PROJECT SUMMARY Agitation is defined as excessive psychomotor activity leading to aggressive and violent behavior in patients. Those presenting with agitation in the emergency setting represent the most marginalized populations. Coercive measures like physical restraints are currently used routinely on agitated individuals, but are associated with physical trauma, respiratory depression, and death. Recent studies have shown disproportionate use of physical restraint on Black patients, those who are homeless, and those with public or no insurance. Identification of specific interpersonal and structural factors that affect heuristics and decision- making of healthcare workers regarding restraint use is needed to mitigate systemic bias and discrimination against these marginalized patients. However, current research is limited to analyzing structured quantitative data elements, while narrative text better reflects sociocultural contexts, interpersonal interactions, and clinician thought processes. Natural language processing is an informatics discipline that can parse free text within clinical notes into quantifiable variables on a large scale that can be combined with mediation analysis to uncover factors leading to disparities in restraint use. A complementary tool is cognitive task analysis, which uses qualitative methods to understand how mediators of bias affect clinical decision-making at the bedside. Our overall objective is to use the combination of these innovative analytical methods to overcome deficiencies of standard health service research methods in identifying individual, interpersonal, institutional, and systems factors leading to bias in physical restraint use. In Aim 1, we will use natural language processing and mediation analysis on a large database of emergency department clinical narrative notes across our regional healthcare system to extract and identify candidate variables that lead minority populations to increased risk of physical restraint. This will allow us to verify and test potential factors within our newly derived conceptual model of bias during restraint use that predict discriminatory practices. In Aim 2, we will use cognitive task analyses through qualitative interviews and video-informed focus groups with emergency healthcare workers to characterize drivers and cues that influence decision-making and heuristics against minority populations. This will complement the results from Aim 1 by providing explanatory models for how interpersonal and structural factors that lead to bias are manifested at the bedside. This proposed work will make a positive contribution to minority and health disparities research in the emergency setting by identifying specific interpersonal and structural factors mediating bias and discrimination against minority and socioeconomically disadvantaged individuals with behavioral emergencies. Our study is exploratory and novel as it combines innovative and multidisciplinary approaches from tw...