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.

翻译：电子健康记录（EHR）审计日志是一种高度细粒度的事件流，能够捕捉临床医生的活动轨迹，是研究临床医生在电子健康记录系统中工作流程的重要领域。现有通过EHR审计日志（以下简称审计日志）衡量工作流复杂度的技术主要依赖于基于时间或频率的横截面聚合方法，这些方法无法全面捕捉EHR会话的完整复杂度。本文初步评估了基于Transformer的表格语言模型（tabular LM）在测量工作流中行为序列熵值（即无序程度）方面的应用，并公开了评估后的模型。