Explore This Center


Suicide is one of the leading causes of death worldwide and the 10th leading cause of death in the United States. A major barrier to suicide prevention has been that cutting-edge scientific advances in the past few decades have not been translated and implemented for use by clinicians working on the front lines of this mental health care crisis.

The Center for Suicide Research and Prevention (CSRP) is a multidisciplinary, practice-based center aimed at advancing and implementing innovative new suicide prevention research. It will support the development, deployment, and evaluation of practice-ready and clinically-focused interventions aimed at improving the identification and effective treatment of patients at risk of suicide. This is a collaborative effort between researchers, clinicians, and stakeholders at the Center for Precision PsychiatryMass General Brigham and Harvard University.

The focus of the Center is to improve the identification and prevention of suicide-related behaviors among patients presenting for treatment at emergency departments (EDs) and psychiatric inpatient settings. Decades of research have shown that:

  • 50% of people who die by suicide are seen in a healthcare setting within one month before their death.
  • 40% visit an ED the year before their death.
  • The suicide rate is highest in the weeks immediately following discharge from a psychiatric inpatient setting.

Research has identified the patients are at the highest risk for suicide, and the time periods during which risk is the highest. The CSRP is interested in implementing the best prediction and prevention methods in ED and inpatient settings to decrease the risk of suicidal behavior. All approaches used in the Center have been developed collaboratively with frontline clinicians and leverage digital and highly scalable technologies.

Our Vision

Our vision is to lead the creation and broad dissemination of approaches generalizable to a diverse range of clinicians and patients, significantly advancing suicide prediction and prevention.

The CSRP has 3 main aims:

  1. Build & maintain a cohesive and innovative multidisciplinary Center dedicated to advancing suicide prevention. Drawing on a community of unparalleled depth and breadth in research and clinical excellence, the Center brings together established and early career scientists, cultivates emerging scholars, develops novel methods, engages stakeholders, and builds community partnerships to strengthen the implementation and sustainability of these efforts. The transdisciplinary community includes clinicians, implementation scientists, data scientists, and other stakeholders focused on improving the prediction and prevention of suicide.
  2. Conduct practice-focused research projects that target prediction and prevention of suicide and related behavior (SRB) in acute care settings. Research projects focus on improved prediction and prevention in EDs and inpatient psychiatric hospitalization. Each project leverages cutting-edge data science, innovative technologies, and foundational research completed by members of our team to create a learning health system that iteratively makes discoveries and implements and tests practice-based innovations.
  3. Share, educate, and implement findings with clinicians working with patients at risk for suicide. The Center disseminates the findings from our research to the broad scientific community and helps others implement and test the innovative approaches we develop. This will help support a diverse pool of emerging and collaborating scholars. The CSRP will launch a pilot grant program in addition to a scholars program to encourage interdisciplinary innovations, while holding ongoing seminars and training sessions for practitioners and clinicians alike. We will produce a digital platform of applications to support clinical decisions and enable dissemination of evidence-based tools to the broader research and clinical community.

Research Projects

staff members working at desk in front of large windows overlooking Boston

Signature Project (SIG): Effectiveness and Implementation of a Clinician Decision Support System to Prevent Suicidal Behaviors

The SIG will implement a previously developed machine learning prediction algorithm based on electronic health record (EHR) and self-report data collected in the ED and randomly assign the clinicians patients to receive (experimental condition) or not receive (control condition) the predicted probability that their patient will make a suicide attempt after ED discharge. We will test the impact of this intervention on both the suicide attempt rate and clinician decision-making. The SIG also will examine clinician acceptability and adherence, prediction model improvement, and the development of treatment optimization rules regarding patients’ likelihood of benefiting from hospitalization versus alternative treatments.

SIG Aims
  • Aim I: Test the effects of providing ED clinicians with information about risk of patient suicidal behavior on clinician decision-making and the rate of subsequent suicide attempts. We will implement a recently developed risk calculator that provides ED clinicians with real-time estimates of the probability that their patient will attempt suicide in the next month. This will be tested with 4,000 patients presenting to two EDs, randomly assigning clinicians to receive (vs not) the risk score and testing the effect of providing this information on care and outcomes.
    • Hypotheses: (a) There will be fewer suicide attempts in the experimental (risk score) condition and (b) Clinicians provided with the risk score data will be more likely to hospitalize patients estimated by the model to be high-risk and less likely to hospitalize patients estimated to be at low risk than clinicians in the control condition. A significant part of this Aim will now focus on maximizing the generalizability of these results by examining clinician- and institutional-level facilitators/barriers to implementation in the Mass General Brigham healthcare system, as well as in an independent, less well-resourced, and more racially-, ethnically-, and linguistically diverse setting.
  • Aim II: Improve detection of high-risk patients. Since the development of the risk scoring system noted above, new developments have emerged that would allow the score to be improved by adding more potentially important predictors of several kinds. In addition, new ensemble machine learning prediction methods have recently emerged that are likely to yield more accurate predictions than those based on the method we used in developing our current model.
    • Hypothesis: Prediction accuracy will improve when we revise the model to include the new features and modeling approaches.
  • Aim III: Improve treatment optimization among high-risk patients. We will develop a precision treatment rule (PTR) to help ED clinicians make optimal clinical decisions about whether to treat patients with significantly elevated risk of suicidal behaviors by hospitalization partial hospitalization, or referral to outpatient care.
    • Hypotheses: (a) Significant estimated heterogeneity of treatment effects will be found based on a precision treatment model that uses information available during the ED visit (e.g., EHR, NLP, surveys) to predict subsequent suicide-related behaviors; and (b) The estimated reduction in suicide-related behaviors through treatment assignment based on the PTR will be superior to that based on merely providing information of patient high-risk status to ED clinicians.

Exploratory Project 1 (EXP-1): A Health System/Community Partnership for Enhanced Outreach to Prevent Suicide Attempts

EXP-1 will implement the SIG risk stratification algorithm and pilot a novel “enhanced outreach intervention” (EOI) in collaboration with Samaritans of Boston to deliver best practice interventions for patients in the high-risk period after ED discharge using a scalable, community-based partnership.

EXP-1 Aims
  • Aim I: Pilot test the implementation process of an enhanced EOI for post-ED discharge prevention of SRB, delivered through an Mass General Brigham-Samaritans partnership. This will include (1) developing operational workflows for using our risk algorithm to identify patients for the EOI, (2) conducting a small open pilot, and (3) assessing individual- and system-level barriers and facilitators, and feasibility, acceptability, and fidelity of implementing the EOI at an academic medical setting through mixed methods to inform intervention refinement. We hypothesize that the EOI will be feasible and acceptable, and delivered with fidelity by Samaritans.
  • Aim II: Conduct a Hybrid Type I randomized trial of the EOI vs. care as usual (CAU) after ED discharge to test effectiveness and explore implementation-related factors. After refinement of the EOI, we will conduct a trial comparing outcomes of participants randomized to EOI or CAU. All participants will be drawn from those enrolled in the Signature Project (SIG) who were stratified to the top 50% of predicted suicide risk via our automated risk algorithm. We hypothesize that the EOI will be associated with (a) reductions in SRB and increased outpatient treatment attendance (primary and secondary outcomes) at post-treatment (12 weeks after discharge) and (b) acceptability, feasibility and fidelity (implementation factors).
  • Exploratory Aim III: Explore moderators of intervention effects. Potential moderators of intervention effects will include, age, sex, race/ethnicity, and predicted risk stratum. We hypothesize that such factors will be associated with differential effects of the EOI on reducing post-ED SRB.

Exploratory Project 2 (EXP-2): Improving Suicide Risk Prediction in Racial, Ethnic, and Linguistic Minority Youth

EXP-2 will develop and implement electronic health record (EHR)-based risk algorithms across two health systems, with a particular focus on minoritized adolescents, and explore the utility of incorporating social determinants of health data for enhancing risk prediction.

EXP-2 Aims

Suicide deaths among youth aged 10 to 19 years rose 56% between 2007 and 2016, and the proportion of pediatric inpatient or emergency department visits due to SRB has doubled since 2008. This public health emergency appears to be affecting youth of underrepresented backgrounds in particular. Trends in suicide attempts over the past decade have remained higher or increased among Black and Latinx youth compared to white peers. The need to develop scalable, practice-based tools with near-term potential for targeting suicide risk in youth is therefore particularly acute in the case of racial, ethnic, and linguistic (REL) minority youth.

  • Aim 1: Use machine learning/natural language processing (NLP) with EHR data to develop algorithms to classify suicide risk for REL minority youth. Mindful of the need to reduce mental health inequity through the development of scalable assessment tools that can be implemented in under-resourced settings with REL minority youth, our first aim is to apply ML and NLP to EHR data, to develop risk algorithms to predict ED/inpatient visits for SRB within 3 months of discharge from index hospitalization. We will evaluate the relative performance of co-trained versus locally trained ML algorithms with EHR data from 2 pediatric psychiatric inpatient sites. To determine which of these algorithms is optimal, we will assess their portability with test sets of REL minority youth at both sites.
  • Aim 2: Evaluate the incremental value of SDOH and CAT data in predicting suicide risk in REL minority youth. Our second aim is to generate a suicide risk algorithm for REL minority youth that would be applicable to optimal clinical care settings. That is, we will build on the optimal algorithm identified in Aim 1 by incorporating SDoH data, as well as broad psychopathology collected through CAT as part of standard care at one of the inpatient sites.
  • Exploratory Aim: Evaluate the performance of the algorithms developed in Aims 1 and 2 for specific REL minority subsamples, such as Black or Latinx youth.

Exploratory Project 3 (EXP-3): Micro-randomized Trial to Assess Brief, Just-in-Time Interventions for Reducing Short-Term Suicide Risk

EXP-3 will test a just-in-time adaptive intervention using a micro-randomized trial (MRT) design to connect at-risk patients with care when most needed.

EXP-3 Aims
  • Aim 1: Identify factors key to just-in-time intervention refinement in an initial pilot. Qualitative feedback on the intervention methods, content, amount of support, timing, and triggering, as well as overall acceptability and feasibility, will be collected in an initial pilot and used to refine the interventions before a full MRT. Hypothesis: The just-in-time interventions will be both acceptable and feasible.
  • Aim 2: Determine the proximal effects of just-in-time interventions aimed to promote use of the safety plan and its components in an MRT. Randomly timed, brief follow-up surveys will assess post- intervention use of safety plan components and suicidal thoughts.
    • Hypothesis 1: When collapsing across all intervention methods and content, the interventions will be associated with increased use of the safety plan and its components, as well as reductions in suicidal thoughts.
    • Hypothesis 2: Intervention effects will vary as a function of the intervention method (phone call, text messaging, automated interactive tool, and automated non- interactive pop-up) and the intervention content (reminder to use the safety plan and its components) at high, medium, and low levels of suicidal thoughts.
  • Exploratory Aim 3: Explore internal and external contextual factors as moderators of intervention effects. Potential moderators will include momentary internal (e.g., affect, physiology) and external (e.g., access to social support) factors, assessed with both intensive longitudinal self-report and passive sensing. Hypothesis: Internal and external contextual factors will be associated with differential intervention effects.

Our Team

Jordan SmollerJordan W. Smoller, MD, ScD

Dr. Smoller is a psychiatrist, epidemiologist and geneticist whose research focus has been understanding the genetic and environmental determinants of psychiatric disorders across the lifespan and using big data to advance precision mental health including improved methods to reduce risk and enhance resilience. He earned his undergraduate degree summa cum laude at Harvard University and his medical degree at Harvard Medical School. After completing residency training in psychiatry at McLean Hospital, Dr. Smoller received masters and doctoral degrees in epidemiology at the Harvard School of Public Health.

Matthew NockMatthew Nock, PhD

Dr. Nock is a professor of Psychology and director of the Laboratory for Clinical and Developmental Research in the Department of Psychology at Harvard University. Dr. Nock’s research is aimed at advancing the understanding of why people behave in ways that are harmful to themselves, with an emphasis on suicide and other forms of self-harm. His research is multi-disciplinary in nature and uses a range of methodological approaches (e.g., epidemiologic surveys, laboratory-based experiments, and clinic-based studies) to better understand how these behaviors develop, how to predict them, and how to prevent their occurrence. Dr. Nock received his PhD in psychology from Yale University and completed his clinical internship at Bellevue Hospital and the New York University Child Study Center.

Administrative Core (AC) Team
Methods Core (MC) Team
Signature Project (SIG) Team
Exploratory Project 1 (EXP-1) Team
Exploratory Project 2 (EXP-2) Team
Exploratory Project 3 (EXP-3) Team

Funding Opportunities

Applications for Scholars and Pilot Grant Programs are now open. Find application information and answers to frequently asked questions.

Explore our Scholars and Pilot Grant Programs