Finding people to fill shifts at the last minute can cause major disruptions on your team. What if you could eliminate that stress?
If you take a proactive approach with your shift planning, it benefits the overall effectiveness of your system. By knowing in advance how to schedule the right number of staff at the right times, you and your managers can find ways to optimize labor costs by reducing unnecessary overtime, as well as avoiding turnover and burnout by ensuring proper rejuvenation time between shifts.
This is where machine learning comes into consideration. Machine learning can remove the burden of finding staff to fill sudden gaps in a shift and create more fair and effective schedules for everyone.
But what exactly is machine learning? According to MIT, machine learning is just one way to use artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. A computer program capable of machine learning is able to perform a certain task or improve how it performs a task. That’s why machine learning holds so much promise in healthcare. It can optimize tasks associated with everything from staff scheduling and clinical decisions to personalized medicine and predictive approaches.
The use of machine learning and other AI applications will only continue to grow in the future. Becker’s Health IT reported that healthcare AI was valued at more than $2 billion in 2018 and is projected to exceed $36 billion by 2025.
“Though progress has been made in getting many healthcare systems to bring in new information technology, there is still much room for innovation to be made to improve all aspects of patient care, including safety, patient experience, efficiency, and effectiveness,” according to a paper published in Harvard Science Review. “Machine learning is currently being used in healthcare, but not to its full potential and capabilities, nor is it being applied to the extent that it is used in other industries, such as finance, where it has brought major positive changes and a variety of benefits.”
Staff schedules created well in advance of a shift can have a tremendous impact on the professional and personal lives of healthcare workers. Scrambling to assign shifts and schedules can put unnecessary pressure on your staff and result in last-minute phone calls, group texts, or emails that cause frustration among employees.
The happiness of staff members is important, and scheduling with the use of machine learning can lower their stress and potentially increase engagement levels.
“Indeed, when nurses feel respected, they provide better care, which can boost patient and staff engagement, decrease turnover, and bolster patient outcomes,” reported FierceHealthcare in a recent article.
With Predictive Census, ShiftWizard uses machine learning to give you the power to leverage your facility’s data to simplify and improve scheduling while increasing your team’s satisfaction. Staff is scheduled based on predicted demand, not your budget, which means fewer last-minute schedule changes, fewer wasted resources, reduced burnout, and happier staff.
The forecasting feature within Predictive Census works to help you make long-term, bigger-picture scheduling decisions for you and your team. By utilizing data from up to 120 days in advance, you will be able to identify your census predictions and plan accordingly.
By leveraging real-time data, the machine learning tool also provides short-term projected census, and factors in the current census numbers to inform staffing arrangements within 1-7 days of a shift. This helps ensure that you have the right staff on duty for the projected patient volume.
There is no question that it is a challenge to ensure that your workforce is scheduled cost-effectively while minimizing negative consequences for staff and patient safety. But there is a better way to overcome that obstacle with machine learning.
CLICK HERE to learn how ShiftWizard™, by HealthStream, can help improve your scheduling capabilities.
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