Using Search Engine Data for Detection and Early Intervention in Suicide Prevention

抽象的。这是使用搜索引擎数据补充赠款奖励R01MH123484的请求 应对NOT-OD-22-026行政性自杀预防的发现和早期干预 与生物伦理问题有关的研究和能力建设努力补充资料。这项补充建议 专注于生命伦理学研究。家长奖将决定互联网搜索历史和 在谷歌搜索引擎上的搜索行为由Variable的人捐赠并预期收集 自杀风险程度将成功地确定近端自杀风险。在之前的一项研究中，人们 最近的一次自杀未遂捐赠了从谷歌外卖工具(GTO)下载的追溯数据。 我们能够识别出在30-60天前预测自杀企图的行为和语言模式 事件发生了。目前资助的这项研究将要求1000名有不同自杀风险的人捐献 回顾数据，并继续捐赠这些数据1年。参与者完成一次回顾 每两周进行一次关于自杀行为和自杀企图发生情况的访谈和前瞻性调查。 我们是否应该能够演示以扩展我们在上一个试点项目中发现的相同结果，即数据 这项研究可能会改变近端自杀风险检测的游戏规则。鉴于77%的人 的美国人几乎完全使用谷歌搜索在网上寻找信息，任何风险预测 算法和后续干预应该能够接触到高危美国人，以防止这种严重的公众 健康结果。然而，如果我们取得成功，有许多道德、法律和社会方面的问题 仍然需要解决的影响。为了理解这些含义，我们将定性地采访50名 一系列焦点小组的研究参与者(25人以前没有自杀治疗经验，25人 和20名干预者(临床医生和社区工作者)关于道德和公平的问题 将这种算法应用于预防自杀的干预措施。我们包括以下观点 本研究中的干预主义者，以确定消费者和干预者在道德、法律和 社会影响以及可能存在意见分歧的地方。与伦理学家的磋商将指导 问题的发展和结果的解释。在数字健康框架的指导下，我们将 向参与者展示有关隐私问题的不同场景(选择共享、共享哪些数据)， 风险/收益问题(哪个工程师应有权访问MLA并负责对MLA采取行动 建议)、可获得性和可用性问题(多样性表示和获取； 干预措施是可以接受的，具体着眼于道德和公平的资源分配)，以及数据 管理方面的问题(数据应该存储在哪里以及如何存储)。参与者还将被要求考虑 应该使用哪些潜在的解决方案来解决这些问题。

ABSTRACT. This is a request to supplement grant award R01MH123484 Using Search Engine Data for Detection and Early Intervention in Suicide Prevention in response to NOT-OD-22-026 Administrative Supplement for Research and Capacity Building Efforts Related to Bioethical Issues. This supplement proposal focuses on Bioethics Research. The parent award will determine whether internet search histories and on search behavior on the Google Search Engine donated by and prospectively collected by people with varying degrees of suicide risk will be successful in determining proximal risk of suicide. In a previous study, people with a recent suicide attempt donated retrospective data downloaded from the Google Take Out tool (GTO). We were able to identify behavioral and linguistic patterns that predicted suicide attempts 30-60 days before the event occurred. The currently funded study will ask 1,000 people with varying risk for suicide to donate retrospective data and to continue to donate these data for 1 year. Participants complete a retrospective interview and prospective surveys every two weeks about the occurrence of suicidal behavior and attempts. Should we be able to demonstrate to scale the same results we found in the previous pilot project, the data from this current study could be game changing in the detection of proximal suicide risk. Given that 77 percent of the US population1 seek information online almost entirely using Google Search, any risk prediction algorithm and subsequent intervention should be able to reach at-risk Americans to prevent this serious public health outcome. However, should we be successful, there are a number of ethical, legal, and societal implications that still need to be addressed. To understand these implications, we will qualitatively interview 50 study participants in a series of focus groups (25 with no previous experience with treatment for suicide and 25 with that experience) and 20 interventionists (clinicians and community workers) about ethical and equitable application of such an algorithm to interventions to prevent suicide. We include the perspectives of interventionists in this study to identify where consumers and interventionists agree on ethical, legal, and societal implications and where there maybe divergence of opinion. Consultation with ethicists will guide the development of the questions and interpretation of results. Guided by the Digital Health Framework, we will present participants with different scenarios about privacy concerns (choice to share, what data to share), risk/benefit concerns (which agent should have access to the MLA and be responsible for acting on a MLA recommendation), accessibility and usability concerns (diversity representation and access; which interventions are acceptable with a specific eye toward moral and equitable resource allocation), and data management concerns (where and how the data should be stored). Participants will also be asked to consider what potential solutions should be used to address these concerns.