A large language model-based approach to quantifying the effects of social determinants in liver transplant decisions

TL;DR

This paper presents an AI framework using large language models to analyze social determinants of health from clinical notes, improving prediction of liver transplant decisions and revealing disparities across demographics.

Contribution

Introduces a novel LLM-based method to extract and analyze SDOH factors from unstructured clinical notes, enhancing understanding of transplant decision influences.

Findings

SDOH factors extracted from notes improve prediction accuracy.

Patterns of SDOH prevalence explain racial disparities.

Identifies unmet patient needs to improve transplant equity.

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.