Struggling with staff morale and patient satisfaction? Predictive analytics can help

By Mike Miliard
09:36 AM
Hospitals still not using staffing software are struggling with nurse scheduling which hampers employee morale and, in turn, detracts from patient satisfaction. A new survey said that predictive analytics tools can help with both problems simultaneously.
patient satisfaction predictive analytics

Nearly 75 percent of nurse managers say they're concerned that scheduling challenges are causing low staff morale – and almost 70 percent have worries about the impact inadequate staffing is having on patient satisfaction.

One challenge is that a quarter of nurse managers are still relying on paper-based scheduling methods, a recent survey by staffing services company AMN Healthcare found.

Some 80 percent of nurse managers said they're not even aware of technology that could help manage RN scheduling and staffing, according to the report "Predictive Analytics in Healthcare 2016: Optimizing Nurse Staffing in an Era of Workforce Shortages," from Avantas, a division of AMN.

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Persistent challenges noted in the report include understaffing, last-minute schedule changes, assignment of non-nursing tasks and imbalances of experienced and specialty staff.

More than half of those managers polled say they're "very concerned" about the effect of staffing on care quality

Predictive analytics that can forecast patient demand and workforce requirements could be useful in addressing these scheduling problems that negatively impact staff morale and patient care, according to AMN.

"With shortages of nurses and other healthcare professionals becoming an increasingly chronic problem, optimizing your workforce is imperative,” AMN CEO Susan Salka said. “Knowing future patient demand so healthcare providers can accurately plan workforce scheduling and staffing is an invaluable asset for medical facilities."

The report pointed to some gains by providers that have adopted predictive analytics and advanced labor management tools, such as 97 percent accurate predictions of staffing needs 30 days out from the shift; 75 percent of open shift hours filled more than two weeks in advance; increases in nurse satisfaction and 4 to 7 percent savings in overall labor spending.

“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. “This saves time and frustration for nurse managers and registered nurses, so they can give all their attention to patient care.”

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  Predictive analytics will be among the topics at the HIMSS and Healthcare IT News Big Data & Analytics Forum in Boston, Oct. 24-25. What to expect:

⇒ Charlotte hospitals analyze social determinants of health to cut ER visits
⇒ Big Data: Healthcare must move beyond the hype
⇒ Tips for reading Big Data results correctly
⇒ Small hospital makes minor investment in analytics and reaps big rewards 
 MIT professor's quick primer on two types of machine learning for healthcare
⇒ Must-haves for machine learning to thrive in healthcare

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