Woo Kyungmi, Adams Victoria, Wilson Paula, Fu Li-Heng, Cato Kenrick, Rossetti Sarah Collins, McDonald Margaret, Shang Jingjing, Topaz Maxim
College of Nursing, Seoul National University, Seoul, Republic of Korea.
Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York, NY, USA.
J Am Med Dir Assoc. 2021 May;22(5):1015-1021.e2. doi: 10.1016/j.jamda.2020.12.010. Epub 2021 Jan 9.
Urinary tract infection (UTI) is common in home care but not easily captured with standard assessment. This study aimed to examine the value of nursing notes in detecting UTI signs and symptoms in home care.
The study developed a natural language processing (NLP) algorithm to automatically identify UTI-related information in nursing notes.
Home care visit notes (n = 1,149,586) and care coordination notes (n = 1,461,171) for 89,459 patients treated in the largest nonprofit home care agency in the United States during 2014.
We generated 6 categories of UTI-related information from literature and used the Unified Medical Language System (UMLS) to identify a preliminary list of terms. The NLP algorithm was tested on a gold standard set of 300 clinical notes annotated by clinical experts. We used structured Outcome and Assessment Information Set data to extract the frequency of UTI-related emergency department (ED) visits or hospitalizations and explored time-patterns in documentation of UTI-related information.
The NLP system achieved very good overall performance (F measure = 0.9, 95% CI: 0.87-0.93) based on the test results obtained by using the notes for patients admitted to the ED or hospital due to UTI. UTI-related information was significantly more prevalent (P < .01 for all the tests) in home care episodes with UTI-related ED admission or hospitalization vs the general patient population; 81% of home care episodes with UTI-related hospitalization or ED admission had at least 1 category of UTI-related information vs 21.6% among episodes without UTI-related hospitalization or ED admission. Frequency of UTI-related information documentation increased in advance of UTI-related hospitalization or ED admission, peaking within a few days before the event.
Information in nursing notes is often overlooked by stakeholders and not integrated into predictive modeling for decision-making support, but our findings highlight their value in early risk identification and care guidance. Health care administrators should consider using NLP to extract clinical data from nursing notes to improve early detection and treatment, which may lead to quality improvement and cost reduction.
尿路感染（UTI）在家庭护理中很常见，但通过标准评估不易发现。本研究旨在探讨护理记录在家庭护理中检测UTI体征和症状的价值。
本研究开发了一种自然语言处理（NLP）算法，以自动识别护理记录中与UTI相关的信息。
2014年期间在美国最大的非营利性家庭护理机构接受治疗的89459名患者的家庭护理访视记录（n = 1149586）和护理协调记录（n = 1461171）。
我们从文献中生成了6类与UTI相关的信息，并使用统一医学语言系统（UMLS）来确定术语的初步列表。NLP算法在由临床专家注释的300份临床记录的金标准集上进行了测试。我们使用结构化的结果和评估信息集数据来提取与UTI相关的急诊科（ED）就诊或住院的频率，并探索与UTI相关信息记录的时间模式。
根据使用因UTI入院或住院患者的记录获得的测试结果，NLP系统取得了非常好的总体性能（F值 = 0.9，95%置信区间：0.87 - 0.93）。与UTI相关的急诊入院或住院的家庭护理事件中，与UTI相关的信息明显比普通患者群体更普遍（所有测试的P <.01）；81%与UTI相关的住院或急诊入院的家庭护理事件至少有1类与UTI相关的信息，而在没有与UTI相关的住院或急诊入院的事件中这一比例为21.6%。与UTI相关的信息记录频率在与UTI相关的住院或急诊入院之前增加，在事件发生前几天达到峰值。
护理记录中的信息常常被利益相关者忽视，没有整合到用于决策支持的预测模型中，但我们的研究结果突出了它们在早期风险识别和护理指导方面的价值。医疗保健管理人员应考虑使用NLP从护理记录中提取临床数据，以改善早期检测和治疗，这可能会带来质量改进和成本降低。
