Stakeholder Perspectives on Implementing Suicide Risk Prediction Models

在过去的二十年里，美国的自杀率一直在上升。2017年，超过 47，000美国人死于自杀。卫生保健访问是预防自杀的机会，因为 大多数人在自杀死亡后一年内进行门诊医疗保健访问，几乎一半的人 在他们死后一个月内去探望他们然而，自杀风险并不总是容易识别的临床医生- 传统的临床预测并不比偶然性好。预测建模，识别“大” 来自行政和电子健康记录的“数据”已被证明上级临床自杀风险预测 和常规使用的自杀筛查仪器。虽然预测模型为自杀提供了希望 预防，如何在常规临床实践中实施模型以及 影响其使用是研究不足。任何风险预测模型的潜在益处，包括 旨在识别自杀风险，依赖于确保模型以某种方式部署， 不伤害患者，支持临床护理管理，并可持续提供医疗保健 系统.我们建议在三种情况下进行实施前试点研究，采用一对一的深入研究， 访谈，以探讨卫生系统管理人员，临床医生和患者的期望，经验， 关注，并建议早期使用自杀风险预测模型。在第一种情况下，健康 系统管理员仍在考虑什么可能是最好的实施方法。面试将 帮助我们了解各种利益相关者的期望如何与其他两个领域的实际情况相匹配， 将进行小型试点研究的环境。其中一个背景是计划外展到高风险 患者独立的医疗保健访问，而另一个是计划交付的风险评分点， 在乎通过研究不同的实施策略，我们可以比较相对优势， 缺点我们特别感兴趣的是对临床工作流程，临床医生与患者的关系， 患者的经验。虽然有一个新兴的文献支持预测模型的承诺， 医疗保健、实施因素和患者影响在很大程度上被忽视。然而，关于 设计和建模方法以及实施过程应该由利益相关者的需求驱动。 这项试点研究的结果将具有重要的临床意义，不仅将为大规模的 在全国卫生系统实施自杀风险预测模型，但也将告知 根据利益攸关方的需要，制定未来风险预测模型和相关护理流程 更一般地说（不限于自杀风险）。该试点项目的长期目标是为正在进行的 卫生系统一级努力减少自杀流行率，并通过优化自杀手段预防自杀 风险预测工具。

Age-adjusted suicide rates have been increasing in the U.S. over the past two decades. In 2017, more than 47,000 Americans died of suicide. Health care visits represent opportunities for suicide prevention because most individuals make an outpatient health care visit within a year of their suicide death and almost half have a visit within a month of their death. However, suicide risk is not always easily recognizable to clinicians— traditional clinical prediction is hardly better than chance. Predictive modeling that identifies patterns in “big data” from administrative and electronic health records has proven superior to clinical suicide risk prediction and routinely used suicide screening instruments. While predictive modeling holds promise for suicide prevention, how models should be implemented in routine clinical practice and the contextual factors that influence their use are understudied. The potential benefits of any risk prediction model, including those designed to identify suicide risks, are dependent on making sure that the models are deployed in a manner that does not harm patients, supports clinical care management, and is sustainable for health care delivery systems. We propose a pre-implementation pilot study in three settings, using one-on-one, in-depth interviews to explore health system administrators', clinicians', and patients' expectations, experiences with, concerns, and suggestions for the early use of suicide risk prediction models. In the first setting, health system administrators are still considering what might be the best implementation approach. Interviews will help us understand how various stakeholder expectations match what is actually occurring in the two other settings where small pilot studies will be in process. One of these settings is planning outreach to high-risk patients independent of health care visits while the other is planning delivery of risk scores at the point of care. By studying different implementation strategies, we can compare relative advantages and disadvantages. We are particularly interested in effects on clinical workflows, clinician-patient relationships, and patient experiences. While there is an emerging literature supporting the promise of predictive models in health care, implementation factors and patient impacts have been largely ignored. Yet decisions regarding design and modeling methods and implementation processes should be driven by stakeholder requirements. Results of this pilot study will have important clinical implications and will not only inform large-scale implementation of suicide risk prediction models in health systems across the country but will also inform development of future risk prediction models and associated care processes tailored to stakeholders needs more generally (not limited to suicide risk). The long-term goals of this pilot project are to inform ongoing health system-level efforts to reduce suicide prevalence and prevent suicides by optimizing the use of suicide risk prediction tools.