No more unimplementable nurse workforce planning

Area of Science:

• Nursing Science
• Operations Research
• Decision Science

Background:

• The integration of mathematical programming (MP) in nursing research, particularly for workforce scheduling, has lagged, creating a gap between academic findings and practical application.
• Nurse scientists require enhanced skills in MP and decision science to bridge this gap and ensure nursing values are central to algorithmic approaches.
• The Fourth Industrial Revolution necessitates nurse scientists to be adept in advanced analytical methods and interdisciplinary collaboration.

Approach:

• This paper critically reviews Ang et al.'s (2018) work on emergency department nurse scheduling.
• It engages in a practical discourse on current nurse workforce staffing and scheduling systems.
• The discussion emphasizes the need for nurse scientists to lead in developing implementable MP-integrated knowledge.

Key Points:

• Mathematical Programming (MP) offers significant potential for optimizing nurse staffing and scheduling to improve efficiency and cost-effectiveness.
• There is a critical need for nurse scientists to develop expertise in MP and decision science.
• Interdisciplinary collaboration is essential to prevent the negative impacts of purely algorithm-driven solutions and to ensure alignment with the intrinsic values of nursing care.

Conclusions:

• Optimal nurse staffing and scheduling are paramount given the current global nursing shortage.
• MP tools can assist nurse executives in achieving efficient and cost-effective nurse deployment.
• Nurse scientists must prioritize the development of a new knowledge base in MP that is directly applicable to nursing practice.