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CN119313476A - A medical insurance management agency management system and method based on big data - Google Patents

A medical insurance management agency management system and method based on big data Download PDF

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CN119313476A
CN119313476A CN202411331231.0A CN202411331231A CN119313476A CN 119313476 A CN119313476 A CN 119313476A CN 202411331231 A CN202411331231 A CN 202411331231A CN 119313476 A CN119313476 A CN 119313476A
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鲍卫国
李勃
程玮
邓云进
林锐
焦洋
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Xinhua Zhikang Technology Co ltd
Beijing Red Chengzhang Technology Co ltd
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Abstract

本发明公开了一种基于大数据的医保管理机构管理系统及方法，涉及医保管理机构管理技术领域，包括对医保药品在药品核销过程中的核销异常程度进行分析，得到特征医保药品；获取历史异常医保药品的历史药品销售数据，获取当前周期内的特征医保药品的药品销售数据，分析特征医保药品与历史异常医保药品之间的药品销售近似性，得到目标历史异常医保药品；获取目标历史异常医保药品的历史异常数据，基于特历史异常数据，对当前周期内的特征医保药品在销售过程中不同销售环节的异常性进行评估；对当前周期内特征医保药品的目标异常数据进行获取，基于目标异常数据，医保管理机构对医保药品的销售进行智能管理。

The present invention discloses a management system and method for a medical insurance management agency based on big data, and relates to the technical field of management of medical insurance management agencies, including analyzing the abnormal degree of write-off of medical insurance drugs in the process of drug write-off to obtain characteristic medical insurance drugs; obtaining historical drug sales data of historical abnormal medical insurance drugs, obtaining drug sales data of characteristic medical insurance drugs in a current cycle, analyzing the drug sales similarity between characteristic medical insurance drugs and historical abnormal medical insurance drugs, and obtaining target historical abnormal medical insurance drugs; obtaining historical abnormal data of target historical abnormal medical insurance drugs, and based on the special historical abnormal data, evaluating the abnormality of different sales links in the sales process of characteristic medical insurance drugs in the current cycle; obtaining target abnormal data of characteristic medical insurance drugs in the current cycle, and based on the target abnormal data, the medical insurance management agency intelligently manages the sales of medical insurance drugs.

Description

Medical insurance management mechanism management system and method based on big data
Technical Field
The invention relates to the technical field of management of medical insurance management institutions, in particular to a medical insurance management institution management system and method based on big data.
Background
The data volume that the medical insurance management mechanism needs to process is very huge, cover millions or even hundreds of millions of personnel's participation information, medical records, expense reimbursement, data such as medicine use, these a large amount of and complicated data, therefore, in order to be able to manage the medical insurance management mechanism, big data technology is gradually applied on the management of medical insurance management mechanism, big data technology can provide a large amount of real-time data and data analysis tools, help the medical insurance management mechanism to make the decision fast, rapidly and accurately, thereby improve management efficiency, and utilize big data technology, can help the medical insurance management mechanism to carry out analysis to the unusual mode and the fraudulent behavior in the medical insurance process, thereby take corresponding measure and manage, protect the security of medical insurance fund.
At present, one of the main works of the medical insurance management mechanism is to analyze the sales process of the analysis medical insurance medicine, the medical insurance management mechanism checks and disposes illegal and legal actions in the sales process of the medical insurance medicine according to the verification result, so that the authenticity of the medical insurance medicine transaction is ensured, but the current abnormal analysis of the sales transaction process of the medical insurance medicine mainly depends on periodic audit and assault detection, so that a large amount of manpower and material resources are required, the management cost is increased, the analysis of the data in the sales process of the medical insurance medicine is increased, a large amount of support of specialized staff is required, the resource is likely to be insufficient, and the traditional method is difficult to correspondingly optimize the sales process of the medical insurance medicine after acquiring the abnormality in the sales process of the medical insurance medicine.
Disclosure of Invention
The invention aims to provide a medical insurance management mechanism management system and method based on big data, which are used for solving the problems in the prior art.
In order to achieve the purpose, the invention provides the technical scheme that the medical insurance management mechanism management method based on big data comprises the following steps:
Step 100, acquiring medicine verification data of the medical insurance medicine in the current period, and analyzing the verification abnormality degree of the medical insurance medicine in the medicine verification process to obtain a characteristic medical insurance medicine;
Step 200, acquiring historical medicine sales data of the historical abnormal medical insurance medicine, acquiring medicine sales data of the characteristic medical insurance medicine in the current period, and analyzing medicine sales similarity between the characteristic medical insurance medicine and the historical abnormal medical insurance medicine to obtain a target historical abnormal medical insurance medicine;
step S300, acquiring historical abnormal data of a target historical abnormal medical insurance medicine, and evaluating the abnormality of different sales links of the characteristic medical insurance medicine in the current period based on the special historical abnormal data to obtain the target abnormal data;
Step S400, acquiring target abnormal data of the characteristic medical insurance medicine in the current period, and intelligently managing sales of the medical insurance medicine by a medical insurance management mechanism based on the target abnormal data.
Further, step S100 includes:
Step S101, acquiring medicine verification data of medical insurance medicines sold in the current period from a medical management institution, wherein the medicine verification data comprises medicine codes and verification amounts of the medical insurance medicines;
step S102, according to the medicine codes of the medical insurance medicines, collecting the same medicine names and the medical insurance medicines of manufacturers to obtain a medical insurance medicine set;
Step S103, acquiring the verification and approval amount of each medical insurance medicine in the medical insurance medicine set, and calculating the characteristic data fluctuation value Q of the medical insurance medicine set:
Wherein X i is the amount of the verification of the ith medical insurance drug in the medical insurance drug set, N is the total number of the medical insurance drugs in the medical insurance drug set, and mu is the average value of the amount of the verification of the medical insurance drugs in the medical insurance drug set;
step S104, calculating characteristic verification abnormal values of all the medical insurance medicines in the medical insurance medicine set, wherein the characteristic verification abnormal value L a of the a-th medical insurance medicine in the medical insurance medicine set is as follows:
wherein X a is the amount of the cancel-after-verification of the a-th medical insurance drug in the medical insurance drug set;
and step 105, judging that the a-th medical insurance medicine is abnormal in medicine approval in the medicine approval process when the characteristic approval abnormal value of the a-th medical insurance medicine is larger than a preset characteristic approval abnormal threshold value, and marking the a-th medical insurance medicine as the characteristic medical insurance medicine.
Further, step S200 includes:
Step S201, obtaining historical medicine sales data of historical abnormal medical insurance medicines from a database of a medical management institution, wherein the historical abnormal medical insurance medicines are the same as the characteristic medical insurance medicines in medicine types, and the historical medicine sales data comprise data corresponding to various medicine sales parameters of the historical abnormal medical insurance medicines;
Step S202, acquiring medicine sales data of the characteristic medical insurance medicine, wherein the medicine sales data comprise data corresponding to various medicine sales parameters of the characteristic medical insurance medicine;
step 203, randomly selecting a plurality of historical abnormal medical insurance medicines, respectively preprocessing and feature-coding each medicine sales parameter of the plurality of historical abnormal medical insurance medicines to obtain feature values of each medicine sales parameter, and constructing a data matrix U:
The method comprises the steps of obtaining a p-th medicine sales parameter of a j-th historical abnormal medical insurance medicine, wherein U jp is a characteristic value of the p-th medicine sales parameter of the j-th historical abnormal medical insurance medicine in a plurality of historical abnormal medical insurance medicines, U jp is a characteristic value of the p-th medicine sales parameter of the j-th historical abnormal medical insurance medicine in a plurality of historical abnormal medical insurance medicines, U 1p is a characteristic value of the p-th medicine sales parameter of the 1-th historical abnormal medical insurance medicine in a plurality of historical abnormal medical insurance medicines, U j1 is a characteristic value of the 1-th medicine sales parameter of the j-th historical abnormal medical insurance medicine in a plurality of historical abnormal medical insurance medicines, j is the total number of the j-th historical abnormal medical insurance medicines, and p is the total number of all medicine sales parameters;
Step S204, performing standardization processing on each element of the data matrix U to obtain a normalized data matrix U 'of the data matrix U, and calculating a characteristic covariance matrix C of the data matrix U':
Wherein U 'T is the transpose of the data matrix U';
Step S205, calculating a eigenvalue lambda and an eigenvector v of the eigenvalue covariance matrix C, wherein the eigenvalue lambda and the eigenvector v meet the equation Cv=lambdav, wherein (C-lambdaI) v=0, and carrying out descending order arrangement according to the numerical value of the eigenvalue, and selecting eigenvectors corresponding to preset k eigenvalues to form a projection matrix W;
Step S206, preprocessing data corresponding to each medicine sales parameter of the feature medical insurance medicine based on the medicine sales data, and feature encoding each medicine sales parameter of the feature medical insurance medicine after preprocessing to obtain a feature sales vector group F of the feature medical insurance medicine, and performing dimension reduction on the feature sales vector group F by using a projection matrix to obtain a main feature sales vector group F △ of the feature medical insurance medicine, wherein F △ =FW;
Step S207, acquiring a main feature sales vector group of the historical abnormal medical insurance medicine, and calculating a medicine sales approximate value K g of the g-th historical abnormal medical insurance medicine in the feature medical insurance medicine and a database:
Wherein, B g is expressed as a main characteristic sales vector group of the g-th historical abnormal medical insurance medicine;
Step S208, when the medicine sales approximate value K g is larger than a preset medicine sales approximate threshold, judging that the medicine sales conditions between the characteristic medicine and the g-th historical abnormal medicine are approximate, and marking the g-th historical abnormal medicine as a target historical abnormal medicine of the characteristic medicine;
The standardized processing is carried out on each element in the data matrix in the steps, so that the influence of dimensions among different medicine sales parameters of different historical abnormal medical insurance medicines is eliminated, and the accuracy of the data is ensured.
Further, step S300 includes:
step 301, acquiring historical abnormal data of each target historical abnormal medical insurance medicine of the characteristic medical insurance medicine, wherein the historical abnormal data is a sales link of the detected abnormal target historical abnormal medical insurance medicine;
Step S302, calculating link anomaly values of all sales links in the feature medical insurance medicine, wherein the link anomaly value H β of the beta-th sales link in the feature medical insurance medicine:
M sum represents the total number of the target historical abnormal medical insurance medicines of the characteristic medical insurance medicines, M β represents the total number of the target historical abnormal medical insurance medicines of which the beta-th sales link is detected to be abnormal;
Step S303, when the link anomaly value of the beta-th sales link is larger than a preset environment anomaly threshold value, judging that the beta-th sales link in the characteristic medical insurance medicine has anomaly, and marking the beta-th sales link as a target anomaly sales link of the characteristic medical insurance medicine in the sales process;
And step S304, collecting a plurality of target abnormal sales links of the characteristic medical insurance medicines to obtain target abnormal data of the characteristic medical insurance medicines.
Further, step S400 includes:
Step S401, acquiring each characteristic medical insurance medicine in the current period, acquiring target abnormal data of each characteristic medical insurance medicine, and extracting a target abnormal sales link of the characteristic medical insurance medicine from the target abnormal data;
Step S402, based on the target abnormal sales links of the medical insurance medicines with various characteristics in the current period, the medical insurance management mechanism performs traceability check on the sales links of the medical insurance medicines in the current period, and performs intelligent management on the medical insurance medicine sales in the current period.
In order to better realize the method, the invention also provides a medical insurance management mechanism management system based on big data, wherein the system comprises a characteristic medical insurance medicine module, a target history abnormal medical insurance medicine module, a target abnormal data module and an intelligent management module;
the characteristic medical insurance medicine module is used for analyzing the abnormal verification degree of the medical insurance medicine in the medicine verification process to obtain the characteristic medical insurance medicine;
The target historical abnormal medical insurance medicine module is used for acquiring medicine sales data of the characteristic medical insurance medicine in the current period, analyzing medicine sales similarity between the characteristic medical insurance medicine and the historical abnormal medical insurance medicine, and obtaining the target historical abnormal medical insurance medicine;
The target abnormal data module is used for acquiring historical abnormal data of the target historical abnormal medical insurance medicine, and evaluating the abnormality of different sales links of the characteristic medical insurance medicine in the current period in the sales process to obtain target abnormal data;
The intelligent management module is used for acquiring target abnormal data of the characteristic medical insurance medicine in the current period, and based on the target abnormal data, the medical insurance management mechanism carries out intelligent management on sales of the medical insurance medicine.
Further, the characteristic medical insurance medicine module comprises a characteristic verification abnormal value unit and a characteristic medical insurance medicine unit;
The characteristic verification abnormal value unit is used for collecting the medical insurance medicines according to the medicine codes of the medical insurance medicines to obtain a medical insurance medicine set, and calculating characteristic verification abnormal values of all the medical insurance medicines in the medical insurance medicine set;
And the characteristic medical insurance medicine unit is used for analyzing the medical insurance medicine with abnormal medicine verification according to the characteristic verification abnormal value to obtain the characteristic medical insurance medicine.
Further, the target history abnormal medical insurance medicine module comprises a medicine sales approximation unit and a target history abnormal medical insurance medicine unit;
the medicine sales approximation unit is used for calculating the medicine sales approximation value of the characteristic medical insurance medicine and the historical abnormal medical insurance medicine in the database of the medical insurance management institution;
And the target history abnormal medical insurance medicine unit is used for acquiring the target history abnormal medical insurance medicine of the characteristic medical insurance medicine according to the medicine sales approximate value.
Further, the target abnormal data module comprises a link abnormal value unit and a target abnormal data unit;
the link abnormal value unit is used for calculating the link abnormal values of each sales link in the characteristic medical insurance medicine;
And the target abnormal data unit is used for carrying out abnormality judgment on different sales links in the characteristic medical insurance medicine according to the link abnormal value to obtain target abnormal data of the characteristic medical insurance medicine.
Further, the intelligent management module comprises an intelligent management unit;
The intelligent management unit is used for acquiring each characteristic medical insurance medicine in the current period, acquiring target abnormal data of each characteristic medical insurance medicine, performing traceability inspection on the sales links of the medical insurance medicines based on the target abnormal data, and performing intelligent management on the medical insurance medicine sales in the current period.
Compared with the prior art, the intelligent management method has the beneficial effects that the intelligent management of the medical insurance medicine in the sales process is realized, the abnormal verification and verification degree of the medical insurance medicine in the verification and verification process is analyzed, the characteristic medical insurance medicine with abnormal verification and verification is obtained, the characteristic medical insurance medicine and the historical abnormal medical insurance medicine with problems in the detected sales links are analyzed, the approximation degree of the medicine sales aspect is obtained, the possible abnormal sales links of the characteristic medical insurance medicine are obtained, the medical insurance management mechanism optimizes and adjusts the sales process of the medical insurance medicine according to the distribution situation of the abnormal sales links, the management mode does not only does not need to input a large amount of manpower and material resources, but also can optimize the sales of the medical insurance medicine according to actual situations, the authenticity of the medical insurance medicine transaction is greatly ensured, and the safety of the medical insurance funds is also protected to a certain extent.
Drawings
FIG. 1 is a flow chart of a method of the present invention for a medical insurance management institution management system and method based on big data;
FIG. 2 is a schematic block diagram of a system and method for managing medical insurance authorities based on big data according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
1-2, The invention provides a technical scheme, namely a medical insurance management mechanism management method based on big data, which comprises the following steps:
Step 100, acquiring medicine verification data of the medical insurance medicine in the current period, and analyzing the verification abnormality degree of the medical insurance medicine in the medicine verification process to obtain a characteristic medical insurance medicine;
wherein, step S100 includes:
Step S101, acquiring medicine verification data of medical insurance medicines sold in the current period from a medical management institution, wherein the medicine verification data comprises medicine codes and verification amounts of the medical insurance medicines;
step S102, according to the medicine codes of the medical insurance medicines, collecting the same medicine names and the medical insurance medicines of manufacturers to obtain a medical insurance medicine set;
Step S103, acquiring the verification and approval amount of each medical insurance medicine in the medical insurance medicine set, and calculating the characteristic data fluctuation value Q of the medical insurance medicine set:
Wherein X i is the amount of the verification of the ith medical insurance drug in the medical insurance drug set, N is the total number of the medical insurance drugs in the medical insurance drug set, and mu is the average value of the amount of the verification of the medical insurance drugs in the medical insurance drug set;
For example, the total number N of the medical insurance medicines in the medical insurance medicine set is 3, the cancel amount X 1 of the 1 st medical insurance medicine in the medical insurance medicine set is 200, the cancel amount X 2 of the 2 nd medical insurance medicine in the medical insurance medicine set is 220, the cancel amount X 3 of the 3 rd medical insurance medicine in the medical insurance medicine set is 270, the average value mu of the cancel amounts of the medical insurance medicines in the medical insurance medicine set is 230, and the characteristic data fluctuation value Q of the medical insurance medicine set is calculated:
step S104, calculating characteristic verification abnormal values of all the medical insurance medicines in the medical insurance medicine set, wherein the characteristic verification abnormal value L a of the a-th medical insurance medicine in the medical insurance medicine set is as follows:
wherein X a is the amount of the cancel-after-verification of the a-th medical insurance drug in the medical insurance drug set;
Step 105, when the characteristic verification abnormal value of the a-th medical insurance medicine is larger than a preset characteristic verification abnormal threshold value, judging that the a-th medical insurance medicine is abnormal in medicine verification, and marking the a-th medical insurance medicine as the characteristic medical insurance medicine;
Step 200, acquiring historical medicine sales data of the historical abnormal medical insurance medicine, acquiring medicine sales data of the characteristic medical insurance medicine in the current period, and analyzing medicine sales similarity between the characteristic medical insurance medicine and the historical abnormal medical insurance medicine to obtain a target historical abnormal medical insurance medicine;
Wherein, step S200 includes:
Step S201, obtaining historical medicine sales data of historical abnormal medical insurance medicines from a database of a medical management institution, wherein the historical abnormal medical insurance medicines are the same as the characteristic medical insurance medicines in medicine types, and the historical medicine sales data comprise data corresponding to various medicine sales parameters of the historical abnormal medical insurance medicines;
for example, the various drug sales parameters include, drug sales amount, purchase customer type, purchase address, etc.;
Step S202, acquiring medicine sales data of the characteristic medical insurance medicine, wherein the medicine sales data comprise data corresponding to various medicine sales parameters of the characteristic medical insurance medicine;
step 203, randomly selecting a plurality of historical abnormal medical insurance medicines, respectively preprocessing and feature-coding each medicine sales parameter of the plurality of historical abnormal medical insurance medicines to obtain feature values of each medicine sales parameter, and constructing a data matrix U:
The method comprises the steps of obtaining a p-th medicine sales parameter of a j-th historical abnormal medical insurance medicine, wherein U jp is a characteristic value of the p-th medicine sales parameter of the j-th historical abnormal medical insurance medicine in a plurality of historical abnormal medical insurance medicines, U jp is a characteristic value of the p-th medicine sales parameter of the j-th historical abnormal medical insurance medicine in a plurality of historical abnormal medical insurance medicines, U 1p is a characteristic value of the p-th medicine sales parameter of the 1-th historical abnormal medical insurance medicine in a plurality of historical abnormal medical insurance medicines, U j1 is a characteristic value of the 1-th medicine sales parameter of the j-th historical abnormal medical insurance medicine in a plurality of historical abnormal medical insurance medicines, j is the total number of the j-th historical abnormal medical insurance medicines, and p is the total number of all medicine sales parameters;
For example, the preprocessing process includes cleaning the collected data, processing missing values, repeated values, outliers, etc.;
Step S204, performing standardization processing on each element of the data matrix U to obtain a normalized data matrix U 'of the data matrix U, and calculating a characteristic covariance matrix C of the data matrix U':
Wherein U 'T is the transpose of the data matrix U';
Step S205, calculating a eigenvalue lambda and an eigenvector v of the eigenvalue covariance matrix C, wherein the eigenvalue lambda and the eigenvector v meet the equation Cv=lambdav, wherein (C-lambdaI) v=0, and carrying out descending order arrangement according to the numerical value of the eigenvalue, and selecting eigenvectors corresponding to preset k eigenvalues to form a projection matrix W;
Step S206, preprocessing data corresponding to each medicine sales parameter of the feature medical insurance medicine based on the medicine sales data, and feature encoding each medicine sales parameter of the feature medical insurance medicine after preprocessing to obtain a feature sales vector group F of the feature medical insurance medicine, and performing dimension reduction on the feature sales vector group F by using a projection matrix to obtain a main feature sales vector group F △ of the feature medical insurance medicine, wherein F △ =FW;
Step S207, acquiring a main feature sales vector group of the historical abnormal medical insurance medicine, and calculating a medicine sales approximate value K g of the g-th historical abnormal medical insurance medicine in the feature medical insurance medicine and a database:
Wherein, B g is expressed as a main characteristic sales vector group of the g-th historical abnormal medical insurance medicine;
Step S208, when the medicine sales approximate value K g is larger than a preset medicine sales approximate threshold, judging that the medicine sales conditions between the characteristic medicine and the g-th historical abnormal medicine are approximate, and marking the g-th historical abnormal medicine as a target historical abnormal medicine of the characteristic medicine;
step S300, acquiring historical abnormal data of a target historical abnormal medical insurance medicine, and evaluating the abnormality of different sales links of the characteristic medical insurance medicine in the current period based on the special historical abnormal data to obtain the target abnormal data;
Wherein, step S300 includes:
step 301, acquiring historical abnormal data of each target historical abnormal medical insurance medicine of the characteristic medical insurance medicine, wherein the historical abnormal data is a sales link of the detected abnormal target historical abnormal medical insurance medicine;
Step S302, calculating link anomaly values of all sales links in the feature medical insurance medicine, wherein the link anomaly value H β of the beta-th sales link in the feature medical insurance medicine:
M sum represents the total number of the target historical abnormal medical insurance medicines of the characteristic medical insurance medicines, M β represents the total number of the target historical abnormal medical insurance medicines of which the beta-th sales link is detected to be abnormal;
For example, the total number M sum of each target historical abnormal medical insurance drug of the characteristic medical insurance drug is represented as 100, the total number M 2 of the target historical abnormal medical insurance drugs of which the abnormality is detected in the 2 nd sales link is represented as 20;
calculating a link anomaly value H 2 of a beta-th sales link in the characteristic medical insurance medicine:
For example, each sales link includes a medicine wholesale link, a medicine distribution link, and the like;
Step S303, when the link anomaly value of the beta-th sales link is larger than a preset environment anomaly threshold value, judging that the beta-th sales link in the characteristic medical insurance medicine has anomaly, and marking the beta-th sales link as a target anomaly sales link of the characteristic medical insurance medicine in the sales process;
step S304, collecting a plurality of target abnormal sales links of the characteristic medical insurance medicines to obtain target abnormal data of the characteristic medical insurance medicines;
Step S400, acquiring target abnormal data of the characteristic medical insurance medicine in the current period, and intelligently managing sales of the medical insurance medicine by a medical insurance management mechanism based on the target abnormal data.
Wherein, step S400 includes:
Step S401, acquiring each characteristic medical insurance medicine in the current period, acquiring target abnormal data of each characteristic medical insurance medicine, and extracting a target abnormal sales link of the characteristic medical insurance medicine from the target abnormal data;
Step S402, based on the target abnormal sales links of the medical insurance medicines with various characteristics in the current period, the medical insurance management mechanism carries out traceability check on the sales links of the medical insurance medicines in the current period, and carries out intelligent management on the medical insurance medicine sales in the current period;
In order to better realize the method, the invention also provides a medical insurance management mechanism management system based on big data, wherein the system comprises a characteristic medical insurance medicine module, a target history abnormal medical insurance medicine module, a target abnormal data module and an intelligent management module;
the characteristic medical insurance medicine module is used for analyzing the abnormal verification degree of the medical insurance medicine in the medicine verification process to obtain the characteristic medical insurance medicine;
The target historical abnormal medical insurance medicine module is used for acquiring medicine sales data of the characteristic medical insurance medicine in the current period, analyzing medicine sales similarity between the characteristic medical insurance medicine and the historical abnormal medical insurance medicine, and obtaining the target historical abnormal medical insurance medicine;
The target abnormal data module is used for acquiring historical abnormal data of the target historical abnormal medical insurance medicine, and evaluating the abnormality of different sales links of the characteristic medical insurance medicine in the current period in the sales process to obtain target abnormal data;
The intelligent management module is used for acquiring target abnormal data of the characteristic medical insurance medicine in the current period, and based on the target abnormal data, the medical insurance management mechanism carries out intelligent management on the sales of the medical insurance medicine;
the characteristic medical insurance medicine module comprises a characteristic verification abnormal value unit and a characteristic medical insurance medicine unit;
The characteristic verification abnormal value unit is used for collecting the medical insurance medicines according to the medicine codes of the medical insurance medicines to obtain a medical insurance medicine set, and calculating characteristic verification abnormal values of all the medical insurance medicines in the medical insurance medicine set;
the characteristic medical insurance medicine unit is used for analyzing the medical insurance medicine with abnormal medicine verification according to the characteristic verification abnormal value to obtain the characteristic medical insurance medicine;
The target history abnormal medical insurance medicine module comprises a medicine sales approximation unit and a target history abnormal medical insurance medicine unit;
the medicine sales approximation unit is used for calculating the medicine sales approximation value of the characteristic medical insurance medicine and the historical abnormal medical insurance medicine in the database of the medical insurance management institution;
the target history abnormal medical insurance medicine unit is used for acquiring the target history abnormal medical insurance medicine of the characteristic medical insurance medicine according to the medicine sales approximate value;
The target abnormal data module comprises a link abnormal value unit and a target abnormal data unit;
the link abnormal value unit is used for calculating the link abnormal values of each sales link in the characteristic medical insurance medicine;
the target abnormal data unit is used for judging the abnormality of different sales links in the characteristic medical insurance medicine according to the link abnormal value to obtain target abnormal data of the characteristic medical insurance medicine;
The intelligent management module comprises an intelligent management unit;
The intelligent management unit is used for acquiring each characteristic medical insurance medicine in the current period, acquiring target abnormal data of each characteristic medical insurance medicine, performing traceability inspection on the sales links of the medical insurance medicines based on the target abnormal data, and performing intelligent management on the medical insurance medicine sales in the current period.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.

Claims (10)

1.一种基于大数据的医保管理机构管理方法，其特征在于，所述方法包括：1. A management method for a medical insurance management agency based on big data, characterized in that the method comprises: 步骤S100：获取当前周期内的医保药品的药品核销数据，对医保药品在药品核销过程中的核销异常程度进行分析，得到特征医保药品；Step S100: Obtaining drug reimbursement data of medical insurance drugs in the current period, analyzing the degree of abnormal reimbursement of medical insurance drugs during the drug reimbursement process, and obtaining characteristic medical insurance drugs; 步骤S200：获取历史异常医保药品的历史药品销售数据，获取当前周期内的特征医保药品的药品销售数据，分析特征医保药品与历史异常医保药品之间的药品销售近似性，得到目标历史异常医保药品；Step S200: Obtain historical drug sales data of historical abnormal medical insurance drugs, obtain drug sales data of characteristic medical insurance drugs in the current period, analyze the drug sales similarity between characteristic medical insurance drugs and historical abnormal medical insurance drugs, and obtain target historical abnormal medical insurance drugs; 步骤S300：获取所述目标历史异常医保药品的历史异常数据，基于所述特历史异常数据，对当前周期内的特征医保药品在销售过程中不同销售环节的异常性进行评估，得到目标异常数据；Step S300: Acquire the historical abnormal data of the target historical abnormal medical insurance drug, and based on the historical abnormal data, evaluate the abnormality of different sales links of the characteristic medical insurance drug in the current cycle during the sales process to obtain the target abnormal data; 步骤S400：对当前周期内特征医保药品的目标异常数据进行获取，基于所述目标异常数据，医保管理机构对医保药品的销售进行智能管理。Step S400: Acquire target abnormal data of characteristic medical insurance drugs in the current cycle, and based on the target abnormal data, the medical insurance management agency performs intelligent management on the sales of medical insurance drugs. 2.根据权利要求1所述的一种基于大数据的医保管理机构管理方法，其特征在于，所述步骤S100包括：2. According to the big data-based medical insurance management agency management method of claim 1, wherein step S100 comprises: 步骤S101：从医保管理机构中获取当前周期内销售的医保药品的药品核销数据，所述药品核销数据包括，所述医保药品的药品编码和核销金额；Step S101: obtaining drug reimbursement data of medical insurance drugs sold in the current period from the medical insurance management agency, wherein the drug reimbursement data includes the drug code and reimbursement amount of the medical insurance drugs; 步骤S102：根据医保药品的药品编码，将相同药品名称和生产厂家的医保药品进行汇集，得到医保药品集；Step S102: according to the drug codes of the medical insurance drugs, the medical insurance drugs with the same drug name and manufacturer are collected to obtain a medical insurance drug set; 步骤S103：获取所述医保药品集中的各个医保药品的核销金额，计算所述医保药品集的特征数据波动值Q：Step S103: Obtain the write-off amount of each medical insurance drug in the medical insurance drug set, and calculate the characteristic data fluctuation value Q of the medical insurance drug set: 其中，Xi为所述医保药品集中的第i个医保药品的核销金额；N为所述医保药品集中的医保药品的总个数；μ为所述医保药品集中的医保药品的核销金额的平均值；Wherein, Xi is the write-off amount of the ith medical insurance drug in the medical insurance drug set; N is the total number of medical insurance drugs in the medical insurance drug set; μ is the average write-off amount of the medical insurance drugs in the medical insurance drug set; 步骤S104：计算所述医保药品集中的各个医保药品的特征核销异常值，其中，所述医保药品集中的第a个医保药品的特征核销异常值La：Step S104: Calculate the characteristic write-off abnormal value of each medical insurance drug in the medical insurance drug set, wherein the characteristic write-off abnormal value L a of the a-th medical insurance drug in the medical insurance drug set is: 其中，Xa为所述医保药品集中的第a个医保药品的核销金额；Wherein, Xa is the write-off amount of the ath medical insurance drug in the medical insurance drug set; 步骤S105：当所述第a个医保药品的特征核销异常值，大于预设的特征核销异常阈值，判定所述第a个医保药品在药品核销过程中为药品核销异常，并将所述第a个医保药品记为特征医保药品。Step S105: When the characteristic write-off anomaly value of the ath medical insurance drug is greater than a preset characteristic write-off anomaly threshold, it is determined that the ath medical insurance drug is a drug write-off anomaly during the drug write-off process, and the ath medical insurance drug is recorded as a characteristic medical insurance drug. 3.根据权利要求2所述的一种基于大数据的医保管理机构管理方法，其特征在于，所述步骤S200包括：3. According to the big data-based medical insurance management agency management method of claim 2, characterized in that step S200 comprises: 步骤S201：从医保管理机构的数据库中获取历史异常医保药品的历史药品销售数据，其中，所述历史异常医保药品与所述特征医保药品的药品种类相同，所述历史药品销售数据包括，所述历史异常医保药品的各项药品销售参数对应的数据；Step S201: Acquire historical drug sales data of historical abnormal medical insurance drugs from a database of a medical insurance management agency, wherein the historical abnormal medical insurance drugs are of the same drug type as the characteristic medical insurance drugs, and the historical drug sales data include data corresponding to various drug sales parameters of the historical abnormal medical insurance drugs; 步骤S202：获取所述特征医保药品的药品销售数据，所述药品销售数据包括，所述特征医保药品的各项药品销售参数对应的数据；Step S202: obtaining drug sales data of the characteristic medical insurance drug, wherein the drug sales data includes data corresponding to various drug sales parameters of the characteristic medical insurance drug; 步骤S203：随机选取出若干个历史异常医保药品，对所述若干个历史异常医保药品的各项药品销售参数，分别进行预处理和特征编码，得到所述各项药品销售参数的特征值，构建一个数据矩阵U：Step S203: randomly select a number of historically abnormal medical insurance drugs, perform preprocessing and feature encoding on the various drug sales parameters of the several historically abnormal medical insurance drugs, obtain the feature values of the various drug sales parameters, and construct a data matrix U: 其中，ujp为所述若干个历史异常医保药品中，第j个历史异常医保药品的第p项药品销售参数的特征值；ujp为所述若干个历史异常医保药品中，第j个历史异常医保药品的第p项药品销售参数的特征值；U1p为所述若干个历史异常医保药品中，第1个历史异常医保药品的第p项药品销售参数的特征值；uj1为所述若干个历史异常医保药品中，第j个历史异常医保药品的第1项药品销售参数的特征值；j为所述若干个历史异常医保药品的总个数；p为各项药品销售参数的总项数；Among them, u jp is the characteristic value of the pth drug sales parameter of the jth historical abnormal medical insurance drug among the several historical abnormal medical insurance drugs; u jp is the characteristic value of the pth drug sales parameter of the jth historical abnormal medical insurance drug among the several historical abnormal medical insurance drugs; U 1p is the characteristic value of the pth drug sales parameter of the 1st historical abnormal medical insurance drug among the several historical abnormal medical insurance drugs; u j1 is the characteristic value of the 1st drug sales parameter of the jth historical abnormal medical insurance drug among the several historical abnormal medical insurance drugs; j is the total number of the several historical abnormal medical insurance drugs; p is the total number of various drug sales parameters; 步骤S204：对数据矩阵U的各个元素进行标准化处理，得到数据矩阵U归一化后的数据矩阵U′,计算所述数据矩阵U′的特征协方差矩阵C：Step S204: Standardize each element of the data matrix U to obtain a normalized data matrix U′, and calculate the characteristic covariance matrix C of the data matrix U′: 其中，U′T为所述数据矩阵U′的转置；Wherein, U′ T is the transpose of the data matrix U′; 步骤S205：计算所述特征协方差矩阵C的特征值λ和特征向量v，其中，所述特征值λ和所述特征向量v，满足方程：Cv＝λv，其中，(C-λI)v＝0，根据所述特征值的数值大小进行降序排列，选出预设的k个特征值对应的特征向量构成投影矩阵W；Step S205: Calculate the eigenvalue λ and eigenvector v of the eigencovariance matrix C, wherein the eigenvalue λ and the eigenvector v satisfy the equation: Cv=λv, wherein (C-λI)v=0, and arrange the eigenvalues in descending order according to their numerical values, and select the eigenvectors corresponding to the preset k eigenvalues to form a projection matrix W; 步骤S206：基于所述药品销售数据，对所述特征医保药品的各项药品销售参数对应的数据进行预处理，并对预处理后的特征医保药品的各项药品销售参数进行特征编码，得到所述特征医保药品的特征销售向量组F，使用所述投影矩阵对所述特征销售向量组F进行降维，得到所述特征医保药品的主特征销售向量组F△，其中，F△＝FW；Step S206: Based on the drug sales data, preprocess the data corresponding to each drug sales parameter of the characteristic medical insurance drug, and feature encode each drug sales parameter of the preprocessed characteristic medical insurance drug to obtain a characteristic sales vector group F of the characteristic medical insurance drug, and use the projection matrix to reduce the dimension of the characteristic sales vector group F to obtain a main characteristic sales vector group F △ of the characteristic medical insurance drug, where F △ =FW; 步骤S207：获取所述历史异常医保药品的主特征销售向量组，计算所述特征医保药品与所述数据库中的第g个历史异常医保药品的药品销售近似值Kg：Step S207: Obtain the main characteristic sales vector group of the historical abnormal medical insurance drug, and calculate the drug sales approximation K g between the characteristic medical insurance drug and the g-th historical abnormal medical insurance drug in the database: 其中，Bg表示为所述第g个历史异常医保药品的主特征销售向量组；Wherein, Bg represents the main characteristic sales vector group of the g-th historical abnormal medical insurance drug; 步骤S208：当所述药品销售近似值Kg大于预设的药品销售近似阈值，判定所述特征医保药品与所述第g个历史异常医保药品之间的药品销售状况近似，将所述第g个历史异常医保药品记为所述特征医保药品的目标历史异常医保药品。Step S208: When the drug sales approximation value Kg is greater than the preset drug sales approximation threshold, it is determined that the drug sales status between the characteristic medical insurance drug and the g-th historical abnormal medical insurance drug is similar, and the g-th historical abnormal medical insurance drug is recorded as the target historical abnormal medical insurance drug of the characteristic medical insurance drug. 4.根据权利要求3所述的一种基于大数据的医保管理机构管理方法，其特征在于，所述步骤S300包括：4. The method for managing a medical insurance management institution based on big data according to claim 3, wherein step S300 comprises: 步骤S301：获取所述特征医保药品的各个目标历史异常医保药品的历史异常数据，所述历史异常数据为，目标历史异常医保药品被检测出异常的销售环节；Step S301: acquiring historical abnormal data of each target historical abnormal medical insurance drug of the characteristic medical insurance drug, wherein the historical abnormal data is the sales link at which the target historical abnormal medical insurance drug is detected to be abnormal; 步骤S302：计算所述特征医保药品中的各项销售环节的环节异常值，其中，所述特征医保药品中的第β项销售环节的环节异常值Hβ：Step S302: Calculate the abnormal value of each sales link in the characteristic medical insurance drug, wherein the abnormal value H β of the β-th sales link in the characteristic medical insurance drug is: 其中，Msum表示为所述特征医保药品的各个目标历史异常医保药品的总个数；Mβ表示为所述第β项销售环节被检测出异常的目标历史异常医保药品的总个数；Wherein, M sum represents the total number of target historical abnormal medical insurance drugs of the characteristic medical insurance drugs; M β represents the total number of target historical abnormal medical insurance drugs detected to be abnormal in the β-th sales link; 步骤S303：当第β项销售环节的环节异常值大于预设的环境异常阈值，判定所述特征医保药品中的第β项销售环节具有异常性，将所述第β项销售环节，记为所述特征医保药品的在销售过程中的目标异常销售环节；Step S303: When the abnormal value of the β-th sales link is greater than the preset environmental abnormality threshold, it is determined that the β-th sales link in the characteristic medical insurance drug is abnormal, and the β-th sales link is recorded as the target abnormal sales link in the sales process of the characteristic medical insurance drug; 步骤S304：对所述特征医保药品的若干项目标异常销售环节进行汇集，得到所述特征医保药品的目标异常数据。Step S304: Gathering several target abnormal sales links of the characteristic medical insurance drugs to obtain target abnormal data of the characteristic medical insurance drugs. 5.根据权利要求4所述的一种基于大数据的医保管理机构管理方法，其特征在于，所述步骤S400包括：5. The method for managing a medical insurance management institution based on big data according to claim 4, wherein step S400 comprises: 步骤S401：对当前周期内的各个特征医保药品进行获取，获取所述各个特征医保药品的目标异常数据，从所述目标异常数据中提取出特征医保药品的目标异常销售环节；Step S401: Acquire each characteristic medical insurance drug in the current cycle, acquire target abnormal data of each characteristic medical insurance drug, and extract target abnormal sales links of the characteristic medical insurance drug from the target abnormal data; 步骤S402：基于当前周期内的所述各个特征医保药品的目标异常销售环节，在当前周期内医保管理机构，对医保药品的销售环节进行溯源检查，并对当前周期内的医保药品销售进行智能管理。Step S402: Based on the target abnormal sales links of each characteristic medical insurance drug in the current cycle, the medical insurance management agency in the current cycle conducts a traceability inspection on the sales links of the medical insurance drugs and intelligently manages the sales of the medical insurance drugs in the current cycle. 6.一种基于大数据的医保管理机构管理系统，用于执行权利要求1-5中任意一项所述的一种基于大数据的医保管理机构管理方法，其特征在于，所述系统包括特征医保药品模块、目标历史异常医保药品模块、目标异常数据模块、智能管理模块；6. A medical insurance management agency management system based on big data, used to execute a medical insurance management agency management method based on big data according to any one of claims 1 to 5, characterized in that the system comprises a characteristic medical insurance drug module, a target historical abnormal medical insurance drug module, a target abnormal data module, and an intelligent management module; 所述特征医保药品模块，用于对医保药品在药品核销过程中的核销异常程度进行分析，得到特征医保药品；The characteristic medical insurance drug module is used to analyze the abnormality of the medical insurance drug write-off during the drug write-off process to obtain characteristic medical insurance drugs; 所述目标历史异常医保药品模块，用于对当前周期内的特征医保药品的药品销售数据进行获取，分析特征医保药品与历史异常医保药品之间的药品销售近似性，得到目标历史异常医保药品；The target historical abnormal medical insurance drug module is used to obtain drug sales data of characteristic medical insurance drugs in the current period, analyze the drug sales similarity between characteristic medical insurance drugs and historical abnormal medical insurance drugs, and obtain target historical abnormal medical insurance drugs; 所述目标异常数据模块，用于对所述目标历史异常医保药品的历史异常数据进行获取，对当前周期内的特征医保药品在销售过程中不同销售环节的异常性进行评估，得到目标异常数据；The target abnormal data module is used to obtain the historical abnormal data of the target historical abnormal medical insurance drugs, evaluate the abnormality of different sales links of the characteristic medical insurance drugs in the current cycle during the sales process, and obtain the target abnormal data; 所述智能管理模块，用于对当前周期内特征医保药品的目标异常数据进行获取，基于所述目标异常数据，医保管理机构对医保药品的销售进行智能管理。The intelligent management module is used to obtain target abnormal data of characteristic medical insurance drugs in the current cycle. Based on the target abnormal data, the medical insurance management agency performs intelligent management on the sales of medical insurance drugs. 7.根据权利要求6所述的一种基于大数据的医保管理机构管理系统，其特征在于，所述特征医保药品模块包括特征核销异常值单元、特征医保药品单元；7. A medical insurance management institution management system based on big data according to claim 6, characterized in that the characteristic medical insurance drug module includes a characteristic write-off abnormal value unit and a characteristic medical insurance drug unit; 所述特征核销异常值单元，用于根据根据医保药品的药品编码，对医保药品进行汇集，得到医保药品集，计算所述医保药品集中的各个医保药品的特征核销异常值；The characteristic write-off abnormal value unit is used to collect medical insurance drugs according to the drug codes of the medical insurance drugs to obtain a medical insurance drug set, and calculate the characteristic write-off abnormal value of each medical insurance drug in the medical insurance drug set; 所述特征医保药品单元，用于根据所述特征核销异常值，对医保药品进行药品核销异常进行分析，得到特征医保药品。The characteristic medical insurance drug unit is used to analyze the medical insurance drug write-off anomalies according to the characteristic write-off anomaly values to obtain characteristic medical insurance drugs. 8.根据权利要求6所述的一种基于大数据的医保管理机构管理系统，其特征在于，所述目标历史异常医保药品模块包括药品销售近似值单元、目标历史异常医保药品单元；8. A medical insurance management institution management system based on big data according to claim 6, characterized in that the target historical abnormal medical insurance drug module includes a drug sales approximate value unit and a target historical abnormal medical insurance drug unit; 所述药品销售近似值单元，用于对特征医保药品，与医保管理机构的数据库中的历史异常医保药品的药品销售近似值进行计算；The drug sales approximation unit is used to calculate the drug sales approximation of the characteristic medical insurance drugs and the historical abnormal medical insurance drugs in the database of the medical insurance management agency; 所述目标历史异常医保药品单元，用于根据所述药品销售近似值，获取所述特征医保药品的目标历史异常医保药品。The target historical abnormal medical insurance drug unit is used to obtain the target historical abnormal medical insurance drug of the characteristic medical insurance drug according to the approximate drug sales value. 9.根据权利要求6所述的一种基于大数据的医保管理机构管理系统，其特征在于，所述目标异常数据模块包括环节异常值单元、目标异常数据单元；9. A medical insurance management institution management system based on big data according to claim 6, characterized in that the target abnormal data module includes a link abnormal value unit and a target abnormal data unit; 所述环节异常值单元，用于对特征医保药品中的各项销售环节的环节异常值进行计算；The link abnormal value unit is used to calculate the link abnormal values of each sales link in the characteristic medical insurance drug; 所述目标异常数据单元，用于根据所述环节异常值，对所述特征医保药品中不同销售环节进行异常性判定，得到所述特征医保药品的目标异常数据。The target abnormal data unit is used to determine the abnormality of different sales links of the characteristic medical insurance drugs according to the link abnormal values, and obtain the target abnormal data of the characteristic medical insurance drugs. 10.根据权利要求6所述的一种基于大数据的医保管理机构管理系统，其特征在于，所述智能管理模块包括智能管理单元；10. A medical insurance management institution management system based on big data according to claim 6, characterized in that the intelligent management module includes an intelligent management unit; 所述智能管理单元，用于对当前周期内的各个特征医保药品进行获取，获取所述各个特征医保药品的目标异常数据，基于所述目标异常数据，对医保药品的销售环节进行溯源检查，并对当前周期内的医保药品销售进行智能管理。The intelligent management unit is used to obtain each characteristic medical insurance drug in the current cycle, obtain the target abnormal data of each characteristic medical insurance drug, conduct a traceability inspection on the sales link of the medical insurance drug based on the target abnormal data, and intelligently manage the sales of the medical insurance drugs in the current cycle.
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