CN113948216A - Intelligent illegal medical hospitalizing prevention system based on big data analysis - Google Patents
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Abstract
The invention relates to the field of medical insurance monitoring, in particular to an intelligent violation hospitalization prevention system based on big data analysis, which comprises a data acquisition module, a data cleaning module, a data labeling module, a model analysis module and a big data warehouse storage module, wherein the data acquisition module acquires medical insurance data of patients in various hospitals by adopting a crawler technology, the acquired medical insurance data of the patients are cleaned by the data cleaning module, and the patients are labeled by the data labeling module. According to the invention, medical insurance data of the patient in each hospital is collected by using a crawler technology, whether the patient breaks rules or not when going to a doctor or the doctor advice of the patient who is mainly used in the past is analyzed by the model analysis module, the violation behaviors in the medical insurance reimbursement process are comprehensively analyzed, and a positive prevention effect is played, so that the medical insurance special fund can be continuously and healthily used, and the occurrence of cheating insurance events is avoided or reduced from the source.
Description
Technical Field
The invention relates to the field of medical insurance monitoring, in particular to an intelligent violation hospitalization prevention system based on big data analysis.
Background
The medical insurance system refers to a system for collecting, distributing and using medical insurance funds for solving the problems of preventing and treating diseases of residents according to the insurance principle in a country or a region, the medical insurance system is an effective financing mechanism of the medical health care industry of residents, is a relatively advanced system forming the social insurance system, and is a health expense management mode which is quite common in the world at present, and the medical insurance funds are vital interests related to each citizen in the country, so that the prevention of illegal use of the medical insurance funds is important content for guaranteeing the medical insurance system.
The invention provides a Chinese patent No. 201810582879.3 and discloses a method and a system for medical insurance abnormity detection, which relate to the technical field of information. The workload of medical insurance auditors is greatly reduced, and the accuracy of audit is improved.
Although the 201810582879.3 patent can achieve the effect of detecting medical insurance abnormality, the detection is not comprehensive enough, which results in that the behavior of medical insurance fraud cannot be prevented and reduced, and the treatment plan cannot be directly recommended when the patient is hospitalized, which results in that the patient needs to make many unnecessary examinations when hospitalized, so that the hospitalization time of the patient is delayed, the economic loss of the patient is caused, and the burden of the doctor is increased. Therefore, it is highly desirable to design an intelligent violation hospitalization prevention system based on big data analysis to solve the above problems.
Disclosure of Invention
The invention aims to provide an intelligent violation hospitalization prevention system based on big data analysis, so as to solve the problems that the detection is not comprehensive and a treatment scheme cannot be recommended to a patient in the background art.
The technical scheme of the invention is as follows: intelligent violation preventive system of seeking medical advice based on big data analysis, including data acquisition module, data washing module, data tagging module, model analysis module and big data warehouse storage module, data acquisition module adopts the reptile technique to gather patient's medical insurance data in each hospital, washs the patient's medical insurance data of gathering through data washing module, through data tagging module carries out the tagging to patient, through model analysis module analyzes the data after wasing to carry out the output result with the data of analysis, data washing module includes: removing redundant and miscellaneous data in the acquired medical insurance data, repairing the removed redundant and miscellaneous data according to a data mining technology, performing data sorting on the repaired data according to a mathematical statistics technology, and outputting the sorted data, wherein the model analysis module comprises: the medical insurance policy model, the medical specification model, the other violation risk model and the recommendation knowledge map model are characterized in that the medical insurance policy model comprises the following contents: whether the medicine purchased by the patient is combined with the sex of the patient, whether the number and the types of the purchased medicines are higher than the average value, the number of times of visiting the clinic in the last 30 days and the drug withdrawal amount are higher than the average value, and the contents of the medical standard model comprise: the medical advice content is real or not, the number of times of receiving patients is real or not, the other violation risk models are used for analyzing other violation behaviors, the recommended knowledge map model is used for automatically giving recommended treatment schemes and medication schemes according with medical general knowledge for reference and selection of doctors after the labeled data are analyzed through a series of big data, and the big data warehouse storage module is used for storing the abnormal behaviors analyzed by the model analysis module.
Further, the patient medical insurance data includes: patient information, information of main doctors, total hospitalizing cost of patients and hospitalizing reimbursement cost of patients, wherein the patient information comprises: name, home address, sex, age, last year case.
Further, the number of men and women who buy each medicine is counted respectively through the medical insurance policy model, whether the medicine is related to gender is determined, and information of patients with the medicine not in accordance with the gender of the user is selected from the medicine and output.
Further, on the basis that the quantity of the medicines prescribed by the doctor for the same medical advice is stable under the condition that the average medicine-prescribed quantity is normal, the acquired patient medical insurance data is analyzed through the medical insurance policy model, the doctor information of which the medicines are far away from the average value of the medicine prescribed by the doctor is selected, all the medical advice prescribed by the doctor is analyzed through the medical standard model again, and the abnormal data is output.
Furthermore, based on the fact that the number of times that the same patient visits the outpatients in one month is low, the average number of times that each person visits the outpatients in one month is calculated by the medical insurance policy model, and the patient information with the number of times of visiting more than the average number is separated by a clustering method and output.
Further, the total drug withdrawal amount of the patients is analyzed according to a medical insurance policy model, the monthly drug withdrawal times and the average value of the total drug withdrawal amount of each patient are counted, and whether the total drug withdrawal amount exceeds the average value of the total drug withdrawal amount is found out by adopting clustering analysis.
Further, the redundant data includes: misspell data, illegal value data, null value data, data that does not conform to naming conventions, repeated data.
Further, the data arrangement comprises: and data sorting, wherein the data sorting comprises date sorting and medical expense high-low sorting.
Further, the number of visits to each person per month is 5-7 on average, and a violation of medical practice is found when the number is 2 times higher than the average.
Further, the average value of the total amount of drug returned per month of each patient is 100 yuan, and the average value of the total amount of drug returned per month of more than 200 yuan is illegal medical behavior.
The invention provides an intelligent violation hospitalization prevention system based on big data analysis by improvement, and compared with the prior art, the invention has the following improvements and advantages:
(1) according to the invention, medical insurance data of the patient in each hospital is collected by using a crawler technology, whether the patient breaks rules or not when going to a doctor or the doctor advice of the patient who is mainly used in the past is analyzed by the model analysis module, the violation behaviors in the medical insurance reimbursement process are comprehensively analyzed, and a positive prevention effect is played, so that the medical insurance special fund can be continuously and healthily used, and the occurrence of cheating insurance events is avoided or reduced from the source.
(2) According to the invention, the patient is labeled through the data labeling module, so that the patient can directly give a treatment scheme through recommending the knowledge map model in the hospitalizing process, the burden of a doctor and the time delay and economic loss caused by unnecessary examination of the patient are reduced, and the hospitalizing process of the patient is easier and more convenient.
(3) The invention utilizes the medical standard model to analyze all medical orders of the main doctors who analyze the abnormal behaviors by the medical insurance policy model, and checks the illegal phenomenon of departments, thereby preventing the illegal behaviors of departments and maintaining the normal order of a medical insurance system.
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The invention is further explained below with reference to the figures and examples:
FIG. 1 is an overall schematic view of the present invention;
FIG. 2 is a schematic diagram of a model analysis module of the present invention;
FIG. 3 is a schematic diagram of a data cleansing module of the present invention.
Detailed Description
The present invention will be described in detail with reference to fig. 1 to 3, and the technical solutions in the embodiments of the present invention will be clearly and completely described, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Intelligent violation preventive system of seeking medical advice based on big data analysis, including data acquisition module, data cleaning module, data tagging module, model analysis module and big data warehouse storage module, data acquisition module adopts the reptile technique to gather patient's medical insurance data in each hospital, wash the patient's medical insurance data of gathering through data cleaning module, carry out tagging to patient through data tagging module, analyze the data after wasing through model analysis module, and carry out the output result with the data of analysis, data cleaning module includes: the method comprises the following steps of removing redundant and miscellaneous data in the collected medical insurance data, repairing the removed redundant and miscellaneous data according to a data mining technology, carrying out data sorting on the repaired data according to a mathematical statistics technology, and outputting the sorted data, wherein the model analysis module comprises: the medical insurance policy model, the medical specification model, other violation risk models and the recommended knowledge map model, wherein the medical insurance policy model comprises the following contents: whether the medicine purchased by the patient is combined with the sex of the patient, whether the number and the types of the purchased medicines are higher than the average value, the number of times of visiting the clinic in the last 30 days and the drug withdrawal amount are higher than the average value, and the contents of the medical standard model comprise: the medical advice content is real or not, the number of times of receiving patients is real or not, other violation risk models are used for analyzing other violation behaviors, the recommended knowledge graph model is used for automatically giving recommended treatment schemes and medication schemes which accord with medical general knowledge for reference and selection of doctors after the labeled data are analyzed through a series of big data, and the big data warehouse storage module is used for storing the abnormal behaviors analyzed by the model analysis module.
Further, the patient medical insurance data includes: patient information, information of main doctors, total cost of hospitalizing patients and reimbursement cost of hospitalizing patients, wherein the patient information comprises: name, home address, sex, age, and the last year case, so as to determine whether the purchased medicine is matched according to the age and sex, and further determine whether the doctor cheats the action of taking a medical insurance.
Further, the number of men and women who buy each medicine is counted respectively through a medical insurance policy model so as to determine whether the medicine is related to the gender, and information of patients with the medicine not in accordance with the gender of the user is selected from the medicine and output.
Further, on the basis that the quantity of the medicines prescribed by the doctor for the same medical advice is stable under the condition that the average medicine-prescribing quantity is normal, the acquired patient medical insurance data is analyzed through a medical insurance policy model, the doctor information of which the medicines are far away from the average value of the medicine prescribed prescription is selected, all the medical advice prescribed by the doctor is analyzed through the medical standard model again, and abnormal data is output to determine whether department violation behaviors exist.
Further, based on the fact that the number of times that the same patient visits the outpatients in one month is low, the average number of times that each person visits the outpatients in each month is calculated by using a medical insurance policy model, and the patient information with the number of times that the number of times of the number of times that the times of the number of times of the times that the number of the times of.
Further, the total drug withdrawal amount of the patients is analyzed according to the medical insurance policy model, the monthly drug withdrawal frequency and the average value of the total drug withdrawal amount of each patient are counted, clustering analysis is adopted to find out whether the total drug withdrawal amount exceeds the average value of the drug withdrawal amount, and the patients with more monthly drug withdrawal frequency and total drug withdrawal amount are found out to be used as the objects of violation analysis.
Further, the redundant data includes: wrong spelling data, illegal value data, null value data, data which does not accord with naming habits and repeated data, so that the quality of the data is improved.
Further, the data arrangement comprises: and data sorting, wherein the data sorting comprises date sorting and medical expense high-low sorting, and the data can be observed conveniently according to requirements.
Further, the number of visits to the clinic per month for each person is 5 on average, and the number of visits to the clinic is 2 times higher than the average.
Further, the average value of the total amount of drug returned per month of each patient is 100 yuan, and the average value of the total amount of drug returned per month of more than 200 yuan is illegal medical behavior.
Example two
Intelligent violating medical service prevention system based on big data analysis, intelligent violating medical service prevention system based on big data analysis, including the data acquisition module, data washing module, data tagging module, model analysis module and big data warehouse storage module, the data acquisition module adopts the reptile technique to gather patient's medical insurance data in each hospital, wash the patient's medical insurance data of gathering through data washing module, carry out tagging to patient through data tagging module, analyze the data after wasing through model analysis module, and carry out the output result with the data of analysis, data washing module includes: the method comprises the following steps of removing redundant and miscellaneous data in the collected medical insurance data, repairing the removed redundant and miscellaneous data according to a data mining technology, carrying out data sorting on the repaired data according to a mathematical statistics technology, and outputting the sorted data, wherein the model analysis module comprises: the medical insurance policy model, the medical specification model, other violation risk models and the recommended knowledge map model, wherein the medical insurance policy model comprises the following contents: whether the medicine purchased by the patient is combined with the sex of the patient, whether the number and the types of the purchased medicines are higher than the average value, the number of times of visiting the clinic in the last 30 days and the drug withdrawal amount are higher than the average value, and the contents of the medical standard model comprise: the medical advice content is real or not, the number of times of receiving patients is real or not, other violation risk models are used for analyzing other violation behaviors, the recommended knowledge graph model is used for automatically giving recommended treatment schemes and medication schemes which accord with medical general knowledge for reference and selection of doctors after the labeled data are analyzed through a series of big data, and the big data warehouse storage module is used for storing the abnormal behaviors analyzed by the model analysis module.
Further, the patient medical insurance data includes: patient information, information of main doctors, total cost of hospitalizing patients and reimbursement cost of hospitalizing patients, wherein the patient information comprises: name, home address, sex, age, and the last year case, so as to determine whether the purchased medicine is matched according to the age and sex, and further determine whether the doctor cheats the action of taking a medical insurance.
Further, the number of men and women who buy each medicine is counted respectively through a medical insurance policy model so as to determine whether the medicine is related to the gender, and information of patients with the medicine not in accordance with the gender of the user is selected from the medicine and output.
Further, on the basis that the quantity of the medicines prescribed by the doctor for the same medical advice is stable under the condition that the average medicine-prescribing quantity is normal, the acquired patient medical insurance data is analyzed through a medical insurance policy model, the doctor information of which the medicines are far away from the average value of the medicine prescribed prescription is selected, all the medical advice prescribed by the doctor is analyzed through the medical standard model again, and abnormal data is output to determine whether department violation behaviors exist.
Further, based on the fact that the number of times that the same patient visits the outpatients in one month is low, the average number of times that each person visits the outpatients in each month is calculated by using a medical insurance policy model, and the patient information with the number of times that the number of times of the number of times that the times of the number of times of the times that the number of the times of.
Further, the total drug withdrawal amount of the patients is analyzed according to the medical insurance policy model, the monthly drug withdrawal frequency and the average value of the total drug withdrawal amount of each patient are counted, clustering analysis is adopted to find out whether the total drug withdrawal amount exceeds the average value of the drug withdrawal amount, and the patients with more monthly drug withdrawal frequency and total drug withdrawal amount are found out to be used as the objects of violation analysis.
Further, the redundant data includes: wrong spelling data, illegal value data, null value data, data which does not accord with naming habits and repeated data, so that the quality of the data is improved.
Further, the data arrangement comprises: and data sorting, wherein the data sorting comprises date sorting and medical expense high-low sorting, and the data can be observed conveniently according to requirements.
Further, the number of visits to the clinic per month for each person is 6 on average, and the number of visits to the clinic is 2 times higher than the average.
Further, the average value of the total amount of drug returned per month of each patient is 100 yuan, and the average value of the total amount of drug returned per month of more than 200 yuan is illegal medical behavior.
EXAMPLE III
Intelligent violating medical service prevention system based on big data analysis, intelligent violating medical service prevention system based on big data analysis, including the data acquisition module, data washing module, data tagging module, model analysis module and big data warehouse storage module, the data acquisition module adopts the reptile technique to gather patient's medical insurance data in each hospital, wash the patient's medical insurance data of gathering through data washing module, carry out tagging to patient through data tagging module, analyze the data after wasing through model analysis module, and carry out the output result with the data of analysis, data washing module includes: the method comprises the following steps of removing redundant and miscellaneous data in the collected medical insurance data, repairing the removed redundant and miscellaneous data according to a data mining technology, carrying out data sorting on the repaired data according to a mathematical statistics technology, and outputting the sorted data, wherein the model analysis module comprises: the medical insurance policy model, the medical specification model, other violation risk models and the recommended knowledge map model, wherein the medical insurance policy model comprises the following contents: whether the medicine purchased by the patient is combined with the sex of the patient, whether the number and the types of the purchased medicines are higher than the average value, the number of times of visiting the clinic in the last 30 days and the drug withdrawal amount are higher than the average value, and the contents of the medical standard model comprise: the medical advice content is real or not, the number of times of receiving patients is real or not, other violation risk models are used for analyzing other violation behaviors, the recommended knowledge graph model is used for automatically giving recommended treatment schemes and medication schemes which accord with medical general knowledge for reference and selection of doctors after the labeled data are analyzed through a series of big data, and the big data warehouse storage module is used for storing the abnormal behaviors analyzed by the model analysis module.
Further, the patient medical insurance data includes: patient information, information of main doctors, total cost of hospitalizing patients and reimbursement cost of hospitalizing patients, wherein the patient information comprises: name, home address, sex, age, and the last year case, so as to determine whether the purchased medicine is matched according to the age and sex, and further determine whether the doctor cheats the action of taking a medical insurance.
Further, the number of men and women who buy each medicine is counted respectively through a medical insurance policy model so as to determine whether the medicine is related to the gender, and information of patients with the medicine not in accordance with the gender of the user is selected from the medicine and output.
Further, on the basis that the quantity of the medicines prescribed by the doctor for the same medical advice is stable under the condition that the average medicine-prescribing quantity is normal, the acquired patient medical insurance data is analyzed through a medical insurance policy model, the doctor information of which the medicines are far away from the average value of the medicine prescribed prescription is selected, all the medical advice prescribed by the doctor is analyzed through the medical standard model again, and abnormal data is output to determine whether department violation behaviors exist.
Further, based on the fact that the number of times that the same patient visits the outpatients in one month is low, the average number of times that each person visits the outpatients in each month is calculated by using a medical insurance policy model, and the patient information with the number of times that the number of times of the number of times that the times of the number of times of the times that the number of the times of.
Further, the total drug withdrawal amount of the patients is analyzed according to the medical insurance policy model, the monthly drug withdrawal frequency and the average value of the total drug withdrawal amount of each patient are counted, clustering analysis is adopted to find out whether the total drug withdrawal amount exceeds the average value of the drug withdrawal amount, and the patients with more monthly drug withdrawal frequency and total drug withdrawal amount are found out to be used as the objects of violation analysis.
Further, the redundant data includes: wrong spelling data, illegal value data, null value data, data which does not accord with naming habits and repeated data, so that the quality of the data is improved.
Further, the data arrangement comprises: and data sorting, wherein the data sorting comprises date sorting and medical expense high-low sorting, and the data can be observed conveniently according to requirements.
Further, the number of visits to the clinic per month for each person is 7 on average, and the number of visits to the clinic is 2 times higher than the average.
Further, the average value of the total amount of drug returned per month of each patient is 100 yuan, and the average value of the total amount of drug returned per month of more than 200 yuan is illegal medical behavior.
In the first embodiment, the second embodiment and the third embodiment, the average value of the number of times of visiting the outpatient service per month is set differently, the other parameters are consistent, and the data analyzed in the second embodiment is most accurate through the human analysis of the finally obtained information of the illegal medical patients.
The working principle is as follows: the medical insurance data of the patients in each hospital are collected by a data collection module through a crawler technology, the collected medical insurance data of the patients are cleaned through a data cleaning module, the patients are labeled through a data labeling module, the cleaned data are analyzed through a model analysis module, and the analyzed data are output; removing redundant and miscellaneous data in the acquired medical insurance data by using a data cleaning module, repairing the data from which the redundant and miscellaneous data are removed according to a data mining technology, sorting the data after repairing according to a mathematical statistics technology, and outputting the sorted data; according to the invention, medical insurance data of the patient in each hospital is collected by using a crawler technology, whether the patient breaks rules or not when going to a doctor or the doctor advice of the patient who is mainly used in the past is analyzed by the model analysis module, the violation behaviors in the medical insurance reimbursement process are comprehensively analyzed, and a positive prevention effect is played, so that the medical insurance special fund can be continuously and healthily used, and the occurrence of cheating insurance events is avoided or reduced from the source.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. Intelligent violation of rules and regulations prevention system of seeking medical advice based on big data analysis, its characterized in that: including data acquisition module, data washing module, data tagging module, model analysis module and big data warehouse storage module, data acquisition module adopts the reptile technique to gather patient's medical insurance data in each hospital, washs the patient medical insurance data of gathering through data washing module, through data tagging module carries out the tagging to patient, through model analysis module carries out the analysis to the data after wasing to carry out the output result with the data of analysis, data washing module includes: removing redundant and miscellaneous data in the acquired medical insurance data, repairing the removed redundant and miscellaneous data according to a data mining technology, performing data sorting on the repaired data according to a mathematical statistics technology, and outputting the sorted data, wherein the model analysis module comprises: the medical insurance policy model, the medical specification model, the other violation risk model and the recommendation knowledge map model are characterized in that the medical insurance policy model comprises the following contents: whether the medicine purchased by the patient is combined with the sex of the patient, whether the number and the types of the purchased medicines are higher than the average value, the number of times of visiting the clinic in the last 30 days and the drug withdrawal amount are higher than the average value, and the contents of the medical standard model comprise: the medical advice content is real or not, the number of times of receiving patients is real or not, the other violation risk models are used for analyzing other violation behaviors, the recommended knowledge map model is used for automatically giving recommended treatment schemes and medication schemes according with medical general knowledge for reference and selection of doctors after the labeled data are analyzed through a series of big data, and the big data warehouse storage module is used for storing the abnormal behaviors analyzed by the model analysis module.
2. The intelligent violation hospitalization prevention system based on big data analysis of claim 1, wherein: the patient medical insurance data comprises: patient information, information of main doctors, total hospitalizing cost of patients and hospitalizing reimbursement cost of patients, wherein the patient information comprises: name, home address, sex, age, last year case.
3. The intelligent violation hospitalization prevention system based on big data analysis of claim 1, wherein: and respectively counting the number of men and women who buy each medicine through the medical insurance policy model to determine whether the medicine is related to the sex, and selecting and outputting the information of the patient with the medicine which is inconsistent with the sex of the patient.
4. The intelligent violation hospitalization prevention system based on big data analysis of claim 1, wherein: on the basis that the quantity of the medicines prescribed by the doctor for the same medical advice is stable under the condition that the average medicine-prescribing quantity is normal, the acquired patient medical insurance data are analyzed through the medical insurance policy model, the doctor information of which the medicines are far away from the average value of the prescription is selected, all the medical advice prescribed by the doctor is analyzed through the medical standard model again, and the abnormal data are output.
5. The intelligent violation hospitalization prevention system based on big data analysis of claim 1, wherein: based on the fact that the number of times that the same patient visits the outpatients in one month is low, the average number of times that each person visits the outpatients in one month is counted by the medical insurance policy model, and the patient information with the number of times of visiting the outpatients larger than the average number is separated by a clustering method and output.
6. The intelligent violation hospitalization prevention system based on big data analysis of claim 1, wherein: and analyzing the total drug withdrawal amount of the patient according to a medical insurance policy model, counting the monthly drug withdrawal times and the average value of the total drug withdrawal amount of each patient, and finding out the patient with the total drug withdrawal amount exceeding the average value of the total drug withdrawal amount by adopting cluster analysis.
7. The intelligent violation hospitalization prevention system based on big data analysis of claim 1, wherein: the redundant data comprises: misspell data, illegal value data, null value data, data that does not conform to naming conventions, repeated data.
8. The intelligent violation hospitalization prevention system based on big data analysis of claim 1, wherein: the data arrangement comprises the following steps: and data sorting, wherein the data sorting comprises date sorting and medical expense high-low sorting.
9. The intelligent violation hospitalization prevention system based on big data analysis of claim 5, wherein: the average number of times of visiting the outpatient service per month of each person is 5-7 times, and illegal medical behaviors exist when the average number of times of visiting the outpatient service per month is 2 times higher than the average number of times of visiting the outpatient service per month.
10. The intelligent violation hospitalization prevention system based on big data analysis of claim 6, wherein: the average value of the total monthly withdrawal sum of each patient is 100 yuan, and the average value of the total monthly withdrawal sum of each patient exceeds 200 yuan, so that the patient is the illegal medical behavior.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114757792A (en) * | 2022-06-15 | 2022-07-15 | 南京云联数科科技有限公司 | Medical insurance wind control management method and equipment based on multi-field data |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114757792A (en) * | 2022-06-15 | 2022-07-15 | 南京云联数科科技有限公司 | Medical insurance wind control management method and equipment based on multi-field data |
| CN114757792B (en) * | 2022-06-15 | 2022-09-30 | 南京云联数科科技有限公司 | Medical insurance wind control management method and equipment based on multi-field data |
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