对于医院信息系统中基于人工智能的后续工作的信任:开发和验证一个新的规模
Lili Xie1,2, Ting Guo1, Yi Yang3
1Nursing Department of the Second Affiliated Hospital of Jiangxi Medical College of Nanchang University, Nanchang, 330006, China.
BMC psychology
|December 17, 2025
概括
这项研究开发并验证了基于人工智能的后续信任度量,以衡量患者对人工智能后续系统的信任. 该尺度显示出强大的可靠性和有效性,有助于未来的研究和坚持改进.
科学领域:
- 医疗保健技术 技术 医疗保健 技术
- 人工智能的人工智能
- 患者护理 患者护理
背景情况:
- 基于人工智能 (AI) 的后续系统提高了出院后护理的效率.
- 患者接受受限于信任问题,需要更好的评估工具.
- 现有的工具不足以衡量对人工智能驱动的后续环境的信任.
研究的目的:
- 开发和心理验证一个评估患者对基于人工智能的随访系统的信任度表.
- 为测量对人工智能医疗应用的信任提供可靠的工具.
- 支持人工智能在患者护理中的进步.
主要方法:
- 一项三阶段的方法研究:项目生成,规模开发 (德尔菲方法,试点调查) 和心理测量验证.
- 探索性因素分析 (EFA) 和确认性因素分析 (CFA) 用于结构确定和验证.
- 使用内部一致性 (克朗巴赫的α,麦当劳的 ω) 和测试-重新测试相关性来评估可靠性.
主要成果:
- 最后的27项尺度衡量了患者的处置信任,系统交互信任和环境信任.
- 高含量有效性 (S-CVI/Ave:0.955) 和很好的模型适合于CFA (RMSEA:0.022,CFI:0.999).
- 强大的可靠性得到证实 (克朗巴赫的α:0.931,麦当劳的 ω:0.960,测试-重新测试:0.901).
结论:
- 基于人工智能的后续信任度量是一个可靠和有效的工具.
- 这种尺度可以有效地评估患者对人工智能后续系统的信任.
- 该工具将促进研究和战略,以增强对人工智能驱动的医疗保健的信任和坚持.
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