Min-Jeoung Kang1, Patricia C Dykes2, Tom Z Korach2, Li Zhou2, Kumiko O Schnock2, Jennifer Thate3, Kimberly Whalen4, Haomiao Jia5, Jessica Schwartz6, Jose P Garcia2, Christopher Knaplund6, Kenrick D Cato6, Sarah Collins Rossetti7. 1. Division of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Boston, USA; Harvard Medical School, Boston, USA. Electronic address: mkang6@bwh.harvard.edu. 2. Division of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Boston, USA; Harvard Medical School, Boston, USA. 3. Siena College, Albany, USA. 4. Massachusetts General Hospital, Boston, USA. 5. Columbia University, Department of Biostatistics, New York, USA; Columbia University, School of Nursing, New York, USA. 6. Columbia University, School of Nursing, New York, USA. 7. Columbia University, School of Nursing, New York, USA; Columbia University, Department of Biomedical Informatics, New York, USA.
Abstract
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.
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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