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

Expression of concern Information storage and retrieval Standardized nursing terminology

Journal

International journal of medical informatics
ISSN: 1872-8243
Titre abrégé: Int J Med Inform
Pays: Ireland
ID NLM: 9711057

Informations de publication

Date de publication:
01 2020
Historique:
received: 17 04 2019
revised: 08 09 2019
accepted: 17 10 2019
pubmed: 11 11 2019
medline: 5 3 2020
entrez: 11 11 2019
Statut: ppublish

Résumé

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. 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. 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). 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.

Identifiants

pubmed: 31707264
pii: S1386-5056(19)30421-6
doi: 10.1016/j.ijmedinf.2019.104016
pmc: PMC6957124
mid: NIHMS1542497
pii:
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

104016

Subventions

Organisme : NINR NIH HHS
ID : R01 NR016941
Pays : United States
Organisme : NINR NIH HHS
ID : T32 NR007969
Pays : United States

Informations de copyright

Copyright © 2019 Elsevier B.V. All rights reserved.

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Auteurs

Min-Jeoung Kang (MJ)

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.

Patricia C Dykes (PC)

Division of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Boston, USA; Harvard Medical School, Boston, USA.

Tom Z Korach (TZ)

Division of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Boston, USA; Harvard Medical School, Boston, USA.

Li Zhou (L)

Division of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Boston, USA; Harvard Medical School, Boston, USA.

Kumiko O Schnock (KO)

Division of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Boston, USA; Harvard Medical School, Boston, USA.

Jennifer Thate (J)

Siena College, Albany, USA.

Kimberly Whalen (K)

Massachusetts General Hospital, Boston, USA.

Haomiao Jia (H)

Columbia University, Department of Biostatistics, New York, USA; Columbia University, School of Nursing, New York, USA.

Jessica Schwartz (J)

Columbia University, School of Nursing, New York, USA.

Jose P Garcia (JP)

Division of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Boston, USA; Harvard Medical School, Boston, USA.

Christopher Knaplund (C)

Columbia University, School of Nursing, New York, USA.

Kenrick D Cato (KD)

Columbia University, School of Nursing, New York, USA.

Sarah Collins Rossetti (SC)

Columbia University, School of Nursing, New York, USA; Columbia University, Department of Biomedical Informatics, New York, USA.

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Classifications MeSH