Automated extraction of medical decisions from clinical notes is a critical step to constructing more granular patient health trajectories than what is currently obtainable from structured healthcare data. Here we present a system designed for the MedExACT shared task that employs an ensemble of BERT-based classifiers to account for demographic diversity when extracting mentions of medical decisions from MIMIC-III discharge summaries. A simple voting strategy combined with architectural diversity is demonstrated to work best when training data is limited.