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

基本信息

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
项目类别：
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

项目摘要

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：来自退伍军人事务部和非退伍军人事务部环境的证据表明，在我们之前的工作表明，提供者具有更高水平的“文化能力”（CC）。但我们将使用我们的数据来提供更公平的护理。当前的项目“打开文化能力的黑匣子”（又名黑匣子），我们正在其中分析我们将补充这些内容分析的音频记录的初级保健访问内容。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在医患关系质量差异中所扮演的角色。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，我们将致力于解决这一问题。这是一个以前未探索过的领域，是减少退伍军人事务部护理差异的潜在目标。还解决了 VA HSR&D 的几个优先领域，包括促进健康公平、改善初级保健实践，并推进数据科学。创新：大多数医疗保健沟通研究都集中在沟通内容（即什么是通过检查语言风格（即如何说话），我们将解决相对未开发的潜力此外，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的沟通，本研究将开发和测试潜在的可扩展工具和可以实施以提供实时反馈以改善患者与提供者互动的指标学习型医疗系统致力于改善高质量、公平护理的提供。具体目标： 1) 将计算机文本分析工具应用于初级保健就诊的记录，以生成患者的测量结果以及提供商语言风格和风格匹配 (LSM)。 2) 测试以下各项的关联：a) 退伍军人种族和提供者 CC 与 LSM 和提供者语言风格；以及 b) LSM；以及提供者的语言风格与医患关系的质量。 3) 定性探索具有高和低 LSM 以及提供者语言风格模式的访问示例与关系质量的高低相关。方法：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 个不同地理位置的 VA 医疗机构 408 次初级保健就诊的音频文件在拟议的项目中，我们将转录音频文件并应用计算机化、基于词典的技术。词汇分析工具，用于评估患者和患者之间的功能和语义语音模式以及 LSM 我们将使用在目标 2 中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。最后，我们将对选定的成绩单进行定性审查，以评估其机制。实现了 LSM 以及与关系质量相关的提供者语言风格。实施/后续步骤：这项试点研究旨在开发新的方法和措施，而不是直接而不是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。黑盒研究将是我们的主要运营合作伙伴，我们还将与患者办公室合作。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。将我们的发现转化为改善患者与提供者沟通质量和公平性的步骤。

项目成果

期刊论文数量（1）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Validating computer-generated measures of linguistic style matching and accommodation in patient-clinician communication.

验证计算机生成的患者与临床医生沟通中语言风格匹配和适应的测量。

DOI：
发表时间：
2024-02
期刊：
影响因子：
3.5
作者：
Khaleghzadegan, Salar;Rosen, Michael;Links, Anne;Ahmad, Alya;Kilcullen, Molly;Boss, Emily;Beach, Mary Catherine;Saha, Somnath
通讯作者：
Saha, Somnath