Everyone is talking about applications for AI in healthcare. Nurse workforce transformation is certainly one. This post explores one innovative CNE’s perspective on and successes with leveraging AI to transform her system’s workforce. 

At AONL 2024, Mercy’s senior VP and CNE Betty Jo Rocchio, DNP, CRNA presented on AI integration in nursing. She detailed lessons learned over the past four years implementing AI solutions at Mercy, a 50-hospital, St. Louis-based system. The bulk of her presentation focused on her successes leveraging AI to add efficiency and value to her nurse workforce management strategy. 

In fact, nurse workforce optimization was the first place Mercy started when they began executing their AI integration roadmap. It topped the priority list for a reason. The confluence of growth in demand for nurses and a dwindling supply of nurses was causing myriad staffing issues, including driving costs up and clinician wellbeing down. 

Pre-pandemic, the US Bureau of Labor and Statistics projected a need for 1.06 million nurses by 2022– and that need grew post-pandemic as nurses left the workforce in droves. Even before COVID-19 640,000 baby boomer nurses were set to retire between 2020 and 2030. In 2023, many nurses reported considering leaving their roles due to insufficient staffing and their role’s impact on personal health.

Mercy aimed to use technology to address the issues driving short staffing, nurse burnout, and rising labor costs. Their goal was to find applications for AI-powered automation that would establish a more balanced workforce and add efficiency to workforce management. By implementing AI and automation in their nurse staffing process, Mercy was able to maximize nurse workforce flexibility, improve fill rate and nurse satisfaction, and build a more scalable multi-layer nurse workforce. 

This blog post will cover key strategies Mercy employed to leverage AI-powered technology to transform nurse workforce, including:

• Involving Nurse Stakeholders in Workforce Technology Planning
• Adopting a Platform Strategy to Efficiently Use All Nurse Labor Pools
• Leveraging Technology to Offer Nurse Scheduling Flexibility
• Automating Systems to Reduce Administrative Burden in Nurse Staffing 
  

Read on for a case study on how to successfully leverage AI to improve nurse workforce management. 

Involving Nurse Stakeholders in Workforce Technology Planning

Dr. Rocchio knew that building a sustainable nurse workforce required attracting and retaining nurses from all generations. So, she and her colleagues studied nurse preferences across all age groups, from Gen Z “Zoomers” to Baby Boomers. Universally, nurses indicated a desire for more technology, control over their schedule, work-life balance, and flexible compensation.

In addition to researching nurse workforce preferences by generation, Dr. Rocchio also held focus groups within each of Mercy’s hospitals to hear directly from bedside nurses about what was working and what wasn’t in terms of workforce management. It was crucial for Dr. Rocchio to design a workforce technology solution involving frontline perspectives and buy-in. 

“We didn’t sit at a corporate level and decide how we were going to do this,” Dr. Rocchio shared in a recent interview with Beckers, “We’re not just out there putting AI into workflows. Nurses asked for it. They were involved in the implementation.” 

Based on Dr. Rocchio’s research and focus groups within her organization. Her team defined four primary priorities to improve workforce management through technology: 

1. Maximize flexibility to improve fill rate and satisfaction 

2. Increase options and control in schedule and compensation  

3. Enable access for multiple workforce layers and generations 

4. Offer scalable, on-demand technology with automation and AI to manage schedules 

Adopting a Platform Strategy to Efficiently Access All Nurse Labor Pools

After defining these priorities, Mercy saw implementing a central digital workforce management platform as a starting point to achieve their strategic objectives. Achieving their goals wouldn’t be possible if they continued to handle workforce management by units or by individual RN or LPN techs in spreadsheets and via email. Mercy needed a workforce management platform that would allow for full workforce visibility. In 2019, Mercy started working with our workforce management platform to implement a flexible workforce model and manage every level of their workforce in one place. This included agency, flex, and core nurse labor. 

What a Digital Workforce Management Platform Enabled:

The Works platform was the connector between all workforce layers. It enabled Mercy to automate their entire nurse scheduling workload across all units and the total available labor pool.

Automation of previously manual scheduling and credentialing processes

The platform automates previously manual nurse onboarding, credentialing, and scheduling tasks. Instead of chasing down scattered documents and dealing with lengthy credentialing processes, the platform keeps credentials updated and automates every step along the way. And, with scalable, on demand technology, Mercy is able to see every available nurse, automate posting of available shifts, and match best-fit nurses to shifts. Mercy’s staffing office and nurse leaders are no longer manually dictating schedules–supply, demand, and nurses themselves are.

Nurses shift pick up from mobile app with real-time available shifts

The workforce management platform’s mobile app allows Mercy nurses to pick up the shifts that work best for them from their phones, at their convenience. 

“Today in this workforce platform, I have nurses that can work one day in a clinic setting, one day in an ICU setting, and one day virtual. That has been particularly appealing to our more experienced nurses because they want that variety.” Dr. Rocchio shared in her AONL talk.  

Overall, technology has allowed Mercy to offer more flexibility to their entire nurse workforce, including their core nurses who were leaving the system for more flexible travel opportunities.

AI-powered shift notifications and dynamic pricing that adjusts incentives based on supply and demand

The Works platform leverages AI to match best-fit clinicians with the right shift options and right-sized incentives based on supply and demand. After implementing the platform and AI-powered, need-based incentives, Mercy saw an improvement in fill rate, which also improved nurse workload. Beyond that, the technology also helped Mercy reduce overall premium spend that had skyrocketed during the pandemic. 

"What's going on in the background is AI is producing not only the shift but the amount of extra money that's launched onto that shift," Dr. Rocchio told Beckers. "It's all math in the background delivered through AI."

Efficient Use of All Labor Pools

Implementing a digital nurse workforce platform allowed Mercy to build and leverage their own flexible workforce layer. This layer spans Mercy’s system-wide, regional, and local staffing pool. This flex layer includes their “gig” or “per diem” nursing workforce. (Mercy defines a gig nurse as any nurse that is not a full-time employee, has more than one year of experience, and works more than 12 hours a month.) These nurses are employed by Mercy and onboarded like an agency, but they are under the Mercy umbrella. Mercy then incentivizes its flex nurses to the area of greatest need. For Mercy, achieving the most efficient fill rate included a combination of core staff nurses, some agency labor, and their flex layer. 

“Agency has a spot, but it must be managed within the workforce layers like everything else. On this platform, we manage our agencies in the same place we manage other core layers.” Dr. Rocchio said in her AONL talk. 

By offering more flexible options to their internal flex staff through their nurse workforce platform’s mobile app, they reduced agency labor use, achieved a higher fill rate, gained staffing efficiency, lowered turnover, and reduced total cost to deliver care. 

Leveraging Digital Tools to Offer Nurse Scheduling Flexibility

Automating scheduling in the workforce management platform also enabled Mercy to think beyond the traditional 12-hour shift. Whether a nurse wants to work 12,10, 8, 6, or 4 hours, Mercy’s AI-powered staffing platform matches nurse shift length preferences with actual patient census demand to optimize fill rate.  

Beyond efficiency improvement and cost savings, allowing shift length flexibility is a strategic differentiator and recruiting and retention tool for Mercy. As clinicians had the power to self-select what layer they wanted to be in head count, how long they preferred to work, and which unit they wanted to work in, Mercy retained more nurses. Within one year of implementing the technology, Bedside FTES went up 3.5% and turnover dropped 9%. Due to the success of the platform strategy, Mercy has moved beyond just using it for nursing and is also using it for techs, respiratory, EVS and transport. 

Reducing Administrative Burden for Nurse Managers by Automating Systems

Automating staffing and scheduling and nurse credentialing management, offering nurses a mobile app to view real-time available shifts, and leveraging AI to match best-fit nurses to the area of greatest need removes a large administrative burden from nurse managers.  

When Mercy’s staffing team has needs, they post it in the app and the app assesses the credentialing system. For example, if Mercy needs 10 medical surgical nurses, that need hits the credentialing platform, the system assesses how many nurses Mercy has that can fill this need and automatically pushes out the shift to nurses whose profiles match the need. When a nurse picks up a shift, the app writes it back to the staffing and scheduling system, eliminating time consuming phone calls from nurse managers begging people to work after nurses call off at 4:30 in the morning.  

“My managers are still sleeping because [last minute needs] go into the staffing and scheduling system,” said Dr. Rocchio.  

The earlier a nurse picks up a shift, the more incentive they see because incentives are based on fill rate. The lower the fill rate, the higher the incentive for picking up the shift. 

“And that's where the magic happens with AI. It’s just like Uber and Lyft.” Dr. Rocchio shared in her talk. “Have you ever stood outside a concert, and you're like, why am I paying $50 to get home when yesterday at the same time I paid $30? It’s supply demand in the background.” 

In Mercy’s workforce platform, AI is continuously calculating the optimal rate to fill each shift, and it gets smarter the longer it learns system patterns. Mercy sets the minimum and maximum rates, and the AI works within those parameters. The result over time is lower labor costs and premium spend.  

“Now I have two years' worth of data. It's refined, it knows what it's going to have to launch to get the shift filled. It knows my people over here in the credentialing system. So, I'm not wasting money trying to get the shift filled. No manager makes any phone calls, nobody can launch any different rates. It all happens in the background with math.” Dr. Rocchio said.  

AI Transforms Nurse Workforce Management

The integration of AI in Mercy’s nurse workforce management has led to significant efficiencies and innovations. Under the leadership of Dr. Rocchio, Mercy has demonstrated how technology can address critical challenges such as staffing shortages, nurse burnout, and rising labor costs. 

By leveraging AI-powered automation and a digital workforce management platform, Mercy achieved greater workforce flexibility, improved nurse satisfaction, and reduced administrative burdens. This approach not only optimized the allocation of nursing resources but also provided a scalable model that can adapt to varying demands and preferences. As Mercy continues to refine and expand its AI-driven strategies, it sets a benchmark for other healthcare systems aiming to enhance their workforce efficiency and care delivery through technology.

The content in this post is inspired by a presentation given by Works and Senior Vice President and chief nurse executive of Mercy Betty Jo Rocchio, DNP, CRNA, CENP at AONL 2024.

Learn more about Mercy’s Success with Works

Mercy built a more flexible, efficient clinical workforce leveraging Works. Read the full case study here.

If you’re interested in spearheading similar change at your facility, request a demo today and get started in as little as two weeks.