Yang Fangyu, Ji Meihua, Ding Shu, Wu Ying, Chang Polun, Lin Chiawei, Yang Xin
School of Nursing, Capital Medical University, Beijing, China.
Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.
Stud Health Technol Inform. 2016;225:668-72.
Delirium is a common complication among patients in ICU settings. Although it has been repeatedly confirmed that Confusion Assessment Model for Intensive Care Unit (CAM-ICU), one of the most commonly used ICU delirium assessment tool, is highly accurate in validation studies, it's sensitivity and specificity is relatively low during routine practice among bedside nurses. The aim of this study is to develop a mobile application (app) to detect delirium and to test its reliability and validity both by research nurses and among ICU bedside nurses. The app was programmed with Java and installed on a mobile device with Android system. After completion of reliability and validity testing, the app will be integrated into the existing Hospital Information System in order to automatically retrieve essential information for risk factor identification and formulation of care plan accordingly to prevent or manage ICU delirium.
谵妄是重症监护病房(ICU)患者常见的并发症。尽管已反复证实,重症监护病房意识模糊评估法(CAM-ICU)作为最常用的ICU谵妄评估工具之一,在验证研究中具有很高的准确性,但在床边护士的日常实践中,其敏感性和特异性相对较低。本研究的目的是开发一款用于检测谵妄的移动应用程序(应用),并由研究护士和ICU床边护士测试其可靠性和有效性。该应用使用Java编程,并安装在安卓系统的移动设备上。在完成可靠性和有效性测试后,该应用将被集成到现有的医院信息系统中,以便自动检索基本信息以识别风险因素,并据此制定护理计划,以预防或管理ICU谵妄。