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’ […]
This project aims to develop a clinical scaffolding system to enhance telemental health care by providing real-time coaching and actionable suggestions during video-based sessions. Modeled after live supervision methodologies, it supports clinicians by identifying intervention targets and offering text-based coaching prompts to guide care. Unlike automated chatbots, this approach enables clinicians to adapt suggestions to […]
Alcohol use disorder (AUD) frequently results in serious illness, injuries, and hospitalizations. Surviving illness or injuries related to alcohol use can motivate behavior change that could be harnessed through treatment engagement for AUD in the hospital; however, in general hospital settings, patients are rarely presented with more than a piece of paper with phone numbers […]
The Canoe Journey study is an exploratory study aimed at examining the acceptability and fit of motivational interviewing (MI) and dialectical behavioral therapy (DBT) among American Indian and Alaska Native youth and young adult Canoe Journey participants. The team will develop a list of MI and DBT approaches in collaboration with Canoe Journey partners, and […]
