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

基本信息

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
项目类别：
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

项目摘要

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，当患者面对生活挑战时使他们的护理复杂（例如，缺乏社会支持，竞争责任或经济困难），医疗保健提供者可以改善医疗保健结果，并降低成本，如果他们试图识别这些结果 “上下文因素”并在他们的护理计划中解决它们 - 过程术语“上下文化护理”。这些研究利用一种称为“护理上下文化的内容编码”的数据分析方法（4C）。 4C是一个实验室密集型过程，要求人类编码人员听取医学样本的录音遇到，从每个患者的病历中提取数据，然后跟踪是否确定了上下文已经解决了因素。它具有巨大的社会价值和商业潜力，因为它准确确定减轻社会需求并避免不必要的护理的护理计划。迄今为止，与卫生系统已导致护理的显着改善和住院率降低。这但是，手动4C编码过程是耗时且不计时的。 4C编码的自动化利用自然语言处理（NLP）将实现快速缩放。目的：构建一个执行自动化的4C编码的原型系统的转录音频编码医疗遭遇，并评估其在分类是否被背景下进行分类的准确性，使用人类4C编码的测试数据集作为金标准。方法：我们提出了一个迭代开发和验证过程，利用了现有的数据集超过400多个手动4C的编码成绩单，来自身体医生的医疗遇到。从300开始以前由我们的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化技术来提取文本功能，反映了语言内容的细微差别和话语结构将上下文护理与上下文错误相关，然后促进开发模仿人类4C编码决策的候选分类模型。然后我们会将模型应用于其余的成绩单以预测成绩单和话语级代码，并比较这些具有人体标记的黄金标准的代码，以建立可行性，分析性能和当概括到新的临床相遇时，评估模型的性能。影响：医疗保健系统正处于财务压力下，以通过降低两者来控制成本可预防的住院以及过度使用和错过医疗服务。此阶段1 STTR将建立自动化4C编码的可行性和技术优点为低成本，可扩展的策略提供准确测量和支持临床表现，以增强基于价值的护理。这样的技术尤其及时，因为录制访问越来越普遍，随着虚拟脚本工作远程记录医疗访问，并为患者提供了录音作为信息辅助。