Computerized Assessment of Mental Status (CAMS): Using remote assessment to make mental health resources more accessible to underserved populations
Every year, one in five adults in the U.S. experiences mental illness and over $190 billion in earnings are lost due to the disabling effects of serious mental illness. Members of racial and ethnic minorities face an even greater burden: they are less likely to have access to mental health services, more likely to use emergency rooms for their mental health care, and more likely to receive lower quality care. The overwhelming personal, social, and economic costs associated with mental illness, particularly in underserved communities, call for innovative approaches that address this critical gap in access. Remote assessment of mental health has the potential to transform mental health care delivery, but lack of robust assessment tools presents a major challenge to implementing scalable, cost-effective solutions.
When a person seeks mental health care, the first thing clinicians do is a mental health status interview. Throughout this interview, the clinician looks for signs of mental health or illness by observing their behavior: the language they use, the quality of their voice, and how they move their face and body. Unfortunately, these mental health assessments are 1) expensive in terms of training and administration time, and 2) subjective, meaning that conclusions can vary by clinician and are vulnerable to implicit bias. To address these challenges, we have developed Computerized Assessment of Mental Status (CAMS), an easy-to-use, interactive, cloud-based application that extracts information about people’s behavior from video data in order to objectively assess their mental health status. CAMS has the potential to improve accessibility, precision, and cost-effectiveness of mental health care delivery, whether used for connecting first-time patients to the mental health resources they need, or monitoring people already in treatment.
How it works
In CAMS, participants interact with a virtual assistant, responding to a series of questions and stimuli similar to what is used during in-person mental health assessments (Fig. 1). From these videos of rich social behavior, we extract three types of data: 1) natural language (what the participant says), 2) vocal signals (how they say it), and 3) visual signals (how they move their face and body). Together, these data provide information about a participant’s emotional state and thought process, providing a snapshot of their mental health. When compared to normed data matched on demographic variables, this snapshot can indicate mental health problems and the need for treatment. The more data we have to compare participants’ results to, the more accurate CAMS will be. To ensure that we are building a platform that will serve diverse communities it is critical that data from those populations are adequately represented in the CAMS data.
Fig. 1. Data collection using CAMS. A virtual assistant guides the participant through a series of tasks designed to capture clinically relevant behavior. For example, participants are asked about the last time they felt really happy and what they are most worried about. Participants also watch a series of brief, emotionally evocative videos and answer questions about the videos as shown above. Throughout CAMS, video data of the participant is recorded using a phone’s built-in camera.
The current proposal
Previous efforts to generalize mental health research to diverse communities, especially research using technology, have been largely ineffective due to lack of diverse populations used in the original research. By combining CAMS with the capabilities of the Eureka platform, our goal is to create a tool that can improve access and cost-effectiveness of mental health care for underserved communities. Enrolling a large number of people who are diverse with regards to race, ethnicity, SES, and age, is a priority for three reasons. First, the proposed project will provide a critical first test of the usability of CAMS in diverse populations, enabling us to identify ways CAMS can best serve a wide variety of communities. Second, the project will yield a large dataset that allows us to examine how behavior covaries with symptoms of mental illness and will be the basis for scientific publications that will have immediate impact on the field of mental health. Third, in the long term, the data we will collect in this project will be used in machine learning approaches to ensure that CAMS will be able to accurately assess the mental health status of people from diverse communities.
To meet these goals, we propose to partner with community-based organizations and leverage the Eureka platform to gather video data via smartphones from a large diverse sample (N=2000) of adults using CAMS. Given the large target sample, we intend to recruit outside UCSF patient populations. In this entirely remote study, we would use the Eureka platform for online consenting, remote administration of mental health symptom self-report scales, and remote video data collection. There will be two assessment points at least six months apart during which self-reported symptoms (e.g. depression, psychosis) will be assessed and responses to CAMS will be video recorded. This repeated measures design will allow us to parse between-person and within-person variability in symptoms of mental illness and behavioral responses. On the backend, we will use our suite of behavioral analysis tools (see Fig. 2) to identify behaviors that significantly relate to self-reported mental health symptoms and establish how these relationships may differ between diverse populations. In the short term, these data will improve our understanding of how emotional and cognitive behavior relate to current and future symptoms of mental illness, and in the long term, will allow us to improve CAMS’ ability to bring much-needed mental health resources to diverse communities. At this time, we will focus on English-speaking participants, but we are currently planning a Spanish language version of CAMS.
Fig. 2. Data extracted by CAMS. We analyze three types of data from participant videos to create a snapshot of mental health status.
Our team is led by Josh Woolley, a psychiatrist and Assistant Professor with extensive experience assessing social and emotional behavior in people with mental illness and designing and conducting clinical trials. He also has experience remotely recruiting participants with mental illness for online studies. Dr. Woolley has salary support through a Career Development Award and project funding through 2020. Dr. Bradley is a psychiatrist and VA Research Fellow with experience running clinical trials and recruiting participants with psychosis both within UCSF and across the Bay Area community. Dr. Anderson is a postdoctoral fellow in the UCSF department of psychiatry, and is an expert on the assessment of emotions. He has previously partnered with Bay Area organizations that make outdoors experiences more accessible to underserved communities to study how nature can improve the mental health of at-risk youth. Together, our team has salary support and grant funding to cover participant compensation, project coordination, and cloud-based data storage through December of 2020. We also re-submitted a National Science Foundation (NSF) grant, which received positive reviews, that would potentially fund CAMS for the next 5 years.
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