Abstract：
OBJECTIVE: To determine the incidence of patients presenting in pain to a large Australian inner-city emergency department (ED) using a clinical text deep learning algorithm.
METHODS: A fine-tuned, domain-specific, transformer-based clinical text deep learning model was used to interpret free-text nursing assessments in the electronic medical records of 235,789 adult presentations to the ED over a three-year period. The model classified presentations according to whether the patient had pain on arrival at the ED. Interrupted time series analysis was used to determine the incidence of pain in patients on arrival over time. We described the changes in the population characteristics and incidence of patients with pain on arrival occurring with the start of the Covid-19 pandemic.
RESULTS: 55.16% (95%CI 54.95%-55.36%) of all patients presenting to this ED had pain on arrival. There were differences in demographics and arrival and departure patterns between patients with and without pain. The Covid-19 pandemic initially precipitated a decrease followed by a sharp, sustained rise in pain on arrival, with concurrent changes to the population arriving in pain and their treatment.
CONCLUSIONS: Applying a clinical text deep learning model has successfully identified the incidence of pain on arrival. It represents an automated, reproducible mechanism to identify pain from routinely collected medical records. The description of this population and their treatment forms the basis of intervention to improve care for patients with pain. The combination of the clinical text deep learning models and interrupted time series analysis has reported on the effects of the Covid-19 pandemic on pain care in the ED, outlining a methodology to assess the impact of significant events or interventions on pain care in the ED.
CONCLUSIONS: Applying a novel deep learning approach to identifying pain guides methodological approaches to evaluating pain care interventions in the ED, giving previously unavailable population-level insights.
METHODS: A fine-tuned, domain-specific, transformer-based clinical text deep learning model was used to interpret free-text nursing assessments in the electronic medical records of 235,789 adult presentations to the ED over a three-year period. The model classified presentations according to whether the patient had pain on arrival at the ED. Interrupted time series analysis was used to determine the incidence of pain in patients on arrival over time. We described the changes in the population characteristics and incidence of patients with pain on arrival occurring with the start of the Covid-19 pandemic.
RESULTS: 55.16% (95%CI 54.95%-55.36%) of all patients presenting to this ED had pain on arrival. There were differences in demographics and arrival and departure patterns between patients with and without pain. The Covid-19 pandemic initially precipitated a decrease followed by a sharp, sustained rise in pain on arrival, with concurrent changes to the population arriving in pain and their treatment.
CONCLUSIONS: Applying a clinical text deep learning model has successfully identified the incidence of pain on arrival. It represents an automated, reproducible mechanism to identify pain from routinely collected medical records. The description of this population and their treatment forms the basis of intervention to improve care for patients with pain. The combination of the clinical text deep learning models and interrupted time series analysis has reported on the effects of the Covid-19 pandemic on pain care in the ED, outlining a methodology to assess the impact of significant events or interventions on pain care in the ED.
CONCLUSIONS: Applying a novel deep learning approach to identifying pain guides methodological approaches to evaluating pain care interventions in the ED, giving previously unavailable population-level insights.
摘要：
目的：使用临床文本深度学习算法确定澳大利亚大型城市急诊科（ED）出现疼痛的患者的发生率。
方法：微调，特定域，基于变压器的临床文本深度学习模型用于在三年内向ED提供的235,789份成人报告的电子病历中解释自由文本护理评估。该模型根据患者到达ED时是否有疼痛对呈现进行分类。使用中断时间序列分析来确定患者随时间的疼痛发生率。我们描述了新冠肺炎大流行开始时出现疼痛的患者的人群特征和发病率的变化。
结果：55.16％（95CI54.95％-55.36％）的所有ED患者在到达时出现疼痛。有和没有疼痛的患者之间的人口统计学和到达和离开模式存在差异。Covid-19大流行最初导致了急剧下降，到达时疼痛持续增加，伴随着疼痛和治疗的人口变化。
结论：应用临床文本深度学习模型已成功确定到达时疼痛的发生率。它代表了一个自动化的，从常规收集的医疗记录中识别疼痛的可重复机制。对该人群及其治疗的描述构成了改善疼痛患者护理的干预基础。临床文本深度学习模型和中断时间序列分析的结合报道了新冠肺炎大流行对急诊室疼痛护理的影响，概述了评估重大事件或干预措施对ED疼痛护理影响的方法。
结论：应用一种新颖的深度学习方法来确定疼痛指导方法学方法来评估ED中的疼痛护理干预措施，提供以前无法获得的人口层面的见解。
方法：微调，特定域，基于变压器的临床文本深度学习模型用于在三年内向ED提供的235,789份成人报告的电子病历中解释自由文本护理评估。该模型根据患者到达ED时是否有疼痛对呈现进行分类。使用中断时间序列分析来确定患者随时间的疼痛发生率。我们描述了新冠肺炎大流行开始时出现疼痛的患者的人群特征和发病率的变化。
结果：55.16％（95CI54.95％-55.36％）的所有ED患者在到达时出现疼痛。有和没有疼痛的患者之间的人口统计学和到达和离开模式存在差异。Covid-19大流行最初导致了急剧下降，到达时疼痛持续增加，伴随着疼痛和治疗的人口变化。
结论：应用临床文本深度学习模型已成功确定到达时疼痛的发生率。它代表了一个自动化的，从常规收集的医疗记录中识别疼痛的可重复机制。对该人群及其治疗的描述构成了改善疼痛患者护理的干预基础。临床文本深度学习模型和中断时间序列分析的结合报道了新冠肺炎大流行对急诊室疼痛护理的影响，概述了评估重大事件或干预措施对ED疼痛护理影响的方法。
结论：应用一种新颖的深度学习方法来确定疼痛指导方法学方法来评估ED中的疼痛护理干预措施，提供以前无法获得的人口层面的见解。
