Related Experiment Video
Updated: Jan 5, 2026
14:27
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
16.1K
Using the PARAFAC2 tensor factorization on EHR audit data to understand PCP desktop work
Ioakeim Perros1, Xiaowei Yan2, J B Jones2
1Georgia Institute of Technology, Atlanta, GA, United States.
Journal of Biomedical Informatics
|October 19, 2019
Summary
Electronic Health Record audit logs can reveal primary care provider (PCP) workflow patterns. This study used tensor factorization to automatically identify common clinical tasks and variations in how PCPs complete them.
Area of Science:
- Health Informatics
- Computer Science
- Clinical Workflow Analysis
Background:
- Electronic Health Record (EHR) audit logs are crucial for privacy and security.
- These logs also offer potential insights into clinical desktop workflows.
Purpose of the Study:
- To determine if EHR audit log data can be processed to derive primary care provider (PCP) workflow measures.
- To investigate the utility of PARAFAC2 tensor factorization for identifying PCP tasks and workflow variations.
Main Methods:
- Analyzed 578,394 time-stamped records from 876 PCPs across 17,455 ambulatory care encounters.
- Applied PARAFAC2 tensor factorization to identify clusters of audit log records representing PCP tasks without ground-truth labels.
- Interpreted results using PARAFAC2 factors for task definitions and task frequency per encounter.
Main Results:
- PARAFAC2 automatically identified 4 common clinical encounter tasks: medications access, notes access, order entry access, and diagnosis modification.
- Discovered significant variation in task completion, including 9 distinct variants for notes access, explaining 77% of data variation.
- Mapped discovered variants to known workflows and identified distinct PCP user groups based on notes access methods.
Conclusions:
- EHR audit log data can be efficiently processed to generate higher-level features representing time-stamped PCP tasks.
- This approach enables objective measurement and understanding of clinical workflows from audit data.
