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How Survival Analytics Provides a Lifeline for Hospitals Combating Nursing Turnover

Healthcare Analytics

Chris St. Jeor



For years, hospitals have faced a crisis in staff retention. Saddled with ongoing staffing shortages, facilities turned to traveling nurses who come at much higher costs than full-time employees. Rural hospitals were especially challenged to keep pace with nursing demand, and relied on high-priced staffing firms to fill the gaps. 

Then, the pandemic hit. The impact on staff retention was like pouring gasoline on a fire.

Forced to the frontline of critical care, nurses faced exceptionally long hours, increased stress, and threats to their overall well-being, accelerating turnover in the profession. As the pandemic continued, the monthly costs for rural hospitals ballooned from $500,000 to over $10 million. By January 2022, hospital labor expenses had grown by 57%, and one study revealed hospitals in New Jersey had an increase in contract staff expenses that amounted to $670 million in 2021, which was triple what was spent in 2020.

To continue to provide adequate service to their communities, hospitals need to think differently about staffing. Hiring skilled nurses is essential, but so is retaining an experienced workforce. By leveraging employee data to conduct a statistical method called survival analysis, hospitals like yours can gain a powerful ally in resolving a threat that continues to grow.

Survival Analysis Predicts Retention Issues Before They Occur

Simple surveys and measuring employee satisfaction rates are just two tools for hospitals to identify problem areas impacting employee retention. However, if your institution already possesses a strong vision for data and its integrity, you can apply survival analysis and generate more accurate insights.

Survival analysis is a valuable statistical tool that measures the duration of time until one or more events occur. Its application spans various industries, including manufacturing, which use survival analysis to estimate the life expectancy of equipment. In medical research, survival analysis measures the effectiveness of vaccines and other clinical trials.

The same algorithmic principles apply to employee retention in the healthcare industry. Recent studies in Korea and Saudi Arabia have examined factors contributing to nurse turnover.  Larger healthcare companies have already begun applying these principles to draw conclusions from longitudinal data gathered during the nursing workload to examine the factors impacting an employee’s decision to quit.

Work-Life Data Illuminates Factors Impacting Retention

While a newer application for data analytics, survival analysis provides a way for organizations to look at the details behind employee retention in two ways:

  1. Predict how long a nurse remains with an organization before they quit.
  2. Examine the impact of select parameters on the amount of time until a nurse quits.

Over a set interval of time, survival analysis looks at the working conditions that influence an employee’s tenure at your organization. The algorithm can factor details such as vacation days taken and the number of shifts worked over consecutive days. Then, you can compare those results against employees who quit. From there, your organization can identify the variables impacting retention.

Survival analysis data introduces two options for organizations to improve their retention rates. You can implement department-wide changes in response to the factors leading to high turnover. Plus, you have access to data that will identify individuals who are at risk of leaving and create an intervention.

In both cases, you gain new resources for retaining the skilled medical staff your institution — and its community — need most.

Nursing Turnover Impacts Health Care Institutions Across Multiple Fronts

In 2022, hospital turnover rates increased by 6.4% to 25.9%. But the impact on the nursing profession was even more pronounced. For the first time, turnover for staff RNs exceeded the national average for hospitals at a rate of 27.1, an increase of 8.4%.

The fallout from the decline in nursing retention rates reaches beyond an individual institution. According to the article linked above, RN recruitment remains challenging, and hospitals typically need an average of 87 days to hire new, experienced nurses. Then the institution must also invest in training new employees or turn to high-priced traveling nurses as a stopgap.

Along with adding to administrative costs, a hospital’s staffing expenditures lead to higher healthcare prices to compensate for losses. As a result, those costs are passed along to consumers in the form of higher healthcare premiums.

Failure to Address Nursing Retention Increases Stress on Hospitals

With fewer experienced nurses available, hospitals impacted by nursing turnover experience worse patient outcomes. Higher fatality rates, medical errors, and longer wait times are just a few of the negative results of poor nursing retention rates.

As the staffing shortage continues, your current staff works longer hours and experiences more burnout. The resulting turnover of overworked staff only adds to the crisis.

With the help of an internal data science team or the right consultants, you can implement an advanced analytics process that incorporates survival analysis. Understanding the root causes behind nursing turnover is crucial to improving the work environment and securing higher retention rates.

Addressing Root Causes of Nursing Turnover Is Essential

An emergency like the Coronavirus pandemic shined a spotlight on systemic difficulties in the U.S. healthcare system. Along with exacerbating the staffing shortage in nursing, the past few years drove increased turnover among primary care physicians. According to a 2022 study, more than a quarter of the costs associated with staff turnover were attributable to burnout.

As of last year, voluntary terminations accounted for more than 95% of hospital workforce changes. Faced with such staffing challenges, your organization needs greater visibility into the factors driving employee departures. Personal reasons such as career advancement and retirement may be difficult to guard against. But hospitals have the capacity to better monitor and respond to workload ratios that are simply unsustainable.

With the help of survival analytics, you gain a clearer picture of how your organization can improve staff retention. Ultimately, for hospitals facing the steep costs associated with nursing shortages, data holds the key to combating what is an existential threat to their future.

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