Patient Population: Adults

Brain, Environment, and Alcohol Research (BEAR) Study

This project examines how brain responses to alcohol cues interact with everyday social contexts to shape drinking in young adult heavy drinkers. We pair multimodal neuroimaging (fMRI, EEG) with a 2-week ecological momentary assessment including transdermal alcohol monitoring and photo-based context capture. We test whether neural incentive salience predicts real-life intoxication, how social features (group size, familiarity, gender mix) influence drinking, and how perceived norms mediate these effects. We further assess whether incentive salience moderates context and norm influences. Findings will refine models of alcohol use disorder etiology and inform prevention and intervention strategies by linking precise brain markers with ecologically valid, context-rich assessments.

Evaluation of the VHA Acute Pain Service Expansion Program implementation and impact

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.

Complementary and integrative health stepped care for co-occurring chronic pain and PTSD

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 care approach versus usual care (n=30 per site, N=60 total). Primary clinical outcomes are pain interference and PTSD symptoms at 6-months.

Improving treatment strategies and clinical outcomes in patients with first-episode psychosis and substance use disorders

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 long-acting injectable antipsychotics (LAI) and referrals to outpatient behavioral health are lower for patients diagnosed with first-episode SIP compared to those diagnosed with first-episode psychosis and that (2) patients diagnosed with first-episode SIP will have worse post-discharge outcomes (rehospitalization, ED utilization), in part due to lower use of LAI.

Strengthening financial literacy for people living with serious mental illness

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 gather qualitative and quantitative data from people admitted to the Center for Behavioral Health and Learning, a psychiatric hospital, to understand the need and desire for a financial skills intervention and its role in discharge planning. The assessment will also seek input from family members/caregivers, representative payees/fiduciaries and experts in the community. Ultimately, we hope to create a replicable, standardized intervention that can be evaluated and implemented in inpatient settings and modified as necessary for outpatient settings.

Using Large Language Models to identify video platform interactions indicating suicide risk

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 assess the temporal relationships between suicide-related interaction events and suicidal ideation, behavior and mental health challenges reported by these participants. Building on these analyses, we will proceed to model patterns of interaction, differentiating between user-initiated (e.g. search) and algorithm-prompted (e.g. recommended content without a preceding search) content to characterize the ways in which intentional and algorithmically-driven behavior drive exposure to suicide-related content. In addition, we will develop a prototype of a privacy-preserving risk monitoring tool, which will detect interactions with concerning content and leverage light-touch intervention strategies to mitigate its impact. 

Making generative AI safe for people with mental health conditions

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 present to people with mental health conditions. Our approach will simulate interactions of hundreds of users and LLMs to evaluate safety across a variety of mental health conditions, demographics, and AI failure modes.