Construction of an Intelligent Early Warning System for a Cloud-Based Laboratory Data Platform under the Medical Consortium Model

Abstract

With the continuous advancement of the tiered diagnosis and treatment system, the medical consortium model has gained increasing attention as an important approach to promoting the vertical integration of healthcare resources. Within this context, laboratory data, as a key component of healthcare information systems, urgently requires efficient sharing and intelligent analysis. This paper designs and constructs an intelligent early warning system for laboratory data based on a cloud platform tailored to the medical consortium model. Through standardized data formats and unified access interfaces, the system enables the integration and cleaning of laboratory data across multiple healthcare institutions. By combining medical rule sets with machine learning models, the system achieves graded alerts and rapid responses to abnormal key indicators and potential outbreaks of infectious diseases. Practical deployment results demonstrate that the system significantly improves the utilization efficiency of laboratory data, strengthens public health event monitoring, and optimizes inter-institutional collaboration. The paper also discusses challenges encountered during system implementation, such as inconsistent data standards, security and compliance concerns, and model interpretability, and proposes corresponding optimization strategies. These findings provide a reference for the broader application of intelligent medical early warning systems.