Automating Assessment of Contextualization of Care During the Clinical Encounter

背景：大规模研究表明，当患者努力应对生活挑战时， 使他们的护理复杂化(例如，缺乏社会支持、相互竞争的责任或经济困难)， 如果卫生保健提供者尝试确定这些因素，他们可以改善卫生保健结果并降低成本 “背景因素”，并在他们的护理计划中解决这些问题--这一过程被称为“情景护理”。这些 研究利用了一种数据分析方法，称为“关爱情境的内容编码”(4C)。4C是 这是一个劳动密集型的过程，需要人类编码员收听来自医疗样本的音频记录 从每个患者的医疗记录中提取数据，然后跟踪识别的上下文 这些因素已经得到了解决。它具有巨大的社会价值和商业潜力，因为它准确地 确定缓解社会需求和避免不必要护理的护理计划。到目前为止，共享4C数据 卫生系统大大改善了护理，降低了住院率。这个 然而，手动4C编码过程既耗时又不可扩展。4C编码的自动化 利用自然语言处理(NLP)将实现快速扩展。 目标：构建一个对转录的录音进行自动4C编码的原型系统 医疗接触，并评估其在分类护理规划是否符合背景方面的准确性， 使用具有人类4C编码的测试数据集作为黄金标准。 方法：我们提出了一个迭代开发和验证过程，利用现有的数据集 超过400份来自医生和患者医疗接触的手动4C编码记录。从300开始 之前由我们团队编码的成绩单和4C培训手册中的编码指南，我们将首先 开发自动技术来提取反映语言内容中细微差别的文本特征，并 将情境化关怀从语境错误中分离出来的语篇结构，反过来又促进了 开发模仿人类4C编码决策的候选分类模型。到时候我们会的 将模型应用于其余的文本，以预测文本和发音级别的代码，比较 这些代码使用人类标记的黄金标准来确定可行性、分析性能和 当推广到新的临床情况时，评估模型的性能。 影响：医疗保健系统面临财政压力，需要通过减少这两项开支来控制成本 可预防的住院和过度使用和滥用医疗服务。此阶段1 STTR将建立 自动化4C编码提供低成本、可扩展的策略的可行性和技术优势 准确衡量和促进临床绩效，以增强基于价值的护理。是这样的 技术尤其及时，因为随着虚拟抄写员的工作，录音访问越来越普遍 远程记录医疗访问，并提供音频记录作为对患者的信息帮助。

Background: Large scale studies have demonstrated that when patients struggle with life challenges that complicate their care (e.g., a lack of social support, competing responsibilities, or financial hardships), health care providers can improve health care outcomes and lower costs if they attempt to identify these “contextual factors” and address them in their care plan – a process termed “contextualizing care.” These studies utilize a method of data analysis called “Content Coding for Contextualization of Care” (4C). 4C is a labor-intensive process that requires human coders listen to audio recordings from a sample of medical encounters, extract data from each patient’s medical record, and then track whether identified contextual factors have been addressed. It has enormous social value and commercial potential because it accurately identifies care plans that mitigate social needs and avoid unnecessary care. To date, sharing 4C data with health systems has led to significant improvements in care and a reduction in rates of hospitalization. The manual 4C coding process, however, is time consuming and unscalable. The automation of 4C coding utilizing natural language processing (NLP) would enable rapid scaling. Objective: Build a prototype system that performs automated 4C coding of transcribed audio-recorded medical encounters, and assess its accuracy at classifying whether care planning is contextualized, utilizing a test dataset with human 4C coding as a gold-standard. Method: We propose an iterative development and validation process, leveraging an existing dataset of over 400 manually 4C coded transcripts from physician-patient medical encounters. Starting with 300 transcripts previously coded by our team and coding guidelines from the 4C training manual, we will first develop automated techniques for extracting text features reflective of nuances in linguistic content and discourse structure that disentangle contextualized care from contextual error, in turn facilitating development of candidate classification models that emulate human 4C coding decisions. We will then apply the models to the remaining transcripts to predict transcript- and utterance-level codes, comparing these codes with the human-labeled gold standard to establish feasibility, analyze performance, and assess the models’ performance when generalized to new clinical encounters. Impact: Health care systems are under financial pressure to control costs through a reduction in both preventable hospitalizations and overuse and misuse of medical services. This phase 1 STTR will establish the feasibility and technical merit of automated 4C coding to provide a low cost, scalable strategy for accurately measuring and facilitating clinical performance that enhances value-based care. Such technology is especially timely as audio recording visits is increasingly common as virtual scribes work remotely to document medical visits, and audio recordings are provided as an information aid to patients.