Improved tailoring of depression care using customized clinical decision support

项目摘要 治疗精神健康状况，如单相抑郁症，提供适度的平均效益，但 随着时间的推移，个体之间和个体内部的差异很大。循证定制治疗 协议将通过提供治疗建议来改善许多人的精神卫生保健， 考虑到由于个人特征（如当前健康状况）而导致的潜在变化的个体 状态、症状和对早期治疗的反应。生成定制的治疗方案需要 大量的数据，例如来自卫生系统网络的数据，这些数据可以将电子健康记录与 数以百万计的个人。目前用于发现定制治疗方案的统计方法是有限的 在三个重要方面。 首先，目前的方法依赖于科学家选择用于定制的患者特征 治疗，而不是使用数据来找到患者的特征，这将导致改善，定制护理。 其次，用当前的统计方法发现的定制治疗方案假设没有未观察到的 接受不同治疗方案的个体之间的差异。第三，调查人员没有办法 了解可用数据是否包含足够的信息来发现和比较定制治疗 精确到足以做出临床决策的协议。我们将通过开发 使用电子健康记录数据发现定制治疗方案的新统计工具。我们 研究团队拥有统计学、流行病学和精神卫生保健方面的专业知识和经验。我们将 综合在其他环境中成功使用方法，以改进统计方法， 发现定制的治疗方案，并解决这三个重要的限制。 我们将扩展机器学习工具，用于选择时变数据的重要信息 发现定制治疗方案所需的结构。我们将建立方法， 关于接受不同药物的人群之间未观察到的差异大小的知识 这些差异如何改变研究结果。通过建立在用于估计 在复杂设计的随机试验中，精确度所需的样本量，我们将开发新的公式， 确定在健康状况下需要多少患有特定疾病的人以及服用特定药物的人 系统提供足够准确的信息，以发现定制的治疗方案。 利用15，000多名患者的电子健康记录数据，我们将发现定制的 抑郁症的治疗方案通过改进统计工具和解决目前的局限性， 定制的治疗方案将对患有单相抑郁症的人产生直接影响。的 我们开发的统计工具也将有助于发现定制的治疗方案， 各种各样的心理健康状况。

PROJECT SUMMARY Treatments for mental health conditions such as unipolar depression provide modest average benefit but have wide variation between individuals and within individuals over time. Evidence-based customized treatment protocols would improve the mental health care of many people by providing treatment recommendations for individuals that take into account potential variation because of personal characteristics such as current health status, symptoms, and response to earlier treatment. Generating customized treatment protocols requires large amounts of data, such as from networks of health systems that can link electronic health records from millions of individuals. Current statistical approaches for discovering customized treatment protocols are limited in three important ways. First, current approaches rely on scientists to select the patient characteristics to use to customize treatments instead of using data to find the patient characteristics that will lead to improved, customized care. Second, customized treatment protocols discovered with current statistical methods assume no unobserved differences between individuals who receive various treatment options. Third, investigators do not have ways to know if the available data contain enough information to discover and compare customized treatment protocols precisely enough to make clinical decisions. We will address these three limitations by developing new statistical tools for discovering customized treatment protocols using electronic health records data. Our research team has expertise and experience in statistics, epidemiology, and mental health care. We will integrate methods that have been successfully used in other settings to improve statistical approaches for discovering customized treatment protocols and address these three important limitations. We will extend machine learning tools for selecting important pieces of information to the time-varying data structure required for discovering customized treatment protocols. We will build approaches that use available knowledge about the size of unobserved differences between groups of people who received different treatments to assess how those differences change study results. By building on the math used to estimate the sample sizes needed for precision in randomized trials with complex designs, we will develop new formulas for determining how many people with a particular condition and who took a particular drug are needed in a health system to provide enough accurate information to discover customized treatment protocols. Using data from the electronic health records of more than 15,000 patients, we will discover customized treatment protocols for depression. By improving statistical tools and addressing current limitations, our customized treatment protocols will have immediate impact for people living with unipolar depression. The statistical tools we develop will also be useful for discovering customized treatment protocols for people with a wide variety of mental health conditions.