ArticleJournal of the American Medical Informatics Association : JAMIA2023
Characterizing the macrostructure of electronic health record work using raw audit logs: an unsupervised action embeddings approach.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Predicting Electronic Health Record Usability: Scoping Review of Adoption Models, Metrics, and Future Directions.JMIR human factors · 2026Article
- Hierarchical spatio-temporal graph network for risk prediction.Scientific reports · 2025Article
- Exploring Task Patterns in EHR Workflows Using Action Sequence Embedding and Graph-Based Analysis.Studies in health technology and informatics · 2025Article
- Electronic health record activity changes around new decision support implementation: monitoring using audit logs and topic modeling.JAMIA open · 2025Article
- Emerging Domains for Measuring Health Care Delivery With Electronic Health Record Metadata.Journal of medical Internet research · 2025Review
- Reimagining clinical AI: from clickstreams to clinical insights with EHR use metadata.npj health systems · 2025Review
- Measuring cognitive effort using tabular transformer-based language models of electronic health record-based audit log action sequences.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Harnessing EHR data for health research.Nature medicine · 2024Review
- A Computational Framework to Evaluate Emergency Department Clinician Task Switching in the Electronic Health Record Using Event Logs.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Creating Conversion Factors from EHR Event Log Data: A Comparison of Investigator-Derived and Vendor-Derived Metrics for Primary Care Physicians.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
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 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.