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How Predictive Analytics can Alleviate Nurse Burnout
Pre-pandemic, high burnout rates led to a 17 percent overall nurse turnover rate in the United States, with rates as high as 37 percent based on area and nursing specialty.
Fremont, CA: Well before COVID, nurse burnout was a significant source of concern for hospitals and health systems. Nurses work long shifts and face a unique set of physical and emotional difficulties, and even the slightest error can be catastrophic. A team's ability to provide reliable, high-quality patient care suffers as a result of the lack of continuity among nursing staff. Putting care teams in the best possible place and providing them with the support they need to succeed is the first step toward providing high-quality care. Predictive analytics is a powerful tool that allows teams to prepare staffing needs ahead of time, increase nurse satisfaction, and improve care quality.
Staffing and Deployment
Nursing workers' usage must be balanced around the company, bearing in mind the demanding nature of the work and ever-changing schedules, which often leads to unnecessary overtime and job dissatisfaction, resulting in high turnover and retention. Since new hires take time to train to become as productive as current workers, turnover puts financial pressure on companies to reduce labor costs. It also puts a strain on unit leaders to achieve target staffing levels and productivity.
Since they have little input into the prospective patient census, unit leaders spend the bulk of their time negotiating with the recruiting office and other units to ensure that they have enough resources to care for their patients.
This contingency plan does not promote a healthy work atmosphere or the provision of high-quality patient care. Nurses don't know whether they'll be expected to come in on their day off, work overtime, or work on a different unit if they take this approach, or how difficult their days will be because of a higher patient load.
Future of Staffing with AI
Using predictive analytics tools that integrate historical data with machine learning, hospitals can proactively handle staffing issues, allowing nurse leaders to predict future staffing needs days in advance. Leaders will make confident decisions on when and when to send nursing workers if they use these experiences. Improved patient care, fewer last-minute adjustments, and a greater work-life balance for nurses are all benefits of this proactive strategy.
In the future, hospitals will need to change their workforce strategy to be more proactive and agile. Using predictive, data-driven insights to improve staffing processes helps hospital leaders to anticipate problems, make the best use of all available resources, and change schedules as required. And also the courage to make proactive choices to offer the best treatment possible under the circumstances.