A partnership to provide comprehensive perinatal mental health and parenting support for the first 1,000 days
The Raising Washington Initiative seeks to develop an evidence-based fully integrated perinatal support program that will offer mental health care, parent training and support services for the first 1,000 days of a baby’s life (conception through child’s 2nd birthday) for every high-risk baby born in Washington. This will include creating care pathways informed by the needs of patients and providers, navigators to help guide families through the many care transitions in the perinatal period and accessible information to keep parents and babies healthy.
To learn more this work, please contact Project Manager Lori Ferro, MHA at ljf9@uw.edu.
We are exploring the feasibility of establishing a long-term, residential Therapeutic Community in Washington State for adults living with schizophrenia, schizoaffective disorder, biplolar disorder and other serious mental illnesses. Such a facility would fill a critical gap in our current system, providing a complete spectrum of care for those in our community with chronic mental health conditions. Beyond offering patients and families a safe and therapeutic way to continue their recovery, we would hope to develop this program as a site of research and innovation and a site where we can teach and inspire the next generation of mental health care professionals for our state. Ultimately, we would like to help other communities build programs of their own.
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.
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.
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.
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 and “red-team” stress testing with project confederates.
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.
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’ social and individual determinants. We will then analyze data to assess the extent to which LLMs present social determinants of health (SDOH) versus individual factors and the accuracy of the connections and causations they draw between SDOH, individual factors, and specific disorders. Our study will examine a range of increasingly popular LLMs, such as ChatGPT.
