July 26, 2019 – Recent statistics reveal that the demand for registered nurses continues to grow. The time to address nurse staffing concerns is now. Data analytics in nurse staffing and scheduling is a beneficial, yet largely overlooked, area. Incorporating modern modeling techniques and machine learning methodologies to forecast staffing needs, predictive analytics identifies demand for staff weeks in advance. This article suggests how optimizing a health system’s workforce with best practice strategies and advanced technology can greatly diminish the staffing challenges being felt in hospitals and health care facilities across the United States.
How do I optimize my workforce during a nursing shortage? How do I build a pool of resources when the demand is greater than the supply? These are questions hospital and health system leaders across the country are asking themselves on a regular basis. Although it may seem like an uphill battle, optimizing your current workforce is possible—just look to new, technology-driven solutions.
According to the Bureau of Labor Statistics’ Employment Projections 2014 to 2024, registered nursing is listed among the top occupations in terms of job growth through 2024.1 The RN workforce is expected to grow from 2.7 million in 2014 to 3.2 million in 2024, an increase of 16%. The Bureau of Labor Statistics also projects the need for 649,100 replacement nurses in the workforce, bringing the total number of job openings for nurses due to growth and replacements to 1.09 million by 2024.
Key Points in this article:
- Utilizing an open-shift program is an effective way to fill needs, reducing the amount of dollars typically tied to filling open shifts.
- Having a flexible pool of resources reduces core staff working in extra hours and overtime, decreasing the likelihood of nurse burnout.
- Continuously monitoring key performance indicators keeps staff aligned with organizational goals and effectively tracks progress toward those goals.
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