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

Autoregressive Language Models For Estimating the Entropy of Epic EHR Audit Logs