Essentially, most of the burden results from high ratios where a nurse manager on average is responsible for 85 staff nurses and, in some instances, up to 220. Now consider that about 80% of nurse managers have no previous managerial training, according to our study. Not ideal for creating a supportive culture driven by interpersonal relationships or what is now known as nurse engagement.
The challenge: how can a nurse manager be expected to know what they need to know about their direct reports in order to proactively engage them?
Fortunately, technology has been developed to solve that problem in the form of artificial/augmented intelligence (A.I.) and it has finally reached the healthcare industry.
This past week Kristen Hagerman MS, RN a heathcare informatics expert was a special guest on our laudio webinar to explore the applications of A.I. Kristen has held leadership positions in nursing, hospital operations, and informatics departments, including Systems Director at Christus Health; Vice President, Chief Nursing Informatics Officer at Advocate Health Care; Chief Nursing Officer at a private sector health-tech firm; and Founder of Connected Care Advisors.
In her discussion, she explained 4 key components of a comprehensive A.I. solution, whether used for clinical or non-clinical purposes. According to Kristen, a successful, impactful solution must be:
• Individually-focused – holistic
• Action-oriented – reduces cognitive workload
• Scalability – pilot to full organization
• Speed-to-Impact – implementation
Then, focusing on strategies for A.I. specifically in nurse engagement, Kristen outlined the type of data that nurse managers require to build meaningful interactions with individuals on their team. These data included:
• Time and attendance
• Staffing and scheduling
• Competency and certification information
• Performance evaluations
• Incident reports
• Staff development planning
She also explained that to traditionally access this information, “a number of organizations are looking in different data systems, for example their HR system, their educational repository, and perhaps their time and attendance… often looking at spreadsheets with multiple screens open to correlate it yet there is no official analysis of having all this data layered in and having an immediate picture.” She continued to explain how technology solutions like laudio aggregate this information for nurse managers and – using calculations and machine learning – provide insights, recommendations and tools to engage immediately.
Now equipped with evidence and data at his or her fingertips, the nurse manager can reach out with affirmation to recognize nurses who deserve appreciation and also with intervention for other nurses who may be at risk for turnover, based on predictive analytics.
During the webinar, we highlighted the comprehensive research that informed the algorithm. And we dove into the C-A-R-E-S framework, which classifies all the collected data into 60 observations about each nurse that is instantly made available to nurse managers.