Evaluating the Impact of Social Determinants on Health Prediction in the Intensive Care Unit
This work addresses the problem of improving fairness and bias audits in health prediction models for ICU patients, though it is incremental as it builds on existing EHR data and SDOH integration methods.
The study linked electronic health records (EIMR) to social determinants of health (SDOH) features to evaluate their impact on health prediction tasks in the ICU, finding that community-level SDOH features did not improve overall model performance but enhanced fairness for specific subpopulations and aided in auditing algorithmic biases.
Social determinants of health (SDOH) -- the conditions in which people live, grow, and age -- play a crucial role in a person's health and well-being. There is a large, compelling body of evidence in population health studies showing that a wide range of SDOH is strongly correlated with health outcomes. Yet, a majority of the risk prediction models based on electronic health records (EHR) do not incorporate a comprehensive set of SDOH features as they are often noisy or simply unavailable. Our work links a publicly available EHR database, MIMIC-IV, to well-documented SDOH features. We investigate the impact of such features on common EHR prediction tasks across different patient populations. We find that community-level SDOH features do not improve model performance for a general patient population, but can improve data-limited model fairness for specific subpopulations. We also demonstrate that SDOH features are vital for conducting thorough audits of algorithmic biases beyond protective attributes. We hope the new integrated EHR-SDOH database will enable studies on the relationship between community health and individual outcomes and provide new benchmarks to study algorithmic biases beyond race, gender, and age.