ABSTRACT. Death by suicide is the 2nd leading cause of death among young adults in the United States. While most patients who die by suicide have had recent contact with their health care providers, the medical delivery system is poorly equipped to address this preventable issue. Risk of suicide is not detected or addressed in the majority of cases, particularly in health care settings serving low income and racially and ethnically diverse populations. In this R34 study we utilize human centered design (HCD) supported by the Center Methods Core to study how Ecological Momentary Assessment (EMA) based signals of suicide risk can be utilized in primary health care for Risk Detection, Assessment, Shared Decision-Making and Long-Term Surveillance. Patient and provider partner input is needed to design a system that has utility to the care of this population. EMA systems show promise as indicators of suicide risk and a means of enhancing existing resources in the primary care setting. However, little is known about how to apply these methods in the context of clinical care, nor is it apparent to what extent patients would agree to use EMA for risk prediction and monitoring. Through principles of HCD we propose to create a clinically actionable pathway for EMA derived signals of suicide risk that is acceptable to both young adult patients and their health care providers. We plan to carry out two specific aims to address this issue: (Aim 1) co-design a suicide risk monitoring system, Augmented Momentary Personal Ecological Risk Evaluation (AMPERE), with patients and health care providers through HCD and (Aim 2) conduct a pilot study of acceptability and usability of the prototype AMPERE suicide risk detection and response system as well as patient outcomes and our putative mechanisms of patient and provider self-efficacy and therapeutic alliance. Our goal in this study is to co-design a critical pathway for EMA from an innovative evidence-based suicide risk detection strategy to an acceptable and usable clinical tool that has potential for other effective risk detection strategies to follow.