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，信息给予和对情感的反应，可以 可以在退伍军人与医疗保健提供者之间的人际交往中进行衡量。 调查人员已经开发了可靠的编码模式，以从录音中提取沟通行为 临床相遇。使用这些模式，包括转子相互作用分析系统（RIAS），模式 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）。最近，RIAS已改编 用于远程医疗和异步书面交流（如电子邮件）。 随着安全消息传递的出现，VA有一个新的机会直接衡量沟通行为 写入这些消息中。在过去的五年中，我们的团队证明了交流 行为存在于安全消息中，可以使用相同的编码模式可靠地提取 经过人际关系交流的验证。 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 通过异步安全消息可测量。我们提出以下具体目标： 特定目的1：地雷沟通行为。使用全国性的安全信息，我们将开发 句子分类系统包含机器学习技术以检测安全的通信 初级保健医生和临床人员的消息反应。 特定目标2：定义代表临床意义的沟通行为指标（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 CBI具有资深经验（2.a）和患者报告行为（2.b）的CBI，药物依从性。 我们将识别和调查具有高CBI速率（最高三重）和匹配集的退伍军人样本（情况） （对照）的速率低（底部三重）。 目标2.安全消息传递和CBIS的资深经验：我们假设（H1）案件（退伍军人 沟通行为率高（CBI）将评估医师沟通的经验 通过安全消息传递比控制退伍军人更积极。 AIM 2.B.经验丰富的药物依从性：在亲自交流的先前研究中 沟通行为与药物依从性的度量密切相关。在我们的调查中，我们 将衡量患者报告的药物依从性，并评估依从性报告的关联 安全消息CBIS。我们假设（H2）案件将具有更好的自我报告药物 与对照组相比，依从性。 特定目标3：了解消息中CBI率高的提供者的经验。 卫生秘书秘书的高度优先事项是在VA中收集和传播最佳实践。目标 3，我们将从医生（n = 30）的最佳实践中收集这些积极沟通的率高 来自安全消息的行为，以及CBI速率低的30个比较样本。 1