识别实施注册护士输入的沟通叙述关切的障碍,早期预警系统智能应用程序
Mollie Hobensack1, Jennifer Withall2, Brian Douthit3
1Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York City, New York, United States.
Applied clinical informatics
|April 17, 2024
概括
使用定性访谈更新了CONCERN实施工具包,以帮助其他医院采用这种患者病情恶化的早期预警系统. 这验证了alpha版本,未来的研究将验证beta版本.
科学领域:
- 护理信息学 护理信息学
- 检测临床恶化的检测.
- 卫生系统的实施卫生系统的实施
背景情况:
- 护士在识别患者病情恶化的过程中至关重要.
- 注册护士输入的叙述关切 (CONCERN) 是一个早期预警系统,使用护理数据进行风险预测.
- CONCERN 之前通过随机临床试验实施.
研究的目的:
- 确定实施CONCERN.的最佳资源.
- 开发CONCERN实施工具包,以便更广泛地采用.
- 为了促进CONCERN的扩散到其他医院网站.
主要方法:
- 与护士,处方提供者和IT专家进行了定性采访.
- 在采访数据上,根据Donabedian模型进行了主题分析.
- 基于这些发现,CONCERN实施工具包 (alpha版本) 进行了更新.
主要成果:
- 这项研究包括了32名参与者.
- 确定了12个主题,与Donabedian模型的结构,过程和结果领域保持一致.
- 八个新的资源被纳入了CONCERN实施工具包.
结论:
- 已经验证了CONCERN实施工具包的alpha版本.
- 未来的研究将专注于验证工具包的测试版.
- 研究结果为在急性护理机构实施早期预警系统提供了洞察力.
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