  EHR audit logs are a highly granular stream of events that capture clinician
activities, and is a significant area of interest for research in
characterizing clinician workflow on the electronic health record (EHR).
Existing techniques to measure the complexity of workflow through EHR audit
logs (audit logs) involve time- or frequency-based cross-sectional aggregations
that are unable to capture the full complexity of a EHR session. We briefly
evaluate the usage of transformer-based tabular language model (tabular LM) in
measuring the entropy or disorderedness of action sequences within workflow and
release the evaluated models publicly.
