Augmenting EHR interfaces for enhanced nurse communication and decision making
Abstract
The use of electronic health records (EHRs) in clinical environments provides new opportunities for nurses to integrate data analyses into their practice. While having access to these records has many benefits, the act of recording, retrieving, and analyzing this data can nonetheless introduce communication issues, as navigating and interpreting large amounts of heterogeneous data can be difficult, and conclusions can be hard to validate. In this paper, we describe a series of integrated visual interfaces to help nurses document and reason about patient data and about clinicians' understanding of patient data. The interfaces present the output of a predictive algorithm that makes use of historical EHR data, patient vital signs, and nurse handoff reports in order to classify a patient in terms of their likelihood of experiencing clinical events. Furthermore, the interfaces enable the nurses to quickly explore the original data and to examine other nurses' interpretation of patient activity during previous shifts. We present a series of usage scenarios that introduce our interactive visualization tools in the context of real-world healthcare situations.
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Hunter and Forbes's work suggests a distinct role for AI prediction from traditional CDS: facilitating problem-based report and assessment during handoffs.
“Hunter and Forbes's work56,63,64 suggests a distinct role for AI prediction from traditional CDS: facilitating problem-based report and assessment during handoffs.”
From Potential uses of AI for perioperative nursing handoffs: a qualitative study (2023) · cites this paper as [64]
User-design work for EHR-integrated shift-change handoff support had similar ideas, arriving at a design which blended data and predictive risks.
“User-design work for EHR-integrated shift-change handoff support had similar ideas, arriving at a design which blended data and predictive risks.56,64”
From Potential uses of AI for perioperative nursing handoffs: a qualitative study (2023) · cites this paper as [64]
Several small studies from other nursing contexts have found similar themes.
“Several small studies from other nursing contexts have found similar themes.”
From Potential uses of AI for perioperative nursing handoffs: a qualitative study (2023) · cites this paper as [64]
Forbes and colleagues envisioned a dynamic EHR integrated shift-report summary for nurses including key data, diagnoses, and predicted adverse events.
“Forbes and colleagues56,64 envisioned a dynamic EHR integrated shift-report summary for nurses including key data, diagnoses, and predicted adverse events.”
From Potential uses of AI for perioperative nursing handoffs: a qualitative study (2023) · cites this paper as [64]
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References 19 · Cited by 8
- Authors
- Chetta, A., Carrington, J. M., & Forbes, A. G.
- Affiliations
- University of Illinois Chicago (US)
- University of Arizona (US)
- Venue
- Proceedings of the 2015 Workshop on Visual Analytics in Healthcare
- Publisher
- ACM
- Published
- 2015-10-25
- Pages
- 1-6
- OpenAlex
- W2196146583
- Cited by
- 8
- References
- 19
- Citations by year
- 2023: 1 · 2022: 1 · 2021: 4 · 2018: 1 · 2017: 1
- FWCI
- 1.14
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Workshop on Visual Analytics in Healthcare (VAHC) (publishedVersion)
- Funders
- National Institutes of Health (R01EB020395)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Keywords
- electronic health records, nursing communication, clinical decision-making, interactive visualization, predictive algorithms, clinical event prediction, exploratory data analysis, heterogeneous clinical data, application scenarios
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