Evidence map›Paper›PMID 41394059›Full record

ArticleFrontiers in psychology2025

The impact of nurse-led, AI-assisted perioperative health education on psychological status and quality of life in patients undergoing lung cancer surgery.

Chenminghuang Shen, Yuzhu Huang, Bingbing Wang, Xiaoyan Ye, Jinzhi Jiang

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Article in Frontiers in psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Chenminghuang Shen *Department of Thoracic Surgery, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, Fujian, China.
Yuzhu Huang *Department of Gynaecology and Obstetrics, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, Fujian, China.
Bingbing WangDepartment of Thoracic Surgery, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, Fujian, China.
Xiaoyan YeDepartment of Neonatology, The First Affiliated Hospital of Xiamen University Xiamen University, Xiamen, Fujian, China.
Jinzhi JiangDepartment of Thoracic Surgery, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, Fujian, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To evaluate the clinical value of integrating artificial intelligence (AI) into perioperative health education for patients undergoing surgery for lung cancer. Methods: This retrospective study included patients who underwent radical resection for non-small cell lung cancer (NSCLC) in the Department of Thoracic Surgery at the First Affiliated Hospital of Xiamen University between January and December 2024. According to the perioperative education method, patients who met the inclusion criteria from January to May 2024 were included in the conventional group, whereas those from June to December 2024 were included in the AI-assisted group. All patients received standardized rehabilitation education based on a unified handbook. On this basis, the AI-assisted group additionally received individualized health education and psychological counseling generated by AI, which was verified and supplemented by nurses. Clinical data and validated questionnaire scores were collected and analyzed. Results: A total of 135 patients were included, with 70 in the conventional group and 65 in the AI-assisted group. There were no significant differences in demographic and clinical characteristics between the groups. Compared with the conventional group, patients in the AI-assisted group scored significantly higher on the "confrontation" dimension of the Medical Coping Modes Questionnaire (MCMQ), indicating a stronger tendency toward active coping, whereas no significant differences were observed in the "avoidance" and "acceptance-resignation" dimensions. Regarding psychological status, the AI-assisted group had significantly lower depression and anxiety scores postoperatively, while stress scores showed no significant difference. Quality of life, assessed by the World Health Organization Quality of Life-BREF (WHOQOL-BREF), revealed that the AI-assisted group had significantly higher scores in the psychological and social domains as well as the overall score, with no significant differences in the physical or environmental domains. Conclusion: Nurse-led perioperative health education supported by AI tools can help optimize patients' coping strategies, reduce negative psychological states, and improve quality of life after lung cancer surgery.

Indexed as

AI-assisted educationlung cancerperioperative carepsychological statusquality of life

Identifiers

PMID41394059
PMCPMC12695728

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.