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As data shift or new data become available, updating clinical machinelearning models may be necessary to maintain or improve performance over time.However, updating a model can introduce compatibility issues when the behaviorof the updated model does not align with user expectations, resulting in pooruser-model team performance. Existing compatibility measures depend on modeldecision thresholds, limiting their applicability in settings where models areused to generate rankings based on estimated risk. To address this limitation,we propose a novel rank-based compatibility measure, $C^R$, and a new lossfunction that aims to optimize discriminative performance while encouraginggood compatibility. Applied to a case study in mortality risk stratificationleveraging data from MIMIC, our approach yields more compatible models whilemaintaining discriminative performance compared to existing model selectiontechniques, with an increase in $C^R$ of $0.019$ ($95\%$ confidence interval:$0.005$, $0.035$). This work provides new tools to analyze and update riskstratification models used in clinical care.

Updating Clinical Risk Stratification Models Using Rank-Based  Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team  Performance