在护理实践中整合人工智能与阴风险预测警报:试点过程评估
Denise Spoon1, Annemarie de Vroed2, Steffen Greup3
1Department of Internal Medicine, Division of Nursing Science, Erasmus MC, Erasmus University Medical Centre, Rotterdam, the Netherlands.
Journal of clinical nursing
|January 20, 2026
概括
护士发现基于人工智能 (DRAAI) 的Decubitus风险预测警报是可以接受和可行的,用于预防压力. 将人工智能集成到护理工作流程中,需要持续的支持和明确的沟通,才能成功实施.
科学领域:
- 护理信息学 护理信息学
- 医疗保健中的人工智能
- 患者安全 患者安全
背景情况:
- 压力 (PUs) 在医疗保健环境中构成了重大挑战.
- 准确的风险预测对于有效的PU预防策略至关重要.
- 将人工智能等新技术集成到临床实践中需要仔细评估.
研究的目的:
- 在护士中评估基于人工智能 (DRAAI) 的形风险预测警报的可接受性和可行性.
- 评估DRAAI在医院环境中的实施计划的可行性.
- 了解人工智能驱动的警报对护理工作流程和PU预防的影响.
主要方法:
- 一个试点实施研究的混合方法过程评估.
- 问卷评估了护士对DRAAI的接受性和可行性.
- 现场笔记记录了在实施期间的调整,可接受性和可行性.
- 分析患者数据以生成警报和遵守护理计划.
主要成果:
- 55名护士发现DRAAI预测对PU预防有价值,大多数护士将其整合到他们的工作流程中.
- 调整包括加强教育课程和创建常见问题.
- 对于30%的入院患者,DRAAI产生了风险预测,近80%的患者接受了适当的护理护理计划.
- 总体而言,实施努力被认为是可行的.
结论:
- 持续的护士参与和清晰的沟通对于成功将人工智能集成到临床工作流程中至关重要.
- DRAAI显示了改善PU预防的希望,尽管护士继续独立识别有风险的患者.
- 需要进一步的研究来确定DRAAI对PU预防的临床影响.
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