Unplanned nurse turn­over is a primary threat to a hospital’s sustainability. Foremost, the price of replacing new nurses is exorbitantly expensive, costing a hospital up to $82,000 or more per nurse. Studies also suggest that high nurse turnover can contribute to increases in patient deaths and failure-to-rescue incidents. It has also correlated with increased rates of infection and longer hospital stays in published research. Compounding the problem is that nurses often struggle during periods of high turnover to work closely together in partnerships with physicians and other caregivers. Decreased staff satisfaction and loyalty are common during periods of high nurse turnover.

Causes of Nurse Turnover

There are a number of factors that contribute to high attrition rates in nursing, but here are some of the most common reasons plaguing the new nurse population:

Opinion-based staffing: In many cases, hospital departments still rely on opinion-based staffing practices that provide zero visibility into overtime, unit needs, and patient acuity. Relying on manual paper-based processes, nurse management has to use their gut—not facts—to optimize staff scheduling and management.

Poor patient matching: Opinion-based staffing often matches nurses with patients outside their scope of practice or experience levels. Staffing based on patient needs requires a data-driven process that creates the perfect balance of both objectivity and professional nursing judgment.

Ineffective new nurse onboarding: It has been estimated that 60% of new nurses are leaving their position by their second year due to low job satisfaction and heavy workloads.

Fortunately, new strategies and tools are emerging to stop turnover before it happens. One such example of these innovative approaches has been the adoption of a data-driven staffing approach.

Data-Driven-Nurse-Callout

Benefits of Data-Driven Staffing Plans

Data-driven staffing strategies are one of the most influential tools for preventing and coping with unplanned turnover in nursing. This approach requires a complete and real-time picture of patient acuity, unit needs, available resources, and cost controls. Using a data-driven approach, nurse management can receive detailed information about excessive overtime, low productivity, poor patient matching, and new nurse performance within seconds. By implementing a data-driven process, hospitals can:

• Improve patient and nurse alignment.

• Identify the factors that cause potential
nurse burnout before it happens.

• Nurture new nursing graduates.

For hospitals, it can be easy to simply “explain away” turnover to nursing staff, but doing so fails to address the issues at hand. It’s important to get the data that are needed in order to make the right decisions with regard to staffing. Automated staffing technology using a data-driven approach—combined with efficient nurse surveys and feedback—can reveal why hospitals are failing to retain new nurses and give us guidance on how to stop turnover before it happens.

References

Twibell, R, St. Pierre J, Johnson D, et al. Tripping over the welcome mat: why new nurses don’t stay and what the evidence says we can do about it. American Nurse Today. 2012:6. Available at: http://www.americannursetoday.com/article.aspx?id=9168.

Blegen M, Goode C, Spetz J, Vaughn T, Park SH. Nurse staffing effects on patient outcomes: safety-net and non-safety-net hospitals. Medical Care. 2011;49:406-414.

Lang TA, Hodge M, Olson V, Romano PS, Kravitz RL. Nurse-patient ratios: a systematic review on the effects of nurse staffing on patient, nurse employee, and hospital outcomes. J Nurs Adm. 2004;34:326-337.

Curtin LL. An integrated analysis of nurse staffing and related variables: effects on patient outcomes. Online J Issues Nurs. 2003;8:5.