Data quality control system based on public health refinement management

Abstract

The invention relates to a data quality control system and method based on public health refinement management. The system comprises a data sharing exchange area and a checking module, the data sharing exchange area comprises a data buffer cache, a checking rule area and a data back area, wherein the data buffer cache stores initial data received from a source platform, the checking rule area stores data checking rules, and the data back area stores data which needs to be sent back to the source platform due to the fact that checking fails; the checking module invokes the data checking rules to check the initial data received from the source platform, and checking comprises longitudinal balance checking between tables. By means of the data quality control system and method based on public health refinement management, all public health service system data which has access to a regional health information platform can be effectively monitored, comprehensive evaluation can be conducted on the data transversely and longitudinally and in all dimensions with small probabilities, and automated and intelligent management on the data is formed.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to the technical field of data verification for fine management of public health, and specifically refers to a data quality control system based on fine management of public health. Background technique [0002] With the continuous improvement of social economy and scientific and technological level, the concept and method of public health business management have undergone fundamental changes. The scope of the information system is getting wider and wider, and independent information systems have exposed problems such as process isolation and data islands, which have made it difficult to adapt to business development. Correspondingly, the quality control for data is also for each system, and each business department conducts quality control separately. In recent years, the original manual sampling quality control method has been gradually transformed into an informa...

AI Technical Summary

Problems solved by technology

[0004] -Multiple users enter data together, and the information each person understands is not comprehensive, which may lead to contradictory data entry;

[0005] -Requirements for required items are difficult to control, especially for data that cannot be collected at one time, during the system design process, only part of the required verification can only be given up;

[0006] - Data collection and verification affect efficiency. In order to ensure daily business operation and reduce system load, it is often impossible to do too complicated data verification quality control

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