A method and automatic early warning system for production risk identification of pharmaceutical production enterprises

Abstract

This application provides a production risk identification method and an automatic early warning system for pharmaceutical manufacturing enterprises, including: step 1: collecting and analyzing the ADR report data of the target enterprise, and outputting the ADR data set; step 2: using the three-dimensional risk matrix method to evaluate the same batch number The degree of risk of ADR data records; step 3: sort the batch number risk data set, and judge the high-risk batch number area; step 4: use the Bayesian signal detection method to analyze the association between the drug and the target ADR in the high-risk time range Carry out signal detection to a certain extent to identify suspected risk causes; Step 5: Output the high-risk time range, high-risk batch number and variety, and possible risk causes, and propose corresponding suggested measures. The invention can identify the risk signal of irregular production of the enterprise, judge the risk degree of the batch number, estimate the time range of high risk and the cause of the suspected risk, and provide inspection suggestions to the supervision department according to the different degree of risk signal.

Description

technical field [0001] The invention relates to the field of production management, in particular to a method for identifying production risks of pharmaceutical production enterprises and an automatic early warning system thereof. Background technique [0002] Drug safety has always been a key issue in public health research, which is related to public life, health and safety. In recent years, due to the non-standard production operation of some enterprises, "drug injury incidents" have occurred frequently, and the risk of drug production has attracted more and more widespread attention. Drug production risks are mainly manifested in two aspects: one is the risk of the production line, such as improper sterilization operation of the production line within a certain period of time, resulting in higher risks for all varieties and batch numbers output from the production line during this period; the other is the risk of a single variety, such as a period of time The feeding of...

AI Technical Summary

Problems solved by technology

However, the current domestic and foreign adverse reaction signal detection methods, such as proportional reporting odds ratio (PRR), reporting odds ratio (ROR), comprehensive standard method (MHRA), Bayesian confidence interval neural network transfer method (BCPNN) and The multiple gamma Poisson distribution shrinkage estimation method (MGPS) can only identify the natural risks of drugs, but cannot identify the production risks of enterprises that may lead to "drug injury incidents". There is no scientific and effective signal detection method in China that can be used to Monitoring the production risks of drug manufacturers

[0004] To sum up, the current domestic adverse reaction monitoring has the following problems: 1. There is a lack of a signal detection method for the production risk of the enterprise, which cannot identify the production risk of the production line and variety of the specific enterprise, and cannot target the production line and variety of the drug manufacturer. Carry out effective supervision and inspection; 2. Lack of an automatic early warning system to identify enterprise risk signals, unable to efficiently use abundant adverse reaction database resources, realize dynamic monitoring and real-time monitoring of enterprise production risks, and fail to timely report to regulatory authorities and enterprises Send high-risk signals to prevent major drug accidents

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