Improved tailoring of depression care using customized clinical decision support

使用定制的临床决策支持改进抑郁症护理的定制

• 批准号: 9913586
• 负责人: Erica Moodie
• 金额: $ 37.28万
• 依托单位: KAISER FOUNDATION RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9913586
• 关键词: 13 year old; Address; Antidepressive Agents; Anxiety Disorders; Biometry; Bipolar Disorder; Caring; Characteristics; Chronic; Clinical; Communities; Complex; Custom; Data; Development; Early treatment; Effectiveness; Electronic Health Record; Epidemiology; Funding; Goals; Health Personnel; Health Status; Health system; Healthcare; Individual; Knowledge; Lead; Link; Machine Learning; Mathematics; Mental Depression; Mental Health; Methodology; Methods; National Institute of Mental Health; Outcome; Pathway interactions; Patient Care; Patients; Pattern; Pharmaceutical Preparations; Psychotherapy; Randomized; Recommendation; Reporting; Research; Research Methodology; Research Personnel; Resources; Sample Size; Schizophrenia; Scientist; Selection for Treatments; Sequential Treatment; Statistical Methods; Symptoms; System; Time; Treatment Effectiveness; Treatment Protocols; Unipolar Depression; Variant; Work; base; care providers; clinical decision support; clinical decision-making; clinical implementation; data resource; data structure; depressive symptoms; design; electronic data; evidence base; experience; improved; individual patient; individualized medicine; large scale data; novel; practice setting; precision medicine; randomized trial; response; statistics; structured data; tool; treatment planning; treatment strategy

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.

项目总结 单相抑郁等精神健康疾病的治疗平均受益不大，但有 随着时间的推移，个体之间和个体内部的差异很大。循证定制化治疗 协议将通过提供治疗建议来改善许多人的精神卫生保健 因个人特征(如当前健康状况)而考虑到潜在变化的个人 状态、症状和对早期治疗的反应。生成定制的治疗方案需要 大量数据，例如来自健康系统网络的数据，这些网络可以将电子健康记录从 数百万人。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患者的特征来定制 而不是使用数据来找到将导致改进的定制护理的患者特征。 其次，用目前的统计方法发现的定制治疗方案假定没有未观察到的情况 接受不同治疗选择的个体之间的差异。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定的方案。我们将通过开发以下方法来解决这三个限制 使用电子健康记录数据发现定制治疗方案的新统计工具。我们的 研究团队在统计学、流行病学和精神卫生保健方面拥有专业知识和经验。我们会 整合已在其他环境中成功使用的方法，以改进统计方法 发现定制的治疗方案并解决这三个重要限制。 我们将把机器学习工具扩展到时变数据，以选择重要的信息片段 发现定制治疗方案所需的结构。我们将构建使用可用的方法 关于不同人群之间未观察到的差异大小的知识 评估这些差异如何改变研究结果的治疗方法。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 确定健康需要多少患有特定疾病的人和服用特定药物的人 系统提供足够准确的信息来发现定制的治疗方案。 使用来自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当前的限制，我们的 定制的治疗方案将对单相抑郁症患者产生立竿见影的影响。这个 我们开发的统计工具也将有助于发现定制的治疗方案，为患有 精神健康状况多种多样。