CAR-T cell therapy in advanced practice nursing management. A Statistical and Large Language Model approach to highlight critical issues

Vollständiger Abstract

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Aim To gather nurses’ perceptions nursing hematologic management on patients undergoing CAR-T cell therapy. A combined approach using statistical analysis and Large Language Model for open-ended questionnaire processing (LLMs) was adopted to analyze and interpret open-ended questionnaire responses, focusing on the critical issues in managing patients’ post-CAR T-cell therapy infusion. Design Observational questionnaire-based survey study. Methods We analyzed data through descriptive statistical methods and used generative artificial intelligence to summarize four open-ended answers. Results A total of 89 Italian oncology nurses participated in the present study. The semiautomatic analysis of the open-ended responses, using a procedure based on a freely available large language model, allowed us to identify and summarize the main concerns expressed by the professionals regarding the critical issues in managing post-CAR T-cell infusion patients. Conclusions Addressing the highlighted issues through targeted improvements in staffing, training, and resource allocation could significantly enhance patient outcomes and care quality.