Frontiers in public health · 2026
Evaluation of an AI-assisted digital health follow-up system integrating humanistic care for patients undergoing chemotherapy: a prospective quasi-experimental study.
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Jing Luning, Ye Baoling, Liu Shanshan
Аннотация
BACKGROUND: Chemotherapy imposes substantial physical and psychological burdens that may reduce treatment adherence and quality of life. Traditional follow-up is often fragmented, reactive, and insufficiently personalized. Artificial intelligence (AI) may improve continuity, responsiveness, and patient-centered care; however, its integration with humanistic care remains underexplored.
METHODS: This prospective, single-center, quasi-experimental study was conducted from January to June 2024. A total of 189 patients undergoing chemotherapy were allocated to an AI-assisted follow-up group (n = 96) or a conventional care group (n = 93). The nurse-supervised AI-assisted follow-up system integrated rule-based risk monitoring, DeepSeek-based large language model-assisted communication, personalized follow-up, and data visualization. Outcomes included self-management ability, quality of life (QoL), and patient satisfaction. Between-group comparisons used independent-samples t-tests, with adjusted analyses performed using analysis of covariance.
RESULTS: At 3 months, the AI-assisted group had higher self-management ability scores (168.83 ± 7.03 vs. 150.11 ± 7.39), QoL scores (66.34 ± 4.30 vs. 59.81 ± 4.46), and satisfaction scores (52.35 ± 2.44 vs. 48.75 ± 2.48) than the conventional care group (all P < 0.001). Follow-up completion was 96%, and the 24-h response rate was 98%. Of 214 generated alerts, 87.4% were clinically validated.
CONCLUSION: The nurse-supervised AI-assisted follow-up system was feasible and associated with better short-term patient-reported outcomes than conventional follow-up. Its distinguishing feature was the integration of rule-based risk management, large language model-assisted communication, and operationalized humanistic care within a closed-loop oncology nursing workflow. Randomized multicenter studies are required to confirm these findings.
METHODS: This prospective, single-center, quasi-experimental study was conducted from January to June 2024. A total of 189 patients undergoing chemotherapy were allocated to an AI-assisted follow-up group (n = 96) or a conventional care group (n = 93). The nurse-supervised AI-assisted follow-up system integrated rule-based risk monitoring, DeepSeek-based large language model-assisted communication, personalized follow-up, and data visualization. Outcomes included self-management ability, quality of life (QoL), and patient satisfaction. Between-group comparisons used independent-samples t-tests, with adjusted analyses performed using analysis of covariance.
RESULTS: At 3 months, the AI-assisted group had higher self-management ability scores (168.83 ± 7.03 vs. 150.11 ± 7.39), QoL scores (66.34 ± 4.30 vs. 59.81 ± 4.46), and satisfaction scores (52.35 ± 2.44 vs. 48.75 ± 2.48) than the conventional care group (all P < 0.001). Follow-up completion was 96%, and the 24-h response rate was 98%. Of 214 generated alerts, 87.4% were clinically validated.
CONCLUSION: The nurse-supervised AI-assisted follow-up system was feasible and associated with better short-term patient-reported outcomes than conventional follow-up. Its distinguishing feature was the integration of rule-based risk management, large language model-assisted communication, and operationalized humanistic care within a closed-loop oncology nursing workflow. Randomized multicenter studies are required to confirm these findings.