Using Predictive Analytics to Reduce Nurse Burnout

April 15, 2021
Bryan Dickerson

Burnout among nurses has been a major pain point for hospitals and health systems long before COVID. Nurses work long hours and face a unique combination of physical and emotional challenges, and a seemingly simple mistake can be fatal. As a result, burnout rates among nurses range from 15% to 45%, leading 1 in 3 nurses to leave the bedside within their first two years.

Burnout among nurses has been a major pain point for hospitals and health systems long before COVID. Nurses work long hours and face a unique combination of physical and emotional challenges, and a seemingly simple mistake can be fatal. As a result, burnout rates among nurses range from 15% to 45%, leading 1 in 3 nurses to leave the bedside within their first two years.

High burnout rates have contributed to an average nurse turnover rate of 17% across the U.S. pre-pandemic, reaching as high as 37% depending on the region and nursing specialty. The resulting lack of continuity among nursing staff has a negative impact on a team’s ability to deliver consistent, high-quality patient care. High-quality care delivery starts with putting care teams in the best possible position and equipping them with tools to be successful. Predictive analytics is a powerful tool that enables teams to proactively plan staffing needs, which increases nurse satisfaction and improves the quality of care they provide.

Staffing and Deployment

Many of the factors contributing to nurse burnout are intangible and difficult for hospital administrators and leadership to address, but there are a few key areas worth investigating, mostly around staffing and deployment. Hospital staffing models have largely remained unchanged for decades and several inefficiencies and suboptimal practices have become baked into the process. Current staffing practices are reactive in nature and present several challenges to hospitals and nurses, creating a daily struggle to ensure adequate staffing to accommodate patient demand. Unexpected patient surges can leave hospital units understaffed and managers scrambling for coverage.

Health systems need to balance nursing staff utilization across the enterprise, keeping in mind the demanding nature of the job along with ever-changing schedules that often leads to excessive overtime and job dissatisfaction, which results in turnover and retention issues. Turnover puts financial pressure on organizations to control labor costs and stresses unit leaders to maintain target staffing levels and productivity as new hires take time to train and become as effective as the existing employees. With no visibility into future patient census, unit leaders spend the majority of their day negotiating with the staffing office and other units in an attempt to ensure that they have adequate staff to provide care to their patients.

This reactive approach is not conducive to a positive work environment or to delivering quality care for patients. With this approach, nurses do not know if they will be asked to come in on their day off, work overtime, work on a different unit, or how trying their days will be due to a higher patient load. According to a recent report from the Office of Inspector General, the pandemic has forced nurses to further increase their hours and responsibilities, resulting in above-average turnover rates. In the post-pandemic healthcare landscape, new tools will be needed to reverse the accelerating trend of nurse burnout.

The Future of Staffing with AI

Hospitals can proactively manage staffing dilemmas by leveraging predictive analytics solutions that combine historical data with machine learning so that nurse leaders can accurately predict future staffing needs days in advance. By taking advantage of these insights, leaders can confidently make decisions about where and when to deploy nursing staff. This proactive approach results in improved patient care, less last-minute changes, and better work-life balance for nurses.

These solutions have already proven their value in the real world. For example, MercyOne Des Moines Medical Center, like many hospitals, regularly confronted the recurring nurse staffing challenge of how to allocate the right number of nurses to the right unit in order to meet actual patient census demand amidst high turnover. After deploying a predictive analytics solution, MercyOne Des Moines was able to streamline communication of staffing needs, improve daily allocation of nurses, and proactively plan before problems arose.

A Nursing Director at MercyOne Des Moines provided some insight into how this has changed the way they staff for the better, stating “Prior to using a predictive staffing solution we had eight floors following different staffing practices. There was limited transparency across the units and no proactive planning when it came to properly utilizing our resource pools. Now we have the ability to see enterprise-wide staff balances in advance, allowing us to be proactive and strategic about the incentive levels we offer to fill staffing gaps.”

Looking ahead, hospitals need to transform their staffing approach to be more proactive and agile. Improving staffing processes using predictive, data-driven insights allows hospital leaders to foresee challenges, best utilize all available resources and adjust schedules as needed. Ultimately, having the ability the make proactive decisions to provide the best possible care given the circumstances.

Bryan Dickerson serves as the senior director of Healthcare Workforce Solutions. Bryan has 30 years’ of experience working in healthcare, specifically with product management, customer experience and software solutions. At Hospital IQ, Bryan applies his expertise to create the tech solutions that solve the challenges hospital leaders and administrators face with scheduling and staffing.