Electronic Health Records

Electronic Health Records

Nursing care documentation in electronic health records (EHRs) with standardized nursing terminologies (SNTs) can facilitate nursing’s participation in big data science that involves combining and analyzing multiple sources of data. Before merging SNTs data with other sources, it is important to understand how such data are being used and analyzed to support nursing practice. The main purpose of this systematic review was to identify studies using SNTs data, their aims and analytical methods. A two-phase systematic process resulted in inclusion and review of 35 publications. Aims of the studies ranged from describing most popular nursing diagnoses, outcomes, and interventions on a unit to predicting outcomes using multi-site data. Analytical techniques varied as well and included descriptive statistics, correlations, data mining, and predictive modeling. The review underscored the value of developing a deep understanding of the meaning and potential impact of nursing variables before merging with other sources of data.

Introduction

The main frontline providers of care are nurses who also represent the largest category of health workers in the hospital setting. Among the 2.8 million registered nurses currently working in the United States (U.S.), 61% work in hospitals1 whereas 19% of 297,1002 pharmacists and 41.9% of 854,698 physicians in practice work in hospitals.3,4

Nurses are responsible 24 hours each day for continuously identifying care issues, implementing and adjusting care prescribed by themselves and other providers to achieve desired patient outcomes. To date, however, it has been difficult to effectively evaluate the impact of nursing on patient outcomes. The growing use of electronic health records (EHRs) to document care now offers the opportunity to use the data captured in practice for discovering knowledge to transform health care. Thus, the documentation entered by nurses into EHRs, for the first time ever, is a potential source for discovering the impact of nursing care on patient outcomes and using the knowledge to improve care. In this article, we report our systematic review of studies that utilized nursing EHRs data to answer a variety of research questions from describing nursing care for a specific population to predicting patient outcomes. The publications reviewed provide a foundation for identifying future paths of inquiry involving nursing and other data retrievable from EHRs.

The use of standardized nursing terminologies (SNTs) to document nursing care enables the easy retrieval and analysis of nursing data while also representing the nurse’s clinical reasoning.5 The integration of nursing data into large datasets requires the frequent and rapid input of new valid information from EHRs.6 These can be achieved through the use of controlled vocabularies in EHRs, which helps overcome the major challenges of aggregation, processing and analysis associated with unstructured text data.7-8 In nursing, SNTs are controlled vocabularies that represent nursing care as nursing diagnoses, interventions and outcomes.8 The SNT coded data retrieved from EHRs can be analyzed alone or merged with other EHRs data. The use of SNTs to document nursing practice is a big step toward supporting the aggregation of nursing data to large datasets and big data science.

Different sets of SNTs are used to document nursing care. The American Nurses Association (ANA) recognizes and supports the use of certain nursing terminologies to guide and document care if those have clear and unambiguous concepts, are coded with a unique identifier per concept, and if those terminologies were tested for reliability, clinical usefulness and validity.9 The following nursing terminologies are recognized by ANA: NANDA-International (NANDA-I)10; Nursing Interventions Classification (NIC)11; Nursing Outcomes Classification (NOC)12; International Classification for Nursing Practice (ICNP)13; Omaha System14; Clinical Care Classification (CCC)15; and the Perioperative Nursing Data Set (PNDS).16 While ICNP, Omaha System, CCC and PNDS sets contain diagnoses, interventions and outcomes terms; NANDA-I (diagnoses), NIC (interventions) and NOC (outcomes) are three separate terminologies. Since NANDA-I, NIC and NOC are very often used together, we will refer to them as a terminology set (NNN).

Systematically reporting and analyzing studies that used SNTs nursing data retrieved from EHRs is important to understand the analytic issues related to the complexity and richness of data generated from the use of these

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terminologies. Given the growing emphasis on using existing health care datasets, there is a need for new statistical, computational, and visualization methods to analyze EHRs data, given their complexity and volume.6 For nursing to join this effort, an important first step is to identify and examine the studies that analyze EHR data coded with SNTs.17

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To date, there are few reviews that report secondary analysis of SNTs nursing data. One recently published systematic review18 described study focus, sample characteristics and frequency of publications that studied SNTs. The authors also identified a limited set of studies in which SNTs nursing data were being analyzed.18 However, substantial evaluation and discussion on the analysis of SNTs nursing data were not performed. Another review19 described characteristics of nursing research data (ranging from patient demographics, social history, medical history, medications, among others) and evaluated if a specific index metadata system represents sufficiently nursing data. Our systematic review differentiates from the previous investigations as we focused only on studies that analyzed nursing data coded with SNTs retrieved from EHRs. The earlier literature review19 included all types of unstructured nursing data, and different types of studies, such as controlled clinical trials. The authors also did not restrict their selection criteria for nursing data retrieved from EHRs.19

The systematic review presented below was conducted to describe and critically analyze the body of studies in which secondary analyses of data coded with the ANA recognized SNTs was performed. These nursing data were documented during the delivery of nursing care and retrieved from EHRs. We believe that the findings from the present systematic review will emphasize the importance of coded nursing data and will encourage a wider use of SNTs that will allow greater participation of nursing in big data science initiatives.

Objective

The objective of this systematic review was to uncover the state of the science related to the use of standardized nursing data (coded with SNTs) retrieved from EHRs to answer research questions, describe the analytical techniques employed, and outline the lessons learned applicable big data science and nursing.

Methods

Search strategy

A comprehensive literature search was conducted to identify publications in which secondary analysis was performed on data extracted from EHRs and documented using terms from the ANA recognized nursing terminologies (or sets). There were two phases used in this process: 1) study selection and 2) data collection process.

Building from the work of Tastan et al.18 that reported secondary uses of SNTs documentation in studies up to 2011, the databases PubMed and CINAHL were searched using keywords encompassing all ANA recognized SNTs, along with “nursing” and “electronic health records” from 2010 to 2017. Keywords were defined for each database with the help of a librarian, who was a specialist in Consumer Health, Nursing and Health Education and Behavior. The limiters since 2010, abstract available and published in English were used. Potential publications identified in both databases were downloaded into a reference management program (EndNote X7, Thompson Reuters ISI ResearchSoft), in which duplicates were deleted and abstracts were reviewed. Finally, grey literature search was conducted using Google Scholar to identify possible publications not captured by the traditional search methods, including relevant publications not in PubMed or CINAHL, but in computer science databases like IEEE. Name of authors of publications already reviewed and included in this study’s sample were individually searched in Google Scholar. Publications pertinent to the subject were reviewed and included

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