Fig. 1: Framework for extracting and analyzing psychosocial risk and SDOH information from transplant evaluation notes.

A Schematic overview of the liver transplant care journey. Decision outcomes shown in purple. B Schematic overview of psychosocial risk and SDOH snapshot creation and analysis pipeline. Clinical notes are processed using LLMs to extract both (i) 23 psychosocial risk and SDOH dimensions describing patient circumstances* and (ii) clinical decisions/outcomes not captured in structured data (e.g., psychosocial risk assessments, transplant recommendations). These extracted elements are combined with structured clinical and demographic data from the EHR to create comprehensive patient snapshots at evaluation. The integrated data enables (i) comparison of psychosocial risk and SDOH factor prevalence across demographic groups, (ii) identification of transition points where specific factors impact care progression, and (iii) decomposition analysis of how psychosocial risk and SDOH patterns and clinical factors explain demographic differences in care access. This approach surfaces both individual-level circumstances and population-level patterns that can guide resource allocation and policy decisions. C Accuracy of GPT4-Turbo-128k vs. ground truth annotations (n = 101) for 28 questions, including 23 psychosocial risk and SDOH-related dimensions. D Demographic composition of the study cohort (n = 3704). E Prevalence of key clinical outcomes, including psychosocial recommendation status (Rec) and liver transplant (LT) listing rates. *Psychosocial risk and SDOH colored by related theme (yellow = ‘Substance Use’; green = ‘Social Support’; blue = ‘Access’, and red = ‘Psychological’).