Improving Medication Adherence in Type 2 Diabetes Using Integrated e-Technology

描述（由申请人提供）：越来越多的证据表明 2 型糖尿病患者通常对处方药物的依从性较差。显然，患者坚持药物治疗对于实现代谢控制和降低心血管疾病（CVD）风险至关重要，心血管疾病是这些患者死亡的主要原因。然而，很少有干预措施试图提高糖尿病患者的药物依从性。在临床实践中，通常无法获得依从性信息。因此，医生不会定期与患者讨论药物依从性。为了满足这一需求，我们建议开发、实施和评估一种新颖而复杂的方法，以评估患者对其处方药物的依从性水平，并在临床就诊时将这些信息传达给他们的初级保健提供者。该项目的具体目标是：(1) 为初级保健医生开发计算机化临床决策支持 (CCDS) 工具，帮助他们识别、监测和解决 2 型糖尿病患者的药物依从性问题。这包括一个计算机程序，可根据电子病历系统 (EMR) 中的患者汇总用药历史客观评估患者用药依从性，以及一个基于网络的门户，为患者提供导致不当药物使用的行为和心理数据。这些数据元素将被组合成易于理解的表格和图形格式，由 CCDS 在临床遇到时生成； （2）评估本课题组开发的数据驱动算法的用药依从性测量的准确性以及患者门户和CDSS的可行性和可用性； (3) 在单一的真实临床环境中对该系统进行试点测试，并评估该 CDSS 如何影响患者的治疗结果。两个主要结果是：1) 患者的药物依从性，以及患者报告的药物使用障碍的变化，2) 通过 HbA1c、LDL 和血压衡量的患者结果。次要结果是提供者和患者对系统的态度，包括系统的总体满意度。系统实施后，我们将每隔 3 个月对患者和提供者进行调查。这项研究的结果将构成 R18 应用的基础，该应用旨在通过随机临床试验确定 CDSS 是否可以改善 2 型糖尿病患者的血糖和 CVD 危险因素控制。 公共卫生相关性：不坚持用药是 2 型糖尿病患者的一个主要问题。尽管有效的依从性干预可能比任何其他医疗改进对人群健康产生更大的影响，但医生在实践中并不常规地解决药物依从性问题。用于评估药物依从性并协助提供者与患者解决这一问题的计算机化临床决策支持系统将有助于改善 2 型糖尿病患者的健康结果。

DESCRIPTION (provided by applicant): Growing evidence suggest that patients with Type 2 diabetes often have poor adherence with prescribed medication. Clearly, patient adherence to pharmacologic therapy is crucial to achieving the goal of metabolic control and reducing the risk of cardiovascular disease (CVD), the primary cause of mortality among these patients. However, few interventions have attempted to improve medication adherence in diabetic patients. In clinical practice, adherence information is usually not available. As a result, physicians do not discuss medication adherence with patients routinely. To address this need, we propose to develop, implement and evaluate a novel and sophisticated method to assess patients' levels of adherence to their prescribed medications and communicate this information to their primary care providers at the time of their clinical encounter. The specific aims of the proposed project are to: (1) develop a computerized clinical decision support (CCDS) tool for primary care physicians to assist them in identifying, monitoring and addressing problems with medication adherence in their patients with Type 2 diabetes. This includes a computer program that objectively assesses patient medication adherence based on patient aggregate medication history from an electronic medical record system (EMR), and a web-based portal for patients to provide behavioral and psychological data that contributes to improper medication utilization. These data elements will be combined into an easy to understand table and graphic format that will generated by the CCDS at the time of the clinical encounter; (2) evaluate the accuracy of the data-driven algorithm developed by our group for measuring medication adherence and the feasibility and usability of the patient portal and the CDSS; and (3) pilot test this system in a single real-world clinical setting and evaluate how this CDSS affect patient outcomes. The two primary outcomes are: 1) patients' medication adherence, as well as changes in patient reported barriers to medication use, and 2) patient outcomes measured by HbA1c, LDL and blood pressure. The secondary outcomes are provider and patient attitudes about the system including overall satisfaction of the system. We will survey patients and providers at 3-month interval after the system implementation. The results from this study will form the foundation of R18 application designed to determine in a randomized clinical trial whether this CDSS can improve glycemic and CVD risk factor control in patients with Type 2 diabetes. PUBLIC HEALTH RELEVANCE: Medication non-adherence is a major problem among patients with Type 2 diabetes. Although an effective adherence intervention may have a greater impact on population health than any other medical treatment improvements, physicians do not routinely address medication adherence in practice. The computerized clinical decision support system to assess medication adherence and assist providers in addressing this issue with their patients will help to improve health outcomes of patients with Type 2 diabetes.