Automating Assessment of Contextualization of Care During the Clinical Encounter

在临床遇到的情况下自动评估护理情境化

• 批准号: 10595446
• 负责人: Saul J Weiner
• 金额: $ 22.19万
• 依托单位: INSTITUTE/PRACTICE/PROVIDER/PERF/ IMPROV
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-25 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10595446
• 关键词: Address; Adopted; Affect; Architecture; Assessment tool; Automation; Capital Financing; Caring; Chronic Disease; Classification; Clinical; Clinical Skills; Code; Consent; Consumption; Contracts; Cost Control; Cost Savings; Costs and Benefits; Data; Data Analyses; Data Set; Development; Elements; Emotional; Face; Failure; Feedback; Financial Hardship; Genetic Transcription; Guidelines; Health; Health Personnel; Health system; Healthcare; Healthcare Systems; Hospitalization; Human; Incentives; Inferior; Intervention; Investments; Knowledge; Label; Laboratories; Lead; Length; Life; Linguistics; Machine Learning; Manuals; Marketing; Measurable; Measures; Medical; Medical Records; Medical Transcription; Methods; Modeling; Modernization; Natural Language Processing; Outcome; Patient-Focused Outcomes; Patients; Pattern; Performance; Pharmaceutical Preparations; Phase; Physicians; Primary Care Physician; Process; Provider; Quality of Care; Regimen; Risk; Sampling; Self Management; Services; Small Business Technology Transfer Research; Social Values; Social support; Structure; System; Techniques; Technology; Testing; Text; Time; Training; Transcript; Validation; Visit; Work; artificial intelligence method; behavioral outcome; care outcomes; clinical encounter; cognitive capacity; commercialization; computer science; contextual factors; cost; cost effective; cost estimate; dexterity; evidence base; experience; experimental study; hospital readmission; hospitalization rates; improved; improved outcome; interest; model development; neural; patient health information; phrases; pressure; prevent; prototype; skills; social; social interventions; success; syntax; tool; treatment planning; vector; virtual

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.

背景：大规模研究表明，当患者与生活挑战作斗争时， 使他们的护理复杂化（例如，缺乏社会支持、责任冲突或经济困难）， 如果医疗保健提供者试图识别这些因素， “情境因素”并在他们的护理计划中解决这些因素--这个过程称为“情境化护理”。这些 研究利用了一种称为“用于护理情境化的内容编码”（4C）的数据分析方法。4C是 这是一个劳动密集型的过程，需要人类编码员从医疗样本中收听录音， 从每个患者的医疗记录中提取数据，然后跟踪识别出的上下文是否 这些因素得到了解决。它具有巨大的社会价值和商业潜力，因为它准确地 确定减轻社会需求和避免不必要护理的护理计划。到目前为止，4C数据与 卫生系统使护理工作得到显著改善，住院率下降。的 然而，手动4C编码过程是耗时且不可缩放的。4C编码的自动化 利用自然语言处理（NLP）可以实现快速扩展。 目的：建立一个对转录的音频记录进行自动4C编码的原型系统 医疗遭遇，并评估其在分类护理计划是否情境化时的准确性， 利用人类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的开发和验证过程，利用现有的数据集， 超过400个手工4C编码的记录，来自医生和病人的医疗接触。从300开始 根据我们团队先前编码的成绩单和4C培训手册中的编码指南，我们将首先 开发用于提取反映语言内容中细微差别的文本特征的自动化技术， 话语结构，从语境错误中分离出语境化的关怀，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。然后我们将 将模型应用于剩余的转录本以预测转录本和话语级代码，比较 这些代码具有人类标记的黄金标准，以确定可行性、分析性能并 评估模型在推广到新的临床病例时的性能。 影响：卫生保健系统面临财政压力，需要通过减少两项费用来控制成本。 可预防的住院以及过度使用和滥用医疗服务。第1阶段STTR将建立 自动化4C编码的可行性和技术优点，以提供低成本，可扩展的战略， 准确测量和促进临床表现，增强基于价值的护理。等 技术是特别及时的录音访问是越来越普遍的虚拟书记员的工作 远程记录医疗访问，并提供录音作为对患者的信息帮助。