CE: Nursing Orientation to Data Science and Machine Learning

Abstract

Nurses collect, use, and produce data every day in countless ways, such as when assessing and treating patients, performing administrative functions, and engaging in strategic planning in their organizations and communities. These data are aggregated into large data sets in health care systems, public and private databases, and academic research settings. In recent years the machines used in this work (computer hardware) have become increasingly able to analyze large data sets, or “big data,” at high speed. Data scientists use machine learning tools to aid in analyzing this big data, such as data amassed from large numbers of electronic health records. In health care, predictions for patient outcomes has become a focus of research using machine learning. It's important for nurses and nurse administrators to understand how machine learning has changed our ways of thinking about data and turning data into knowledge that can improve patient care. This article provides an orientation to machine learning and data science, offers an understanding of current challenges and opportunities, and describes the nursing implications for nurses in various roles.

Development and validation of interpretable machine learning models for inpatient fall events and electronic medical record integration

“…Although many ML algorithms have been developed to predict falls, pressure injuries, and delirium [ 13 , 23 , 25 ], there has been no study that is integrated with and applied to the actual EMR. Prospective studies could be evaluated to determine the applicability and usefulness of AI-based CDSS for future studies.…”

Nursing Informatics and Epigenetics

“…35 The article focuses on ethical considerations for DNAm study sample collection and analysis. Although research articles and continuing education are available from NI specialists, 36,37 they do not involve epigenetics, and the vast majority of NI publications deal with medication administration, care coordination issues, and data integration. 38 In recent years, there have been indications of advancements in nursing and epigenetic research.…”

Care Coordination: A Concept Analysis

“…Empirical referents of the first defining attribute of care coordination-purposeful patient targeting-include an increase in the health care literature that places priority on the recognition of complexity as pertaining to social determinants of health through data science and machine learning methodologies to identify patients for care coordination. 5,18,32 The overwhelming presence of complex care coordination programs and the need to target their coordinating interventions to the specificity of chronicity and multimorbidity is a clear empirical referent of targeting as a defining attribute. 17 An empirical referent of the second defining attribute of care coordination-complex information exchange-might stem from the increasing emphasis on patient experience metrics, another value-based payment mechanism measured by the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey, which includes direct patient-level questions on the effectiveness of the coordination of care and the communication of providers regarding transitions to home.…”

“…Empirical referents of the first defining attribute of care coordination—purposeful patient targeting—include an increase in the health care literature that places priority on the recognition of complexity as pertaining to social determinants of health through data science and machine learning methodologies to identify patients for care coordination 5, 18, 32. The overwhelming presence of complex care coordination programs and the need to target their coordinating interventions to the specificity of chronicity and multimorbidity is a clear empirical referent of targeting as a defining attribute 17…”