Artificial intelligence-enhanced nurse navigation for monitoring and care of long COVID
Year: 2026
Type: article
Abstract: INTRODUCTION: Long COVID is a multisystem condition with challenging diagnosis. Nurse-navigation, a patient-centered intervention, can enhance education and care access. Analyzing patient-nurse text message exchanges using natural language processing (NLP) enables automated extraction of clinical information, potentially supporting early identification of long COVID. We aimed to evaluate a digital nurse navigation platform integrating a predictive model for long COVID identification as a triage-assisting tool and to assess user acceptance. METHODS: This observational study included patients and healthcare professionals diagnosed with COVID-19 from January to July 2024. Participants received nurse-navigation support for 16 weeks with monthly interactions via a WhatsApp-integrated platform. Structured sociodemographic and clinical data were combined with text-message insights using NLP techniques such as term frequency-inverse document frequency (TF-IDF), and analyzed using language models (Gemini 1.5 Pro, BERTimbau) and probabilistic linkage. The dataset was split into 70% training and 30% testing, and eight machine learning models were evaluated. Performance metrics included accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUROC). User satisfaction was assessed with the Net Promoter Score (NPS). RESULTS: Among 177 participants, 141 (78%) were female, with an overall mean age of 51 years. A total of 7,016 messages were processed. Long COVID was identified in 60 participants (33%), most frequently reporting memory loss, dyspnea, cognitive fatigue, and hair loss. Participants received structured education, and 20 were referred for further evaluation. The XGBoost-minor model achieved the highest classification performance with an accuracy of 72%, sensitivity of 38%, specificity of 88%, PPV of 63%, NPV of 74%, and AUROC 0.59. Predictive factors included age, COVID-19 episodes, vaccination, comorbidities, and respiratory symptoms. The NPS was 92, indicating strong endorsement. CONCLUSION: An AI-enhanced triage process within nurse navigation represents a promising and scalable strategy to support the identification and monitoring of patients at risk for long COVID.
Authors: Ana Paula Bandeira Barboza, Renata Luciria Monteiro, Alessandra Luna Muschi, Alexandra Rodrigues dos Santos Silva, Dênis Araújo da Silva, Rubens Carvalho Silveira, Cecília Salete Alencar Silva, Gabriela Salim de Castro, José Vangenor Santana, Rogerio dos Santos Soares Silva, Giovanni Guido Cerri, Antonio José Rodrigues Pereira, Marco Antonio Beco, Gabriel Romitelli, Julia Ribeiro Veneziani, Evandro Matheus dos Santos Silva, Antonio dos Santos Barboza, Luciana Lischewski Mattar, Ester Cerdeira Sabino, Rogério Silicani Ribeiro, Silvia Figueiredo Costa
Language: English
Subfield: Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,150
OpenAlex ID: W7166333667