# Technologic Innovation to Enhance the Scalability and Sustainability of Trauma Center Provider Training in Suicide Safety Planning

> **NIH NIH K23** · UNIVERSITY OF WASHINGTON · 2022 · $139,122

## Abstract

PROJECT SUMMARY/ABSTRACT
Over 44,000 people died by suicide in the U.S. in 2016 and national rates continue to increase. The majority of
people who died by suicide had contact with the health care system in the year prior to their death. Major
hurdles to implementing suicide prevention in healthcare settings include the lack of scalable and sustainable
methods for training routine healthcare providers in suicide prevention. Innovations in machine learning and
artificial intelligence may overcome these hurdles as it is now possible for technology to assess the quality of
provider skill in intervention delivery and provide opportunities for skill acquisition and practice. The candidate's
long-term goal is to harness technological advances in artificial intelligence, natural language processing, and
machine learning to improve the scalability and sustainability of training among general medical providers in
suicide prevention. The proposed research and training activities will take place at the University of
Washington at Harborview Medical Center in Seattle, WA, a county safety-net hospital and level I trauma
center serving patients across Washington, Wyoming, Alaska, Montana and Idaho. The research aims to adapt
and deploy existing scalable technology to train frontline trauma center providers (e.g., nurses) to
collaboratively engage patients in a suicide safety planning intervention (SPI) and conduct a pilot feasibility trial
of the resultant training. Aim 1 includes focus groups with trauma nurses to identify individual, setting, and
organizational-level implementation barriers and facilitators based on the Theoretical Domains Framework and
inform strategies for engaging nurses in training and delivery of the SPI with patients. Aim 1 also includes the
user-centered design method of contextual inquiry, including task analysis, with nurses to inform workflow-
integration. Aim 2 includes user-centered design methods to identify technology refinements and adaptations
based on nurse preferences to increase usability. The technologies are a 1) conversational agent, with
simulated patient role-play and real-time feedback, and 2) AI-based feedback of counseling performance from
SPI audio recordings. Aim 3 is to conduct a pilot randomized trial of a technology-enhanced provider training
as compared to a web-based didactic only condition. The longitudinal trial will include 20 nurses (10 per
condition), each with 3 patients, and support submission of an NIMH R01 full-scale trial. The K23 training goals
include building knowledge and skills in 1) technology-focused team science, 2) the application and integration
of implementation science, user-centered design, and adult learning theory for technology adaptation and
integration for nurse training, 3) acute care suicide prevention clinical trials research, including the responsible
conduct of research with patients at-risk for suicide, and statistical methods for low base-rate outcomes and
nested longitudin...

## Key facts

- **NIH application ID:** 10426124
- **Project number:** 5K23MH118361-04
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Doyanne Aspen Darnell
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $139,122
- **Award type:** 5
- **Project period:** 2019-07-05 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10426124

## Citation

> US National Institutes of Health, RePORTER application 10426124, Technologic Innovation to Enhance the Scalability and Sustainability of Trauma Center Provider Training in Suicide Safety Planning (5K23MH118361-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10426124. Licensed CC0.

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