Disease prevalence measuring method based on medical insurance big data

Abstract

The invention discloses a disease prevalence measuring method based on medical insurance big data. The number of all new and old cases of a certain disease in a total population within a certain timeperiod is used as a numerator for calculating the prevalence. The total population in a certain time period is used as a denominator for calculating the prevalence. A number of key parameters of monthly medical insurance data are summarized, including the total number of insured individuals per month, the number of new insured individuals per month, the total number of monthly medical records, andthe total number of diagnosis depletion of monthly medical records. The information of the numerator and denominator for prevalence calculating is acquired, and then the prevalence is calculated. According to the invention, the method is based on medical insurance big data, is simple, fast and effective, can replace the existing epidemiological population survey which consumes labor, money and materials, transforms a prevalence calculation policy based on panel data, optimizes the efficiency of data storage and operation, reduces the risk of privacy leakage and a data sharing threshold, and promotes the transformation application of medical insurance big data.

Description

technical field [0001] The present invention relates to data processing technology, in particular to a method for measuring and calculating disease prevalence based on medical insurance big data, specifically performing statistical operations on the numerator and denominator of the high-efficiency counting prevalence in the summary data format. Background technique [0002] Medical insurance data (Claims data) is the data obtained during the medical insurance business process. Since it does not require sampling surveys, it naturally covers a large-scale population and records the medical information of the population within a certain period of time. It is increasingly used Mostly used in disease prevalence studies. [0003] In conventional epidemiological research, the calculation of the prevalence rate requires the population size of the denominator to be determined and the number of cases corresponding to a certain period of time to be counted. However, for the huge amoun...

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Problems solved by technology

Foreign research is based on individual raw data, and the amount of data matches the performance of computing resources. However, my country’s medical insurance population exceeds 1.3 billion, and in the context of massive data with many times and years, the traditional computing strategy of building individual panel data will lead to data sparseness, Problems such as information redundancy, storage and computing difficulties

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