使用人工智能预测患者住院时间:PACU工作人员对开发和实施人工智能系统的需求和期望
Sara Lundsten1, Maritha Jacobsson2, Patrik Rydén3
1Department of Nursing, University of Umeå, Umeå 901 87, Sweden.
Journal of nursing management
|April 14, 2025
概括
在麻醉后护理单位 (PACU) 中,医疗保健工作人员在很大程度上倾向于使用人工智能驱动的患者流系统. 这些机器学习 (ML) 系统可以提高效率和患者护理,但需要强有力的领导和清晰的沟通才能成功采用.
科学领域:
- 医疗保健技术采用 医疗保健技术采用
- 医学中的人工智能
- 患者流量管理患者流量管理
背景情况:
- 医疗保健系统面临资源限制,需要创新的技术解决方案.
- 麻醉后护理单位 (PACU) 对患者流量管理提出了独特的挑战.
- 使用机器学习 (ML) 预测的数字患者流系统提供了潜在的改进.
研究的目的:
- 调查员工对基于人工智能的数字患者流系统在PACU的需求和期望.
- 探索ML驱动的患者流量管理的潜在好处和缺点.
- 评估拟议的系统特征与技术接受模型 (TAM2) 的一致性.
主要方法:
- 质量研究涉及20名医疗保健专业人员 (护士经理和工作人员) 的采访.
- 使用反射主题分析 (熟悉,编码,主题生成) 分析数据.
- 根据修改技术接受模型 (TAM2) 评估的主题.
主要成果:
- 员工预计ML系统将提高PACU吞吐量,停留时间预测和患者流量概述.
- 由于效率的提高,可能会增加患者互动时间.
- 发现了有关患者保密和员工对新技术多样化的接受度的担忧.
结论:
- 受访者普遍支持在PACU中实施拟议的ML系统.
- 护士经理在患者工作流程,安全和成功的数字化方面发挥着至关重要的作用.
- 有效的领导和沟通策略对于系统实施至关重要.
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