Developing a model for quantifying staffing requirements in the post-anaesthesia care unit.

  • Published In: Nursing Management - UK, 2023, v. 30, n. 5. P. 19 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Bagstaff, Katie 3 of 3

Abstract

Why you should read this article: • To recognise the challenges related to quantifying staffing requirements in a post-anaesthesia care unit (PACU) • To increase your understanding of the suitability of different types of data for quantifying PACU staffing requirements • To read about factors to consider when developing a model for quantifying PACU staffing requirements Nurse managers in charge of a post-anaesthesia care unit (PACU) face the task of optimising staffing levels and must be able to justify staffing needs to the wider operational team. The high variability in patient numbers and acuity that characterises the PACU, as well as the broader factors that affect patient flow to and from the PACU, make it challenging to quantify staffing requirements. Staffing models often fail to reflect accurately the needs of patients and therefore the needs of the unit and there is no recommended model for quantifying PACU staffing requirements. In this article, the author describes the challenges of quantifying PACU staffing requirements and the suitability of different types of data. The author also discusses factors to consider when developing model for quantifying PACU staffing requirements. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Nursing Management - UK. 2023/10, Vol. 30, Issue 5, p19
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2023
  • ISSN:1354-5760
  • DOI:10.7748/nm.2023.e2096
  • Accession Number:174199479
  • Copyright Statement:Copyright of Nursing Management - UK 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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