4.7 Article

Identifying nurses' concern concepts about patient deterioration using a standard nursing terminology

Journal

Publisher

ELSEVIER IRELAND LTD
DOI: 10.1016/j.ijmedinf.2019.104016

Keywords

Standardized nursing terminology; Expression of concern; Information storage and retrieval

Funding

  1. NINR [1R01NR016941-01]
  2. National Institute of Nursing Research Reducing Health Disparities Through Informatics (RHeaDI) [T32NR007969]
  3. CRICO/Risk Managment Foundation of the Harvard Medical Institues: Resilience in Clinical Deterioration Survival. Learning from different Outcomes in Critical and Acute Care

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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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