TY - JOUR
T1 - Characterizing the macrostructure of electronic health record work using raw audit logs
T2 - An unsupervised action embeddings approach
AU - Lou, Sunny
AU - Liu, Hanyang
AU - Harford, Derek
AU - Lu, Chenyang
AU - Kannampallil, Thomas
N1 - Publisher Copyright:
© 2022 The Author(s). Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved.
PY - 2023/3/1
Y1 - 2023/3/1
N2 - 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 typically performed across diverse contexts. Our approach represents an important methodological advance in raw audit log research, facilitating the future development of metrics and predictive models to measure clinician behaviors at the macroscale.
AB - 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 typically performed across diverse contexts. Our approach represents an important methodological advance in raw audit log research, facilitating the future development of metrics and predictive models to measure clinician behaviors at the macroscale.
KW - clinical workflow
KW - raw audit logs
KW - unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=85148249816&partnerID=8YFLogxK
U2 - 10.1093/jamia/ocac239
DO - 10.1093/jamia/ocac239
M3 - Article
C2 - 36478460
AN - SCOPUS:85148249816
SN - 1067-5027
VL - 30
SP - 539
EP - 544
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - 3
ER -