OBJECTIVE To assess the utility of applying natural language processing (NLP) to electronic health records (EHRs) to identify individuals with chronic mobility disability. DESIGN We used EHRs from the Research Patient Data Repository, which contains EHRs from a large Massachusetts health care delivery system. This analysis was part of a larger study assessing the effects of disability on diagnosis of colorectal cancer. We applied NLP text extraction software to longitudinal EHRs of colorectal cancer patients to identify persons who use a wheelchair (our indicator of mobility disability for this analysis). We manually reviewed the clinical notes identified by NLP using directed content analysis to identify true cases using wheelchairs, duration or chronicity of use, and documentation quality. SETTING EHRs from large health care delivery system PARTICIPANTS: Patients 21-75 years old who were newly diagnosed with colorectal cancer between 2005-2017. INTERVENTIONS Not applicable MAIN OUTCOME MEASURE(S): Confirmation of patients' chronic wheelchair use in NLP-flagged notes; quality of disability documentation RESULTS: We identified 14,877 patients with colorectal cancer with 303,182 associated clinical notes. NLP screening identified 1,482 (0.5%) notes that contained 1+ wheelchair-associated keyword. These notes were associated with 420 patients (2.8% of colorectal cancer population). Of the 1,482 notes, 286 (19.3%, representing 105 patients, 0.7% of the total) contained documentation of reason for wheelchair use and duration. Directed content analysis identified three themes concerning disability documentation: (1) wheelchair keywords used in specific EHR contexts; (2) reason for wheelchair not clearly stated; and (3) duration of wheelchair use not consistently documented. CONCLUSIONS NLP offers an option to screen for patients with chronic mobility disability in much less time than required by manual chart review. Nonetheless, manual chart review must confirm that flagged patients have chronic mobility disability (are not false positives). Notes, however, often have inadequate disability documentation.

中文翻译：

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开发带有电子健康记录的自然语言处理方法来识别慢性行动障碍人士的挑战


目的 评估将自然语言处理 (NLP) 应用于电子健康记录 (EHR) 来识别患有慢性行动障碍的个人的效用。设计我们使用了研究患者数据存储库中的电子病历，其中包含来自马萨诸塞州大型医疗保健服务系统的电子病历。这项分析是一项更大规模研究的一部分，该研究评估残疾对结直肠癌诊断的影响。我们将 NLP 文本提取软件应用于结直肠癌患者的纵向 EHR，以识别使用轮椅的人（我们用于此分析的行动障碍指标）。我们使用定向内容分析手动审查 NLP 识别的临床记录，以识别使用轮椅的真实病例、使用的持续时间或长期性以及文档质量。从大型医疗保健服务系统中设置 EHR 参与者：2005 年至 2017 年间新诊断出结直肠癌的 21 至 75 岁患者。干预措施 不适用 主要结果测量：在 NLP 标记的笔记中确认患者长期使用轮椅；残疾记录的质量 结果：我们确定了 14,877 名结直肠癌患者，并提供了 303,182 份相关临床记录。 NLP 筛选发现 1,482 (0.5%) 条笔记包含 1 个以上与轮椅相关的关键词。这些注释与 420 名患者（占结直肠癌患者的 2.8%）相关。在 1,482 份笔记中，286 份（19.3%，代表 105 名患者，占总数的 0.7%）包含使用轮椅的原因和持续时间的记录。定向内容分析确定了有关残疾记录的三个主题：(1) 在特定 EHR 环境中使用的轮椅关键词； （二）未明确说明使用轮椅的原因的； (3) 轮椅使用时间的记录不一致。 结论 NLP 提供了一种筛选慢性行动障碍患者的选择，其时间比手动图表审查所需的时间短得多。尽管如此，手动图表审查必须确认标记的患者患有慢性行动障碍（不是误报）。然而，注释通常没有足够的残疾文件。