Tamara G R Macieira1, Tania C M Chianca2, Madison B Smith1, Yingwei Yao3, Jiang Bian4, Diana J Wilkie3, Karen Dunn Lopez5, Gail M Keenan6. 1. College of Nursing, University of Florida, Gainesville, Florida, USA. 2. Department of Basic Nursing, School of Nursing, Federal University of Minas Gerais, Belo Horizonte, Brazil. 3. Department of Biobehavioral Nursing Science, College of Nursing, University of Florida, Gainesville, Florida, USA. 4. Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA. 5. Biomedical and Health Information Science, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, Illinois, USA. 6. Department of Family, Community and Health Systems Science, College of Nursing, University of Florida, Gainesville, Florida, USA.
Abstract
OBJECTIVE: The study sought to present the findings of a systematic review of studies involving secondary analyses of data coded with standardized nursing terminologies (SNTs) retrieved from electronic health records (EHRs). MATERIALS AND METHODS: We identified studies that performed secondary analysis of SNT-coded nursing EHR data from PubMed, CINAHL, and Google Scholar. We screened 2570 unique records and identified 44 articles of interest. We extracted research questions, nursing terminologies, sample characteristics, variables, and statistical techniques used from these articles. An adapted STROBE (Strengthening The Reporting of OBservational Studies in Epidemiology) Statement checklist for observational studies was used for reproducibility assessment. RESULTS: Forty-four articles were identified. Their study foci were grouped into 3 categories: (1) potential uses of SNT-coded nursing data or challenges associated with this type of data (feasibility of standardizing nursing data), (2) analysis of SNT-coded nursing data to describe the characteristics of nursing care (characterization of nursing care), and (3) analysis of SNT-coded nursing data to understand the impact or effectiveness of nursing care (impact of nursing care). The analytical techniques varied including bivariate analysis, data mining, and predictive modeling. DISCUSSION: SNT-coded nursing data extracted from EHRs is useful in characterizing nursing practice and offers the potential for demonstrating its impact on patient outcomes. CONCLUSIONS: Our study provides evidence of the value of SNT-coded nursing data in EHRs. Future studies are needed to identify additional useful methods of analyzing SNT-coded nursing data and to combine nursing data with other data elements in EHRs to fully characterize the patient's health care experience.
OBJECTIVE: The study sought to present the findings of a systematic review of studies involving secondary analyses of data coded with standardized nursing terminologies (SNTs) retrieved from electronic health records (EHRs). MATERIALS AND METHODS: We identified studies that performed secondary analysis of SNT-coded nursing EHR data from PubMed, CINAHL, and Google Scholar. We screened 2570 unique records and identified 44 articles of interest. We extracted research questions, nursing terminologies, sample characteristics, variables, and statistical techniques used from these articles. An adapted STROBE (Strengthening The Reporting of OBservational Studies in Epidemiology) Statement checklist for observational studies was used for reproducibility assessment. RESULTS: Forty-four articles were identified. Their study foci were grouped into 3 categories: (1) potential uses of SNT-coded nursing data or challenges associated with this type of data (feasibility of standardizing nursing data), (2) analysis of SNT-coded nursing data to describe the characteristics of nursing care (characterization of nursing care), and (3) analysis of SNT-coded nursing data to understand the impact or effectiveness of nursing care (impact of nursing care). The analytical techniques varied including bivariate analysis, data mining, and predictive modeling. DISCUSSION: SNT-coded nursing data extracted from EHRs is useful in characterizing nursing practice and offers the potential for demonstrating its impact on patient outcomes. CONCLUSIONS: Our study provides evidence of the value of SNT-coded nursing data in EHRs. Future studies are needed to identify additional useful methods of analyzing SNT-coded nursing data and to combine nursing data with other data elements in EHRs to fully characterize the patient's health care experience.
Authors: Sevinc Tastan; Graciele C F Linch; Gail M Keenan; Janet Stifter; Dawn McKinney; Linda Fahey; Karen Dunn Lopez; Yingwei Yao; Diana J Wilkie Journal: Int J Nurs Stud Date: 2013-12-18 Impact factor: 5.837
Authors: M Rodríguez-Álvaro; P R Brito-Brito; A M García-Hernández; A Aguirre-Jaime; D A Fernandez-Gutierrez Journal: Int J Nurs Knowl Date: 2018-01-23 Impact factor: 1.222
Authors: Fabiana Cristina Dos Santos; Tamara G R Macieira; Yingwei Yao; Samantha Hunter; Olatunde O Madandola; Hwayoung Cho; Ragnhildur I Bjarnadottir; Karen Dunn Lopez; Diana J Wilkie; Gail M Keenan Journal: J Palliat Med Date: 2022-01-26 Impact factor: 2.947
Authors: Tamara G R Macieira; Yingwei Yao; Madison B Smith; Jiang Bian; Diana J Wilkie; Gail M Keenan Journal: Nurs Res Date: 2020 Mar/Apr Impact factor: 2.381