Play your part in shaping the future.

AI has become a trending topic among healthcare executives. The launch of ChatGPT in 2022 and OpenAI demystified AI by allowing for tangible and easy-to-use applications within our everyday lives. This consumerism of AI has prompted healthcare leaders to consider exploring the technology’s potential.

Since beginning my doctoral work in 2015, I’ve pursued AI to solve nurse staffing and scheduling challenges. The literature revealed that these issues present some of the most mathematically complex optimization problems. That means we need approaches such as predictive modeling, machine learning, regression models, and logic rules to create schedules and inform staffing decisions.

Managers and leaders would no longer have to guess or rely on an average midnight census to build schedules or perform staffing actions. They would have prescriptive guidance to ensure improved accuracy. However, a significant research-to-practice gap exists. Although I discovered abundant literature, most AI models weren’t incorporated into existing scheduling and staffing technology, which limited their application.

To close this gap, I built a predictive scheduling model in 2017 with a provisional patent. I did a small test of change, implementing model predictions into an existing schedule for two medical–surgical departments. I achieved better-aligned scheduling and staffing needs for both. Thrilled with this outcome, I endeavored to bring my AI model alive in practice. I pitched it to several scheduling vendors and health systems but never secured a partnership and folded my patent journey at the end of 2019. I was heartbroken because I knew AI could solve the nurse staffing crisis.

However, I remained committed to solve nurse staffing complexity through best practice approaches, such as contemporizing and streamlining organizational policies like scheduling, staffing, incentives, floating, and timekeeping. I implemented three scheduling and timekeeping technologies, from 2015 to 2023, for three large enterprise health systems and learned as much about the technology as I could. I built and managed central staffing offices, which oversaw scheduling, timekeeping, and staffing. I pushed the limits on flexible workforce programs to incorporate a gig economy, an internal travel agency, and in-house float pools. I pursued a mission to change practices and solve the nurse staffing problem from every angle. Although I never lost hope in predictive modeling and AI as the necessary solution to support best practice, I also understood timing. Healthcare just wasn’t ready for AI.

Fast forward to 2024 and healthcare is now willing to embrace conversations around AI, automation, and prediction in both clinical and operational spaces. My 9-year quest finally became a reality. The lessons learned and the practical knowledge gained have allowed for greater clarity and a deeper understanding of the complexity of nurse staffing. My professional and academic experience has come full circle, with a unique pivot into technology. I now lead solution design aimed at solving nurse hiring, scheduling, and staffing through AI and automation. I want to ensure the frontline healthcare leader’s perspective guides the use of technology.

The takeaway? Don’t lose sight of your North Star. If you have a passion, follow it and persevere. It could just be a timing issue. Most importantly, unfamiliar paths and new horizons like AI must be explored in practice and embraced by leaders so we can continue to advance our profession and solve challenges. We’re on the edge of a new reality. It’s our professional duty to shape the future, otherwise others will shape it for us.  AN

Danielle (Dani) Bowie, an American Nurse Journal editorial board member, is senior vice president of solutions design and workforce AI at Aya Healthcare in San Diego, CA.

References

Bowie D. The era of digitization in nursing workforce management: An overview of contemporary nurse scheduling and staffing practices. In: Weberg D, Mangold K, eds. Leadership in Nursing Practice: The Intersection of Innovation and Teamwork in Healthcare Systems. 4th ed. Burlington, MA: Jones & Bartlett Learning; 2022; 194-229.

Bowie D, Fischer R, Holland ML. Development and implementation of a forecasting model for nurse scheduling. Nurs Econ. 2019;37(3):144-151.

Bowie D, Shelley K, Weigel K, Scherzinger T. How to build a flexible nursing workforce program: Making the dream a reality. Nurse Lead. 2022;20(4):410-14. doi:10.1016/j.mnl.2022.03.006

Drake RG. The nurse rostering problem: From operational research to organizational reality? J Adv Nurs. 2014;70(4):800-10. doi:10.1111/jan.12238