Detecting Language Associated With Home Healthcare Patient's Risk for Hospitalization and Emergency Department Visit.
Detecting Language Associated With Home Healthcare Patient's Risk for Hospitalization and Emergency Department Visit.
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DOI：
10.1097/nnr.0000000000000586
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发表时间：
2022-07-01
期刊：
影响因子：
2.5
通讯作者：
Topaz, Maxim
中科院分区：
文献类型：
--
作者：
Song, Jiyoun;Ojo, Marietta;Bowles, Kathryn H.;McDonald, Margaret V.;Cato, Kenrick;Rossetti, Sarah Collins;Adams, Victoria;Chae, Sena;Hobensack, Mollie;Kennedy, Erin;Tark, Aluem;Kang, Min-Jeoung;Woo, Kyungmi;Barron, Yolanda;Sridharan, Sridevi;Topaz, Maxim
关键词：
大约五分之一接受家庭医疗保健（HHC）服务的患者在家庭护理期间住院或去急诊室（艾德）就诊。早期识别有风险的患者可以防止这些负面结果。然而，风险指标，包括临床记录中表明患者担忧的语言，通常隐藏在整个HHC事件的叙述性文件中。开发自动自然语言处理（NLP）算法，以识别指示HHC患者住院或艾德访视风险的相关语言。这项研究使用了奥马哈系统-一个标准化的护理术语，描述了问题/体征/症状，可以发生在社区设置。首先，五位HHC专家反复审查奥马哈系统，并确定了指示HHC患者住院或艾德就诊风险的相关概念。接下来，我们开发并测试了一种NLP算法，以自动识别HHC临床笔记中的这些相关概念。由此产生的NLP算法应用于2015年至2017年期间在美国东北部一家大型HHC机构住院的66，317例独特患者（n = 87，966次HHC发作）记录的大量叙述性笔记（230万条笔记）。共确定了160个Omaha系统体征/症状与HHC中住院或艾德访视的概念有关。这些迹象/症状属于42个可用的奥马哈系统问题中的31个。总体而言，NLP算法在识别临床笔记中的相关概念方面表现出良好的性能。超过18%的临床记录被检测到至少有一个相关的概念，超过90%的HHC事件包括至少一个奥马哈系统问题。最常记录的相关概念是疼痛，其次是与神经肌肉骨骼功能、循环、心理健康和传染性/感染性疾病相关的问题。我们的研究结果表明，可能增加住院或艾德访视风险的相关问题或症状经常记录在叙述性临床记录中。NLP可以自动从叙述性临床笔记中提取信息，以提高我们对HHC护理需求的理解。下一步是评估临床记录中识别的哪些相关概念可预测住院或艾德访视。
About one in five patients receiving home health care (HHC) services are hospitalized or visit an emergency department (ED) during a home care episode. Early identification of patients at risk can prevent these negative outcomes. However, risk indicators, including language in clinical notes that indicate a concern about a patient, are often hidden in narrative documentation throughout their HHC episode. To develop an automated natural language processing (NLP) algorithm to identify concerning language indicative of HHC patient risk of hospitalizations or ED visits. This study used the Omaha System—a standardized nursing terminology that describes problems/signs/symptoms that can occur in the community setting. First, five HHC experts iteratively reviewed the Omaha System and identified concerning concepts indicative of HHC patient risk of hospitalizations or ED visits. Next, we developed and tested an NLP algorithm to identify these concerning concepts in HHC clinical notes automatically. The resulting NLP algorithm was applied on a large subset of narrative notes (2.3 million notes) documented for 66,317 unique patients (n = 87,966 HHC episodes) admitted to one large HHC agency in the Northeast United States between 2015 and 2017. A total of 160 Omaha System signs/symptoms were identified as concerning concepts for hospitalizations or ED visits in HHC. These signs/symptoms belong to 31 of the 42 available Omaha System problems. Overall, the NLP algorithm showed good performance in identifying concerning concepts in clinical notes. More than 18% of clinical notes were detected as having at least one concerning concept, and more than 90% of HHC episodes included at least one Omaha System problem. The most frequently documented concerning concepts were pain, followed by issues related to neuro-musculoskeletal function, circulation, mental health, and communicable/infectious conditions. Our findings suggest that concerning problems or symptoms that could increase the risk of hospitalization or ED visit were frequently documented in narrative clinical notes. NLP can automatically extract information from narrative clinical notes to improve our understanding of care needs in HHC. Next steps are to evaluate which concerning concepts identified in clinical notes predict hospitalization or ED visit.