Use of machine learning to transform complex standardized nursing care plan data into meaningful research variables: a palliative care exemplar. (27th September 2021)

• Record Type: Journal Article
• Authors: Macieira, Tamara G R; Yao, Yingwei; Keenan, Gail M
• Is Part Of: Journal of the American Medical Informatics Association. Volume 28:Number 12(2021)
• Page Start: 2695
• Page End: 2701
• Publication Date: 2021-09-27
• Subjects: nursing -- nursing informatics -- electronic health records -- standardized nursing terminology -- machine learning; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocab205
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: The aim of this article was to describe a novel methodology for transforming complex nursing care plan data into meaningful variables to assess the impact of nursing care. We extracted standardized care plan data for older adults from the electronic health records of 4 hospitals. We created a palliative care framework with 8 categories. A subset of the data was manually classified under the framework, which was then used to train random forest machine learning algorithms that performed automated classification. Two expert raters achieved a 78% agreement rate. Random forest classifiers trained using the expert consensus achieved accuracy (agreement with consensus) between 77% and 89%. The best classifier was utilized for the automated classification of the remaining data. Utilizing machine learning reduces the cost of transforming raw data into representative constructs that can be used in research and practice to understand the essence of nursing specialty care, such as palliative care.