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.
@article{arxiv.2311.06401,
title = {Autoregressive Language Models For Estimating the Entropy of Epic EHR Audit Logs},
author = {Benjamin C. Warner and Thomas Kannampallil and Seunghwan Kim},
journal= {arXiv preprint arXiv:2311.06401},
year = {2023}
}
Comments
Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2023, December 10th, 2023, New Orleans, United States, 10 pages