# Artificial intelligence in outpatient management: a simulation-based study in a Chinese tertiary hospital

## TL;DR

This study uses simulations to show that AI can improve outpatient management in Chinese hospitals by reducing waiting times and boosting efficiency.

## Contribution

The study introduces a simulation-based evaluation of AI's impact on outpatient management in a Chinese hospital context.

## Key findings

- AI improved outpatient flow prediction accuracy by about 15% compared to traditional models.
- AI implementation reduced patient waiting times by approximately 30% in simulations.
- AI increased doctors' work efficiency and satisfaction according to survey responses.

## Abstract

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.