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住院儿童的败血症预测:临床决策支持设计和部署
Rebecca J Stephen1,2,3, Kate Lucey1,2,3, Michael S Carroll1,4
1Department of Pediatrics, Northwestern Feinberg School of Medicine, Chicago, Illinois.
这项研究实施了一种电子健康记录败血症预测模型,配合临床决策支持 (CDS) 工具和聚集工作流. 该系统实现了高合规性和低警报率,改善了儿科患者的败血症识别.
科学领域:
- 儿科重症监护 儿科重症监护
- 医疗信息学 医疗信息学
- 质量改进科学 质量改进科学
背景情况:
- 此前已经开发了一种经过验证的败血症预测模型.
- 该研究的重点是部署临床决策支持 (CDS) 工具和 pediatric sepsis识别工作流程.
研究的目的:
- 引导CDS工具和临床医生工作流程的设计和部署,以改善儿科败血症识别.
- 实施基于电子健康记录的败血症预测模型,使用质量改进和安全方法.
主要方法:
- 开发和实施的CDS工具和败血症聚集工作流程.
- 利用模拟和安全科学原则进行主动分析和改进.
- 采用快速的计划-做-研究-行动 (PDSA) 循环,由用户反和指标数据提供信息.
主要成果:
- 实现了89%的床边聚会完成率.
- 每月每1000个非ICU患者日产生10个败血症警报.
- 没有观察到可以归因于败血症的突发转移之间的天数有显著差异.
结论:
- 成功实施了基于EHR的自动化败血症预测模型,CDS工具和聚集工作流.
- 通过CDS,模拟和质量改进的最佳实践,实现了毒性查过程的高利用率.
- 展示了一个具有低中断警报和高度遵守床边聚集的系统.
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