Center for Home Care Policy and Research, Visiting Nurse Service of New York, New York, NY; Columbia University School of Nursing, Columbia University Data Science Institute, New York, NY.
University of Pittsburgh School of Nursing, Pittsburgh, PA.
Nurs Outlook. 2021 May-Jun;69(3):435-446. doi: 10.1016/j.outlook.2020.12.007. Epub 2020 Dec 29.
Nurses often document patient symptoms in narrative notes.
This study used a technique called natural language processing (NLP) to: (1) Automatically identify documentation of seven common symptoms (anxiety, cognitive disturbance, depressed mood, fatigue, sleep disturbance, pain, and well-being) in homecare narrative nursing notes, and (2) examine the association between symptoms and emergency department visits or hospital admissions from homecare.
NLP was applied on a large subset of narrative notes (2.5 million notes) documented for 89,825 patients admitted to one large homecare agency in the Northeast United States.
NLP accurately identified symptoms in narrative notes. Patients with more documented symptom categories had higher risk of emergency department visit or hospital admission.
Further research is needed to explore additional symptoms and implement NLP systems in the homecare setting to enable early identification of concerning patient trends leading to emergency department visit or hospital admission.
护士经常在叙述性护理记录中记录患者的症状。
本研究使用一种称为自然语言处理（NLP）的技术：（1）自动识别家庭护理叙述性护理记录中七种常见症状（焦虑、认知障碍、情绪低落、疲劳、睡眠障碍、疼痛和幸福感）的记录，（2）检查症状与家庭护理中急诊科就诊或住院之间的关联。
NLP 应用于在美国东北部的一家大型家庭护理机构住院的 89825 名患者的大量叙述性记录（250 万条记录）的子集上。
NLP 可以准确识别叙述性记录中的症状。记录的症状类别越多的患者，急诊科就诊或住院的风险越高。
需要进一步研究以探索其他症状，并在家护理环境中实施 NLP 系统，以尽早识别导致急诊科就诊或住院的患者病情变化趋势。
