Predictive Analytics Help Optimize Nurse Staffing

By Christine Kern, contributing writer

New report assesses how big data can help ease scheduling.

Faced with a nursing shortage, hospitals and healthcare organizations are struggling to find ways to effectively schedule. Predictive analytics is one tool that can help optimize nurse staffing at these facilities according to the findings of an AMN Healthcare/Avantas report. The survey, Predictive Analytics in Healthcare 2016: Optimizing Nurse Staffing in an Era of Workforce Shortages, is an assessment of over 5,600 nurse managers and includes interviews with 35 nurse managers, finance managers, and registered nurses.

According to the accompanying infographic, predictive analytics could provide accurate forecasting of future patient demand and workforce needs, something that could prove very valuable in solving common nurse scheduling and staffing problems that negatively impact staff morale and patient care. However, 80 percent of Nurse Managers reported they were unaware of such available technology.

“With shortages of nurses and other healthcare professionals becoming an increasingly chronic problem, optimizing your workforce is imperative,” said Susan Salka, President and CEO of AMN Healthcare. “Knowing future patient demand so healthcare providers can accurately plan workforce scheduling and staffing is an invaluable asset for medical facilities. Fortunately, that information can be available and applied to workforce planning and management.”

The study findings demonstrate hospitals are struggling to provide or utilize tools to assist with nurse staffing. With shortages of nurses and other healthcare professionals becoming a chronic problem, optimizing the nursing workforce is imperative to maintaining quality patient care, reducing staff burnout, and increasing nurse retention. The survey also revealed:

• one quarter of managers use paper-based staffing tools; while 23 percent do not use any scheduling tools at all
• 19 percent of nurse managers use simple digital spreadsheets, and even of those nurse managers with access to technology-enhanced scheduling tools, 43 percent still depend on manual scheduling
• 80 percent of nurse manager remain unaware of available technology that can accurately forecast patient demand and staff needs

“Although most don’t know it is available, predictive analytics can take the guesswork out of nurse scheduling and staffing through accurate forecasting of patient demand months in advance of the shift,” said Jackie Larson, president of Avantas, an AMN Healthcare company. “This saves time and frustration for nurse managers and registered nurses, so they can give all their attention to patient care.”

And there’s more at stake than under or over staffing. Nurse managers said scheduling and staffing problems directly impact the quality of patient care and staff morale with 70 percent of those surveyed saying they are very concerned about the impact on patient satisfaction; more than half are concerned about the effect on the quality of care; and a full 94 percent said that understaffing has a negative impact on workplace morale.