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Claire Su-Yeon Park, MSN, RN, Nursing Decision Scientist

Received 31 October 2018, Revised 15 February 2019, Accepted 15 February 2019


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.
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.

Keywords: Mathematical Programming, Optimization, Decision-making, Optimal safe staffing, Nurse scheduling, Nursing workforce, Social justice

Correspondence to: Claire Su-Yeon PARK, MSN, RN, Nursing Decision Scientist, CEO, Center for Econometric Optimization in the Nursing Workforce, Address: 112-1702 Samsung Raemian 1st APT, Gireum 1-dong, Seoungbuk-ku, Seoul 136-770, Republic of Korea. Tel. +82 10.6248.9947, Email:, @YeonClaire

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Journal of Learning and Teaching in Digital Age 2019. © 2019. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.


I read multiple times, with great interest, the article entitled “Nurse workforce scheduling in the emergency department: A sequential decision support system considering multiple objectives,” published in the Journal of Nursing Management in May of 2018 (Ang, Lam, Pasupathy, & Ong, 2018). As decision-making amid uncertainty and the risk in real situations becomes more and more apparent (Park & Glenn, 2017), such advanced, mathematics-driven evidence—specifically, Mathematical Programming (optimization) (MP)-based nurse scheduling systems—becomes higher valued. Accordingly, this evidence is appearing more frequently in nursing literature; however, it is still rare. Its complexity and profoundness could make nurse scientists feel resistant, rather than willing to change, which supports the fact that MP has so far rarely been utilized in nursing research, as opposed to statistics. Nurse scientists may, therefore, be unfamiliar with the scientific research regarding MP-based nurse scheduling.

Nurse scientists may also question whether or not such studies fall within the scope of nursing science. For example, in 2015, my MP-integrated theory synthesis article proposing a solution to the global nursing shortages by pinpointing optimal safe nurse staffing levels (Park, 2017), was rejected without peer review for this very reason by the editorial teams of International Journal of Nursing Studies and Nursing Outlook, both well-regarded nursing journals. I published the paper in another nursing journal, the Journal of Advanced Nursing, in May of 2017, after rigorous peer reviews and a 51-page response letter explaining two sets of revisions over 9 months. The long peer review period and intensive revision process demonstrate how difficult it is for MP-based research to be accepted by the nursing science community. Because the paper won the 2017 Nursing Policy Scholarly Award from the Korean Nurses Association on 21 Feb 2018, and due to the rising importance of developing competent educators in both nursing science and data science with mathematics at its core (Park, 2018a), interest in MP among nurse scientists is, fortunately, rising inch by inch.

MP-based nurse scheduling scientific research is actually found in great numbers, mostly in the fields of industrial engineering and health business administration, going back nearly 30 years (since Hung’s (1991) report). Why has nursing science failed to integrate such high-quality research evidence that could make a difference in nursing practice? It is time to think critically about existing research evidence regarding nurse workforce scheduling systems and the reasons nursing science has been so slow to integrate it. We are scholars seeking to advance science, not simply working for the sake of publication.

Ang and colleagues’ (2018) literature review points out that one of the challenges of MP-based research in the nurse scheduling system is nurse managers’ distrust of mathematical modelling (p. 433; Hung, 1991). I have served as an abstract reviewer since 2015 for the Annual North American Meeting of the Society for Medical Decision Making ( in the following review categories: 1) Decision Psychology and Shared Decision Making; 2) Health Services, Outcomes and Policy Research; 3) Quantitative Methods and Theoretical Developments; and 4) Patient/Stakeholder Preferences and Engagement. Increasingly, the best future forecasting simulation(s), given a set of plausible scenarios, are being studied, while Artificial Intelligence (AI)-driven or MP-based decision-making support aids are also continuously entering into the literature, reflecting a rising need to enhance the quality of decision making against entropy in a system (Park & Glenn, 2017). Medical/pharmaceutical science already has two notable scholarly journals that concentrate on such mathematics-integrated decision-making dynamics (decision science), i.e., Medical Decision Making ( and Value in Health (; however, neither journal focuses on nursing science. Further, nursing journals specializing in decision science do not exist at present. This lack of trust, interest, and literature explains why current nurse scientists’ knowledge and experience in decision science, encompassing behavioral economics, econometrics, decision psychology, operations research, applied mathematics, and computer science (, do not include competence in advanced predictive decision-making analytical intellection (Park, 2018a).

Worse, there seem to be no active interdisciplinary collaborations between nursing science and decision science. Even, nurse scientists have rarely co-authored MP-based nurse scheduling studies. Analysts’ ability to demonstrate the invisible yet significant nature and value of nursing care in their data analyses may significantly improve if they had the opportunity to work with nurse scientists who were skilled in decision science (Park, 2018a). Such a partnership is urgently necessary if we are to address the tangible and intangible barriers preventing multidisciplinary consilience and meet the demands of the new age of the Fourth Industrial Revolution.

MP-based modelling can be directly used for the development of an algorithm-driven automatic system such as AI, which differs from traditional statistics-based research evidence. Some may propose that statistical parameters/estimates derived from big data-based advanced statistical analytics can be applied to settings in other areas; however, statistics-based research evidence hypostatically works within the limits of the datasets (Tabachnick & Fidell, 2013). It cannot be considered as a population no matter how big data is used in the study (though it is, at present, getting closer to a population than any other samples thanks to data warehouses) (Tabachnick & Fidell, 2013). That is to say, the statistical parameters/estimates have ever-changing variance by data, whereas an MP-based algorithm is a sophisticatedly planned sequence of steps in a process to produce an answer, making it unalterable by data and leading to exceptional applicability.
It suggests that unlimited interconnectivity such as the real estate-based Virtual Reality platform may make a “winner-takes-all” system possible (Park, 2018b). This sounds ideal. Unfortunately, however, it might also jeopardize a fair social justice system (Park, 2018b). As such, a stereotyped algorithm could prejudice health equity or social justice while not fully considering the rights of the individual, necessitating ethical engagement in order to ensure justice (O’Neil, 2016). Nurse scientists thus have to be fully equipped with MP to prevent such a tragedy from happening in the future through both self-education and interdisciplinary collaborations.

As a nurse scientist who synthesized MP to determine the optimal safe staffing levels (Park, 2017, 2018c) and would like to contribute to creating a party-to-party shared value (Park, 2018d) and achieving a better value-based nursing workforce system in order to mitigate the worsening global nursing shortage (Park, 2018e), I was delighted to read such an ingenious study in a leading journal of nursing management. I hope that this scholarly discourse and Ang and colleagues’ (2018) work will inspire blossoming scholars and lead to a shift and expansion in their way of thinking, the starting point for innovation in nursing science.


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