Background

Rates of child maltreatment (CM) obtained from electronic health records are much lower than national child welfare prevalence rates indicate. There is a need to understand how CM is documented to improve reporting and surveillance.

Objectives

To examine whether using natural language processing (NLP) in outpatient chart notes can identify cases of CM not documented by ICD diagnosis code, the overlap between the coding of child maltreatment by ICD and NLP, and any differences by age, gender, or race/ethnicity.

Methods

Outpatient chart notes of children age 0–18 years old within Kaiser Permanente Washington (KPWA) 2018–2020 were used to examine a selected set of maltreatment-related terms categorized into concept unique identifiers (CUI). Manual review of text snippets for each CUI was completed to flag for validated cases and retrain the NLP algorithm.

Results

The NLP results indicated a crude rate of 1.55 % to 2.36 % (2018–2020) of notes with reference to CM. The rate of CM identified by ICD code was 3.32 per 1000 children, whereas the rate identified by NLP was 37.38 per 1000 children. The groups that increased the most in identification of maltreatment from ICD to NLP were adolescents (13–18 yrs. old), females, Native American children, and those on Medicaid. Of note, all subgroups had substantially higher rates of maltreatment when using NLP.

Conclusions

Use of NLP substantially increased the estimated number of children who have been impacted by CM. Accurately capturing this population will improve identification of vulnerable youth at high risk for mental health symptoms.

中文翻译：

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使用自然语言处理识别卫生系统中的儿童虐待

背景

从电子健康记录中获得的儿童虐待 (CM) 率远低于全国儿童福利流行率显示的水平。有必要了解如何记录 CM 以改进报告和监督。

目标

检查在门诊病历记录中使用自然语言处理 (NLP) 是否可以识别 ICD 诊断代码未记录的 CM 病例、ICD 和 NLP 对儿童虐待的编码之间的重叠，以及年龄、性别或种族之间的任何差异/种族。

方法

华盛顿凯撒永久医疗机构 (KPWA) 2018-2020 年 0-18 岁儿童的门诊病历记录用于检查一组选定的虐待相关术语，这些术语被分类为概念唯一标识符 (CUI)。已完成对每个 CUI 的文本片段的手动审查，以标记已验证的案例并重新训练 NLP 算法。

结果

NLP 结果表明，参考 CM 的票据粗利率为 1.55% 至 2.36% (2018-2020)。ICD 代码识别出的 CM 率为每 1000 名儿童 3.32 名，而 NLP 识别出的 CM 率为每 1000 名儿童 37.38 名。从 ICD 到 NLP，在识别虐待方面增加最多的群体是青少年（13-18 岁）、女性、美国原住民儿童和那些享受医疗补助的人。值得注意的是，所有亚组在使用 NLP 时的虐待率都高得多。

结论

NLP 的使用大大增加了受 CM 影响的儿童的估计数量。准确捕捉这一人群将有助于识别处于精神健康症状高风险中的弱势青年。