Nurses “Seeing Forest for the Trees” in the Age of Machine Learning: Using Nursing Knowledge to Improve Relevance and Performance

Nurses “Seeing Forest for the Trees” in the Age of Machine Learning: Using Nursing Knowledge... Although machine learning is increasingly being applied to support clinical decision making, there is a significant gap in understanding what it is and how nurses should adopt it in practice. The purpose of this case study is to show how one application of machine learning may support nursing work and to discuss how nurses can contribute to improving its relevance and performance. Using data from 130 specialized hospitals with 101 766 patients with diabetes, we applied various advanced statistical methods (known as machine learning algorithms) to predict early readmission. The best-performing machine learning algorithm showed modest predictive ability with opportunities for improvement. Nurses can contribute to machine learning algorithms by (1) filling data gaps with nursing-relevant data that provide personalized context about the patient, (2) improving data preprocessing techniques, and (3) evaluating potential value in practice. These findings suggest that nurses need to further process the information provided by machine learning and apply “Wisdom-in-Action” to make appropriate clinical decisions. Nurses play a pivotal role in ensuring that machine learning algorithms are shaped by their unique knowledge of each patient's personalized context. By combining machine learning with unique nursing knowledge, nurses can provide more visibility to nursing work, advance nursing science, and better individualize patient care. Therefore, to successfully integrate and maximize the benefits of machine learning, nurses must fully participate in its development, implementation, and evaluation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png CIN: Computers, Informatics, Nursing Wolters Kluwer Health

Nurses “Seeing Forest for the Trees” in the Age of Machine Learning: Using Nursing Knowledge to Improve Relevance and Performance

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Publisher
Wolters Kluwer Health
Copyright
Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.
ISSN
1538-2931
eISSN
1538-9774
DOI
10.1097/CIN.0000000000000508
Publisher site
See Article on Publisher Site

Abstract

Although machine learning is increasingly being applied to support clinical decision making, there is a significant gap in understanding what it is and how nurses should adopt it in practice. The purpose of this case study is to show how one application of machine learning may support nursing work and to discuss how nurses can contribute to improving its relevance and performance. Using data from 130 specialized hospitals with 101 766 patients with diabetes, we applied various advanced statistical methods (known as machine learning algorithms) to predict early readmission. The best-performing machine learning algorithm showed modest predictive ability with opportunities for improvement. Nurses can contribute to machine learning algorithms by (1) filling data gaps with nursing-relevant data that provide personalized context about the patient, (2) improving data preprocessing techniques, and (3) evaluating potential value in practice. These findings suggest that nurses need to further process the information provided by machine learning and apply “Wisdom-in-Action” to make appropriate clinical decisions. Nurses play a pivotal role in ensuring that machine learning algorithms are shaped by their unique knowledge of each patient's personalized context. By combining machine learning with unique nursing knowledge, nurses can provide more visibility to nursing work, advance nursing science, and better individualize patient care. Therefore, to successfully integrate and maximize the benefits of machine learning, nurses must fully participate in its development, implementation, and evaluation.

Journal

CIN: Computers, Informatics, NursingWolters Kluwer Health

Published: Apr 1, 2019

References

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