The objective of the project is to evaluate the implementation and impact of the Acute Pain Services Expansion Program (APSEP), an expansion of the independent and formal set of services that provide comprehensive inpatient pain management consultative services and is designed to meet perioperative care needs of veterans receiving inpatient pain management.
The aim of the project is to conduct a pragmatic pilot trial of a CIH-based stepped care approach v. treatment as usual in two primary care settings (one rural and one urban). The pilot trial will focus on feasibility, acceptability, and appropriateness for providers and patients (e.g., randomization, retention, and treatment satisfaction) of the stepped […]
We are conducting a study to understand the role of problem-solving in challenging behaviors for children with Down syndrome so that we can better understand the development of and treat these behaviors. If you agree to participate, this study will involve questionnaires, some of which may be completed at home. You would also attend an […]
Our project will seek to identify factors associated with gaps in transitions of care for psychiatric inpatients who presented with substance-induced psychosis (SIP) for the first time. We will analyze historical electronic health record data of patients who were treated for psychosis at Harborview Medical Center. We will test the hypotheses that (1) treatment with […]
Improved financial literacy among people living with serious mental illness (SMI) is associated with a higher quality of life, fewer hospitalizations, and better treatment adherence. Yet people living with SMI frequently express how their lack of financial knowledge has negative personal consequences and that they don’t know where to turn for assistance. This project will […]
This project will identify interaction patterns with online video platforms that are indicative of suicide risk, focusing on YouTube and TikTok. Leveraging archival data including over 5 million interaction events collected from participants in previous research, we will use combinations of neural language models to identify suicide-related “like”, “search” and “watch” events. We will then […]
This QI project aims to expand from general medical wards to inpatient psychiatry the use of predictive risk-modeling for violence or restraint, using Natural Language Processing of clinical notes. We will also assess whether NLP paired with generative AI can accurately summarize a wider range of clinical notes relevant to behavioral emergencies
The objective of this project is to leverage Artificial Intelligence (AI) to create COACH: an on-device AI-driven digital navigator system that will support patients’ effective use of Digital Mental Health Technologies. We aim to: 1. Develop a prototype chatbot-based digital navigator; 2. Conduct preliminary evaluation of the system including lab-based usability testing with healthy participants […]
Hundreds of millions of people are already using Large Language Models (LLMs), including for mental health purposes, which has led to inadvertent harms. Critically, people with mental health conditions may be especially vulnerable to such harms. In this project, we will develop the first computational framework to systematically quantify and benchmark the risks that LLMs […]
This project aims to evaluate LLMs’ bias and accuracy in conveying the causes of mental health disorders (e.g., anxiety). We will address the absence of related data and the challenge of annotating data by responsibly collecting human-LLM conversations about mental health advice and working with domain experts to create fact sheets about mental health conditions’ […]
