Evaluating the predictive ability of natural language processing in identifying tertiary/quaternary cases in prioritization workflows for interhospital transfer Article Swipe
Timothy Lee
,
Paul J. Lukac
,
Sitaram Vangala
,
Kamran Kowsari
,
Vu Vu
,
Spencer Fogelman
,
Michael A. Pfeffer
,
Douglas S. Bell
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/jamiaopen/ooad069
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/jamiaopen/ooad069
Objectives Tertiary and quaternary (TQ) care refers to complex cases requiring highly specialized health services. Our study aimed to compare the ability of a natural language processing (NLP) model to an existing human workflow in predictively identifying TQ cases for transfer requests to an academic health center. Materials and methods Data on interhospital transfers were queried from the electronic health record for the 6-month period from July 1, 2020 to December 31, 2020. The NLP model was allowed to generate predictions on the same cases as the human predictive workflow during the study period. These predictions were then retrospectively compared to the true TQ outcomes. Results There were 1895 transfer cases labeled by both the human predictive workflow and the NLP model, all of which had retrospective confirmation of the true TQ label. The NLP model receiver operating characteristic curve had an area under the curve of 0.91. Using a model probability threshold of ≥0.3 to be considered TQ positive, accuracy was 81.5% for the NLP model versus 80.3% for the human predictions (P = .198) while sensitivity was 83.6% versus 67.7% (P<.001). Discussion The NLP model was as accurate as the human workflow but significantly more sensitive. This translated to 15.9% more TQ cases identified by the NLP model. Conclusion Integrating an NLP model into existing workflows as automated decision support could translate to more TQ cases identified at the onset of the transfer process.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/jamiaopen/ooad069
- https://academic.oup.com/jamiaopen/article-pdf/6/3/ooad069/51142352/ooad069.pdf
- OA Status
- gold
- Cited By
- 3
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385933780
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385933780Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/jamiaopen/ooad069Digital Object Identifier
- Title
-
Evaluating the predictive ability of natural language processing in identifying tertiary/quaternary cases in prioritization workflows for interhospital transferWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-04Full publication date if available
- Authors
-
Timothy Lee, Paul J. Lukac, Sitaram Vangala, Kamran Kowsari, Vu Vu, Spencer Fogelman, Michael A. Pfeffer, Douglas S. BellList of authors in order
- Landing page
-
https://doi.org/10.1093/jamiaopen/ooad069Publisher landing page
- PDF URL
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https://academic.oup.com/jamiaopen/article-pdf/6/3/ooad069/51142352/ooad069.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://academic.oup.com/jamiaopen/article-pdf/6/3/ooad069/51142352/ooad069.pdfDirect OA link when available
- Concepts
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Workflow, Prioritization, Artificial intelligence, Computer science, Natural language processing, Receiver operating characteristic, Electronic health record, Transfer of learning, Database, Machine learning, Tertiary care, Health care, Medicine, Emergency medicine, Management science, Economic growth, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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