Underserved populations with missing race ethnicity data differ significantly from those with structured race/ethnicity documentation
Underserved populations with missing race ethnicity data differ significantly from those with structured race/ethnicity documentation
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DOI：
10.1093/jamia/ocz040
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发表时间：
2019-08-01
影响因子：
6.4
通讯作者：
Campion, Thomas R., Jr.
中科院分区：
文献类型：
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作者：
Sholle, Evan T.;Pinheiro, Laura C.;Campion, Thomas R., Jr.
目的：我们旨在通过从非结构化临床记录中识别黑人和西班牙裔患者，并评估有或没有结构化种族/民族数据的患者之间的差异，解决种族和民族结构化电子健康记录（EHR）数据的缺陷。材料和方法：利用16 665名在初级保健实践中遇到的患者的电子病历记录，我们开发了基于规则的自然语言处理（NLP）算法，将患者分类为黑人/西班牙裔。我们根据注释金标准评估了该方法的性能，比较了NLP衍生和结构化EHR数据之间的种族和民族，并比较了仅使用NLP识别为黑人或西班牙裔患者与仅在结构化EHR数据中识别为黑人或西班牙裔患者的特征。结果：在16665例患者的样本中，NLP确定了948例黑人患者，增加了26%，665例西班牙裔患者，增加了20%。与结构化EHR数据中确定为黑人或西班牙裔的患者相比，仅通过NLP确定为黑人或西班牙裔的患者年龄更大，更有可能是男性，更不可能有商业保险，更有可能有更高的合并症。讨论：种族和民族的结构化电子病历数据受到数据质量问题的影响。用nlp衍生的种族和民族来补充结构化的电子病历种族数据，可以让研究人员更好地评估人口的人口构成，并得出更准确的关于群体间健康结果差异的结论。结论：在结构化的电子病历种族/民族领域中未被记录的黑人或西班牙裔患者与被记录的患者存在显著差异。相对简单的NLP可以帮助解决这个限制。
Objective: We aimed to address deficiencies in structured electronic health record (EHR) data for race and ethnicity by identifying black and Hispanic patients from unstructured clinical notes and assessing differences between patients with or without structured race/ethnicity data.Materials and Methods: Using EHR notes for 16 665 patients with encounters at a primary care practice, we developed rule-based natural language processing (NLP) algorithms to classify patients as black/Hispanic. We evaluated performance of the method against an annotated gold standard, compared race and ethnicity between NLP-derived and structured EHR data, and compared characteristics of patients identified as black or Hispanic using only NLP vs patients identified as such only in structured EHR data.Results: For the sample of 16 665 patients, NLP identified 948 additional patients as black, a 26% increase, and 665 additional patients as Hispanic, a 20% increase. Compared with the patients identified as black or Hispanic in structured EHR data, patients identified as black or Hispanic via NLP only were older, more likely to be male, less likely to have commercial insurance, and more likely to have higher comorbidity.Discussion: Structured EHR data for race and ethnicity are subject to data quality issues. Supplementing structured EHR race data with NLP-derived race and ethnicity may allow researchers to better assess the demographic makeup of populations and draw more accurate conclusions about intergroup differences in health outcomes.Conclusions: Black or Hispanic patients who are not documented as such in structured EHR race/ethnicity fields differ significantly from those who are. Relatively simple NLP can help address this limitation.