I-Corps: Translation Potential of Stochastic Optimization to Enable Real-Time Staffing Needs in Hospitals

This I-Corps project is based on the development of an advanced decision-support system that optimizes workforce allocation in healthcare settings. The system addresses critical staffing shortages by dynamically assigning medical professionals across multiple facilities based on real-time demand, patient needs, and workforce availability. By reducing understaffing and reliance on expensive temporary workers, this solution enhances operational efficiency, improves patient care, and mitigates burnout among healthcare workers. Beyond healthcare, the technology has potential applications in other industries that require dynamic workforce distribution, such as emergency response, retail, and logistics. By leveraging advanced analytical techniques to improve decision-making in complex, resource-constrained environments, this project contributes to economic sustainability and workforce resilience. The commercial potential lies in its ability to provide scalable, data-driven staffing recommendations, offering significant cost savings and efficiency gains for large organizations that face fluctuating demand and constrained labor resources. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a predictive and prescriptive analytics system that integrates machine learning with optimization techniques to generate real-time staffing recommendations. Unlike existing workforce management tools that focus on single facility, this technology continuously adapts to changes in demand, hospital conditions, and workforce availability across facilities. The research behind this project builds on state-of-the-art methods from operations research and machine learning to develop a decision-support system that optimally balances workforce distribution while maintaining operational efficiency and resilience. The research underlying this system has demonstrated its ability to reduce staffing inefficiencies and improve resource allocation through simulation and pilot testing in real-world settings. By enhancing the adaptability and responsiveness of workforce planning, this project advances the field of decision analytics and has the potential to drive widespread adoption of intelligent resource management solutions across multiple sectors. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.