The link between staff morale and patient care, and how predictive analytics can help

Healthcare leaders have put an emphasis on having the right workplace "culture" in recent years, as organizations have made the connection that having the right people in the right places is their most valuable resource.

Making smart hiring and staffing decisions drives a positive workforce and sees a greater return on investment. Put simply, happy employees equals greater productivity and lower turnover for an organization. For healthcare, that translates to improved patient care and satisfaction.

Staff morale is an important metric for all organizations to gauge, but it should be vitally so when staff is providing patient care. It is a trying time for healthcare, as the industry continues to grow while facing a tremendous nursing shortage, along with many unknowns of the future. Competition to recruit experienced providers is intense, leaving organizations grappling to find ways to leverage the staff they currently have to keep up with the patient demand.

This climate puts a tremendous pressure on nurse managers when it comes to appropriately staffing their units. According to a 2016 survey conducted by AMN Healthcare and Avantas, nearly all nurse managers agree that scheduling and staffing problems have a negative impact on staff morale. Afraid of burning out their nurses, managers must find a fair way to balance their staffing needs while being respectful of their staff members' schedules. Additionally, nearly 70% of nurse managers say they are very concerned about the impact of staffing and scheduling issues has on patient satisfaction. Not being able to provide the highest level of care to patients due to situational short staffing is the biggest concern and greatest cause for low morale among nurses.

Knowing the effect scheduling has on staff morale and the link to patient care and satisfaction elevates this issue. This is where labor management solutions can have their greatest impact. One such solution is utilizing predictive analytics and technology-enabled systems to accurately forecast patient demand and staffing to that need up to 120 days in advance.

Predictive analytics can take the guesswork out of nurse scheduling and staffing by accurately predicting patient demand months in advance of the shift. This returns valuable time back to nurse managers so they can focus their attention on patient care and supporting their staff. Appropriately scheduling staff to the forecasted demand optimizes the workforce, aligning schedules to volume, and reducing frequent occurrences of cancellations and overtime, as well as time-consuming and often expensive last-minute recruitment efforts.

Technology-enabled systems and predictive analytics has encouraged provider organizations to adopt a strategic enterprise approach. Standardizing practices and using "big data" driven by predictive analytics to accurately forecast patient demand and staffing needs gives organizations a competitive edge to be ahead of the ball. Providers that are not using these cutting-edge practices and automated systems are at an increasing disadvantage.

Healthcare enterprises that have adopted predictive analytics and advanced labor management strategies have realized outcomes that include reductions in agency nursing, increased staff satisfaction scores, improved nurse retention, and significant annual savings in labor spending. This kind of impact is huge. At a time when the gap of unfilled healthcare jobs continues to widen, utilization of predictive analytics aligned with advanced labor management strategies offers a solution to scheduling and staffing problems that frustrates managers and drains staff morale.

The views, opinions and positions expressed within these guest posts are those of the author alone and do not represent those of Becker's Hospital Review/Becker's Healthcare. The accuracy, completeness and validity of any statements made within this article are not guaranteed. We accept no liability for any errors, omissions or representations. The copyright of this content belongs to the author and any liability with regards to infringement of intellectual property rights remains with them.

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