 We propose a metric that measures a models ability to potentially augment medical decision-making by reducing uncertainty in specific clinical scenarios. Practically, we envision this metric being used during the early phases of model development i.e., before calculating net benefit for multiclass models in dynamic care environments like critical care, which are becoming increasingly common ... Making machine learning matter to clinicians: model actionability... 