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384 A systematic approach to understanding nursing documentation tasks
Published online by Cambridge University Press: 11 April 2025
Abstract
Objectives/Goals: Healthcare organizations must track electronic health record (EHR) activity at the user level, including logons, accessed records, and viewed or entered documentation. There is little standardization in EHR audit logs and nurse workload has not been explored using these data. In this project, we characterized nurse actions from EHR audit logs. Methods/Study Population: We performed an analysis of EHR audit log data collected from 8,149 nurses over 5 years at University of Utah Health. We preprocessed nursing-centric EHR audit logs from the Epic EHR by cleaning and preparing the data for analysis. We calculated basic statistics for the variables labeled user_id (nurse) and metric_id (action). We reviewed the actions used by nurses and categories the actions as navigation, view, and entry. To capture the clinical context of the actions, two nurses categorized each action. A third nurse resolved any discrepancies. Results/Anticipated Results: We found that of the 4,419 available metrics, nurses used 1,461 unique metrics during the timeframe. The actions most used by nurses were 1) report with patient data viewed, 2) inpatient system list, and 3) storyboard viewed. Most of the metrics were categorized as navigation. The number of nurses interacting with the EHR increased each year and on average, we found that 1000 unique metrics were used by each nurse user in a 24-hour period. The expected outcome is a set of actions that can be mapped to higher level nursing interventions and in the future contribute to models for nursing workload measurement. Discussion/Significance of Impact: We found great value in using EHR audit logs to provide insights into nursing actions. Information gleaned can benefit organizations that are crafting interventions to decrease workload. Ultimately, the goal is to ensure that nurses have an appropriate workload allowing for safe and high-quality patient care while maintaining their well-being.
- Type
- Informatics, AI and Data Science
- Information
- Creative Commons
- This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
- Copyright
- © The Author(s), 2025. The Association for Clinical and Translational Science