A large language model-based approach to quantifying the effects of social determinants in liver transplant decisions
Journal:
arXiv
Published Date:
Dec 10, 2024
Abstract
Patient life circumstances, including social determinants of health (SDOH),
shape both health outcomes and care access, contributing to persistent
disparities across gender, race, and socioeconomic status. Liver
transplantation exemplifies these challenges, requiring complex eligibility and
allocation decisions where SDOH directly influence patient evaluation. We
developed an artificial intelligence (AI)-driven framework to analyze how
broadly defined SDOH -- encompassing both traditional social determinants and
transplantation-related psychosocial factors -- influence patient care
trajectories. Using large language models, we extracted 23 SDOH factors related
to patient eligibility for liver transplantation from psychosocial evaluation
notes. These SDOH ``snapshots'' significantly improve prediction of patient
progression through transplantation evaluation stages and help explain liver
transplantation decisions including the recommendation based on psychosocial
evaluation and the listing of a patient for a liver transplantation. Our
analysis helps identify patterns of SDOH prevalence across demographics that
help explain racial disparities in liver transplantation decisions. We
highlight specific unmet patient needs, which, if addressed, could improve the
equity and efficacy of transplant care. While developed for liver
transplantation, this systematic approach to analyzing previously unstructured
information about patient circumstances and clinical decision-making could
inform understanding of care decisions and disparities across various medical
domains.
