Measuring, Mining and Understanding Communication Behaviors: Markers for Quality Healthcare

测量、挖掘和理解沟通行为：优质医疗保健的标志

• 批准号: 9883635
• 负责人: Dezon Finch
• 金额: --
• 依托单位: EDITH NOURSE ROGERS MEMORIAL VETERANS HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9883635
• 关键词: Address; Adherence; Area; Audiotape; Behavior; Caring; Classification; Client satisfaction; Clinic Visits; Clinical; Code; Collaborations; Communication; Computers; Consultations; Data; Data Sources; Electronic Mail; Emotions; Feedback; Finches; Foundations; Future; Health; Health Communication; Health Personnel; Health Services Research; Healthcare Systems; Informatics; Institute of Medicine (U.S.); Knowledge; Learning; Letters; Machine Learning; Measurable; Measurement; Measures; Methods; Mining; Names; Nurses; Patient Outcomes Assessments; Patient Self-Report; Patient-Centered Care; Patients; Pattern; Personal Communication; Persons; Physicians; Primary Health Care; Provider; Published Comment; Reporting; Research; Research Personnel; Roter; Sampling; Science; Scientist; Secure; Self Management; Surveys; System; Systems Analysis; Techniques; Testing; Text; Time; Trust; Veterans; Work; base; clinical encounter; clinical practice; communication behavior; communication theory; data mining; eHealth; experience; health care quality; health information technology; innovation; medication compliance; member; patient health information; patient oriented; patient portal; performance based measurement; response; telehealth

Communication behaviors, including information seeking, information giving, and responding to emotions, can be measured within in-person interpersonal health communication between Veterans and healthcare providers. Investigators have developed reliable coding schemas to extract communication behaviors from audiotapes of clinical encounters. Using these schemas, including the Roter Interaction Analysis System (RIAS), patterns of communication behaviors have been positively associated with patient satisfaction, trust in providers, and positive changes in Veteran self-management (e.g., medication adherence). Recently, RIAS has been adapted for use with telehealth and asynchronous written communication (like email). With the advent of Secure Messaging, VA has a new opportunity to directly measure communication behaviors written into these messages. Over the past five years, our team has demonstrated that communication behaviors are present in Secure Messages and can reliably be extracted using the same coding schemas validated for in-person interpersonal exchanges. In this project, we propose to advance knowledge and methods related to communication behaviors measurable through asynchronous Secure Messages. We propose the following specific aims: Specific Aim 1: Mine communication behaviors. Using a national corpus of Secure Messages, we will develop a sentence classification system incorporating machine learning techniques to detect communication in Secure Message responses from primary care doctors and clinical staff. Specific Aim 2: Define communication behavior indicators (CBIs) that represent clinically meaningful measures of Secure Message communication patterns between Veterans and Clinical Teams, then test the association of CBIs with measures of Veteran Experience (2.a) and Patient-reported behavior (2.b), medication adherence. We will identify and survey a sample of Veterans (CASES) with high CBI rates (top tertile) and a matched set of (CONTROLS) with low rates (bottom tertile). Aim 2.a Veteran experience with Secure Messaging and CBIs: We hypothesize (H1) that CASES (Veterans with high rates of communication behaviors (CBIs)) will rate the experience with physician communication through Secure Messaging more positively than CONTROL Veterans. Aim 2.b. Veteran-reported medication adherence: In prior studies of in-person communication, patterns of communication behaviors are strongly associated with measures of medication adherence. In our survey, we will measure patient-reported medication adherence and assess the association of adherence reports with secure messaging CBIs. We hypothesize (H2) that CASES will have better self-reported medication adherence, compared with CONTROLS. Specific Aim 3: Understand experiences of providers with high rates of CBIs in messages. A high priority for the VA Under Secretary for Health is to collect and disseminate best practices in VA. In Aim 3, we will collect best practices from physicians (N = 30) with high rates of these positive communication behaviors from Secure Messages, and a comparison sample of 30 with low rates of CBIs. 1

沟通行为，包括信息寻求、信息给予和情绪反应，可以 在退伍军人和医疗保健提供者之间的面对面的人际健康沟通中进行衡量。 研究人员开发了可靠的编码模式，可以从录音带中提取交流行为 临床遭遇。使用这些模式，包括 Roter 交互分析系统 (RIAS)， 沟通行为与患者满意度、对提供者的信任以及 退伍军人自我管理的积极变化（例如，药物依从性）。近期，RIAS进行了适配 用于远程医疗和异步书面通信（如电子邮件）。 随着安全消息传递的出现，VA 有了直接测量通信行为的新机会 写入这些消息中。在过去的五年里，我们的团队已经证明，沟通 行为存在于安全消息中，并且可以使用相同的编码模式可靠地提取 经验证可用于面对面的人际交流。 在这个项目中，我们建议推进与沟通行为相关的知识和方法 可通过异步安全消息进行测量。我们提出以下具体目标： 具体目标1：挖掘沟通行为。使用国家安全消息语料库，我们将开发 结合机器学习技术来检测安全通信的句子分类系统 来自初级保健医生和临床工作人员的消息回复。 具体目标 2：定义代表有临床意义的测量的沟通行为指标 (CBI) 退伍军人和临床团队之间的安全消息通信模式，然后测试关联 CBI 衡量退伍军人经验 (2.a) 和患者报告的行为 (2.b)、药物依从性。 我们将确定并调查具有高 CBI 率（前三分位数）的退伍军人样本（案例）和一组匹配的退伍军人样本 （控制）的比率较低（底部三分位）。 目标 2.a 退伍军人在安全消息传递和 CBI 方面的经验：我们假设 (H1) 案例（退伍军人） 具有高沟通行为 (CBI) 率）将评价与医生沟通的体验 比 CONTROL 退伍军人更积极地通过安全消息传递。 目标 2.b。退伍军人报告的药物依从性：在之前的面对面交流研究中， 沟通行为与药物依从性密切相关。在我们的调查中，我们 将测量患者报告的药物依从性并评估依从性报告与 安全消息传递 CBI。我们假设 (H2) CASES 将有更好的自我报告药物治疗 与对照相比，依从性。 具体目标 3：了解消息中 CBI 率较高的提供商的经验。 退伍军人事务部卫生部副部长的首要任务是收集和传播退伍军人事务部的最佳实践。瞄准 3、我们将从积极沟通率较高的医生（N = 30）那里收集最佳实践 来自安全消息的行为，以及 30 个具有低 CBI 率的比较样本。 1