AI Agent-Based Operational Data Intelligent Analysis and Practice
Yaxi Kang, Jiayi Chen, Guoguang Wang, Haifeng Yang, Wenlong Luo, Qi Wang, Shujian Hu · 2025
In the daily operations of the healthcare industry, data analysis and processing play a crucial role [1]. However, traditional healthcare operational data analysis faces multiple challenges such as low efficiency, low accuracy, insufficient depth and breadth, and poor adaptability. This article explores the adoption of intelligent agent technology (LLM Agent and function calling) to construct a new data retrieval and analysis paradigm[2]. By training LLM Agents with deep learning algorithms, they are enabled to accurately understand users' query intentions and needs, and to invoke a set of AI tools[3][5][6], including analysis budget tools, chart tools, multimodal tools, etc., to perform data retrieval tasks [4]. These toolsets not only efficiently retrieve the required data but also design data processing tasks, selecting the most appropriate data and chart tools to present the analysis results[7]. After completing data retrieval and processing, the system further summarizes the data, distilling valuable information and insights[8]. A detailed analysis report is quickly generated and fed back to the user[9]. The report includes not only the analysis results of the data but also provides reasons for fluctuations, helping users better understand the business logic behind the data[10]. In this way, the efficiency of data retrieval and analysis is improved by leveraging artificial intelligence technology, resulting in more accurate, in-depth, and comprehensive analysis results, thereby supporting the formulation of operational management decisions[11]. Through actual testing and application, compared with traditional methods, the efficiency and accuracy of data analysis have been enhanced[12]. Moreover, it is capable of in-depth analysis of complex operational data, providing key information such as reasons for fluctuations, helping management better understand the business logic behind the data, and thus make wiser decisions.