eVent in the Human Patient Simulator
eVENT: An Expert System for Detecting Critical Events During Anesthesia (Human Patient Simulator Study)
研究概览
详细说明
The development of new sensors or the intelligent synthesis of existing signals (smart sensors) cannot reliably prevent adverse events unless the investigators assimilate the data produced from these devices and provide it to the clinician in a format that is easy to comprehend. The expert system that the investigators are developing will provide intelligent synthesis of data. The clinician focus can then be directed toward the patient rather than continual observation of the monitors.
Clinicians need an expert system to augment vigilance and situation awareness, and to aid in decision making to prevent patient harm.
The development of an expert system to assist the everyday anesthesiologist in the operating room is a significant challenge that is in urgent need of being addressed. Assistance is required in interpreting the overwhelming stream of physiological data, intelligently extracting key features from these data, and bringing them to the attention of the clinician.
研究类型
注册 (实际的)
联系人和位置
学习地点
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British Columbia
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Vancouver、British Columbia、加拿大、V6H 3V4
- BC Children's Hospital
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参与标准
资格标准
适合学习的年龄
- 孩子
- 成人
- 年长者
接受健康志愿者
有资格学习的性别
取样方法
研究人群
描述
Inclusion Criteria:
- Staff anesthesiologist, fellows and 3rd - 5th year residents will be able to participate in this study.
- Subjects willing to provide informed consent.
Exclusion Criteria:
- Inability/failure to provide informed consent.
- Presence of any of the following conditions oPrevious participation in similar experiment oTaken sedative medication within last 24 hours
- Inability/failure to obtain 75% or above in the post-training quiz on the correct use of the eVent.
- Evaluation will be immediately discontinued at request of subject at any point during the study.
学习计划
研究是如何设计的?
设计细节
- 观测模型:案例交叉
- 时间观点:预期
合作者和调查者
调查人员
- 首席研究员:Mark Ansermino、University of British Columbia
出版物和有用的链接
研究记录日期
研究主要日期
学习开始
初级完成 (实际的)
研究完成 (实际的)
研究注册日期
首次提交
首先提交符合 QC 标准的
首次发布 (估计)
研究记录更新
最后更新发布 (实际的)
上次提交的符合 QC 标准的更新
最后验证
更多信息
与本研究相关的术语
其他研究编号
- H10-02621
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