Implementation and development experience of an AI‐assisted rostering system in a Hong Kong emergency department

Abstract Background Manual emergency department (ED) rostering is labour‐intensive and prone to inconsistency. We developed and implemented an artificial intelligence (AI)‐assisted rostering system that combined large language model (LLM)–supported coding with a constraint solver. This study describes its development, implementation and lessons learnt from real‐world use. Methods This implementation‐science project involved a clinician‐led team building a Python‐based rostering programme using ChatGPT for code generation and Google OR‐Tools for optimisation. Development followed iterative cycles of prototyping, testing and user feedback. The solver first generated a basic roster backbone of A (morning), P (afternoon), N (night) and O (off) duties under fixed, adjustable and soft constraints. A post‐processing module then translated these into duty subtypes to improve coverage. Implementation outcomes included efficiency, roster quality and coverage, assessed by workload balance, reduction of unfavourable patterns, fairness metrics and staff feedback. Results The system produced feasible rosters across five consecutive monthly cycles and reduced drafting time by over 90%. Roster quality improved with more balanced coverage among ranks, fairer duty and off‐day allocation and about 30% fewer unfavourable patterns. The model maintained consistent rest rules and equitable workload distribution. Early phases required constraint tuning and human verification, which decreased as the model stabilised. Informal feedback noted improved predictability, fairness and coverage stability. Conclusion An AI‐assisted rostering system was successfully developed and deployed in a clinical setting through iterative human–AI collaboration. LLM‐assisted programming enabled nonprogrammers to create adaptable operational tools. The modular backbone–post‐processing design allows replication in other EDs with minimal modification.