Understanding practical alcohol measures in primary care to prepare for measurement-based care: Scaled EHR measures of alcohol use and DSM-5 AUD symptoms

项目摘要。初级保健(PC)处于有利地位，可以发现和解决不健康的饮酒和酒精问题 使用障碍(AUD)，然而大多数PC设置在提供与酒精相关的护理方面存在显著差距。一种方法 改善与酒精相关的护理是使用标准化的测量方法来筛选不健康的饮酒行为，并 评估AUD症状，通过检测不健康的饮酒，评估AUD的严重性， 告知有关AUD治疗强度的决策，并随着时间的推移监测临床结果。研究人员 国家机构越来越多地呼吁使用不健康饮酒的标准化措施和 常规PC设置中的AUD症状；然而，令人惊讶的是，人们对这些测量如何执行知之甚少 或者当它们在常规个人电脑的环境中使用时，即在 常规预约并记录在电子健康记录(EHR)中。我们建议利用一大笔 和来自Kaiser Permanente Washington的新型EHR数据集，这是一个大型综合区域卫生系统。这个 数据集将包括：AUDIT-C酒精筛查措施，作为年度筛查的一部分由 &gt；250,000名患者(参加PC预约的成年人中有)；一种新的患者报告的AUD症状 基于11个DSM-5和AUD标准的检查表(“DSM-5检查表”)，由4,000多名患者填写 (&gt；70%的AUDIT-C评分为≥7)；以及eHR提供的各种健康结果衡量标准， 管理和索赔数据。为期2年的发育R21阶段包括心理测量分析， 评估DSM-5核对表，将其作为衡量AUD严重性及其性能一致性的衡量标准 在人口统计上不同的亚群中；据我们所知，这是第一次评估 标准化的患者报告的AUD症状测量集成到常规PC中。横断面分析将 检查AUDIT-C、DSM-5核对表和已知的重要健康结果指标之间的关联 与饮酒有关，包括收缩压，病人报告的抑郁症状， 因酒精引起的疾病住院、全因住院和全因死亡。R21 还将实现里程碑，证明为R33阶段做好了准备。在为期3年的R33阶段， 纵向分析将测试AUDIT-C和DSM-5核对表是否与后续 健康结果以及人员内部随时间变化的AUDIT-C分数是否与 健康结果的变化。据我们所知，这项工作将是第一次评估患者报告的AUD 任何常规PC设置中的症状核对表，以及在任何非资深常规中第一个评估AUDIT-C的人 电脑设置。这项研究将通过帮助提供者和患者了解 这些分级的患者报告措施提供的信息及其与重要不良反应的关联 健康结果。这项工作还将为未来使用患者报告的EHR的研究提供基础。 以务实研究、实施试验和质量改进举措为基础的措施。

Project Summary. Primary care (PC) is well-positioned to detect and address unhealthy drinking and alcohol use disorder (AUD), yet most PC settings have notable gaps in providing alcohol-related care. One way to improve alcohol-related care is to use standardized measurements to screen for unhealthy alcohol use and to assess AUD symptoms, which informs clinical care by detecting unhealthy drinking, assessing AUD severity, informing decisions about AUD treatment intensity, and monitoring clinical outcomes over time. Researchers and national agencies are increasingly calling for the use of standardized measures of unhealthy drinking and AUD symptoms in routine PC settings; however, surprisingly little is known about how such measures perform or what they indicate about health risks when they are used in the context of routine PC—i.e., administered in routine appointments and documented in electronic health records (EHRs). We propose to leverage a large and novel EHR dataset from Kaiser Permanente Washington, a large integrated regional health system. The dataset will include: the AUDIT-C alcohol screening measure, completed as part of an annual screen by >250,000 patients (89% of adults who attend PC appointments); a novel patient-reported AUD symptom checklist that is based on the 11 DSM-5 AUD criteria (“DSM-5 checklist”), completed by over 4,000 patients (>70% of those with AUDIT-C scores ≥ 7); and diverse health outcome measures available from EHR, administrative, and claims data. The 2-year developmental R21 phase includes psychometric analyses that evaluate the DSM-5 checklist as a scaled measure of AUD severity and the consistency of its performance across demographically diverse subgroups; to our awareness, this is the first study to evaluate any standardized patient-reported AUD symptom measure integrated into routine PC. Cross-sectional analyses will examine associations between the AUDIT-C, DSM-5 checklist, and important health outcome measures known to be associated with drinking, including systolic blood pressure, patient-reported depression symptoms, hospitalizations for alcohol-attributable conditions, all-cause hospitalizations, and all-cause mortality. The R21 will also achieve milestones demonstrating readiness for the R33 phase. During the 3-year R33 phase, longitudinal analyses will test whether the AUDIT-C and DSM-5 checklist are associated with subsequent health outcomes and whether within-person changes in AUDIT-C scores over time are associated with changes in health outcomes. To our awareness, this work will be the first to evaluate a patient-reported AUD symptom checklist in any routine PC setting and the first to evaluate the AUDIT-C in any non-veteran routine PC setting. This research will improve clinical care by helping providers and patients understand the information provided by these scaled patient-reported measures and their associations with important adverse health outcomes. The work will also provide a foundation for future studies that use patient-reported EHR- based measures in pragmatic studies, implementation trials, and quality improvement initiatives.