Risk control method for deducing illegal behavior of medical insurance through data analysis

Abstract

The invention relates to a risk control method for deducing an illegal behavior of medical insurance through data analysis, and the method comprises the steps: 1, obtaining the current and historicalclinical consultation behavior data of a risk control object and the personal information and public data correlated with the risk control object; 2, obtaining the features correlated with the clinical consultation behavior of the risk control object from the data obtained at step 1; 3, classifying the clinical consultation behavior of each clinical consultation in the current and historical clinical consultation behaviors of the risk control object into a corresponding clinical consultation class according to the extracted features, and forming a clinical consultation class sequence; 4, taking the clinical consultation class sequence formed at step 3 as an observation sequence, taking a clinical consultation purpose as a hidden state, calculating the most possible hidden state sequence according to a hidden Markov model, wherein the most possible hidden state sequence comprises the most possible clinical consultation purpose of the risk control object; 5, outputting the medical data correlated with the current and historical clinical consultation behaviors of the risk control object if the most possible clinical consultation purpose, in the hidden state sequence, of the risk control object is corresponding to an abnormal purpose.

Description

technical field [0001] The present invention relates to the technical field of Internet data processing, in particular to a risk control method that infers medical insurance violations through data analysis and provides a basis for system audit or manual audit. Background technique [0002] In the medical insurance social system, tens of thousands of outpatient and hospital medical transaction data occur every day, mainly including transactions between patients and medical institutions, and transactions between medical institutions and insurance institutions. At present, the existing medical insurance processing system is difficult to accurately identify the real needs of patients when processing payment transactions, and the insured person or medical institution may seek improper benefits from it. The existence of medical fraud has seriously affected the balance of income and expenditure of the medical insurance fund, and violated the interests of the insured and social wel...

AI Technical Summary

Problems solved by technology

However, most of the existing medical insurance risk control schemes are based on setting threshold red lines to monitor violations such as fraud, waste, and abuse. Since violations often change with medical insurance policies, payment methods, and supervision, simple threshold divisions are not suitable. The actual application environment of multiple scenarios, the composition of insured personnel, and policy replacement

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