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A Scalable and Extensible Logical Data Model of Electronic Health Record Audit Logs for Temporal Data Mining
Victoria L Tiase1, Katherine A Sward1,2, Julio C Facelli1
1Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States.
JMIR Nursing
|June 24, 2024
Summary
A new logical data model, RNteract, can analyze nurse electronic health record (EHR) interactions from audit logs. This approach quantifies nursing workload to help reduce nurse burnout and improve patient safety.
Area of Science:
- Health Informatics
- Nursing Informatics
- Data Science
Background:
- Increased workload, particularly electronic health record (EHR) documentation, is a primary driver of nurse burnout, negatively impacting patient safety and satisfaction.
- Traditional workload analysis methods are often administrative (e.g., nurse-patient ratios) or subjective snapshots (e.g., time-motion studies), failing to capture the dynamic nature of nursing care.
- Examining EHR audit logs offers a scalable and unobtrusive method to quantify nursing workload, provided the complex data is structured for advanced analysis.
Purpose of the Study:
- To conceptualize a logical data model for analyzing nurse-EHR interactions using EHR audit log data.
- To facilitate the development of temporal machine learning (ML) models for understanding nursing workload patterns.
- To create a foundation for data-driven interventions aimed at mitigating nurse burnout.
Main Methods:
- Conducted a preliminary review of EHR audit logs to identify nursing-specific data points.
- Formulated a logical data model, incorporating literature and prior experience with temporal biomedical data patterns.
- Designed the model to describe nurse-EHR interactions, influencing characteristics, and workload outcomes in a scalable and extensible manner.
Main Results:
- Introduced RNteract, a logical data model structured from EHR audit log data relevant to nursing workload.
- Conceptually demonstrated RNteract's capability to support temporal unsupervised ML and advanced AI predictive modeling.
- Highlighted the model's potential for uncovering complex temporal patterns in nurse-EHR interactions.
Conclusions:
- The RNteract logical data model is adaptable for various AI-based systems and generalizable across different EHR systems and healthcare settings.
- Quantitatively analyzing temporal patterns of nurse-EHR interactions is crucial for developing targeted interventions.
- This approach provides a foundational step towards addressing nurse burnout and improving the nursing documentation workload.
Keywords:
AIEHRMLalgorithmartificial intelligenceburnoutdata modelingdata setelectronic health recordmachine learningnursenursingpractical modelpredictive analyticspredictive modelprofessionaltemporal machine learningMore Related Videos
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