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Strategic Study of Chinese Academy of Engineering

Design and Application of Clinical Big Data Management System for Oncology

1. Academic Development Office, Peking University Health Science Center, Beijing 100191, China;

2. Advanced Institute of Information Technology, Peking University, Hangzhou 311215, China;

3. National Institute of Health Data Science at Peking University, Beijing 100191, China

Received:2022-08-21 Revised:2022-09-27 Available online:2022-11-10

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Cancer is a serious threat to human life and health. Along with the development of medical informatization in China, healthcare institutions have cumulated a great quantity of clinical data in oncology; however, these data have not been fully explored owing to the disunity of data standards and great difficulties in data management. Hence, establishing a national clinical big data management system for oncology based on artificial intelligence could potentially promote the application of clinical data in oncology, further improving the quality and efficiency of clinical management for oncology. This study conducted an in-depth analysis of the problems and challenges of clinical data management and application for oncology and presented the significant values of an oncology clinical data management system. Considering the complexity of multi-source and multi-modal data in oncology, we explored the possible mechanisms and pathways of applying artificial  intelligence to the management and research of clinical data for oncology Furthermore, a full-circle solution was designed, and the  construction framework and technology systems were promoted for the clinical data management system for oncology, including the development of common data models for oncology, data collection and security management, data standardization and structuring, data analysis and application, and data quality control. Besides, we validated the feasibility and benefits of the promoted system in clinical practice by taking the clinical data management for lung cancer in a tertiary hospital as an example. Finally, we proposed some suggestions on the research directions of the clinical big data management system for oncology.

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