Applying Novel Analytic Methods to Address the Impact of Race on Patient-Provider Communication

应用新颖的分析方法来解决种族对医患沟通的影响

• 批准号: 10187911
• 负责人: SOMNATH SAHA
• 金额: --
• 依托单位: PORTLAND VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10187911
• 关键词: Address; African American; Area; Attention; Awareness; Caring; Code; Communication; Complement; Data; Data Science; Diabetes Mellitus; Dictionary; Dimensions; Disadvantaged; Discrimination; Education; Elements; Feedback; Future; Geography; Goals; Health Care Research; Health Personnel; Health system; Healthcare; Healthcare Systems; Human; Intervention; Intervention Studies; Language; Lead; Learning; Linguistics; Manuals; Measures; Medical; Medical center; Methodology; Methods; Minority; Mission; Natural Language Processing; Outcome; Parents; Patient Care; Patient-Centered Care; Patient-Focused Outcomes; Patients; Pattern; Pilot Projects; Primary Health Care; Provider; Quality of Care; Race; Relationship-Building; Research; Role; Roter; Semantics; Social Distance; Source; Specificity; Speech; Surveys; System; Systems Analysis; Testing; Text; Time; Transcript; Translating; Translations; Veterans; Visit; Work; analytical method; barrier to care; base; care outcomes; care providers; computerized; cost; cultural competence; design; disparity reduction; distrust; ethnic minority population; experience; health care delivery; health care disparity; health care quality; health care service utilization; health equity; health equity promotion; improved; innovation; lexical; novel; operation; patient population; patient-clinician communication; racial difference; racial disparity; racial minority; skills; tool; trustworthiness

Background: Evidence from VA and non-VA settings demonstrates widespread racial disparities in healthcare delivery. Our prior work suggests that providers with higher levels of “cultural competence” (CC) deliver more equitable care. But how CC translates into better care is unclear. We will use data from our current project, “Opening the Black Box of Cultural Competence” (aka Black Box), in which we are analyzing communication content from audio recorded primary care visits. We will complement these content analyses with computerized linguistic analysis methods to generate and test novel measures of patient-provider communication and examine their role in disparities in patient-provider relationship quality. Significance/Impact: Delivering high-quality care to all Veterans is central to VA's mission. We will address a previously unexplored area that represents a potential target for reducing disparities in VA care. Our study also addresses several VA HSR&D priority areas, including promoting health equity, improving primary care practice, and advancing data science. Innovation: Most studies of healthcare communication have focused on communication content (i.e., what is said). By examining linguistic style (i.e., how things are said), we will address a relatively unexplored potential source of racial disparities. In addition, by applying computerized, natural language processing (NLP) methods to evaluate patient-provider communication, this study will develop and test potentially scalable tools and metrics that can be implemented to provide real-time feedback to improve patient-provider interactions as part of a learning health system striving to improve the delivery of high-quality, equitable care. Specific Aims: 1) Apply computerized text analysis tools to transcripts of primary care visits to generate measures of patient and provider linguistic style and style matching (LSM). 2) Test associations of: a) Veteran race and provider CC with LSM and provider linguistic style; and b) LSM and provider linguistic style with the quality of patient-provider relationships. 3) Qualitatively explore examples of visits with high and low LSM and with provider linguistic style patterns associated with high and low relationship quality. Methodology: In the Black Box study, we are analyzing communication content, using the Roter Interaction Analysis System, directly from the audio files of 408 primary care visits at 4 geographically diverse VA medical centers. In the proposed project, we will transcribe the audio files and apply computerized, dictionary-based lexical analysis tools to evaluate functional and semantic speech patterns and LSM between patient and provider. We will test the associations described in Aim 2 using patient and provider survey data collected in the parent study. Finally, we will qualitatively review selected transcripts to evaluate the mechanisms by which LSM, and provider linguistic styles associated with relationship quality, are achieved. Implementation/Next Steps: This pilot study is designed to develop novel methods and measures rather than lead directly to a larger intervention study. The VHA Office of Health Equity (OHE), our current partner on the Black Box study, will be our primary operations partner. We will also engage the Office of Patient- Centered Care and Cultural Transformation. We will review our findings with these stakeholders to plan next steps in translating our findings into improvements in patient-provider communication quality and equity.

背景：来自VA和非VA环境的证据表明， 医疗保健服务。我们之前的工作表明，具有较高水平的“文化能力”（CC）的供应商 提供更公平的护理。但CC如何转化为更好的护理尚不清楚。我们将使用我们的数据 目前的项目，“打开文化能力的黑匣子”（又名黑匣子），我们正在分析 来自音频记录的初级保健就诊的通信内容。我们将补充这些内容分析 使用计算机语言分析方法来生成和测试患者-提供者的新措施， 沟通，并检查他们的作用，在病人与提供者的关系质量的差距。 意义/影响：为所有退伍军人提供高质量的护理是VA使命的核心。我们将解决 这是一个以前未探索的领域，代表了减少VA护理差异的潜在目标。我们的研究 还涉及几个VA HSR&D优先领域，包括促进健康公平，改善初级保健 实践和推进数据科学。 创新：大多数医疗保健传播研究都集中在传播内容上（即，是什么 说）。通过检查语言风格（即，如何说的话），我们将解决一个相对未开发的潜力 种族差异的根源。此外，通过应用计算机化的自然语言处理（NLP）方法， 为了评估患者与提供者之间的沟通，本研究将开发和测试潜在的可扩展工具， 可以实现的指标，以提供实时反馈，以改善患者-提供者的互动， 一个学习型卫生系统，努力改善高质量、公平护理的提供。 具体目标： 1)将计算机化文本分析工具应用于初级保健访问的成绩单，以生成患者 并提供语言风格和风格匹配（LSM）。 2)测试：a）退伍军人种族和提供者CC与LSM和提供者语言风格的关联;以及B）LSM 以及提供者的语言风格与患者与提供者关系的质量。 3)Qualitimes探索具有高和低LSM以及提供者语言风格模式的访问示例 关系质量的高低。 方法论：在黑盒研究中，我们使用Roter交互来分析通信内容。 分析系统，直接来自4个地理位置不同的VA医疗中心的408次初级保健访视的音频文件 中心.在拟议的项目中，我们将转录音频文件，并应用计算机化的，基于字典的 词汇分析工具，以评估功能和语义的语音模式和LSM之间的病人和 提供商我们将使用收集的患者和提供者调查数据来测试目标2中描述的关联， 家长研究。最后，我们将定性审查选定的成绩单，以评估的机制， LSM，和供应商的语言风格与关系质量，实现。 实施/下一步：这项试点研究旨在制定新的方法和措施， 而不是直接导致更大规模的干预研究。VHA健康公平办公室（OHE），我们目前的合作伙伴 将是我们的主要合作伙伴我们还将邀请患者办公室- 中心关怀与文化转型。我们将与这些利益相关者一起审查我们的调查结果，以制定下一步计划 将我们的研究结果转化为改善医患沟通质量和公平性的步骤。