CN111696659A - Medical insurance big data-based tumor morbidity information monitoring method and device - Google Patents
Medical insurance big data-based tumor morbidity information monitoring method and device Download PDFInfo
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Abstract
The invention discloses a method and a device for monitoring tumor morbidity information based on medical insurance big data, wherein the method comprises the following steps: obtain multiunit medical insurance data, every group medical insurance data includes: disease diagnostic data and corresponding disease encoding data; according to the tumor keyword library, one or more groups of first medical insurance data of which the disease diagnosis data comprise tumor keywords are selected from the multiple groups of medical insurance data; according to a tumor coding database, one or more groups of second medical insurance data with tumor codes contained in the disease coding data are selected from the multiple groups of medical insurance data; and comparing the selected first medical insurance data with the second medical insurance data, selecting one or more groups of medical insurance data from the multiple groups of medical insurance data according to the comparison result, and monitoring the tumor morbidity information according to the one or more groups of medical insurance data. The tumor onset data does not need to be manually registered and checked, tumor onset events are extracted through medical insurance data, tumor onset information is obtained, manpower and material resources are saved, the efficiency is improved, and the data timeliness is guaranteed.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to a method and a device for monitoring tumor morbidity information based on medical insurance big data.
Background
Chronic non-infectious diseases, the second leading cause of death, tumors pose a serious threat to human health. Accurate, dynamic and real-time tumor morbidity data can provide important scientific basis for the work such as policy making, resource allocation and the like of tumor prevention and control work.
Currently, acquisition of tumor incidence data is mainly achieved by tumor enrollment. The fixed-point medical institutions in all regions perform manual report filling according to medical insurance data, staff in the register periodically performs comprehensive inspection, evaluation and feedback on the registration working quality of each institution, such as whether report is missed or not and whether the report card item is completely filled, and each institution verifies, supplements, modifies and reports the registration data again according to the feedback result. The whole tumor registration and verification work needs to consume a large amount of manpower and material resources, the working efficiency is extremely low, the time required in the data summarization process is long, and the timeliness is lacked.
Disclosure of Invention
The embodiment of the invention provides a tumor morbidity information monitoring method based on medical insurance big data, which is used for improving the working efficiency of tumor registration and verification and obtaining tumor morbidity data with timeliness, and comprises the following steps:
obtain multiunit medical insurance data, every group medical insurance data includes: disease diagnostic data and corresponding disease encoding data;
according to the tumor keyword library, one or more groups of first medical insurance data of which the disease diagnosis data comprise tumor keywords are selected from the multiple groups of medical insurance data;
according to a tumor coding database, one or more groups of second medical insurance data with tumor codes contained in the disease coding data are selected from the multiple groups of medical insurance data;
and comparing the selected first medical insurance data with the second medical insurance data, selecting one or more groups of medical insurance data from the multiple groups of medical insurance data according to the comparison result, and monitoring the tumor morbidity information according to the one or more groups of medical insurance data.
The embodiment of the invention provides a tumor morbidity information monitoring device based on medical insurance big data, which is used for improving the working efficiency of tumor registration and verification and obtaining tumor morbidity data with timeliness, and comprises the following steps:
the data acquisition module is used for acquiring a plurality of groups of medical insurance data, and each group of medical insurance data comprises: disease diagnostic data and corresponding disease encoding data;
the first selection module is used for selecting one or more groups of first medical insurance data of which the disease diagnosis data comprise tumor keywords from the multiple groups of medical insurance data according to the tumor keyword library;
the second selecting module is used for selecting one or more groups of second medical insurance data with disease coded data containing tumor codes from the multiple groups of medical insurance data according to the tumor coded data base;
and the data comparison module is used for comparing the selected first medical insurance data with the second medical insurance data, selecting one or more groups of medical insurance data from the multiple groups of medical insurance data according to the comparison result, and monitoring the tumor morbidity information according to the one or more groups of medical insurance data.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can be run on the processor, wherein when the processor executes the computer program, the tumor morbidity information monitoring method based on the medical insurance big data is realized.
The embodiment of the invention also provides a computer readable storage medium, which stores a computer program for executing the tumor morbidity information monitoring method based on the medical insurance big data.
Compared with the scheme of manually registering and checking the tumor morbidity data in the prior art, the embodiment of the invention acquires a plurality of groups of medical insurance data, and each group of medical insurance data comprises: disease diagnostic data and corresponding disease encoding data; according to the tumor keyword library, one or more groups of first medical insurance data of which the disease diagnosis data comprise tumor keywords are selected from the multiple groups of medical insurance data; according to a tumor coding database, one or more groups of second medical insurance data with tumor codes contained in the disease coding data are selected from the multiple groups of medical insurance data; and comparing the selected first medical insurance data with the second medical insurance data, selecting one or more groups of medical insurance data from the multiple groups of medical insurance data according to the comparison result, and monitoring the tumor morbidity information according to the one or more groups of medical insurance data. According to the embodiment of the invention, the medical insurance data are respectively screened according to the tumor keyword library and the tumor coding data library, and whether the medical insurance data selected twice can correspond is checked through comparison, so that the tumor onset data is determined, the tumor onset data does not need to be manually registered and checked one by one, and the tumor onset event can be extracted only through the medical insurance data, so that the tumor onset information is obtained, a large amount of manpower and material resources are saved, the working efficiency is improved, and the timeliness of the data is guaranteed.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
FIG. 1 is a schematic diagram of a method for monitoring tumor morbidity information based on medical insurance big data in an embodiment of the present invention;
FIG. 2 is a schematic diagram of a method for monitoring tumor incidence information based on medical insurance big data according to an embodiment of the present invention;
FIG. 3 is a database of new cases of tumors in accordance with an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating the results of the main indicators of tumor incidence according to an embodiment of the present invention;
FIG. 5 is a schematic illustration of a tumor incidence trend in an embodiment of the present invention;
fig. 6 is a structural diagram of a tumor morbidity information monitoring device based on medical insurance big data in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
As mentioned previously, the acquisition of tumor incidence data is currently achieved primarily through tumor enrollment. The tumor registration mode adopts a mode of establishing registration points (mainly depending on hospitals) in parts of the country, data acquisition depends on the registration points to report, the tumor registration is a huge system, a large amount of manpower and material resources are needed, the working efficiency is extremely low, the time needed in the data summarizing process is long, and the timeliness is lacked. The inventor finds that the tumor onset events can be extracted only by using medical insurance reimbursement data, so that information such as the number of the tumor onset events can be obtained, a large amount of manpower and material resources are saved, the working efficiency is improved, the data timeliness is guaranteed, and the method is a new mode advocated in the big data era.
In order to improve the work efficiency of tumor registration and verification and obtain tumor onset data with timeliness, an embodiment of the present invention provides a tumor onset information monitoring method based on medical insurance big data, as shown in fig. 1, the method may include:
102, selecting one or more groups of first medical insurance data of which the disease diagnosis data comprise tumor keywords from the multiple groups of medical insurance data according to the tumor keyword library;
103, selecting one or more groups of second medical insurance data with disease coding data containing tumor codes from the multiple groups of medical insurance data according to the tumor coding database;
and 104, comparing the selected first medical insurance data with the selected second medical insurance data, selecting one or more groups of medical insurance data from the multiple groups of medical insurance data according to the comparison result, and monitoring the tumor morbidity information according to the one or more groups of medical insurance data.
As shown in fig. 1, in the embodiment of the present invention, multiple sets of medical insurance data are obtained, where each set of medical insurance data includes: disease diagnostic data and corresponding disease encoding data; according to the tumor keyword library, one or more groups of first medical insurance data of which the disease diagnosis data comprise tumor keywords are selected from the multiple groups of medical insurance data; according to a tumor coding database, one or more groups of second medical insurance data with tumor codes contained in the disease coding data are selected from the multiple groups of medical insurance data; and comparing the selected first medical insurance data with the second medical insurance data, selecting one or more groups of medical insurance data from the multiple groups of medical insurance data according to the comparison result, and monitoring the tumor morbidity information according to the one or more groups of medical insurance data. According to the embodiment of the invention, the medical insurance data are respectively screened according to the tumor keyword library and the tumor coding data library, and whether the medical insurance data selected twice can correspond is checked through comparison, so that the tumor onset data is determined, the tumor onset data does not need to be manually registered and checked one by one, and the tumor onset event can be extracted only through the medical insurance data, so that the tumor onset information is obtained, a large amount of manpower and material resources are saved, the working efficiency is improved, and the timeliness of the data is guaranteed.
When the method is specifically implemented, a plurality of groups of medical insurance data are acquired, and each group of medical insurance data comprises: disease diagnostic data and corresponding disease encoding data.
In an embodiment, the disease diagnosis data may be text information in a diagnosis report, the disease coding data may be ICD-10 disease coding, and the ICD-10 disease coding is coding for a disease in international classification of Diseases-10th version 10 (international classification of Diseases-10th vision).
In an embodiment, each set of medical insurance data further includes: patient identity data corresponding to the disease diagnosis data; after acquiring the plurality of groups of medical insurance data, encrypting the patient identity data in each group of medical insurance data; or encrypting the patient identity data in each group of medical insurance data when each group of medical insurance data is acquired. The inventor finds that data security and privacy protection are the first requirements of public big data development and are important bases and prerequisites for medical insurance data analysis work. Therefore, after or while the data is acquired, the embodiment of the invention encrypts the patient identity data, thereby effectively protecting the privacy of the patient. The patient identification data may include personal sensitive data such as name, identification card number, medical card number, contact details, and specific home address. In this embodiment, a hexadecimal encryption algorithm may be employed to generate a meaningless unique identification code to mark the patient identity for data transmission and cleaning.
In an embodiment, each set of medical insurance data further includes: the hospital grade of the visit corresponding to the disease diagnosis data; after the multiple groups of medical insurance data are obtained, selecting medical insurance data of which the hospitalizing hospital grade is greater than the preset grade from the multiple groups of medical insurance data; or each group of medical insurance data is acquired, whether the hospital grade of the doctor in the group of medical insurance data is greater than the preset grade is judged, and if the hospital grade of the doctor in the group of medical insurance data is greater than the preset grade, the group of medical insurance data is selected. The inventor finds that the disease diagnosis data issued by some low-qualification hospitalization hospitals lack accuracy, and the accuracy of the analysis result is seriously influenced by adopting the data in the medical insurance data analysis work, so that the embodiment of the invention refers to official data (hqms.org.cn/usp/roster/index.jsp) of the national health committee of the people's republic of China, the grades of the hospitalization hospitals are calibrated to be ' third grade ', ' second grade ' and ' first grade or unclassified ' according to the treatment qualification from high to low, and after a plurality of groups of medical insurance data are obtained, the medical insurance data of which the grade of the hospitalization hospitals is greater than the preset grade in the plurality of groups of medical insurance data are selected; or each group of medical insurance data is acquired, whether the hospital grade of the doctor in the group of medical insurance data is greater than the preset grade is judged, and if the hospital grade of the doctor in the group of medical insurance data is greater than the preset grade, the group of medical insurance data is selected. For example, medical insurance data of primary or unclassified hospitals is excluded, thereby ensuring the accuracy of disease diagnosis data.
In an embodiment, according to the tumor onset time period to be analyzed, a time window which is before and adjacent to the time period may be set, when acquiring a plurality of sets of medical insurance data, the medical insurance data in the time window is also required to be acquired in addition to the medical insurance data in the time period to be analyzed, and the medical insurance data which simultaneously appears in the time period to be analyzed and the time window is removed, so that the influence of existing patients is eliminated. The inventor finds that medical data in the tumor incidence period to be analyzed exist in the current incidence, and if the medical data is added in the analysis, the incidence result is higher than the actual incidence. Therefore, the embodiment of the invention removes the medical insurance data which simultaneously appear in the time period to be analyzed and the time window by setting the time window, thereby eliminating the influence of the current case.
In an embodiment, according to the onset time of the tumor to be analyzed, a time window which is adjacent to the time window after the time window can be set, when acquiring multiple sets of medical insurance data, the medical insurance data in the time window is acquired in addition to the medical insurance data in the time window to be analyzed, and the influence of reimbursement delay is removed by adding the medical insurance data appearing in the time window. The inventors found that there were cases admitted the same year but reimbursed the next year and if this part of the data was missed in the analysis, the morbidity results obtained would be lower than the actual morbidity. Therefore, the embodiment of the invention adds the medical insurance data appearing in the time window by setting the time window, thereby eliminating the influence of reimbursement delay.
In specific implementation, one or more groups of first medical insurance data of which the disease diagnosis data comprise tumor keywords are selected from the multiple groups of medical insurance data according to the tumor keyword library.
In the embodiment, according to the ICD-10 chinese version, all tumor-prompting keywords in the text diagnosis are extracted, and a tumor keyword library is established, for example: "cancer", "sarcoma", "malignant tumor", "leukemia", "lymphoma", "Hodgkin", "Franklin disease", "alpha heavy chain disease", etc. And then selecting one or more groups of first medical insurance data of which the disease diagnosis data comprise tumor keywords from the multiple groups of medical insurance data according to the tumor keyword library.
In specific implementation, one or more groups of second medical insurance data with disease coding data containing tumor codes are selected from the multiple groups of medical insurance data according to the tumor coding database.
In an embodiment, all tumor-related codes are extracted and a tumor-encoding database is built, such as codes beginning with C00-C97, based on the ICD-10 Chinese version. And then one or more groups of second medical insurance data with disease coding data containing tumor codes are selected from the multiple groups of medical insurance data according to the tumor keyword library.
In specific implementation, the selected first medical insurance data and the second medical insurance data are compared, one or more groups of medical insurance data are selected from the multiple groups of medical insurance data according to the comparison result, and the tumor morbidity information is monitored according to the one or more groups of medical insurance data.
In an embodiment, the selecting one or more sets of medical insurance data from the plurality of sets of medical insurance data according to the comparison result includes: for each group of medical insurance data, if the medical insurance data are respectively selected as the first medical insurance data and the second medical insurance data, and the disease name data corresponding to the disease diagnosis data and the disease coded data in the medical insurance data are the same, the group of medical insurance data is selected.
In an embodiment, the selecting one or more sets of medical insurance data from the plurality of sets of medical insurance data according to the comparison result includes: and for each group of medical insurance data, if the medical insurance data is only selected as the first medical insurance data or only selected as the second medical insurance data, or the medical insurance data is respectively selected as the first medical insurance data and the second medical insurance data, but the disease name data corresponding to the disease diagnosis data and the disease coding data in the medical insurance data are different, performing a manual review link. Manual interpretation and review to determine diagnosis is performed by panel discussions, expert consultation.
In an embodiment, the selected medical insurance data may be used for statistics, and the population may be calculated according to gender and age group. For example, at age 5 years, the onset age was divided into 19 groups: less than 1 year old, 1 to 4 years old, 5 to 9 years old, 10 to 14 years old, … … 75 to 79 years old, 80 to 84 years old, 85 years old and above.
In the examples, the gross incidence reflecting the local actual tumor incidence and disease burden in the current year can be calculated as follows:
in the examples, the gender and age incidence rate reflecting the high-incidence age group and gender difference of the tumor can be calculated according to the following formula, and a gender and age incidence rate curve can be drawn:
in the embodiment, the age adjustment rate can eliminate the influence of age structure difference on the coarse morbidity, and is convenient for comparing the morbidity levels of different periods or different areas in the same area. The Chinese standard population refers to the population composition of the fifth census nationwide in 2000, and the world standard population refers to the Segi's standard population composition. The age adjustment rate, i.e., the normalization rate, can be calculated as follows:
in the embodiment, the classification composition reflecting the local common tumor types and the health burden of residents can be calculated according to the following formula, namely the composition percentage of the number of each type of new tumor cases:
in the examples, the Annual Percentage Change (APC) for analyzing the time trend of tumor onset can be calculated as follows. And (3) setting the annual incidence of y as Ry, according to a regression model: log (ry) ═ b0+b1y, wherein b0In order to obtain the intercept of the signal,b1for the slope, y is year, and the formula for calculating APC from y year to y +1 year is as follows:
wherein R isy+1Is the incidence of y +1 years, RyThe incidence of y years, e is the natural constant, b1Is the slope.
In the examples, Stata 15.0 was used for data processing, and MS-Excel 2016 was used to calculate the incidence. The onset of the disease was examined by Joinpoint 4.6.0.0. The Joinpoint model assumes that the natural logarithm of incidence is linear with the year of onset.
The following provides a specific example to illustrate a specific application of the tumor incidence information monitoring method based on medical insurance big data in the embodiment of the present invention. In the embodiment, it is assumed that the incidence of all tumors in 2017, the gender and the age distribution of common tumors in city A and the incidence trend of tumors in 2014-2017 need to be counted. The medical insurance data time span required to be acquired is from 1 month and 1 day in 2012 to 6 months and 30 days in 2018. A city medical insurance data analysis method in 2014-2017 is shown in figure 2. The sites of tumor incidence were divided into 27 groups, including 26 major sites and 1 other site. For each tumor diagnosis for each patient, only one record with the earliest admission time was kept to exclude duplicate diagnoses. The local new tumor case database of 2014-2017 is shown in the figure 3. In FIG. 3, "a" indicates an excluded nasopharyngeal carcinoma and "b" indicates an excluded C16.0. Furthermore, statistical analysis was performed, and the results of the main indicators of urban tumor incidence in 2017 a in the analysis results are shown in fig. 4. The onset trend of tumors in 2014-2017 is shown in FIG. 5.
Based on the same inventive concept, the embodiment of the invention also provides a device for monitoring the tumor morbidity information based on the medical insurance big data, which is described in the following embodiment. Because the principles for solving the problems are similar to the tumor morbidity information monitoring method based on medical insurance big data, the implementation of the device can be referred to the implementation of the method, and repeated parts are not described again.
Fig. 6 is a structural diagram of a tumor morbidity information monitoring device based on medical insurance big data in an embodiment of the present invention, and as shown in fig. 6, the device includes:
the data acquisition module 201 is configured to acquire multiple sets of medical insurance data, where each set of medical insurance data includes: disease diagnostic data and corresponding disease encoding data;
a first selecting module 202, configured to select one or more sets of first medical insurance data including a tumor keyword from the multiple sets of medical insurance data according to the tumor keyword library;
the second selecting module 203 is configured to select one or more groups of second medical insurance data with disease coded data including tumor codes from the multiple groups of medical insurance data according to the tumor coded data base;
the data comparison module 204 is configured to compare the selected first medical insurance data with the selected second medical insurance data, select one or more groups of medical insurance data from the multiple groups of medical insurance data according to a comparison result, and monitor the tumor morbidity information according to the one or more groups of medical insurance data.
In one embodiment, the data alignment module 204 is further configured to:
for each group of medical insurance data, if the medical insurance data are respectively selected as the first medical insurance data and the second medical insurance data, and the disease name data corresponding to the disease diagnosis data and the disease coded data in the medical insurance data are the same, the group of medical insurance data is selected.
In one embodiment, each set of medical insurance data further includes: patient identity data corresponding to the disease diagnosis data;
after acquiring the plurality of groups of medical insurance data, encrypting the patient identity data in each group of medical insurance data; or
And encrypting the patient identity data in each group of medical insurance data when each group of medical insurance data is acquired.
In one embodiment, each set of medical insurance data further includes: the hospital grade of the visit corresponding to the disease diagnosis data;
after the multiple groups of medical insurance data are obtained, selecting medical insurance data of which the hospitalizing hospital grade is greater than the preset grade from the multiple groups of medical insurance data; or
And when a group of medical insurance data is acquired, judging whether the hospital grade of the doctor in the group of medical insurance data is greater than a preset grade, and if so, selecting the group of medical insurance data.
In summary, in the embodiment of the present invention, by acquiring multiple sets of medical insurance data, each set of medical insurance data includes: disease diagnostic data and corresponding disease encoding data; according to the tumor keyword library, one or more groups of first medical insurance data of which the disease diagnosis data comprise tumor keywords are selected from the multiple groups of medical insurance data; according to a tumor coding database, one or more groups of second medical insurance data with tumor codes contained in the disease coding data are selected from the multiple groups of medical insurance data; and comparing the selected first medical insurance data with the second medical insurance data, selecting one or more groups of medical insurance data from the multiple groups of medical insurance data according to the comparison result, and monitoring the tumor morbidity information according to the one or more groups of medical insurance data. According to the embodiment of the invention, the medical insurance data are respectively screened according to the tumor keyword library and the tumor coding data library, and whether the medical insurance data selected twice can correspond is checked through comparison, so that the tumor onset data is determined, the tumor onset data does not need to be manually registered and checked one by one, and the tumor onset event can be extracted only through the medical insurance data, so that the tumor onset information is obtained, a large amount of manpower and material resources are saved, the working efficiency is improved, and the timeliness of the data is guaranteed.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A tumor morbidity information monitoring method based on medical insurance big data is characterized by comprising the following steps:
obtain multiunit medical insurance data, every group medical insurance data includes: disease diagnostic data and corresponding disease encoding data;
according to the tumor keyword library, one or more groups of first medical insurance data of which the disease diagnosis data comprise tumor keywords are selected from the multiple groups of medical insurance data;
according to a tumor coding database, one or more groups of second medical insurance data with tumor codes contained in the disease coding data are selected from the multiple groups of medical insurance data;
and comparing the selected first medical insurance data with the second medical insurance data, selecting one or more groups of medical insurance data from the multiple groups of medical insurance data according to the comparison result, and monitoring the tumor morbidity information according to the one or more groups of medical insurance data.
2. The method of claim 1, wherein selecting one or more sets of medical insurance data from the plurality of sets of medical insurance data based on the comparison comprises:
for each group of medical insurance data, if the medical insurance data are respectively selected as the first medical insurance data and the second medical insurance data, and the disease name data corresponding to the disease diagnosis data and the disease coded data in the medical insurance data are the same, the group of medical insurance data is selected.
3. The method of claim 1, wherein each set of medical insurance data further comprises: patient identity data corresponding to the disease diagnosis data;
after acquiring the plurality of groups of medical insurance data, encrypting the patient identity data in each group of medical insurance data; or
And encrypting the patient identity data in each group of medical insurance data when each group of medical insurance data is acquired.
4. The method of claim 1, wherein each set of medical insurance data further comprises: the hospital grade of the visit corresponding to the disease diagnosis data;
after the multiple groups of medical insurance data are obtained, selecting medical insurance data of which the hospitalizing hospital grade is greater than the preset grade from the multiple groups of medical insurance data; or
And when a group of medical insurance data is acquired, judging whether the hospital grade of the doctor in the group of medical insurance data is greater than a preset grade, and if so, selecting the group of medical insurance data.
5. A tumor morbidity information monitoring device based on medical insurance big data is characterized by comprising:
the data acquisition module is used for acquiring a plurality of groups of medical insurance data, and each group of medical insurance data comprises: disease diagnostic data and corresponding disease encoding data;
the first selection module is used for selecting one or more groups of first medical insurance data of which the disease diagnosis data comprise tumor keywords from the multiple groups of medical insurance data according to the tumor keyword library;
the second selecting module is used for selecting one or more groups of second medical insurance data with disease coded data containing tumor codes from the multiple groups of medical insurance data according to the tumor coded data base;
and the data comparison module is used for comparing the selected first medical insurance data with the second medical insurance data, selecting one or more groups of medical insurance data from the multiple groups of medical insurance data according to the comparison result, and monitoring the tumor morbidity information according to the one or more groups of medical insurance data.
6. The apparatus of claim 5, wherein the data alignment module is further configured to:
for each group of medical insurance data, if the medical insurance data are respectively selected as the first medical insurance data and the second medical insurance data, and the disease name data corresponding to the disease diagnosis data and the disease coded data in the medical insurance data are the same, the group of medical insurance data is selected.
7. The apparatus of claim 5, wherein each set of medical insurance data further comprises: patient identity data corresponding to the disease diagnosis data;
after acquiring the plurality of groups of medical insurance data, encrypting the patient identity data in each group of medical insurance data; or
And encrypting the patient identity data in each group of medical insurance data when each group of medical insurance data is acquired.
8. The apparatus of claim 5, wherein each set of medical insurance data further comprises: the hospital grade of the visit corresponding to the disease diagnosis data;
after the multiple groups of medical insurance data are obtained, selecting medical insurance data of which the hospitalizing hospital grade is greater than the preset grade from the multiple groups of medical insurance data; or
And when a group of medical insurance data is acquired, judging whether the hospital grade of the doctor in the group of medical insurance data is greater than a preset grade, and if so, selecting the group of medical insurance data.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 4.
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| CN112509659A (en) * | 2020-11-27 | 2021-03-16 | 北京市肿瘤防治研究所 | Medical insurance and death cause monitoring data-based tumor patient survival monitoring method and device |
| CN112635068A (en) * | 2020-11-27 | 2021-04-09 | 北京市肿瘤防治研究所 | Tumor present and affected monitoring method and device based on medical insurance and death factor monitoring data |
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