Autoregressive Language Models For Estimating the Entropy of Epic EHR Audit Logs
This addresses the need for better characterization of clinician workflow in EHR systems, though it appears incremental as it applies existing methods to a new data type.
The paper tackled the problem of measuring workflow complexity in EHR audit logs by using a transformer-based tabular language model to estimate entropy, releasing the evaluated models publicly.
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.