08-23-2024
In the face of a critical nurse shortage, my collaborator and I developed an innovative nurse deployment program that covers the entire state of Indiana and makes use of advanced data and decision analytics.
Indiana University’s Jonathan Helm and I partnered with Indiana University Health (IUH) System to create the Delta Coverage internal travel nursing program. The program works on an unprecedented state-wide scale, in contrast to existing nurse scheduling tools that cater to single hospitals or units.
The program runs on our Delta Coverage Analytics Suite, which we launched in October 2021 as a Microsoft Power BI application. The suite has two critical components that are seamlessly integrated: a novel patient census forecast based on a deep generative model (currently under patent process) capturing complex spatial-temporal correlations and avoiding error accumulation common in traditional time-series models; and a stochastic optimization that prescribes optimal on-call and call-in decisions based on the forecasted patient census, hospital size, patient acuity, and staff availability.
Our pilot, conducted from May to June 2023, produced a remarkable reduction in understaffing, with estimated annual savings of $1.8 to $2.5 million in IUH and over $7 billion on a national scale compared to the conventional solution of travel nursing. As the first known program of its kind, our efforts establish new benchmarks for evidence-based and data-driven nurse workforce management, potentially transforming how healthcare institutions approach the national nursing shortage crisis.
The decades-long nurse shortage crisis has elevated to the level of a global health emergency, with the United States projected to face a deficit of half a million nurses within the next two years and annual burnout and turnover rates exceeding 20%. The accelerating shortage of nurses combined with large spikes in demand has prompted hospitals and health systems to explore innovative solutions for both the short and long term. We believe our solution is a breakthrough more hospitals can adopt.
Read more about our work here. Our paper is forthcoming in the INFORMS Journal on Applied Analytics, and won first place in INFORMS’ 2024 Innovative Applications in Analytics Award.
Pengyi Shi is an associate professor of management in the Supply Chain and Operations Management area in the Mitch Daniels School of Business. She is a faculty affiliate of the Regenstrief Center for Healthcare Engineering and the Integrative Data Science Initiative. Shi’s research focuses on building data-driven, high-fidelity models and developing predictive and prescriptive analytics to support decision making under uncertainty in healthcare and service systems. One of her main research streams is to develop patient flow models to improve hospital operations and patient outcomes.
