Artificial intelligence is starting to transform the way the healthcare industry operates, from front-office tasks to financial workflows to clinical decision-making. One of the areas with the most potential is nurse staffing, which has reached a breaking point since the start of the pandemic and is now characterized by chronic staff shortages and high contingent labor costs, which have soared by nearly 300% over the last three years. Today, the rates of nurse turnover and nurse vacancy are significantly higher than they were pre-pandemic.
AI has emerged as a powerful tool to help the industry combat these issues and transform nursing into a more sustainable profession, with applications across a variety of care settings. In specialized units like critical care, pediatrics and oncology, AI can help match nurses with the appropriate skills and certifications, ensuring the availability of qualified staff and maintaining high-quality care. It can help larger hospitals and multi-facility healthcare systems sort through massive amounts of data, thus streamlining staffing practices, reducing manual labor, automating basic processes and creating far-reaching efficiencies. At rural facilities who struggle with recruitment and staffing, AI can help predict demand, optimize resource allocation and minimize unnecessary spending.
Finally, AI’s ability to match available nurses with open shifts in the most optimal way means that it has immense value for both internal and external staffing agencies. These organizations can leverage it to optimize assignments based on nurse skills and preferences and streamline scheduling processes, ultimately resulting in placements that are better for the nurse and the facility.
Within the field of staffing, there are five processes that are ripe for disruption from AI. These include:
Recruiting. AI tools can help hospitals find the best staff for a given system or facility by automating things like interview scheduling, resume screening and skill assessment. It can also help remove bias from these processes to ensure a more diverse and equitable workforce.
Scheduling. Scheduling can often represent the biggest single drain on nurse managers’ time and can negatively impact hospitals’ ability to retain these individuals. AI-powered platforms can help alleviate much of this burden by using historical data to predict scheduling needs and using staff preferences to match available nurses with open shifts based on their availability, skills and certifications. It can also create a balanced schedule by factoring in optimal nurse-to-patient ratios and current patient acuity.
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Compensation. AI can determine payment rates and shift pricing by collecting and considering data across a variety of variables, including:
- Staff preferences, including shift desirability and available staff supply
- Historical patient data like admissions and census that indicates seasonal patterns and demand throughout the year
- Real-time monitoring that accounts for current trends and situational information
- Performance data that reflects patient outcomes and other quality metrics
- Benchmarks against local and national staffing compensation rates
- Associated staffing costs such as agency fees, overtime expenses and penalties for understaffing
Performance. AI-powered systems can monitor and make sense of performance metrics like patient outcomes and adherence to best practices in order to identify areas for improvement, provide feedback and support ongoing professional development for nurses.
Retention. By analyzing data on nurse workload, scheduling patterns and job satisfaction surveys, AI systems can flag potential attrition risks and suggest strategies for workload redistribution and scheduling optimization as well as cultural changes your organization may want to consider.
The potential for AI is vast, and organizations who have begun experimenting with it are already seeing impressive results in efficiency, patient outcomes, staff satisfaction, cost and workplace culture. And while many hospitals can benefit from AI, it is not a one-size-fits-all tool, which is why I’ll be sharing more about the questions that every organization should ask before implementing AI to address their staffing needs in a follow-up post.
