Equal Credit or Disease Priority? Dynamic Credit Routing in Offline Multi-Agent Reinforcement Learning for Comorbid Critical Care

Clinical reinforcement learning (RL) has largely optimized treatment for a single disease, leaving cross-disease coordination in critically ill patients underexplored. Sepsis–acute kidney injury (AKI) exemplifies this challenge, where the same intervention may benefit one disease while worsening the other, and the clinically dominant disease may shift over time. This requires a policy to decide, at each moment, whose priority weight (i.e., Sepsis's or AKI's) should dominate learning, and how their competing treatment preferences are reconciled into a single action. We provide the first offline multi-agent reinforcement learning (MARL) formulation of comorbid care and introduce our model BIFOCAL, which coordinates disease-conditioned value branches through dynamic credit routing: a telescope (future deterioration risk) sets which disease deserves priority, a magnifying glass (present treatment conflict) determines when that priority takes effect, and their coupling decides which disease dominates learning and reconciles preferences into a single action. Across 4 diverse ICU datasets from U.S. and China comprising 87,566 trajectories with 1,285,977 decision points, BIFOCAL consistently achieves higher estimated policy value than clinician behavior. On MIMIC-IV, it further outperforms baselines in the comorbid cohort and two disease-specific methods in disease-only cohorts under 5 off-policy and 2 action-consistency metrics. Its lead on single-disease cohorts suggests that BIFOCAL's utility extends beyond comorbidity to single-disease care. Furthermore, a blinded physician evaluation of 30 comorbid cases over six dimensions supports its clinical potential. These results establish BIFOCAL as the first large-scale offline MARL framework for cross-disease treatment coordination in critical care.