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，现在每10个美国成年人中就有4个以上患有肥胖症， 许多不利的健康结果，包括心肌梗死、中风、2型糖尿病（T2 DM）、高血压， 睡眠呼吸暂停、关节炎等。有效的外科手术，药物和行为治疗肥胖是 这些治疗方法是可行的，并且支持广泛使用这些治疗肥胖症的证据是非常明确的。 然而，肥胖的积极管理定义为处方或转介肥胖的成年人的生活方式， 肥胖症的药物或手术治疗大大未得到充分利用。主要的根本原因 有效的肥胖治疗利用不足包括：（a）患者和初级保健医生（PCC） 经常低估肥胖治疗的有效性和潜在益处;和（B）两种患者 临床医生通常无法获得基于证据的、针对患者的潜在获益估计， 适当的患者特异性肥胖治疗选择的风险。 为了解决这个问题，我们整合了外部验证的预测方程，估计收益和风险 将T2 DM成人患者的各种肥胖治疗方案转化为广泛使用和成功的临床决策 支持系统，以提供适当的患者特定的肥胖治疗建议， 在乎我们在一项随机试验中实施了一种可扩展的、基于网络的床旁决策支持干预， 40个初级保健诊所，15 814名合格患者，并评估干预措施对以下初级保健诊所的影响： 结果：（a）适当转诊符合条件的患者进行代谢减肥手术（MBS）评价;（B） 适当启动FDA批准的减肥药物;（c）体重轨迹;和（d）患者- 报告与他们的PCC关于减肥和减肥意图的对话。此外，本发明还提供了一种方法， 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 促进向其他提供护理的机构广泛传播这一干预战略。 这项创新项目将（a）为大量肥胖症治疗提供最先进的科学证据 肥胖的美国成年T2 DM患者及其PCC的护理点;（B）帮助PCC识别适当的 （c）在草根医疗服务推行网上电子健康记录系统， 治疗临床决策支持模型，使用最先进的HIT标准，具有广泛的可扩展性，易于 根据证据变化进行更新，并进行优化，以便向患者和PCC明确传达信息;以及 (d)提高私人和公共部门对门诊健康持续进行的大规模投资的临床回报率 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.