BACKGROUND: 

Methods that can automate, support, and streamline the preanesthesia evaluation process may improve resource utilization and efficiency. Natural language processing (NLP) involves the extraction of relevant information from unstructured text data. We describe the utilization of a clinical NLP pipeline intended to identify elements relevant to preoperative medical history by analyzing clinical notes. We hypothesize that the NLP pipeline would identify a significant portion of pertinent history captured by a perioperative provider.

METHODS: 

For each patient, we collected all pertinent notes from the institution’s electronic medical record that were available no later than 1 day before their preoperative anesthesia clinic appointment. Pertinent notes included free-text notes consisting of history and physical, consultation, outpatient, inpatient progress, and previous preanesthetic evaluation notes. The free-text notes were processed by a Named Entity Recognition pipeline, an NLP machine learning model trained to recognize and label spans of text that corresponded to medical concepts. These medical concepts were then mapped to a list of medical conditions that were of interest for a preanesthesia evaluation. For each condition, we calculated the percentage of time across all patients in which (1) the NLP pipeline and the anesthesiologist both captured the condition; (2) the NLP pipeline captured the condition but the anesthesiologist did not; and (3) the NLP pipeline did not capture the condition but the anesthesiologist did.

RESULTS: 

A total of 93 patients were included in the NLP pipeline input. Free-text notes were extracted from the electronic medical record of these patients for a total of 9765 notes. The NLP pipeline and anesthesiologist agreed in 81.24% of instances on the presence or absence of a specific condition. The NLP pipeline identified information that was not noted by the anesthesiologist in 16.57% of instances and did not identify a condition that was noted by the anesthesiologist’s review in 2.19% of instances.

CONCLUSIONS: 

In this proof-of-concept study, we demonstrated that utilization of NLP produced an output that identified medical conditions relevant to preanesthetic evaluation from unstructured free-text input. Automation of risk stratification tools may provide clinical decision support or recommend additional preoperative testing or evaluation. Future studies are needed to integrate these tools into clinical workflows and validate its efficacy.

中文翻译：

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通过自然语言处理引擎识别麻醉前历史元素

背景： 

可以自动化、支持和简化麻醉前评估过程的方法可以提高资源利用率和效率。自然语言处理 (NLP) 涉及从非结构化文本数据中提取相关信息。我们描述了临床 NLP 管道的使用，旨在通过分析临床记录来识别与术前病史相关的元素。我们假设 NLP 管道将识别围手术期提供者捕获的相关历史的重要部分。

方法： 

对于每位患者，我们从该机构的电子病历中收集了所有相关注释，这些记录在术前麻醉门诊预约前 1 天可用。相关注释包括自由文本注释，包括病史和身体、会诊、门诊、住院进展和之前的麻醉前评估注释。自由文本注释由命名实体识别管道处理，这是一种 NLP 机器学习模型，经过训练可以识别和标记与医学概念相对应的文本范围。然后将这些医学概念映射到麻醉前评估感兴趣的医学状况列表。对于每种情况，我们计算了所有患者的时间百分比，其中 (1) NLP 管道和麻醉师都捕获了这种情况；(2) NLP pipeline 捕捉到了病情，但麻醉师没有捕捉到；(3) NLP 管道没有捕捉到这种情况，但麻醉师捕捉到了。

结果： 

共有 93 名患者被纳入 NLP 管道输入。从这些患者的电子病历中提取自由文本注释，共计 9765 条注释。NLP 管道和麻醉师在 81.24% 的实例中同意特定条件的存在或不存在。NLP 管道在 16.57% 的实例中识别出麻醉师未注意到的信息，在 2.19% 的实例中未识别出麻醉师审查时注意到的情况。

结论： 

在这项概念验证研究中，我们证明了 NLP 的利用产生了一个输出，该输出从非结构化的自由文本输入中识别出与麻醉前评估相关的医疗状况。风险分层工具的自动化可以提供临床决策支持或推荐额外的术前测试或评估。未来的研究需要将这些工具整合到临床工作流程中并验证其有效性。