Personalized Physician Learning Intervention to Improve Hypertension Control

描述(由申请人提供)：每年有4600万患有失控高血压(HT)的美国人去看约15万名初级保健医生(PCP)，就诊人数超过1.5亿人次。每一次访问都代表着一个加强HT治疗的机会，但超过80%的时间，治疗没有改变。这些数据表明，有效的HT改善干预必须减少初级保健中的治疗惰性，并且足够简单和廉价，以促进广泛传播。个性化医生学习(PPL)是一种强大的策略，可以改变尚未应用于PCP提供的HT护理的行为。 在这个项目中，我们评估了两种PPL干预措施的影响，这两种干预措施在如何识别医生在HT护理中的决策模式方面存在差异。第一个干预，REAL-PPL，使用真实的EMR派生数据来指导个性化学习干预。第二个干预，SIM-PPL，利用医生在模拟病例上的表现来指导个性化学习干预。这两种干预措施都通过网络传递给PCP，他们完成至少12个模拟的HT学习案例，解决治疗惰性和每个医生在HT护理中特定识别的决策失误。两种干预中使用的学习案例都体现了适应性学习的原则，每个学习案例平均提供三种学习反馈(实际BP值、图形数据显示和具体的临床管理建议)。 为了验证这些干预措施改善高血压控制的假设，我们将39个诊所的120名PCP和6,000名患有未控制的高血压的成年患者随机分成三个研究组之一：(A)REAL+PPL干预，(B)SIM+PPL干预，或(C)不干预(对照组)。采用包含嵌套数据的分层Logistic模型(MLwiN)比较每个干预措施与对照组的差异，并评估REAL+PPL和SIM+PPL干预措施之间的差异。二次分析量化了干预措施对治疗惰性的影响，以及对一组明确的医生决策在HT护理中的具体失败的影响。正式的成本效益分析提供关键信息，指导干预措施的传播，如果成功的话。 传播得到以下方面的支持：(A)SIM+PPL的低成本和易运输性，(B)REAL+PPL对急诊患者医疗群体的吸引力，(C)失控高血压的患病率、临床并发症和成本，以及(D)PCP和管理护理领导人对该项目的参与。结果有可能改善数百万美国人的护理，他们默默地遭受着有史以来最具破坏性和最难控制的慢性病的蹂躏。在美国，改善高血压护理的一个主要障碍是医生未能及时有效地加强治疗。公共卫生相关性：该项目利用电子病历技术来(A)评估医生的高血压护理模式，(B)基于每个医生观察到的护理模式，制定并通过网络提供一套针对医生的复杂的学习干预措施，以及(C)评估这些学习干预措施对向真实的高血压患者提供的护理质量的影响。结果有可能改善数百万美国人的护理，他们默默地遭受着有史以来最具破坏性、最难控制的慢性病之一的蹂躏。

DESCRIPTION (provided by applicant): Each year 46 million Americans with uncontrolled hypertension (HT) make well over 150 million office visits to about 150,000 primary care physicians (PCPs). Each of these visits represents an opportunity to intensify HT therapy, but over 80% of the time, no change in therapy is made. These data suggest that an effective HT improvement intervention must reduce therapeutic inertia in primary care, and be simple and inexpensive enough to facilitate broad dissemination. Personalized Physician Learning (PPL) is a powerful strategy to change behavior that has yet to be applied to HT care provided by PCPs. In this project we assess the impact of two PPL interventions that differ in how they identify patterns of physician decision making in HT care. The first intervention, REAL-PPL, uses real EMR-derived data to direct the personalized learning intervention. The second intervention, SIM-PPL, uses physician performance on simulated cases to direct the personalized learning intervention. Both interventions are delivered via the web to PCPs who complete a minimum of 12 simulated HT learning cases that address therapeutic inertia and each physician's specific identified failures of decision making in HT care. The learning cases used in both interventions embody principles of adaptive learning, and provide three kinds of learning feedback (of actual BP values, graphic data displays, and specific clinical management suggestions) an average of 15 times per learning case. To test the hypothesis that these interventions improve HT control, we group randomize 39 clinics with their 120 PCPs and 6,000 adult patients with uncontrolled HT to one of three study arms: (a) REAL+PPL Intervention, (b) SIM+PPL Intervention, or (c) No Intervention (control group). Hierarchical logistic models (MLwiN) that accommodate nested data are used to compare each intervention to control group, and to evaluate differences between the REAL+PPL and SIM+PPL interventions. Secondary analysis quantifies the impact of interventions on therapeutic inertia and on a defined set of specific failures of physician decision making in HT care. Formal cost-effectiveness analysis provides key information to guide dissemination of interventions, if successful. Dissemination is supported by (a) the low cost and easy transportability of SIM+PPL, (b) the appeal of REAL+PPL to medical groups with EMRs, (c) the prevalence, clinical complications, and costs of uncontrolled HT, and (d) participation of PCP and managed care leaders in the project. Results have the potential to improve the care of millions of Americans who silently suffer the ravages of one of the most devastating and hard to control chronic diseases of all time.A major obstacle to better hypertension care in the United States is failure of physicians to intensify therapy in a timely and effective way. PUBLIC HEALTH RELEVANCE: This project takes advantage of electronic medical record technology to (a) assess physician patterns of hypertension care, (b) based on each physician's observed patterns of care, develop and deliver via the Web a sophisticated physician-specific set of learning interventions, and (c) assess the impact of these learning interventions on quality of care delivered to real patients with hypertension. Results have the potential to improve the care of millions of Americans who silently suffer the ravages of one of the most devastating and hard to control chronic diseases of all time.