Implementation and development experience of an AI‐assisted rostering system in a Hong Kong emergency department.
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| Title: | Implementation and development experience of an AI‐assisted rostering system in a Hong Kong emergency department. |
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| Authors: | Sin, Chi‐Kit, Kung, Shu‐Wing |
| Source: | Hong Kong Journal of Emergency Medicine; Dec2025, Vol. 32 Issue 6, p1-6, 6p |
| Subject Terms: | ARTIFICIAL intelligence, SCHEDULING software, HEALTH facilities, PYTHON programming language, EMPLOYEES' workload, HOSPITAL emergency services, MACHINE learning, RESEARCH protocols |
| Geographic Terms: | HONG Kong (China) |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Hong Kong Journal of Emergency Medicine is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Biomedical Index |
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