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: decision-making; mathematical programming; nurse scheduling; nursing workforce; optimal safe staffing; optimisation; social justice.