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.
CAR-T cell therapy in advanced practice nursing management. A Statistical and Large Language Model approach to highlight critical issues
Conte L.;De Nunzio G.;
2026-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
