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Risk Stratified Enhancements to Clinical Care: Targeting Care for Patients Identified Through Predictive Modeling as Being at High Risk for Suicide, with the Office of Mental Health Operations

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Despite work done to strengthen Veterans Health Administration (VHA) mental health services and suicide prevention, suicide rates in VHA have been stable [1]. These rates stand in contrast to increasing rates in other Americans, especially middle-aged men [2,3] and in Veterans who do not utilize VHA services [4,5], suggesting that VHA programs may have mitigated expected increases although this has not been determined. Nevertheless, the finding that suicide rates in VHA remain high represents a strong call for action. Novel approaches that reduce the incidence of suicide-related events are needed earlier, ideally before suicide- related behaviors occur. One innovative approach recently validated in VHA is predictive modeling that identifies Veterans at risk and thus facilitates implementation of targeted prevention. The VHA predictive model has identified the top 5% of VHA patients who were at the highest predicted risk for suicide. This model provides new information about who is at risk; fewer than 2% of the 5% of patients identified as high risk received clinical flags for being at risk. For those identified as high risk, VHA?s Office of Suicide Prevention has implemented a national suicide prevention outreach program entitled Recovery Engagement and Coordination for Health ? Veterans Enhanced Treatment (REACH VET). REACH VET utilizes a dashboard to provide the names of patients identified by the model to coordinators at each VA medical facility. REACH VET coordinators are responsible for notifying providers of the patient?s status and prompting providers to re-evaluate care and take any appropriate steps if they are not already occurring (e.g., contacting the patient to re-engage in care). To further strengthen REACH VET, an effective, low-cost suicide prevention intervention, caring letters, is being added as a REACH VET augmentation. Caring letters involves the sending of recurring brief notes to high risk patients expressing care, concern, and offers of help if needed. It is one of the only suicide prevention interventions that have reduced suicide mortality rates in a randomized controlled trial [8?12]. Specific Aim 1: Evaluate the impact of virtual external facilitation versus standard implementation by conducting a formative evaluation to identify barriers and facilitators to implementation to define and refine virtual external facilitation strategies and a summative evaluation of virtual external facilitation versus standard implementation. Specific Aim 2: Develop and evaluate the augmentation of REACH VET using caring letters, an evidence- based suicide prevention intervention by conducting a formative evaluation of the augmentation of REACH VET with caring letters and refining the caring letter intervention for scale up to the VHA-wide REACH VET program. The overall design will be a hybrid effectiveness-implementation controlled program evaluation using a mixed methods approach. REACH VET is currently being implemented in VHA using an adaptive design. The Run-in Phase began in November and included coordinator training and dashboard rollout. Phase 1 will include randomization of all facilities classified as non-responders to either continued standard implementation or virtual external facilitation. Also during Phase 1, caring letters will be implemented at four sites to develop a centralized model for scale up. In Phase 2, those facilities that did not receive virtual external facilitation will receive it if they are still classified as non-responders. Pending available budget and resources, in Phase 2, 30 sites will be randomized to receive centralized support for caring letters in a stepped-wedge trial. The anticipated impact on the VA health care system of this project is to improve targeting of VA suicide prevention resources through understanding the effectiveness of the intervention package at reducing suicide behaviors and how to best implement such an intervention across a system.

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