Ji Meihua, Wu Ying, Chang Polun, Yang Xin, Yang Fangyu, Xu Shuang
School of Nursing, Capital Medical University, Beijing, China.
Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.
Stud Health Technol Inform. 2015;216:899.
Delirium is a common complication among patients in ICU settings. The accuracy of using the assessment tool CAM-ICU to detect delirium is relatively low during routine practice among bedside nurses. The aim of this study is to develop a mobile application (app) to detect delirium in early stage and to test its usability among ICU nurses. The app was developed with Java and installed on a mobile device. A questionnaire was created based on the Technology Acceptance Model (TAM) measuring their response to the four domains of TAM: perceived usefulness (PU), perceived ease of use (PEOU), attitudes towards usage (ATU) and behavioral intention to use (BIU). One hundred and two ICU nurses completed the survey. The result indicated that the app we developed has easy to use interfaces and is easier to use compared to the regular CAM-ICU.
谵妄是重症监护病房（ICU）患者常见的并发症。在床边护士的日常工作中，使用评估工具CAM-ICU检测谵妄的准确性相对较低。本研究的目的是开发一款移动应用程序（应用）以早期检测谵妄，并在ICU护士中测试其可用性。该应用使用Java开发并安装在移动设备上。基于技术接受模型（TAM）创建了一份问卷，测量护士对TAM四个维度的反应：感知有用性（PU）、感知易用性（PEOU）、使用态度（ATU）和使用行为意向（BIU）。102名ICU护士完成了调查。结果表明，我们开发的应用具有易于使用的界面，并且与常规的CAM-ICU相比更易于使用。
