Construction of a precise supervision mechanism for medical insurance dual-channel drugs based on collaborative filtering algorithm in the era of intelligent medical insurance

In recent years, China's health insurance drug negotiation has become increasingly improved, and the speed of access to the health insurance catalog has increased dramatically.Under the implementation of health insurance negotiated drugs and dual-channel policy, this study investigates the application of negotiated drugs in a certain region to explore their accessibility and affordability.On this basis, it links the health insurance department, designated pharmacies and medical institutions to explore the precise regulatory mechanism of dual-channel drugs in health insurance.For the drug safety supervision therein, collaborative filtering algorithms, attention mechanisms and multi-task learning are utilized to construct an adverse drug reaction prediction model.It is found that under the influence of the health insurance dual-channel policy, the accessibility and affordability models of medicines are enhanced, the types of negotiated medicines, the number and total amount of purchases are increased year by year, and the total amount of purchases by medical institutions and retail pharmacies are enhanced by 3.42 times and 2.36 times, respectively.The proposed prediction model has good accuracy and applicability in predicting adverse drug reactions, with AUC and AUPR values of 0.93 and 0.83 on different datasets, which are better than the comparison methods.It is recommended to continuously promote the construction of the "dual-channel" management mechanism of designated medical institutions and retail pharmacies to enhance the convenience and sense of access to medical care of the insured.