SBIR Phase II: Optimizing emergency department nurse scheduling via a novel operational intelligence platform

Medecipher, Inc., Denver CO

Investigators

Abstract

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to improve emergency clinical care. Emergency Departments are facing complex nursing utilization problems, fueled in part by scheduling systems built on flawed assumptions. Escalating nursing shortages and burnout must be addressed as fatigue is rampant. With hospitals facing significant revenue losses, there is no room for inefficient use of nurses. Nurse shifts must be intelligently sequenced in a way that optimizes available assets and balances complex sets of tradeoffs. The proposed solution uses artificial intelligence (AI) and operations research to predictively right-size clinical resources. The platform will be packaged as a cloud-based solution that reduces nursing staff churn, decreases patient wait times, reduces healthcare delivery costs, and improves revenue. Sophisticated mathematical approaches beyond what is available in the calendar and an Excel spreadsheet – specifically this project’s constraint-based algorithms, simulation methods and machine learning – will be the turning point in optimizing emergency department operational performance. This will improve the health system’s patient care, operational, and financial outcomes while reducing effort, waste, cost, and nurse burnout. This proposed project addresses the multi-stage emergency department nurse staffing problem by increasing scalability and usability. The proposed solution scales a set of multi-objective optimization algorithms based on operations research, sophisticated data science and queueing theory models to create an end-to-end decision support platform. The platform will provide longitudinal decision support for emergency department nurse staffing: nurse managers will use it daily for flexing decisions, monthly for schedule planning and assignment, and annually for budgeting. Objectives include: (1) Data science: refine and automate the forecasting, adapt the optimization model, generate and maintain input files, automate output files; (2) Front-end: create UI components, create service layer/API, wire service layer and UI, develop integration and component testing; (3) Back-end: implement container orchestration system, upgrade security, implement data ingest protocols, support development operations; and (4) Validate recommendation and create client ROI calculator. 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.