人工智能症状模型在瘤护理中的感知:混合方法评估研究
Bridget Nicholson1, Elizabeth A Sloss1, Aref Smiley2
1College of Nursing, University of Utah, 10 2000 E, Salt Lake City, UT, United States.
JMIR nursing
|February 4, 2026
概括
瘤护士认为,人工智能 (AI) 症状模型可以通过早期干预来改善癌症护理. 采用的关键因素包括模型透明度,护士参与,以及对患者症状管理的明显好处.
科学领域:
- 瘤学护理 癌症护理
- 医疗保健中的人工智能
- 症状管理 症状管理
背景情况:
- 癌症患者面临着严重的症状负担,影响治疗.
- 目前的症状管理依赖于患者报告和临床反应.
- 使用人工智能的预测性症状模型提供了早期风险识别,以便及时干预.
研究的目的:
- 探索瘤护士对癌症护理中的预测性症状模型的看法.
- 确定影响采用人工智能驱动的症状护理创新的因素.
主要方法:
- 这项研究以罗杰斯的创新扩散理论为指导.
- 瘤护士使用利克特尺度对AI症状模型的感知进行了评分,并提供了定性评论.
- 数据使用描述性统计和用归纳编码进行内容分析.
主要成果:
- 护士们同意人工智能模型可以增强症状管理和早期干预 (67%-86%).
- 所有护士发现症状信息有帮助,73%的护士认为它可以节省时间.
- 主要采用主题包括模型兼容性,患者感知益处,改善的临床过程,准确性问题和实施因素.
结论:
- 瘤护士支持预测性症状模型,以改善癌症症状管理.
- 采用必不可少的:人工智能模型的透明度,护士参与开发,以及可观察到的好处.
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