An eHealth intervention to increase depression treatment initiation and adherence among Veterans referred for mental health services

背景：抑郁症是VHA中最常见的心理健康障碍， 与残疾和自杀死亡有关，尤其是在未经治疗的情况下。了解 脱离护理的患者的概况将有助于开发支持系统以改善护理 利用情况和结果。根据Levesque的框架，相关的患者特征 将护理访问映射到包含确定医疗保健需求和愿望的流程中 对于护理、医疗保健寻求、接触和利用，所有这些都会导致医疗保健结果。使用这个 框架下，拟议的综合发展评估采取双管齐下的方法，以应对护理服务未得到充分利用的问题 在抑郁症患者中：通过分析方法开发风险预测模型 利用情绪和症状自我监测的作用作为抑郁症的关键组成部分 管理层。意义/影响：通过该CDA开发的知识具有长期性 对OEF/OIF退伍军人的影响，他们是抑郁症和自杀的高危人群。抑郁症 对退伍军人、提供者和退伍军人管理局有重大影响。这是一种与 退伍军人事务部面临巨大的医疗和经济负担。抑郁是发展的一个危险因素 以及维持医疗和精神状况(即创伤后应激障碍、创伤后应激障碍)。尽管坚持不懈地努力 为了加强对抑郁症的护理，治疗指南专门针对那些从事 关心。治疗前干预有可能提高精神卫生保健的利用率和 减轻患者和退伍军人事务部与抑郁症相关的负担。这样的干预可以最大限度地减少提供者 通过减少不露面和增加坚持来增加负担。创新：研究表明， 退伍军人管理局有潜力促进工具的开发，以加强退伍军人的精神卫生保健。 为了填补在加强对精神健康问题的护理方面使用分析和技术方面的空白， 建议的工作在两个方面具有创新性：1)我们建议使用大数据和分析工具来 确定与心理健康治疗参与度和风险增加相关的患者概况 放弃护理；2)开发技术驱动的干预措施，以提高自我效能感和积极性 参与心理健康护理。具体目标：RA1：确定相关的风险概况(分数) 配合抑郁症治疗使用。使用VHA电子健康记录(EHR)测试预测模型。 在目标1中计算的风险分数将用于选择有风险并符合资格的患者 拟议的干预措施.TA1：熟练掌握电子病历/大数据的方法和分析。RA2：设计 使用技术驱动的自我监控的电子健康干预。TA2：培养技能和知识 关于干预性发展。RA3：对电子健康干预进行形成性评估和试点。TA3： 熟练掌握形成性评价。方法：RA1将采用回溯性队列设计。 利用电子病历数据和分析工具的优势，我们将调查风险模型以确定 与治疗开始和依从性相关的患者概况。预测值将从 结构化数据。RA2是一个发展目标。我们建议设计和形式化地开发一个 电子健康干预主要使用技术驱动的抑郁情绪自我监控和 症状。RA3是一项形成性评估和试点目标，重点是在 OEF/OIF退伍军人可能患有抑郁症(N=15)。下一步/实施：本CDA将 帮助为未来关于增加干预措施的有效性/有效性研究奠定基础 退伍军人抑郁症患者的治疗利用情况。结果将被用来通知提交 在CDA的第三年进行随机对照试验IIR，以评估这项干预的效果/效果。工具 在本CDA中开发的将为退伍军人管理局的创新目标做出贡献。

Background: Depression is the most prevalent mental health disorder in VHA and is strongly associated with disability and suicide mortality, especially when untreated. Understanding the profiles of patients that disengage from care will help develop support systems to improve care utilization and outcomes. According to Levesque’s framework, relevant patient characteristics that lead to care access map onto a process that incorporates identifying health care needs and desire for care, healthcare seeking, reaching, and utilization, all leading to health care outcomes. Using this framework, the proposed CDA takes a two-prong approach in response to underutilization of care among those with depression by: developing risk predictive models through analytics methodology and leveraging the role of mood and symptom self-monitoring as key components in depression management. Significance/Impact: The knowledge developed through this CDA has long term implications for OEF/OIF Veterans who are at highest risk for depression and suicide. Depression has a significant impact on Veterans, providers, and the VA. It is a disorder that is linked to substantial medical and economic burden in the VA. Depression is a risk factor for the development and maintenance of medical and psychiatric conditions (i.e., PTSD, TBI). Despite persistent efforts to increase care for depression, treatment guidelines are exclusively focused on those engaging in care. Pre-treatment interventions have the potential to increase mental health care utilization and reduce depression related burden on patients and the VA. Such interventions can minimize provider burden by reducing no shows and by increasing adherence. Innovation: Research shows that the VA has the potential to foster the development of tools to enhance mental health care for Veterans. To fill gaps in the use of analytics and technology in enhancing care for mental health concerns, the proposed work is innovative in two ways: 1) we propose the use of big data and analytics tools to identify patient profiles associated with mental health treatment engagement and increased risk for drop out of care; 2) develop a technology driven intervention to increase self-efficacy and active engagement in mental health care. Specific Aims: RA1: Identify risk profiles (scores) associated with depression treatment use. Test prediction models using VHA electronic health records (EHR). Risk scores computed in Aim 1 will be used in selection of patients at risk and eligible for the proposed intervention.TA1: Gain proficiency in methods and analysis of EHR/big data. RA2: Design an eHealth intervention using technology driven self-monitoring. TA2: Develop skills and knowledge about intervention development. RA3: Formatively evaluate and pilot the eHealth intervention. TA3: Gain proficiency in formative evaluation. Methodology: RA1 will use a retrospective cohort design. Leveraging the strengths of EHR data and analytics tools, we will investigate risk models to identify patient profiles associated with treatment initiation and adherence. Predictors will be extracted from structured data. RA2 is a development aim. We propose to design and formatively develop an eHealth intervention primarily using technology driven self-monitoring of depressed mood and symptoms. RA3 is a formative evaluation and pilot aim focused on the use of the intervention among OEF/OIF Veterans with probable depression (N= 15). Next Steps/Implementation: This CDA will help to establish a foundation for future efficacy/effectiveness research on interventions to increase treatment utilization among Veterans with depression. Results will be used to inform the submission of a RCT IIR in year 3 of the CDA to evaluate the efficacy/effectiveness of this intervention. Tools developed in this CDA will contribute to VA innovation goals.