Assessing the effect of race on machine learning models predicting hospital admissions from emergency department

Machine learning (ML) models are increasingly being developed to support healthcare delivery. However, concerns remain about their potential to perpetuate existing biases rooted in the data used to develop them. We aim to assess the impact of using race in predicting hospital admission probabilities for patients visiting the emergency department (ED).

Data from the MIMIC-IV ED dataset were used to train2 ML models predicting hospital admission: one included race; the other did not. Differences in predicted admission probabilities were evaluated across racial groups under multiple validation conditions.

Including race as a model input was associated with meaningful differences in predicted admission probabilities for White (3.2%), Black (-1.5%), and Hispanic (-3.0%) patients, while minimal differences were observed for Asian (0.2%) and Other (0.5%) patients. These differences were associated with large Cohen's d effect sizes in the baseline model for White (d = 1.00), Black (d = -1.23), and Hispanic (d = -1.35) patients. After balancing racial group prevalence, the effects persisted for White (1.22%; d = 1.22) and Hispanic (-1.00%; d = -1.00) patients.

These findings suggest that race is associated with differences in ML-based hospital admission predictions for ED patients, underscoring the need for caution when incorporating race into clinical prediction models and the importance of rigorous bias assessment.

As race was associated with predictions, there is a crucial need to address underlying social factors and the use of broader, more equitable clinical data for ML model training.