A Scholarly Discourse on Research Evidence on the Nurse Workforce Scheduling Systems

Claire Su-Yeon Park

View Counter: Abstract - 17 times| PDF - 4 times| HTML - 14 times|


Aim: This perspective aims to spur thought-provoking scholarly debates on current nurse workforce scheduling systems in relation to a critical review of the article “Nurse workforce scheduling in the emergency department: A sequential decision support system considering multiple objectives” published in Journal of Nursing Management in May of 2018.

Background: Mathematical Programming (optimization) (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.

Evaluation: A scholarly discourse in connection with the published article.

Key issues/Conclusions: Nurse scientists’ knowledge of decision science encompassing MP is insufficient, as are their interdisciplinary collaborations, setting back the advancement of nursing science through multidisciplinary consilience. Above all, nurse scientists skilled in decision science are desperately needed for analytic intellection rooted in the ‘intrinsic nature and value of nursing care.’ Nurse scientists are thus required to be well-prepared for the new age of the Fourth Industrial Revolution through both self-education in MP and interdisciplinary collaborations with decision science experts.


Mathematical Programming; Optimization; Decision-making; Optimal Safe Staffing; Nurse Sheduling; Nursing Workforce; Social Justice

Full Text:



Ang, B. Y., Lam, S.W.S., Pasupathy, Y., & Ong, M.E.H. (2018). Nurse workforce scheduling in the emergency department: A sequential decision support system considering multiple objectives. Journal of Nursing Management, 26(4), 432-441. doi: 10.1111/jonm.12560

Hung, R. (1991). A cyclical schedule of 10-hour, four-day workweeks. Nursing Management, 22, 30–33.

O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. New York, NY, U.S.A.: William Morris Endeavor Entertainment, LLC.

Park, C. S. (2017). Optimizing staffing, quality and cost in home healthcare nursing: Theory synthesis. Journal of Advanced Nursing, 73(8), 1838-1847. doi: 10.1111/jan.13284

Park, C. S. (2018a). Act now: A call to action for scholarly preparation in mathematics among nurse scientists [Perspectives]. Journal of Learning and Teaching in Digital Age, 3(2), 1-2. Retrieved from http://joltida.org/index.php/joltida/article/view/65

Park, C. S. (2018b). The dark shadow of Virtual Reality [Perspective]. Journal of Learning and Teaching in Digital Age, 3(1), 1-2. Retrieved from http://joltida.org/index.php/joltida/article/view/40

Park, C. S. (2018c). Thinking outside the box [Editorial]. Journal of Advanced Nursing, 74(2), 237-238. doi:10.1111/jan.13312

Park, C. S. (2018d). Challenging rules, creating values: Park’s sweet spot theory-driven central-‘optimum nurse staffing zone’ [Editorial]. Journal of Advanced Nursing, 74(6), 1231-1232. doi:10.1111/jan.13496

Park, C. S. (in press). Is your idea safe? [Editorial]. Journal of Advanced Nursing. doi:10.1111/jan.13862

Park, Y. S., & Glenn, J. (2017). The millennium project: World future report 2055. Seoul, Republic of Korea: The Business Books Co. Ltd.

Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Boston, MA, U.S.A.: Pearson.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.


Journal of Learning and Teaching in Digital Age. All rights reserved, 2016. ISSN:2458-8350