ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Medicine and Public Health
Artificial Intelligence in Outpatient Management: A Simulation-Based Study in a Chinese Tertiary Hospital
Provisionally accepted
- 1Wuhan University, Wuhan, China
- 2Shenzhen Second People's Hospital, Shenzhen, China
- 3Shenzhen Polytechnic University, Shenzhen, China
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The Chinese healthcare system faces significant challenges such as an aging population and uneven resource distribution, necessitating technological innovations to enhance service efficiency and quality. This study explores the application, potential value, and challenges of artificial intelligence (AI) in outpatient management in China using simulated data and a physician survey. Simulation results, based on a comparison between an LSTM model and a traditional ARIMA model, demonstrate that the deep learning-based approach outperforms in predicting outpatient flow, improving forecasting accuracy by approximately 15%. Under simulated conditions, AI implementation reduced patient waiting times by about 30% and increased doctors' work efficiency and satisfaction, as supported by survey responses. These findings suggest that AI can optimize resource allocation and patient experience in outpatient settings. However, this study is primarily based on simulation, and real-world applicability may be limited. Additional concerns regarding data privacy, regulatory compliance, and physician acceptance remain critical.
Keywords: artificial intelligence, Chinese tertiary hospital, deep learning, Resource optimization, simulation study
Received: 01 Sep 2025; Accepted: 08 Dec 2025.
Copyright: © 2025 Quan, Wang, Pan and Fang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xiaoxiao Quan
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.