Prediction of emergency department resource requirements during triage: An application of current natural language processing techniques Article Swipe
Nicholas W. Sterling
,
Felix Brann
,
Rachel E. Patzer
,
Mengyu Di
,
Megan Koebbe
,
Madalyn Burke
,
Justin D. Schrager
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1002/emp2.12253
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1002/emp2.12253
OBJECTIVE: Accurate triage in the emergency department (ED) is critical for medical safety and operational efficiency. We aimed to predict the number of future required ED resources, as defined by the Emergency Severity Index (ESI) triage protocol, using natural language processing of nursing triage notes. METHODS: We constructed a retrospective cohort of all 265,572 consecutive ED encounters from 2015 to 2016 from 3 separate clinically heterogeneous academically affiliated EDs. We excluded encounters missing relevant information, leaving 226,317 encounters. We calculated the number of resources used by patients in the ED retrospectively and based outcome categories on criteria defined in the ESI algorithm: 0 (30,604 encounters), 1 (49,315 encounters), and 2 or more (146,398 encounters). A neural network model was trained on a training subset to predict the number of resources using triage notes and clinical variables at triage. Model performance was evaluated using the test subset and was compared with human ratings. RESULTS: Overall model accuracy and macro F1 score for number of resources were 66.5% and 0.601, respectively. The model had similar macro F1 (0.589 vs 0.592) and overall accuracy (65.9% vs 69.0%) compared to human raters. Model predictions had slightly higher F1 scores and accuracy for 0 resources and were less accurate for 2 or more resources. CONCLUSIONS: Machine learning of nursing triage notes, combined with clinical data available at ED presentation, can be used to predict the number of required future ED resources. These findings suggest that machine learning may be a valuable adjunct tool in the initial triage of ED patients.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/emp2.12253
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/emp2.12253
- OA Status
- gold
- Cited By
- 20
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3096339397
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3096339397Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1002/emp2.12253Digital Object Identifier
- Title
-
Prediction of emergency department resource requirements during triage: An application of current natural language processing techniquesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-10-14Full publication date if available
- Authors
-
Nicholas W. Sterling, Felix Brann, Rachel E. Patzer, Mengyu Di, Megan Koebbe, Madalyn Burke, Justin D. SchragerList of authors in order
- Landing page
-
https://doi.org/10.1002/emp2.12253Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/emp2.12253Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/emp2.12253Direct OA link when available
- Concepts
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Triage, Emergency department, Artificial intelligence, Retrospective cohort study, Machine learning, Macro, Medicine, Computer science, Medical emergency, Emergency nursing, Nursing, Pathology, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
20Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 3, 2023: 7, 2022: 5, 2020: 1Per-year citation counts (last 5 years)
- References (count)
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20Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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