开发一种人工智能辅助工具,识别多病症和复杂多药症患者,以改善药物审查过程:定性访谈和焦点小组研究
Aseel S Abuzour1,2, Samantha A Wilson3, Alan A Woodall3,4
1Academic Unit for Ageing & Stroke Research, University of Leeds, Leeds, United Kingdom.
Journal of medical Internet research
|January 8, 2026
概括
人工智能 (AI) 工具可以增强多种疾病和复杂处方患者的结构化药物评估 (SMR). 成功实施需要以用户为中心的设计,无集成和有效性的明确证据来克服采用障碍.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 药物管理 药物管理
背景情况:
- 结构化药物评价 (SMR) 对于优化多病症和多药症患者的药物治疗至关重要.
- 目前的SMR流程面临着数据复杂性,时间限制和专业间协调等挑战.
- 本研究调查了医疗保健专业人员 (HCP) 关于将人工智能辅助工具集成到SMR中的观点.
研究的目的:
- 确定用户对人工智能工具的要求,以提高SMR的效率和有效性.
- 专注于人工智能工具对多病症,复杂多药性和脆弱性患者的需求.
- 探索在SMR中采用AI工具的潜在好处和障碍.
主要方法:
- 使用焦点小组和半结构面试对英国的医疗保健工作者和患者进行定性研究.
- 参与者包括医生,药剂师,临床药理学家,精神科医生,政策制定者和患者.
- 对AI工具功能,工作流集成,UI/UX和可用性数据进行主题分析 (诱导和演).
主要成果:
- 人工智能有可能识别有风险的患者,并检测处方问题,如药物相互作用.
- 医疗人员希望能够直观地可视化数据 (例如,颜色编码,时间表) 和共享决策接口.
- 无的EHR集成,用户友好的设计和有效性的证据是关键促进因素;数字排斥和AI准确性问题是障碍.
结论:
- 人工智能辅助工具可以显著简化和优化复杂患者群体的SMR.
- 成功实施人工智能取决于解决工作流集成,用户接受和证明有效性.
- 以用户为中心的设计对于人工智能工具来说至关重要,以支持医疗人员提供高质量的,以患者为中心的护理,而不会造成警报疲劳.
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