EAGER: Curating and representing mental health data to support therapists in personalized care

University Of California-Irvine, Irvine CA

Investigators

Abstract

This research addresses the growing need to support mental health therapists in managing their workload and client support through technology. Every year, 20% of the US adult population experiences depression and anxiety, these numbers increased during COVID-19 to over 40% of the population. As practices of mental health providers have moved online during COVID-19 through telehealth, providers took on more care responsibilities related to clients. A key aspect of mental health treatment is managing care outside of therapy, which is achieved through personalized care plans. This relies on the therapist developing deep understanding of the client’s personal data and context, which is cognitively challenging, and difficult to achieve due to limited tools to support therapists’ data gathering. The proposed research investigates the role of visual curation in supporting reflection and tailoring of care plans. This research contributes novel representations of care plans and client data for supporting reflection. It investigates how visual curation and synthesis, data organization, and annotation can support therapists in representing subjective goals, behaviors, and thoughts over time, to gain insights and identify future plans of action. The research has the potential to make clinical care more effective by supporting therapists in better understanding their clients and managing client care, providing more tailored care, and building stronger relationships with clients. This is expected to help therapists better manage their caseloads, maintain client engagement, and provide successful care. By supporting therapists, it has the potential to enable citizens at large to receive better mental health care, addressing the immediate mental health crisis and the nation’s long-term needs. The exploratory research grant will contribute to the fields of human-computer interaction and psychology by investigating new techniques for how technology can facilitate therapists in capturing, representing, and comprehending evolving, multifaceted, and complex goals and experiences related to mental health. The research involves two phases to develop and evaluate approaches for personalizing care through reflection on visual data. In Phase 1, the research team establishes visual representations beneficial for reflection and future planning in mental health. This phase outlines data representation techniques for mental health data and establishes design principles for visual data curation of multifaceted, subjective, and difficult to quantify data. In Phase 2, the research team develops and evaluates a prototype for visual curation and representation of longitudinal mental health data to support reflection. This phase establishes initial contributions of approaching therapy data curation using visual representations of mental health data, which include longitudinal information about client multiple goals, behaviors, thoughts, and progress, to support clients and therapists in reflective activities, and inform personalization of mental health care plans. This exploratory research contributes design guidelines and frameworks addressing how visual forms can be used to represent psychological and environmental client data and support mental health therapists reflecting on the data while providing personalized care. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.