Optimizing the Treatment of Diabetes Patients

描述（由申请人提供）：优化糖尿病患者的治疗。医学上复杂的病人对实施质量测量计划和提高美国医疗保健系统的护理质量提出了重要的挑战。2型糖尿病患者是一个不断增长的复杂患者群体，其治疗方案复杂，合并症数量多。由于糖尿病患者平均有3.5种合并症，其中高血压和高脂血症最为常见，因此在这一人群中确定治疗优先级变得越来越重要。越来越多的人强调评估以证据为基础的糖尿病护理提供者的表现。然而，这些措施假设一刀切，并没有考虑到年龄、疾病状态和合并症患者不断变化的需求和偏好。因此，初级保健临床医生往往很难优先考虑糖尿病患者的干预措施，这些干预措施既要对患者的偏好敏感，又要对外部质量测量要求做出反应。在接下来的两年里，我们将通过提高对糖尿病最佳治疗方法的认识来弥补这一差距，该方法以患者的质量调整寿命、治疗依从性、治疗费用以及糖尿病相关并发症给卫生系统带来的成本来衡量。具体来说，我们将重点发展数学模型，以确定开始和加强治疗糖尿病，高血压和高脂血症的管理的最佳时间。利用纵向数据集，我们旨在更好地了解糖尿病患者的服药行为，最重要的是与依从性相关的方面。这些分析的结果将被纳入我们的数学模型。因此，我们的模型将提供最佳的治疗时机，假设完美的依从性，但也在不同程度的不依从性。纳入非依从性将为我们提供与治疗相关的更现实的情景，并有更大的机会影响患者的寿命和生活质量。这项研究的结果将被转化为决策辅助工具。这些决策辅助工具可以在实际的随机试验中进行测试，以评估它们在多大程度上可以通过有效的成本提高结果和护理质量来实现医疗保健价值的改善。

DESCRIPTION (provided by applicant): Optimizing the Treatment of Diabetes Patients. Medically complex patients present an important challenge for implementing quality measurement programs and for improving the quality of care in the U.S. healthcare system. Patients with type 2 diabetes are a growing population of complex patients with complicated treatment regimens and high number of comorbid conditions. Treatment prioritization in this population is increasingly important since patients with diabetes have, on average, 3.5 comorbid conditions with hypertension and hyperlipidemia being the most common. There has been an increasing emphasis on evaluating provider performance for evidence-based diabetes care. However, these measures assume one-size fits all, and do not account for changing needs and preferences of the patients with age, disease state, and comorbidities. Thus, it is often difficult for primary care clinicians to prioritize interventions for diabetes patients that would be sensitive to patient preferences and at the same time respond to external quality measurement requirements. Over the next two years, we will bridge this gap by advancing knowledge regarding the optimal treatment of diabetes as measured by the patient's quality adjusted lifespan, adherence to treatment, the costs of treatment, and cost of diabetes-related complications to the health system. Specifically, we will focus on developing mathematical models to determine the optimal time for initiating and intensifying treatment for the management of diabetes, hypertension, and hyperlipidemia. Using longitudinal datasets, we aim to better understand the medication taking behavior in diabetes patients, most importantly the aspects related to adherence. The results of these analyses will then be incorporated into our mathematical models. Thus, our models will provide optimal timing of treatment assuming perfect adherence, but also at different levels of non-adherence. Incorporating non- adherence will provide us more realistic scenarios related to treatment and greater opportunity to influence the patient's length and quality of life. The findings from this study will then be translated to decision aids. These decision aids can be tested in practical randomized trials to evaluate the extent to which they can realize improved value of healthcare by enhancing outcomes and quality of care at an efficient cost.