Algorithmic bias in health care: Opportunities for nurses to improve equality in the age of artificial intelligence
View File(s)
- Author(s)
- Details
-
Siobhan O’Connor - Division of Nursing, Midwifery and Social Work, The University of Manchester, Manchester, United Kingdom; Richard G. Booth - The Arthur Labatt Family School of Nursing, Western University, London, Ontario, Canada
- Sigma Affiliation
- Unknown
- Contributor Affiliation(s)
- The University of Manchester, Manchester, United Kingdom
Artificial Intelligence (AI) consists of a range of sophisticated computational techniques, encompassing machine learning algorithms and natural language processing among others, that are lauded as a way to improve clinical decision making, patient care, and health service delivery. A recent systematic review of AI in nursing and midwifery found many clinical, managerial, and educational applications of these predictive algorithms over the last 20 years covering areas such as wound care, critical care, falls, infection control, emergency care, older adult care, and education among others... Despite numerous identified benefits of AI in health care, it can also introduce a host of risks – one of the most pressing being algorithmic bias.
The authors of this work did not submit this material directly to the Sigma Repository. This work is indexed and appears in the Sigma Repository pursuant to a Creative Commons Attribution 4.0 International License attached to the work upon its publication in the journal acknowledged in this record. Please refer to the attached license (the icon at the bottom of this entry) for further information and terms. All terms of the license have been followed. There are no changes in this article from the original posting
| Type | Article |
| Acquisition | Indexed from External Source (Per Rights Granted in Creative Commons License) |
| Review Type | External Review: Previously Published Material |
| Format | Text-based Document |
| Evidence Level | N/A |
| Research Approach | N/A |
| Keywords | Algorithms; Artificial Intelligence; Bias; Health Care; Machine Learning; Natural Language Processing; Neural Networks; Nursing |
| Publisher | Elsevier |
| Date | 2022-11-14 |
| Version | Publisher’s Version |
| DOI | https://doi.org/10.1016/j.outlook.2022.09.003 |
| Citation | O'Connor, S., & Booth, R. G. (2022). Algorithmic bias in health care: Opportunities for nurses to improve equality in the age of Artificial Intelligence. Nursing Outlook, 70(6), 780–782. https://doi.org/10.1016/j.outlook.2022.09.003 |
License
The following license files are associated with this item:
Related items
Showing items related by title, author, creator and subjects.
-
Using Twitter to engage nurses in policy debate to inform health strategy
(2017-07-18)Purpose: Health policy can often be designed with little input from frontline nursing or managerial staff, whose clinical and organisational expertise could add value to the development of national health ... -
Derek Hoy: The legacy of the first Scottish nursing informatician
(2017-07-20)Purpose: Derek Hoy was a prominent Scottish nursing informatician. This paper aims to explore his career and its impact on nursing, patients and the health services in Scotland and beyond. Methods: A ... -
Improving patient care outcomes through better delegation-communication among nurses and assistive personnel
(2016-06-08)In acute care settings, registered nurses must depend on and delegate to unlicensed assistive personnel when providing direct care to patients. The nurses’ ability to effectively delegate can affect the safety and ... -
Nutrition education content in nurse practitioner curricula: What are Michigan NP's learning about nutrition?
(2016-05-27)Background and Purpose: Unhealthy diets are linked to common chronic diseases in the U.S. and Michigan. Healthy People 2020 calls for more nutrition counseling in primary care office visits. The last known survey of nutrition ... -
Implementing obesity management in primary care: Linking evidence based guidelines with a nurse practitioner model of care
(2016-06-03)Background: Obesity is a rising epidemic, affecting over one third of U.S. adults and over 13% of the world’s population, carrying significant morbidity and mortality. There have been decreasing rates of obesity ...
