Virtual Assistant Based on Recurrent Neural Networks: Scope and Coverage of Health Insurance

In Peru, the use of health services provided by state institutions has decreased, largely due to perceived deficiencies in care quality, such as delays in medical attention and administrative barriers that hinder timely access to information on insurance scope and coverage. This study develops a web-based application with an integrated chatbot that leverages Artificial Intelligence (AI) and a Recurrent Neural Network (RNN) to provide accurate and accessible information about institutional insurance, including the characteristics of different insurance types and specific coverage cases. The application integrates a chatbot powered by an AI model based on Natural Language Processing (NLP) from OpenAI, with intent recognition handled by a dedicated classifier. In comparison to existing chatbots in the healthcare field, the model proposes a hybrid approach based on RNN and GPT-3.5, optimized for health insurance queries in public sector contexts. The system was evaluated with 50 representative questions scored on four criteria: clarity, relevance, coherence, and accuracy. The chatbot achieved an overall mean response accuracy of 82% and a mean user satisfaction score of 4.59/5, indicating strong acceptance and usability. These results suggest that the combination of these technologies constitutes an effective alternative for addressing queries relevant to users of state health systems.