No more unimplementable nurse workforce planning

Abstract

Objective: This paper aims to spur thought-provoking practical debates on current nurse workforce staffing and scheduling systems in relation to a critical review of Ang and colleagues’ (2018) article entitled “Nurse workforce scheduling in the emergency department: A sequential decision support system considering multiple objectives.”

Design: Discussion paper on a practical discourse in connection with the aforementioned published article.

Discussion: Mathematical Programming (optimisation) (MP)-based nursing research has been published for nearly thirty years almost exclusively in industrial engineering or health business administration journals, demonstrating a widening gap between nursing research and practice. Nurse scientists’ knowledge and skill of MP is insufficient, as are their interdisciplinary collaborations, setting back the advancement of nursing science. Above all, nurse scientists skilled in decision science are desperately needed for that analytic intellection which is rooted in the ‘intrinsic nature and value of nursing care.’ It is imperative that nurse scientists be well-prepared for the new age of the Fourth Industrial Revolution through both an education in MP and interdisciplinary collaboration with decision science experts in order to prevent potential stereotyped MP-based algorithm-driven destructive influences.

Conclusions: The current global nursing shortage makes optimal nursing workforce staffing and scheduling more important. MP helps nurse executives and leaders to ensure the most efficient number of nurses with the most effective composition of nurse staffing at the right time for a reasonable cost. Nurse scientists urgently need to produce a new nursing knowledge base that is directly implementable in nursing practice.

Impact Statement: Nurse scientists should take the leading role in producing the mathematical programming-integrated knowledge base that is directly implementable in practice.

Keywords:

• mathematical programming
• optimisation
• decision-making
• optimal safe staffing
• nurse scheduling
• nursing workforce
• social justice

Acknowledgement

This article went through scholarly discussions with Dr. Rose Sherman, the editor-in-chief of the Nurse Leader in 2019 and has been refined with her constructive comments. The present research has also been conducted by the research grants of the Korea National University of Transportation and the Kwangwoon University in 2020, 2022. The authors truly thank Dr. Sherman, the Korea National University of Transportation, and the Kwangwoon University for their contribution and dedication.

Author contributions

The authors have agreed on the final version and have met both of the following criteria (recommended by the ICMJE): 1) substantial contributions to conception and design, acquisition of data or analysis and interpretation of data; and 2) drafting the article or revising it critically for important intellectual content.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Geolocation information

Seoul, Republic of Korea

Additional information

Funding

This paper was funded by research grants (no specific grant numbers) from the Korea National University of Transportation and the Kwangwoon University in 2020, 2022; the recipients were Dr. Haejoong Kim and Dr. Sangmin Lee. The funders (https://www.kw.ac.kr/en/index.jsp; https://www.ut.ac.kr/english.do) did not and will not have a role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.