center through the implementation of a chatbot that was trained with natural language processing and the Transformer model. The method consists of collecting data from the user to provide answers that are efficient and in accordance with expectations. The chatbot, designed with an intuitive interface, provides users with access to virtual and online functions. It interprets the data provided by the user in text form, generating valid recommendations according to the requesting user. Care time reduction is achieved by automating routine non-medical tasks, such as appointment management and frequent consultations, allowing healthcare staff to focus on more complex cases. The chatbot does not replace face-to-face medical care, but acts as a support tool to optimize resource allocation. The results obtained showed varied opinions regarding the decongestion and management of the application, with 81.3% having no problems using it, while the rest did. The most frequently reported problems included technical difficulties in interacting with the chatbot and errors in interpreting complex requests. Overall, this approach aims to improve healthcare services through technology to provide personalized and relevant information to users.