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Movement Retraining using Real-time Feedback of Performance
Published on: January 17, 2013
建模精度反知识为医疗保健专业的学习和质量改进
Zach Landis-Lewis1, Yidan Cao1, Hana Chung1,2
1Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI.
医疗保健提供者需要更好的学习支持. 精确反通过动机潜力优先考虑信息,通过新的知识基础增强学习健康系统.
科学领域:
- 医疗信息学 医疗信息学
- 医学教育 医学教育
- 学习健康系统 学习健康系统
背景情况:
- 由于生物医学知识的快速扩展,医疗保健提供者需要不断学习.
- 电子健康记录 (EHR) 数据越来越多地用于临床绩效测量,需要改进学习支持.
- 现有的支持系统往往缺乏个性化,未能根据个人提供者需求优化学习.
研究的目的:
- 开发一个精确反知识库 (PFKB) 作为一个开放的资源.
- 支持为医疗保健提供者创建精确反系统.
- 通过建模反个性化,为学习卫生系统提供基础设施.
主要方法:
- 设计和开发了PFKB,结合了反个性化知识模型.
- 包括关键组件,如质量测量,反消息模板,因果路径模型,信号探测器和优先级算法.
- 建立了PFKB作为一个开放的资源,用于更广泛的实施.
主要成果:
- 成功开发了PFKB,包含了针对个性化反的结构化知识.
- 该知识库整合了有效的精确反系统所必需的各种组件.
- 目前,PFKB正在国家麻醉护理质量改善联盟中实施.
结论:
- PFKB提供了一个基础资源,通过个性化的反来增强提供者的学习.
- 精确反,由PFKB支持,有潜力改善学习健康系统.
- 实施的系统旨在优化提供者反电子邮件,促进持续的专业发展.
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