Characterizing the macrostructure of electronic health record work using raw audit logs: an unsupervised action embeddings approach.

ABSTRACT: Raw audit logs provide a comprehensive record of clinicians' activities on an electronic health record (EHR) and have considerable potential for studying clinician behaviors. However, research using raw audit logs is limited because they lack context for clinical tasks, leading to difficulties in interpretation. We describe a novel unsupervised approach using the comparison and visualization of EHR action embeddings to learn context and structure from raw audit log activities. Using a dataset of 15 767 634 raw audit log actions performed by 88 intern physicians over 6 months of EHR use across inpatient and outpatient settings, we demonstrated that embeddings can be used to learn the situated context for EHR-based work activities, identify discrete clinical workflows, and discern activities

SUBMITTER: Lou SS 

PROVIDER: S-EPMC9933072 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature