Nurse productivity: using evidence to enhance nurses’ use of time.
Published In: Nursing Standard, 2024, v. 39, n. 5. P. 30 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Morgan, Sara 3 of 3
Abstract
Why you should read this article: • To recognise some of the issues related to measuring and enhancing nurse productivity • To learn about several product improvement initiatives that have been implemented in healthcare • To consider various approaches that may be beneficial in optimising the use of nurses’ time. The UK is experiencing a nursing shortage, making it challenging to maintain the staffing levels required to deliver effective patient care. One way of enhancing the care delivered by the existing workforce could be to optimise nurse productivity; however, previous efforts to do this have been largely ineffective, due in part to a focus on the processes of care delivery rather than the nursing activities within these processes. In this article, the author explores the concept of nurse productivity and suggests that enhancing productivity requires the identification of nursing activities and consideration of how these may be undertaken in a more time-efficient manner – or removed altogether. The author discusses two such activities: intentional (hourly) rounding, and fixed-time manual vital signs for patients on general wards. The author also considers the potential of using automatic continuous remote monitoring on general hospital wards to free up nurses’ time for other care activities. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Nursing Standard. 2024/05, Vol. 39, Issue 5, p30
- Document Type:Article
- Subject Area:Nursing and Allied Health
- Publication Date:2024
- ISSN:0029-6570
- DOI:10.7748/ns.2024.e12251
- Accession Number:176928828
- Copyright Statement:Copyright of Nursing Standard is the property of Royal College of Nursing of the United Kingdom (The) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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