Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

Abstract

Background The term “data science” encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications. Objectives This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature. Methods We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care–acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture. Results Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing. Conclusion This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

Identifying High-Need Primary Care Patients Using Nursing Knowledge and Machine Learning Methods

“… 7 9 10 Nurses, who make up the largest number of health care professionals nationwide, are increasingly participating in the design and development of precision health algorithms using machine learning (ML) approaches. 11 12 Nursing has increasingly used ML learning methods to develop a variety of algorithms that address important clinical issues ranging from standardizing nomenclature for machine-identified topic models, through the development of complex care management systems, identifying mortality risk using nurse-generated data, exploring predictors of hospice use, and for addressing important nursing workforce issues such as burnout and staffing. 12 13 14 15 16 However, there remains a need for nursing to lead the expansion of social determinants of health (SDOH) and biopsychosocial features into precision health algorithms to advance health equity and access to care.…”

“…11,12 Nursing has increasingly used ML learning methods to develop a variety of algorithms that address important clinical issues ranging from standardizing nomenclature for machine-identified topic models, through the development of complex care management systems, identifying mortality risk using nurse-generated data, exploring predictors of hospice use, and for addressing important nursing workforce issues such as burnout and staffing. [12][13][14][15][16] However, there remains a need for nursing to lead the expansion of social determinants of health (SDOH) and biopsychosocial features into precision health algorithms to advance health equity and access to care. 12,17 To address these concerns, we have embarked on the preliminary phase of our personalized cross-sector transitional care management project (PC-TCM) and commenced design of an actionable clinical classification model of high-need persons that incorporates relevant medical and behavioral health factors as well as psychosocial phenotypes.…”

“…[12][13][14][15][16] However, there remains a need for nursing to lead the expansion of social determinants of health (SDOH) and biopsychosocial features into precision health algorithms to advance health equity and access to care. 12,17 To address these concerns, we have embarked on the preliminary phase of our personalized cross-sector transitional care management project (PC-TCM) and commenced design of an actionable clinical classification model of high-need persons that incorporates relevant medical and behavioral health factors as well as psychosocial phenotypes. 18 The original conceptualization of psychosocial phenotyping is based on the work of Kim and colleagues who sought to more effectively personalize care by identifying key psychological and social features of biological and environmental conditions that influence health outcomes.…”

Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature

“…The idea of conducting a 'nursing data science year in review' was conceived by the Center for Nursing Informatics' Data Science Workgroup 14 where we originally sought to help readers remain abreast of the latest research in which data science was used to address selected patient and health care system outcomes. In our earlier reviews, we described the data science models in projects that focused on particular clinical problems such as patient falls, nosocomial infections, and pressure injuries 15,16 . We noted that the variables included in most statistical models were similar (i.e.…”

“…In our earlier reviews, we described the data science models in projects that focused on particular clinical problems such as patient falls, nosocomial infections, and pressure injuries. 15,16 We noted that the variables included in most statistical models were similar (i.e., demographics, diagnoses, laboratories), and the major data science models (i.e., supervised machine learning) were also a commonality across the spectrum of clinical problems we considered. What remained unclear to us at the conclusion of these reviews was the extent to which the data science models that were developed had been used in actual episodes of care or incorporated into health information systems and CDS.…”