紧急心脏护理的人为中心的人工智能:评估RAPIDxAI与PROLIFERATE_AI
Maria Alejandra Pinero de Plaza1, Kristina Lambrakis2, Fernando Marmolejo-Ramos1
1Caring Futures Institute, Flinders University, Adelaide, South, Australia.
International journal of medical informatics
|February 2, 2025
概括
像RAPIDx AI这样的人工智能 (AI) 提高了经验丰富的临床医生胸痛诊断的准确性. 然而,新手用户面临着可用性挑战,这表明需要量身定制的培训和界面改进,以便在紧急部门更广泛地采用AI.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 紧急医疗 紧急医疗
背景情况:
- 在紧急护理中,胸痛诊断存在挑战,因为症状重叠,导致诊断不确定性.
- 人工智能 (AI) 工具,如RAPIDx AI,通过整合临床和生化数据,有可能提高诊断准确性.
- 成功采用人工智能需要解决应急部门内的可用性,可解释性和无工作流集成问题.
研究的目的:
- 评估RAPIDx AI在急救部门的临床工作流程中的整合.
- 识别和解决阻碍采用RAPIDx AI的可用性障碍.
- 在紧急情况下优化RAPIDx AI的实施和使用.
主要方法:
- 从2022年7月到2024年1月,PROLIFERATE_AI框架在12个紧急部门 (ED) 中使用.
- 专家知识诱导 (EKE) 报告了对24名ED临床医生进行的调查,评估RAPIDx AI的可用性和采用性.
- 贝叶斯推理和蒙特卡洛模拟量化了用户的理解,参与,使用和偏好,而主题分析确定了障碍和优化需求.
主要成果:
- 登记人员对RAPIDx AI的理解和偏好最高,而居民/实习生显示得分最低.
- 注册护士报告说,他们对人工智能工具有强烈的情感参与.
- 新手用户遇到可用性和工作流集成问题,与经验丰富的临床医生关于自动化和简化流程的建议形成鲜明对比. RAPIDx人工智能在训练有素的用户中显示出"良好的影响",但对于新手来说需要改进.
结论:
- RAPIDx AI 提高了经验丰富的医疗保健专业人员的诊断准确性和效率.
- 新手用户面临的可用性挑战需要针对性的培训计划和界面修改,以有效地整合人工智能.
- PROLIFERATE_AI框架提供了一个强大的方法来评估和扩展复杂的医疗保健系统中的AI解决方案.
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