DRG grouping method based on artificial intelligence

Abstract

The invention discloses a DRG grouping method based on artificial intelligence, and the method comprises the steps: 1), obtaining medical record data and settlement data, automatically obtaining a data feature set through an AI technology, and carrying out the cutting of the obtained data; 2) dividing the data into 26 MDC according to the main diagnosis, and subdividing the 25 MDC into ADRG through combination with main surgical operation; 3) further subdividing the ADRG into three groups by combining CC and MCC on the basis of the ADRG; 4) selecting a core characteristic factor in the characteristic set, respectively clustering the subdivided three groups of medical record data of the ADRG by utilizing a clustering algorithm, and obtaining DRG; and 5) processing the clustered DRG by usinga decision forest, and combining partial groups to obtain a final DRG group. Disease diagnosis grouping DRG screening characteristic factors are automatically screened by using the artificial intelligence (AI) technology, and grouping is carried out in combination with clinical practice, so that the empirical constraints are broken through, and the grouping accuracy and effectiveness are improved.

Description

technical field [0001] The present application relates to artificial intelligence technology, in particular to an artificial intelligence-based DRG grouping method. Background technique [0002] In recent years, DRGs (Diagnosis Related Groups) have gained considerable attention in the healthcare services industry. In 1988, DRG was introduced to China, which started the beginning of China's DRG system technology research; in 2004, Beijing began to pay for DRG research. In 2015, the National Health and Family Planning Commission led the national DRG research work. The country has accumulated more than ten years of DRG application experience, covering Quality control of medical records, disease grouping, fund allocation, performance management and other aspects. However, the promotion of DRG groups has been limited, and the main problems are concentrated in three aspects. One is that the degree of adaptation and recognition of diagnostic coding and surgery is low, which leads...

AI Technical Summary

Problems solved by technology

One is that the degree of adaptation and recognition of diagnostic coding and surgery is low, which leads to poor quality of collected data and affects the grouping effect; second, the method adopted in the grouping relies heavily on manual experience, has serious regional characteristics, and is highly dependent on the region. The promotion is poor; the third is that the processing method after grouping is not universal, and the effect of grouping and adjustment methods are limited by resources, which makes it difficult to guarantee the effect of grouping and difficult to apply

Images