# Project-001

> **NIH NIH P50** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $788,907

## Abstract

Suicide is one of the leading causes of death worldwide and the 10th leading cause of death in the US. A major
barrier to suicide prevention has been that the cutting-edge scientific advances that have occurred in the past
few decades have not yet been translated and implemented into clinical practice settings. We propose the
development of a practice-based Center for Suicide Research and Prevention (CSRP) that will support the
development, deployment, and evaluation of practice-ready and deployment-focused interventions aimed at
improving the identification and effective treatment of those at risk of suicide. This Center will be a collaborative
effort between researchers, clinicians, and stakeholders at Mass General Brigham (MGB) and Harvard
University. Our focus is on improving the identification and prevention of suicide-related behaviors (SRBs)
among patients presenting for treatment at emergency departments (EDs) and psychiatric inpatient units.
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, and the suicide rate is highest in the
weeks immediately following discharge from a psychiatric inpatient hospitalization. Our first aim is to build and
maintain a cohesive and innovative transdisciplinary Center dedicated to advancing suicide prevention. This
will be accomplished via the work of our proposed Administrative Core and Methods Core. Our second aim
is to conduct four practice-focused research projects that target prediction and prevention of suicidal behaviors
in ED and inpatient settings. Our Signature Project (SIG) will implement a previously-develop machine learning
prediction algorithm based on electronic health record (EHR) and self-report data collected in the ED and
randomly assign the clinicians of 4,000 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 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. Our three Exploratory Projects (EXP): (EXP1) will use the SIG prediction model to identify ED
patients at risk of suicidal behavior and experimentally test the effectiveness of an enhanced outreach
intervention administered in collaboration with a community partner – Samaritans of Boston; (EXP2) will
implement and test EHR-based risk algorithms in two inpatient units with a special focus on the use of social
determinants of health to improve prediction among under-represented adolescents; and (EXP3) will test a
just-in-time adaptive intervention using an innovative micro-randomized trial ...

## Key facts

- **NIH application ID:** 10901840
- **Project number:** 5P50MH129699-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** MATTHEW K NOCK
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $788,907
- **Award type:** 5
- **Project period:** 2023-08-08 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10901840

## Citation

> US National Institutes of Health, RePORTER application 10901840, Project-001 (5P50MH129699-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10901840. Licensed CC0.

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