Application of Artificial Intelligence Software to Identify Emotions of Lung Cancer Patients in Preoperative Health Education: A Cross-Sectional Study.
Xiaoxue Chen, Ziya Xin, Dong Yang and 8 others
PMID 39957550WHAT IT FOUND
Lung cancer patients recorded during nurse-led preoperative education were mostly neutral, then sad.
Explanatory tone and educator negative words were linked to lower emotional arousal, but the study cannot show that changing wording causes this.
Key findings
01Health education content did not significantly predict overall emotional arousal, but explanatory tone (r = -0.038) and educator negative emotion (r = -0.050) had significant negative associations.
02During preoperative education delivered by senior nurses, patients' facial emotion frames were mostly neutral (79.51%), with sadness the most common non-neutral emotion (11.21%).
03Individual differences accounted for 54.1% of the total variance in emotional arousal, leaving the rest to sentence-level education factors.
STILL TO COME
How it was doneWhat they foundWhat it means for RNs
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What it does not show
The design was cross-sectional, so it cannot show that education wording changed patients' emotions. The study was done in one hospital in Guangzhou, China, so the results may not apply to other settings. The study included lung cancer patients only and did not include caregivers. Patients' emotions were inferred from facial expression software, and the paper notes no evidence that lung cancer patients' facial expressions differ from nonpatients. The authors state that the cross-sectional design limited deeper analysis of emotional states during health education.
Declared interests
The authors declare no conflicts of interest.
The easy way to misread this
Do not conclude that a particular tone or word choice caused patients' emotions. The study only observed correlations during routine preoperative education and did not test a communication change.