Reducing Clinical Inertia in Obesity Management of Diabetes in Primary Care: Cluster-Randomized Trial

项目摘要 肥胖症的患病率一直在稳步上升，现在超过40%的美国成年人受到影响，导致 许多不良健康后果，包括心肌梗塞、中风、2型糖尿病、高血压、 睡眠呼吸暂停、关节炎等。有效的手术、药物和行为治疗肥胖症 而且，支持广泛使用这些治疗方法治疗肥胖症的证据也非常充分。 然而，积极的肥胖管理定义为处方或转介肥胖成年人的生活方式， 药物，或肥胖症的外科治疗，都被大大低估了。主要的潜在原因是 有效的肥胖治疗利用不足包括：(A)患者和初级保健临床医生(PCCs) 经常低估肥胖治疗的有效性和潜在益处；以及(B)两名患者 临床医生通常无法获得基于证据的、特定于患者的潜在益处估计和 适当的针对患者的肥胖治疗方案的风险。 为了解决这个问题，我们集成了外部验证的预测方程，估计了收益和风险 将成人T2 DM患者的各种肥胖治疗方案纳入广泛使用和成功的临床决策 支持系统，以便在以下点提供适当的针对患者的肥胖治疗建议 关心。我们在一项随机试验中实施了一种可扩展的、基于Web的护理点决策支持干预 40家初级保健诊所，15,814名符合条件的患者，并评估干预对以下初级保健诊所的影响 结果：(A)适当转介符合条件的患者接受代谢性减肥手术(MBS)评估；(B) 适当启动FDA批准的减肥药物；(C)体重轨迹；和(D)患者- 报告了与他们的PCC关于减肥和减肥意图的对话。此外, 我们收集和分析临床医生报告和患者报告的数据，以确定可能阻碍或 促进将这一干预战略广泛传播到其他护理提供环境。 这一创新项目将(A)为大量肥胖治疗提供最先进的科学证据 (B)帮助肥胖的、患有2型糖尿病的美国成年人和他们的初级保健中心确定适当的 针对患者的肥胖治疗选择；(C)在初级保健中实施基于网络的电子病历相关肥胖 使用最先进的HIT标准的治疗临床决策支持模式，具有广泛的可扩展性，易于 随着证据的变化而更新，并进行优化，以便向患者和项目控制中心清楚地传达信息；以及 (D)提高对门诊健康的持续大规模私人和公共投资的临床回报 信息系统。

Project Summary Obesity has been steadily increasing in prevalence and now affects more than 4 in 10 U.S. adults, leading to many adverse health outcomes including myocardial infarction, stroke, type 2 diabetes (T2DM), hypertension, sleep apnea, arthritis, and others. Effective surgical, pharmaceutical, and behavioral treatments for obesity are available, and the evidence to support the broad use of these treatments for obesity is very well established. However, active management of obesity defined as prescribing or referring adults with obesity for lifestyle, pharmaceutical, or surgical treatment of obesity, is greatly underused. Major underlying reasons for underutilization of effective obesity treatments include: (a) both patients and primary care clinicians (PCCs) frequently underestimate the effectiveness and potential benefits of obesity treatments; and (b) both patients and clinicians typically lack access to evidence-based, patient-specific estimates of the potential benefits and risks of appropriate patient-specific obesity treatment options. To address this problem, we integrate externally validated prediction equations that estimate benefits and risk of various obesity treatment options in adults with T2DM into a widely-used and successful clinical decision support system in order to deliver appropriate patient-specific obesity treatment suggestions at the point of care. We implement a scalable, web-based point-of-care decision-support intervention in a randomized trial in 40 primary care clinics with 15,814 eligible patients, and assess intervention impact on the following primary outcomes: (a) appropriate referral of eligible patients for evaluation for metabolic bariatric surgery (MBS); (b) appropriate initiation of FDA-approved medications for weight loss; (c) weight trajectories; and (d) patient- reported conversations with their PCC about weight loss and intentions to engage in weight loss. In addition, we collect and analyze clinician-reported and patient-reported data to identify factors that may impede or facilitate broad dissemination of this intervention strategy to other care delivery settings. This innovative project will (a) provide state-of-the-art scientific evidence on obesity treatment to large numbers of obese American adults with T2DM and their PCCs at the point of care; (b) help PCCs identify appropriate patient-specific obesity treatment options; (c) implement in primary care a web-based EHR-linked obesity treatment clinical decision support model that uses state-of-the-art HIT standards, is broadly scalable, easy to update as evidence changes, and optimized for clear communication of information to patients and PCCs; and (d) improve the clinical return on ongoing massive private and public investments in outpatient health information systems.