CE: Nursing Orientation to Data Science and Machine Learning.

Journal: The American journal of nursing
Published Date:

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.

Authors

  • Roxanne L O'Brien
    Roxanne L. O'Brien is retired from her position as lecturer at the School of Nursing, California State University, Fullerton. Matt W. O'Brien is a data scientist at the Center for Digital Health Innovation, University of California San Francisco. Contact author: Roxanne L. O'Brien, roxanne.obrien@gmail.com . The authors and planners have disclosed no potential conflicts of interest, financial or otherwise. A podcast with the authors is available at www.ajnonline.com .
  • Matt W O'Brien