Hospital Workforce Scheduling and Management System Using AI-Based Optimization

Hospital workforce scheduling is one of the most operationally critical and administratively burdensome tasks in healthcare management. Manual scheduling is error-prone, time- intensive, and frequently results in unfair shift distribution, staff burnout, and inadequate resource allocation. This paper presents the design, implementation, and evaluation of a Hospital Workforce Scheduling and Management System that leverages Generative AI — specifically OpenAI's GPT-3.5-turbo — to automate and optimize weekly shift scheduling across multiple hospital departments. The system integrates a Flask-MongoDB backend with a dual-mode scheduling engine: an AI-primary mode that generates context-aware, department-specific shift al- locations, and a deterministic Round-Robin fallback that ensures 100% scheduling availability even when the external API is unavailable. The platform supports four distinct user roles — Doctor, Nurse, Receptionist, and Administrator — each with tailored dashboards and access controls. A comprehensive Leave Management System with automated replacement assignment from a general backup pool and email-based notifications is also implemented. Experimental results demonstrate a reduction in scheduling time of approximately 80%, schedule generation in under 5 seconds via AI, and sub-100 ms generation via the fallback algorithm. Security vulnerabilities are identified and a remediation roadmap is established. The system serves as a production-ready, cost-effective, and extensible reference imple- mentation for AI-powered healthcare workforce management.