Optimizing the Treatment of Diabetes Patients

描述(申请人提供)：优化糖尿病患者的治疗。医学上复杂的患者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挑战。2型糖尿病患者是越来越多的复杂患者，他们的治疗方案复杂，合并疾病的数量也很多。在这一人群中，治疗的优先顺序越来越重要，因为糖尿病患者平均有3.5种并存情况，其中高血压和高脂血症是最常见的。人们越来越重视评估循证糖尿病护理提供者的表现。然而，这些方法假设一刀切，没有考虑到随着年龄、疾病状态和合并症而改变的患者的需求和偏好。因此，初级保健临床医生通常很难对糖尿病患者的干预措施进行优先排序，这些干预措施将对患者的偏好敏感，同时对外部质量测量要求做出回应。在接下来的两年里，我们将通过增进关于糖尿病最佳治疗的知识来弥合这一差距，该知识的衡量标准是患者的质量调整后的寿命、对治疗的坚持程度、治疗成本以及与糖尿病相关的并发症对医疗系统的成本。具体地说，我们将重点开发数学模型，以确定开始和强化治疗糖尿病、高血压和高脂血症的最佳时间。利用纵向数据集，我们的目标是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，假设完全依从性，但也在不同程度的不依从性。纳入不依从性将为我们提供与治疗相关的更现实的情景，并提供更大的机会来影响患者的长度和生活质量。这项研究的结果将被转化为决策辅助工具。这些辅助决策工具可以在实际的随机试验中进行测试，以评估它们能够在多大程度上通过以有效的成本提高结果和护理质量来实现更高的医疗保健价值。

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.