Nguyen Phuong, Schiaffino Melody K, Zhang Zhan, Choi Hyung Wook, Huh-Yoo Jina
Department of Computer Science, University of Iowa, Iowa City, Iowa, USA.
School of Public Health, San Diego State University, San Diego, California, USA.
JAMIA Open. 2023 Aug 7;6(3):ooad061. doi: 10.1093/jamiaopen/ooad061. eCollection 2023 Oct.
Combined with mobile monitoring devices, telehealth generates overwhelming data, which could cause clinician burnout and overlooking critical patient status. Developing novel and efficient ways to correctly triage such data will be critical to a successful telehealth adoption. We aim to develop an automated classification framework of existing nurses' notes for each alert that will serve as a training dataset for a future alert triage system for telehealth programs.
We analyzed and developed a coding framework and a regular expression-based keyword match approach based on the information of 24 931 alert notes from a community-based telehealth program. We evaluated our automated alert triaging model for its scalability on a stratified sampling of 800 alert notes for precision and recall analysis.
We found 22 717 out of 24 579 alert notes (92%) belonging to at least one of the 17 codes. The evaluation of the automated alert note analysis using the regular expression-based information extraction approach resulted in an average precision of 0.86 (SD = 0.13) and recall 0.90 (SD = 0.13).
The high-performance results show the feasibility and the scalability potential of this approach in community-based, low-income older adult telehealth settings. The resulting coded alert notes can be combined with participants' health monitoring results to generate predictive models and to triage false alerts. The findings build steps toward developing an automated alert triaging model to improve the identification of alert types in remote health monitoring and telehealth systems.
远程医疗与移动监测设备相结合会产生海量数据，这可能导致临床医生倦怠并忽视关键的患者状况。开发新颖且高效的方法来正确分类此类数据对于成功采用远程医疗至关重要。我们旨在为每个警报开发一个现有的护士记录自动分类框架，该框架将作为未来远程医疗项目警报分类系统的训练数据集。
我们基于一个社区远程医疗项目的24931条警报记录信息，分析并开发了一个编码框架和基于正则表达式的关键词匹配方法。我们在800条警报记录的分层抽样上评估了我们的自动警报分类模型的可扩展性，以进行精确率和召回率分析。
我们发现24579条警报记录中有22717条（92%）至少属于17个代码中的一个。使用基于正则表达式的信息提取方法对自动警报记录分析的评估结果显示，平均精确率为0.86（标准差 = 0.13），召回率为0.90（标准差 = 0.13）。
高性能结果表明该方法在基于社区的低收入老年成人远程医疗环境中的可行性和可扩展性潜力。生成的编码警报记录可与参与者的健康监测结果相结合，以生成预测模型并对误报进行分类。这些发现为开发自动警报分类模型以改善远程健康监测和远程医疗系统中警报类型的识别奠定了基础。
