The limits of debiased clinical language models for cross-hospital generalization.

As clinical language models are increasingly deployed across healthcare institutions, ensuring equitable predictions for all demographic groups becomes critical. While fairness interventions have shown promise in medical AI, a fundamental question remains underexplored: do models that achieve fairness at one hospital maintain that fairness when deployed elsewhere? We investigated this question through extensive cross-database experiments on ICU mortality prediction, fine-tuning ClinicalBERT on MIMIC-IV and evaluating on eICU-CRD. Our findings reveal that debiasing can backfire: fairness-only training achieves the best in-domain equalized odds gap (0.039) but the worst out-of-domain fairness (0.182), representing a striking 4.7× degradation that completely reverses the intended intervention. We find evidence consistent with a domain leakage mechanism, whereby hospital-specific documentation patterns encoded in learned representations can interact unpredictably with demographic signals across sites, such that in-domain fairness constraints may fail or even reverse when deployed out-of-domain. Crucially, we find that domain leakage, not demographic leakage, is most strongly associated with fairness transfer failures (Pearson [Formula: see text]). To address this challenge, we propose a leakage-aware training framework combining domain confusion, group robustness, and equalized odds penalties, along with MDLS (Minimum-Domain-Leakage Selection), a model selection criterion requiring no out-of-domain labels. Our method reduces the out-of-domain equalized odds gap to 0.064 while improving AUROC from 0.741 to 0.802. These results demonstrate that debiasing without domain awareness can produce models that are locally fair yet globally unfair, underscoring the necessity of domain-aware auditing before cross-institutional deployment of clinical NLP systems.