Identifying nurses’ concern concepts about patient deterioration using a standard nursing terminology
Open accessAbstract
Objectives Nurse concerns documented in nursing notes are important predictors of patient risk of deterioration. Using a standard nursing terminology and inputs from subject-matter experts (SMEs), we aimed to identify and define nurse concern concepts and terms about patient deterioration, which can be used to support subsequent automated tasks, such as natural language processing and risk predication. Methods Group consensus meetings with nurse SMEs were held to identify nursing concerns by grading Clinical Care Classification (CCC) system concepts based on clinical knowledge. Next, a fundamental lexicon was built placing selected CCC concepts into a framework of entities and seed terms to extend CCC granularity. Results A total of 29 CCC concepts were selected as reflecting nurse concerns. From these, 111 entities and 586 seed terms were generated into a fundamental lexicon. Nursing concern concepts differed across settings (intensive care units versus non-intensive care units) and unit types (medicine versus surgery units). Conclusions The CCC concepts were useful for representing nursing concern as they encompass a nursing-centric conceptual framework and are practical in lexicon construction. It enabled the codification of nursing concerns for deteriorating patients at a standardized conceptual level. The boundary of selected CCC concepts and lexicons were determined by the SMEs. The fundamental lexicon offers more granular terms that can be identified and processed in an automated fashion.
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CONCERN leverages evidence that nurses alter their documentation behavior or use selected language in their narrative notes when they are concerned about a patient's changing clinical state.
“As such, it leverages evidence that nurses alter their documentation behavior or use selected language in their narrative notes when they are concerned about a patient’s changing clinical state [21-24].”
From Factors influencing clinician trust in predictive clinical decision support systems for in-hospital deterioration: qualitative descriptive study (2022) · cites this paper as [21]
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Schwartz, J. M., George, M., Rossetti, S. C., Dykes, P. C., Minshall, S. R., Lucas, E., & Cato, K. D. (2022) · JMIR Human Factors · doi:10.2196/33960 · Cited by 88 · see citing works
“As such, it leverages evidence that nurses alter their documentation behavior or use selected language in their narrative notes when they are concerned about a patient’s changing clinical state [21-24].”
BackgroundSelf-citationRossetti, S. C., Knaplund, C., Albers, D., Dykes, P. C., Kang, M. J., Korach, T. Z., Zhou, L., Schnock, K., Garcia, J., Schwartz, J., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab006 · Cited by 42 · see citing works
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- Authors
- Kang, M.-J., Dykes, P. C., Korach, T. Z., Zhou, L., Schnock, K. O., Thate, J., Whalen, K., Jia, H., Schwartz, J., Garcia, J. P., Knaplund, C., Cato, K. D., & Rossetti, S. C.
- Affiliations
- Brigham and Women's Hospital (US)
- Siena College (US)
- Massachusetts General Hospital (US)
- Columbia University (US)
- Corresponding author
- Kang, Min-Jeoung
- ORCID
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 133
- Pages
- 104016
- ISSN
- 1386-5056 (ISSN-L)
- PMC
- PMC6957124
- OpenAlex
- W2982496000
- Cited by
- 24
- References
- 0
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 6 · 2023: 1 · 2022: 5 · 2021: 3
- FWCI
- 5.47
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- International Journal of Medical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Nursing Research
- Topics
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- clinical deterioration, nurses' concerns, nursing terminology, nursing notes, lexicon construction, intensive care unit, risk prediction, natural language processing
- MeSH terms
- Standardized Nursing Terminology, Humans, Intensive Care Units, Natural Language Processing, Nurses
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