Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.
Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.
Int J Med Inform. 2024 Dec;192:105632. doi: 10.1016/j.ijmedinf.2024.105632. Epub 2024 Sep 17.
Teleconsultation is anticipated to have a long-term role in primary care. However, conducting virtual physical examinations is a well-known limitation. To anticipate unmet needs general practitioners (GPs) and patients may experience during teleconsultation, this study aims to automatically identify physical examinations typically conducted during in-person GP consultation.
This study utilizes 281 GP in-person consultations (de-identified transcripts & video recordings) within UK general practices, where 169 eligible ones were included in this study. We propose an automated text-based approach using regular expressions on keywords in GP-patient consultation dialogue (e.g., "roll up your sleeves") to identify physical examinations (e.g. blood pressure measurement). This approach involves the construction of conceptual diagrams to visually inspect the relationship between keywords and physical examinations, syntax analysis to identify patterns between keywords and generate regular expressions, and the use of these regular expressions in consultation transcripts to detect potential instances of physical examinations, where matching video frames were subsequently retrieved. The performance of our automated text-based approach is compared to manual classification by 2 independent researchers using 5-fold cross-validation (precision, recall, and F1-score).
Among the 169 eligible GP in-person consultations, 133 (79%) required a physical examination, while the other 33 visits were for psychological reasons. Out of these 133 consultations, a total of 283 physical examinations were observed, with 21 instances conducted behind a curtain. We identified 42 distinct types of physical examinations from these 283 instances, grouped into 10 physical examination categories based on body areas and physical artefacts. The most frequent category of physical examinations is Vital Signs 26.80% (76/283). Overall, blood pressure measurement (also belonging to the Vital Signs category) is the most frequent physical examination at 59.2% (45/76). The comparison between manual classification and the regular expression model demonstrates an average precision of 88.3%, recall of 78.9%, and an F1-score of 83.3% from 5-fold cross-validation, providing significant insights into the frequency and types of physical examinations conducted during in-person GP consultations.
By using regular expressions in consultation dialogues between GPs and patients, we can automatically identify physical examinations in GP consultations with a precision of 88.3%. Findings from this study, i.e. physical examinations during in-person GP consultations, provide insights into areas where GPs and patients may need support during teleconsultation.
远程咨询预计将在初级保健中发挥长期作用。然而，进行虚拟体检是一个众所周知的限制。为了预测全科医生（GP）和患者在远程咨询期间可能遇到的未满足的需求，本研究旨在自动识别通常在 GP 当面咨询中进行的体检。
本研究利用了英国普通诊所的 281 次 GP 当面咨询（已识别的记录和视频记录），其中有 169 次符合条件的咨询被纳入本研究。我们提出了一种基于正则表达式的自动文本方法，该方法使用 GP-患者咨询对话中的关键字（例如，“卷起袖子”）来识别体检（例如，血压测量）。该方法涉及构建概念图，以直观检查关键字和体检之间的关系，进行语法分析以识别关键字之间的模式并生成正则表达式，以及在咨询记录中使用这些正则表达式来检测潜在的体检实例，随后检索匹配的视频帧。我们的自动文本方法的性能通过 2 位独立研究人员使用 5 折交叉验证（精度、召回率和 F1 分数）进行比较。
在 169 次符合条件的 GP 当面咨询中，有 133 次（79%）需要进行体检，而其他 33 次咨询是出于心理原因。在这 133 次咨询中，共观察到 283 次体检，其中 21 次在幕后进行。我们从这 283 次体检中确定了 42 种不同类型的体检，根据身体部位和体检器具将其分为 10 个体检类别。最常见的体检类别是生命体征 26.80%（76/283）。总体而言，血压测量（也属于生命体征类别）是最常见的体检，占 59.2%（45/76）。手动分类和正则表达式模型之间的比较表明，5 折交叉验证的平均精度为 88.3%，召回率为 78.9%，F1 得分为 83.3%，这为在 GP 当面咨询中进行的体检的频率和类型提供了重要见解。
通过在 GP 和患者之间的咨询对话中使用正则表达式，我们可以以 88.3%的精度自动识别 GP 咨询中的体检。本研究中的发现，即 GP 当面咨询中的体检，为远程咨询中 GP 和患者可能需要支持的领域提供了见解。
