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， 与残疾和自杀死亡率相关，特别是在未经治疗的情况下。了解 脱离护理的患者概况将有助于发展支持系统，以改善护理 利用和成果。根据Levesque的框架， 导致将护理访问映射到结合识别健康护理需要和期望过程 对于护理，医疗保健寻求，到达和利用，所有这些都导致医疗保健结果。使用此 在这个框架下，拟议的综合发展援助采取双管齐下的方法，以应对护理利用不足的问题 通过分析方法开发风险预测模型 并利用情绪和症状自我监控作为抑郁症的关键组成部分 管理意义/影响：通过本CDA开发的知识具有长期意义 OEF/OIF退伍军人谁是抑郁症和自杀的最高风险的影响。抑郁 对退伍军人，供应商和VA有重大影响。这种疾病与 医疗和经济负担沉重。抑郁症是发展的危险因素 以及维持医疗和精神状况（即，PTSD、TBI）。尽管作出了不懈努力， 为了增加对抑郁症的护理，治疗指南专门针对那些参与 在乎治疗前干预有可能提高精神卫生保健的利用率， 减轻患者和VA的抑郁相关负担。这种干预措施可以最大限度地减少供应商 通过减少不显示和增加遵守的负担。创新：研究表明， VA有潜力促进工具的开发，以加强退伍军人的心理健康护理。 为了填补在使用分析和技术以加强对心理健康问题的护理方面的空白， 拟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， 确定与心理健康治疗参与相关的患者概况， 退出护理; 2）开发技术驱动的干预措施，以提高自我效能和积极性 参与精神卫生保健。具体目标：RA 1：确定相关风险特征（评分） 抑郁症治疗的使用。使用VHA电子健康记录（EHR）测试预测模型。 在目标1中计算的风险评分将用于选择有风险且有资格参加 提议的干预措施。TA 1：熟练掌握EHR/大数据的方法和分析。RA 2：设计 使用技术驱动的自我监测的电子健康干预。TA 2：培养技能和知识 关于干预发展。RA 3：正式评估和试点电子卫生干预措施。TA3： 熟练掌握形成性评价。方法学：RA 1将采用回顾性队列设计。 利用EHR数据和分析工具的优势，我们将研究风险模型， 与治疗开始和依从性相关的患者概况。预测因子将从 结构化数据RA 2是一个发展目标。我们建议设计和形成发展一个 电子健康干预主要使用技术驱动的抑郁情绪自我监测， 症状RA 3是一项形成性评价和试点目标，重点是在以下人群中使用干预措施： OEF/OIF退伍军人可能患有抑郁症（N= 15）。后续步骤/实施：本CDA将 有助于为今后关于干预措施的功效/效果研究奠定基础， 抑郁症退伍军人的治疗利用率。结果将用于通知提交 在CDA的第3年进行RCT IIR，以评价该干预的疗效/有效性。工具 本CDA中开发的技术将有助于实现VA创新目标。

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.