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

交流行为，包括寻求信息、提供信息和对情绪的反应，可以 在退伍军人和医疗保健提供者之间的面对面人际健康交流中进行测量。 研究人员已经开发出可靠的编码模式来从录音中提取交流行为 临床上的接触。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 退伍军人自我管理方面的积极变化(例如，坚持服药)。最近，RIAS已被改编为 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，退伍军人管理局有了直接测量通信行为的新机会 写进了这些信息中。在过去的五年里，我们的团队展示了这种沟通 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 对面对面的人际交流进行验证。 在这个项目中，我们提出了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。我们提出了以下具体目标： 具体目标1：地雷传播行为。使用全国安全消息语料库，我们将开发 一种结合机器学习技术的句子分类系统，用于检测Secure中的通信 初级保健医生和临床工作人员的消息回复。 具体目标2：定义沟通行为指标(CBI)，代表具有临床意义的措施 退伍军人和临床团队之间的安全消息通信模式，然后测试 CBIS包括退伍军人经验(2.a)和患者报告的行为(2.b)，服药依从性。 我们将确定和调查CBI比率较高的退伍军人(病例)样本(前四分位数)和匹配的一组 低比率(底部四分音符)的(对照)。 目标2.使用安全消息传递和CBIS的资深经验：我们假设(H1)案例(退伍军人 高沟通行为(CBIS)会给医生的沟通体验打分 通过安全消息传递比控制退伍军人更积极。 目标2.b.退伍军人报告的服药依从性：在先前的面对面沟通研究中， 沟通行为与服药依从性的衡量标准密切相关。在我们的调查中，我们 将衡量患者报告的服药依从性，并评估依从性报告与 安全报文传送CBIS。我们假设(H2)病例会有更好的自我报告药物 依从性，与对照组相比。 具体目标3：了解报文中CBIS使用率较高的供应商的经验。 退伍军人事务部卫生部副部长的一个高度优先事项是收集和传播退伍军人管理局的最佳做法。在AIM 3、我们将从医生(N=30)那里收集这些积极沟通的高比率的最佳实践 来自安全消息的行为，以及30个具有低CBIS比率的比较样本。 1

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