WO2020078058A1 - Medical data abnormality identification method and device, terminal, and storage medium - Google Patents

Abstract

A medical data abnormality identification method and device based on big data, a terminal, and a computer readable storage medium. The method comprises: when detecting an abnormality detection request, obtaining a target hospital corresponding to the abnormality detection request and medical data to be detected of the target hospital (S10); grouping said medical data according to a preset dimension to obtain grouped subsets (S20); analyzing each of the subsets on the basis of a preset rule corresponding to the preset dimension, and determining whether there is target medical data satisfying abnormal features in said medical data (S30); and if the target medical data satisfying the abnormality features exists in said medical data, marking the target medical data with abnormality (S40). By means of the method, disaggregated hospitalization behaviors can accurately identified, thereby effectively preventing fraudulent insurance behaviors.

Description

 Medical data abnormality identification method, device, terminal and storage medium The

This application requires the priority of the Chinese patent application filed on October 19, 2018 in the Chinese Patent Office with the application number 201811226809.0 and the invention titled "Medical Data Anomaly Recognition Method, Device, Terminal and Storage Medium", the entire content of which is cited by reference Incorporated in the application.

Technical field

The present application relates to the technical field of medical data processing, and in particular, to a method, device, terminal, and computer-readable storage medium for identifying abnormal medical data.

Background technique

The medical insurance system is a kind of social compulsory insurance for the state to prevent and share the medical expenses caused by diseases. The expenses are jointly paid by the employer and the individual, and the medical insurance money is paid by the medical insurance institution to solve the laborers ’illness or illness. Medical risks from injury.

The medical insurance department will evaluate the hospital. Among them, the number of single hospital stays is an important evaluation index of the hospital's medical efficiency. The more the average number of single hospital stays, the lower the hospital's medical efficiency. Therefore, in actual operation, the hospital will improve its evaluation score , Break down one hospitalization into multiple hospitalizations, or handle false hospitalizations to reduce the number of single hospitalizations. This is obviously a practice that violates medical insurance regulations and harms the public interest. Therefore, a method for identifying abnormal medical data is needed. The

Summary of the invention

The main purpose of the present application is to provide a method for identifying abnormal medical data, aiming to solve the technical problem that the medical insurance card behavior data cannot be detected abnormally.

In order to achieve the above object, the present application provides a medical data abnormality identification method, characterized in that the medical data abnormality identification method includes the following steps:

When an abnormality detection request is detected, the target hospital corresponding to the abnormality detection request and the medical data to be detected of the target hospital are obtained;

Group the medical data to be tested according to a preset dimension to obtain a grouped subset;

Analyzing each of the subsets based on a preset rule corresponding to a preset dimension to determine whether there is target medical data satisfying abnormal characteristics in the medical data to be detected;

If there is target medical data satisfying abnormal characteristics in the medical data to be detected, the target medical data is marked abnormally.

In addition, in order to achieve the above object, the present application also provides a medical data abnormality identification device, the medical data abnormality identification device includes:

An obtaining module, configured to obtain the target hospital corresponding to the abnormal detection request and the medical data to be detected of the target hospital when the abnormal detection request is detected;

A grouping module, configured to group the medical data to be detected according to a preset dimension to obtain a grouped subset;

An anomaly analysis module, configured to analyze each of the subsets based on a preset rule corresponding to a preset dimension, and determine whether there is target medical data satisfying an abnormal feature in the medical data to be detected;

The abnormal marking module is configured to mark the target medical data abnormally if there is target medical data satisfying abnormal characteristics in the medical data to be detected.

In addition, in order to achieve the above object, the present application also provides a medical data abnormality identification terminal, the medical data abnormality identification terminal includes a processor, a memory, and a computer stored on the memory and executable by the processor. Read instructions, where the computer-readable instructions are executed by the processor to implement the steps of the medical data abnormality identification method as described above.

In addition, in order to achieve the above object, the present application also provides a computer-readable storage medium having computer-readable instructions stored on the computer-readable storage medium, wherein when the computer-readable instructions are executed by a processor, the implementation is as described above Steps of the method for identifying abnormal medical data.

BRIEF DESCRIPTION

FIG. 1 is a schematic structural diagram of a medical data abnormality recognition terminal in a hardware operating environment involved in an embodiment of the present application;

2 is a schematic flowchart of a first embodiment of a medical data abnormality identification method of the application;

3 is a schematic flowchart of a second embodiment of a medical data abnormality identification method of the present application;

4 is a schematic flowchart of a fifth embodiment of a medical data abnormality identification method of the present application;

FIG. 5 is a schematic diagram of functional modules of the first embodiment of the medical data abnormality recognition device of the present application.

The implementation, functional characteristics and advantages of the present application will be further described in conjunction with the embodiments and with reference to the drawings.

detailed description

It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

Please refer to FIG. 1, which is a schematic diagram of the hardware structure of the medical data abnormality identification terminal provided by the present application.

The medical data abnormality identification terminal may be a PC or a device terminal with a display function such as a smart phone, tablet computer, portable computer, desktop computer, etc. Optionally, the medical data abnormality identification terminal may be a server device that exists A back-end management system for medical data abnormality identification, and a user manages the medical data abnormality identification terminal through the back-end management system.

The medical data abnormality identification terminal may include components such as a processor 101 and a memory 201. In the medical data abnormality recognition terminal, the processor 101 is connected to the memory 201, and the memory 201 stores computer-readable instructions, and the processor 101 can call the computer-readable instructions stored in the memory 201, and The steps of each embodiment of the medical data abnormality recognition method described below are implemented.

The memory 201 can be used to store software computer readable instructions and various data. The memory 201 may mainly include an area for storing computer-readable instructions and an area for storing data. In addition, the memory 201 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

The processor 101 is the control center of the medical data abnormality recognition terminal, and uses various interfaces and lines to connect the various parts of the entire medical data abnormality recognition terminal, by running or executing software computer readable instructions and / or modules stored in the memory 201 , And call the data stored in the memory 201 to perform various functions and process data of the medical data abnormality recognition terminal, thereby performing overall monitoring of the medical data abnormality recognition terminal. The processor 101 may include one or more processing units; optionally, the processor 101 may integrate an application processor and a modem processor, where the application processor mainly processes an operating system, a user interface, and application computer-readable instructions, etc. The modem processor mainly handles wireless communication. It can be understood that the foregoing modem processor may not be integrated into the processor 101.

Those skilled in the art can understand that the structure of the medical data abnormality identification terminal shown in FIG. 1 does not constitute a limitation on the medical data abnormality identification terminal, and may include more or less components than the illustration, or a combination of certain components, Or different component arrangements.

Based on the above hardware structure, various embodiments of the method of the present application are proposed, and the identification terminal in the following is an abbreviation of a medical data abnormality identification terminal.

This application provides a method for identifying abnormal medical data.

Referring to FIG. 2, FIG. 2 is a schematic flowchart of a first embodiment of a medical data abnormality recognition method of this application.

In this embodiment, the medical data abnormality identification method includes the following steps:

Step S10, when an abnormality detection request is detected, the target hospital corresponding to the abnormality detection request and the medical data to be detected of the target hospital are obtained;

The anomaly detection request is an instruction that triggers the start of the computer-readable instruction corresponding to the medical data anomaly detection method of the present application. The anomaly detection request can be triggered by a preset external event / page request, such as a click operation for anomaly detection input by a user; it can also be passed Use the timed JOB (task) method as the starting interface for computer-readable instructions, which triggers anomaly detection of medical data at regular intervals. It can also perform anomaly detection immediately after receiving medical data to be detected. Anomaly detection is performed after the amount of medical data to be detected reaches a certain threshold. Optionally, the anomaly detection of medical data in this application may be one or more process nodes in the medical data review process, and the abnormality detection of medical data may be automatically triggered by the flow of the process nodes.

In this embodiment, in order to identify the abnormal medical data with the hospital as the subject of violation, the medical data of the target hospital to be detected is used as the basis for abnormal identification. The anomaly detection request contains the identification of the corresponding target hospital. The medical data to be detected is the specific medical data pointed to by the abnormal detection request and the target hospital requested for detection. The medical data includes but is not limited to: type of treatment, identification of treatment items, time of treatment, diagnosis results, medication situation, amount of treatment, amount of medication Wait. The

After the insured person swipes the medical insurance card, the card swiping device will upload the relevant medical data to the hospital terminal, and then the hospital terminal sends it to the identification terminal or directly uploads it to the identification terminal, which can be directly stored in the memory of the identification terminal, when performing abnormal detection , The identification terminal directly obtains the medical data to be detected from the local storage according to the abnormal detection request, or it can be stored in the data storage terminal / cloud of the medical data, so that the identification terminal can detect the abnormal medical data from the data according to the abnormal detection request The storage terminal / cloud obtains medical data to be tested.

Step S20: Group the medical data to be detected according to a preset dimension to obtain a grouped subset;

The preset dimension includes at least one of the hospital dimension and the personal dimension. The hospital dimension is based on the analysis of all medical data of the target hospital to determine whether there is abnormal medical data, and the personal dimension is based on the medical data analysis of each participant to determine whether there is abnormal medical data . Each preset dimension can be set as a clickable control, and the preset dimension for analysis selected by the user can be determined according to the user's click operation, or the default preset dimension can be directly obtained.

The preset dimensions are different, and the medical data to be tested are grouped in different ways. When the preset dimension is a hospital, the identification terminal groups the medical data to be tested according to the disease type, and a corresponding subset of each disease type can be obtained; when the preset dimension is an individual, the identification terminal groups the medical data to be tested according to the insured person By grouping, you can get a subset of each insured person.

Optionally, the identification terminal may simultaneously and separately analyze the medical data to be detected according to the hospital dimension and the individual dimension to identify abnormal data.

Step S30: Analyze each of the subsets based on a preset rule corresponding to a preset dimension to determine whether there is target medical data satisfying abnormal characteristics in the medical data to be detected;

Each preset dimension has its own corresponding preset rule, and each subset is the analysis object of the preset rule. Specifically, in an embodiment, when the preset dimension is a hospital dimension, the medical data to be tested are grouped according to disease types to obtain a first subset corresponding to each disease type, based on the Calculate the average number of single hospitalization days corresponding to each disease type, and / or average number of hospitalizations per person, and / or average amount of single hospitalization, and calculate the average number of single hospitalization days corresponding to each disease type, and / or per person The average number of hospitalizations, and / or the average single hospitalization amount are compared with preset values corresponding to each disease type, and it is determined whether the first subset corresponding to each disease type meets the abnormal feature.

Abnormal characteristics refer to the characteristics set in advance to limit abnormal medical data. For example, the average number of days of hospitalization corresponding to each disease type is compared with the preset value corresponding to each type of disease. The number of hospitalization days is greater than the preset value corresponding to each disease type, then the first subset corresponding to the disease type meets the abnormal characteristics.

Step S40, if there is target medical data satisfying abnormal characteristics in the medical data to be detected, the target medical data is marked abnormally.

If the medical data to be detected has abnormal target medical data, the target medical data is marked abnormally, and the abnormal detection result is output to prompt the user to perform offline verification. If there is no abnormal target medical data in the medical data to be detected, it is prompted that there is no abnormality.

Further, in order to better mark the abnormality, so as to identify the terminal and / or the user to select the corresponding processing method for the target medical data to process, and obtain the abnormal type of the target medical data; according to the abnormal type, obtain the corresponding abnormality identifier to detect medical treatment The data is marked abnormally. Further, when the correction instruction input after the offline verification by the user is detected, the state of the target medical data is modified to be normal.

In this embodiment, when an abnormality detection request is detected, the target hospital corresponding to the abnormality detection request and the medical data to be detected of the target hospital are obtained; the medical data to be detected are grouped according to a preset dimension to obtain the grouped Sub-sets; analyze each of the sub-sets based on preset rules corresponding to preset dimensions to determine whether there is target medical data satisfying abnormal characteristics in the medical data to be tested; For target medical data, the target medical data is abnormally marked. By taking the target hospital to be audited / abnormally identified as the screening condition, the medical data to be tested of the target hospital can be obtained, and the target hospital can be targeted to analyze and identify whether the target hospital violates the medical insurance regulations; Grouped analysis can effectively detect abnormal medical data; mark abnormal medical target data that meets abnormal characteristics in the medical data to be detected, and distinguish the abnormal medical data for subsequent targeted treatment of abnormal medical data to standardize medical treatment The normal operation of the expense reimbursement system.

Further, based on the above embodiment, as shown in FIG. 3, in the second embodiment of the medical data abnormality identification method of the present application, when the preset dimension is a hospital dimension, the step S20 includes:

Step S21: Group the medical data to be tested according to the disease type, and obtain a first sub-set corresponding to each disease type;

Disease types, including primary diagnosis (such as diabetes) and secondary diagnosis / complication (kidney disease, fundus disease, neuropathy, etc.), when grouping, according to the primary diagnosis + secondary diagnosis, that is, the medical data is grouped according to the breakdown .

The step S30 includes:

Step S310: Obtain the total number of hospitalizations and the total number of hospitalization days of each disease type from the first subset corresponding to each disease type, and calculate and obtain the average number of each disease type based on the total number of hospitalizations and the total number of hospitalization days Number of hospital stays;

The first sub-collection corresponding to each disease type contains all medical data corresponding to each disease type of the target hospital, and the insured person is not used as the criterion. From the first subset corresponding to each disease type, the total number of hospitalizations and the total number of hospitalization days corresponding to each disease type are counted. The average number of single hospitalization days can be obtained by dividing the total number of hospitalization days by the total number of hospitalizations. For example, if diabetes is complicated by lactic acidosis, the total number of hospitalizations is 20, and the total number of hospitalization days is 140 days, then the average number of single hospitalization days for the disease is 140/20 = 7 days / time.

The average number of hospital days per disease refers to the average number of hospital days for a disease. The average number of hospital stays for different diseases is different. Therefore, the average number of hospital stays for each disease is calculated based on the first subset corresponding to each disease.

Optionally, in order to ensure more accurate data and ensure compatibility with accidental accidents, the medical data of a single hospitalization day above / below the first preset value can be marked in the first subset corresponding to the disease type Come out and further verify, and at the same time, calculate the average number of days of hospitalization for the disease based on the remaining medical data. As in the above example, in the 20 hospitalizations, the number of hospitalization days is mostly between 4-10 days, but once the hospitalization was 2 days and once the hospitalization was 14 days, the two abnormal data were marked for further verification, and the These two abnormal data are eliminated, and the average number of hospital stays is calculated based on the remaining 18 data.

Step S311: Compare the average number of hospitalization days of each disease type with the preset days corresponding to each disease type, obtain the difference between the average number of hospitalization days of each disease type and the corresponding preset days, and calculate each disease type The ratio of the difference in the number of days to the corresponding preset number of days to obtain the abnormal factor for the number of days corresponding to each disease type;

The preset number of days is calculated based on the average number of hospital stays in the same class and the same type of disease in the same type of medical institutions that have been reviewed. The same level refers to the general hospital grades such as "Third Class A Hospital" and "Second Class A Hospital", and the category refers to the same specialty. For example, they belong to the specialty of children, and the same disease includes the main diagnosis and the auxiliary diagnosis. The preset number of days can be a range.

The abnormal number of days refers to the parameter used to identify the abnormal degree of the average number of days in a single hospital stay. In this embodiment, the ratio of the difference between the average number of days of hospitalization for each disease type and the number of days corresponding to the preset number of days and the number of days corresponding to the preset number of days is used as the abnormal factor for the number of days corresponding to each disease type. For example, if the preset number of days for diabetes complicated with lactic acidosis is 15 days, and the average number of single hospital stays in the target hospital is 7 days, the difference is 8 days, then it is calculated based on the preset days, average number of single hospital stays, and the difference in days Days anomaly factor, the days anomaly factor in the above example is (15-7) / 15.

When the number of days abnormal factor is 0, that is, the difference between the average number of days of single hospitalization and the corresponding preset days is 0, indicating that the average number of days of corresponding single hospitalization of the corresponding disease is not abnormal; when the number of days abnormal factor <0, it is the average single The number of hospitalization days is greater than the corresponding preset number of days, indicating that there can be no decomposition hospitalization for the corresponding disease, that is, the average number of single hospitalization days is not abnormal; when the abnormality factor of the number of days> 0, the average number of single hospitalization days is less than the corresponding preset number of days, indicating that the corresponding disease The average number of hospitalization days for each species is abnormal, and the value indicates the degree of abnormality.

Step S312, when the abnormal number of days corresponding to any disease type is greater than the first preset threshold, the first subset corresponding to the disease type satisfies the abnormal characteristics.

The first preset threshold refers to a value preset by the user and can be adjusted according to actual conditions.

Optionally, after calculating the abnormal number of days corresponding to each disease type, the abnormal score of the target hospital may be calculated according to the abnormal number of days corresponding to each disease type. In the overall evaluation of the target hospital, the abnormal number of days corresponding to each disease type is integrated to calculate the abnormal score of the target hospital. Optionally, the sum of all the abnormal factors of the number of days can be used as the abnormal score of the target hospital. The degree of instability of the disease (a disease with a large individual difference) is assigned a reference value, and the sum of the product of the reference value corresponding to each disease and the abnormal number of days is used as the abnormal score of the target hospital.

In this embodiment, the average number of single hospitalization days of each disease type is obtained by calculation, and the average number of single hospitalization days of each disease type of the target hospital is compared with the average number of single hospitalization days of the same disease type and medical institution of the same class (ie Preset days) to compare, identify the medical data in the target hospital's medical data that does not meet the usual situation, and characterize the degree of abnormality by the number of days abnormal factor, when the number of days abnormal factor is greater than the first preset threshold, the corresponding disease type The first sub-set is determined to satisfy the abnormal features and realize the identification of abnormal medical data.

Further, based on the foregoing embodiment, in the third embodiment of the medical data abnormality recognition method of the present application, the step S30 includes:

Step S313: Obtain the number of insured persons and the total number of hospitalizations of each disease type from the first subset corresponding to each disease type, and calculate the average per capita corresponding to each disease type based on the number of insured persons of each disease type and the total number of hospitalizations Number of hospitalizations;

The first subset corresponding to each disease type contains all medical data corresponding to each disease type in the target hospital. From the first subset corresponding to each disease type, the number of insured persons corresponding to each disease type and the total number of hospitalizations are counted. The average number of hospitalizations per person can be obtained by dividing the total number of hospitalizations by the number of insured persons. For example, if diabetes is complicated by lactic acidosis, the total number of hospitalizations is 100, and the number of insured persons is 14, then the average number of hospitalizations per person for this disease is 100/14.

Step S314: Compare the average number of hospitalizations per person for each disease type with the corresponding preset number of times, obtain the difference between the number of average hospitalizations per person for each disease type and the corresponding preset number of times, and calculate the number of times for each disease type The ratio of the difference to the corresponding preset number of times to obtain the abnormal factor of the number of times corresponding to each disease type;

The preset number of times is calculated based on the average number of hospitalizations of the same diagnosis (primary diagnosis / subdiagnosis) of the same disease in the same level and the same category of medical institutions under review. The average number of hospitalizations for the same diagnosis (primary diagnosis / subdiagnosis) of the same type of disease.

The number of abnormal factors refers to the parameter used to identify the abnormal degree of the average number of hospitalizations per person. In this embodiment, the ratio between the average number of hospitalizations per person of each disease type and the number of times corresponding to the preset number of times, and the ratio of the number of times corresponding to the preset number of times as the abnormality factor of the number of times corresponding to each disease type. For example, if the average number of hospitalizations per person for diabetes complicated with lactic acidosis is 10, and the average number of hospitalizations per person for the target hospital is 17, a difference of 7 times, according to the preset number of times, the average number of hospitalizations per person, the number of differences Calculate the frequency anomaly factor. The frequency anomaly factor in the above example is (17-10) / 10.

When the number of abnormal factors is 0, that is, the difference between the average number of hospitalizations per person and the corresponding preset number of times is 0, indicating that the average number of hospitalizations per person for the corresponding disease type is not abnormal; when the number of abnormal factors <0, the average The number of hospitalizations is less than the corresponding preset number, indicating that there can be no decomposition hospitalization for the corresponding disease, that is, the average number of hospitalizations per person is not abnormal; when the number of abnormal factors is> 0, the average number of hospitalizations per person is greater than the corresponding preset number, indicating the corresponding disease The average number of hospitalizations per person for each species is abnormal, and the value indicates the degree of abnormality. The larger the value, the greater the degree of abnormality.

Step S315, when the abnormality factor of the number of times corresponding to any disease type is greater than the second preset threshold, the first subset corresponding to the disease type satisfies the abnormal characteristics.

The second preset threshold refers to a value preset by the user and can be adjusted according to actual conditions.

Optionally, after calculating the abnormal factor of the number of times corresponding to each disease type, the abnormal score of the target hospital may be calculated according to the abnormal factor of the number of times corresponding to each disease type. In the overall evaluation of the target hospital, the abnormal factors of the number of times corresponding to each disease are integrated to calculate the abnormal score of the target hospital. Optionally, the sum of all the abnormal factors of the number of times can be used as the abnormal score of the target hospital; The degree of instability of the disease type (the disease type with large individual difference) is assigned a reference value, and the sum of the product of the reference value corresponding to each disease type and the number of abnormal factors is used as the abnormal score of the target hospital; The abnormal number of days and the abnormal number of times are combined to calculate the overall abnormal score of the target hospital, with more participation dimensions and more accurate abnormal scores.

In this embodiment, the average number of hospitalizations per person of each disease type is obtained by calculation, and the average number of hospitalizations per person of each disease type in the target hospital is compared with the average number of hospitalizations per person of the same disease type and medical institution of the same class (ie Preset times) to compare, identify the medical data in the target hospital's medical data that does not meet the usual situation, and characterize the degree of abnormality by the number of times abnormal factor, when the number of times abnormal factor is greater than the second preset threshold, the corresponding disease type The first sub-set is determined to satisfy the abnormal features and realize the identification of abnormal medical data.

Further, based on the foregoing embodiment, in the fourth embodiment of the medical data abnormality recognition method of the present application, the step S30 includes:

Step S316: Obtain the total number of hospitalizations and total hospitalization amount of each disease type from the first subset corresponding to each disease type, and calculate and obtain the average bill of each disease type based on the total number of hospitalizations and total hospitalization amount of each disease type Amount of second hospitalization;

From the first subset corresponding to each disease type, the total number of hospitalizations and total hospitalization amount corresponding to each disease type are counted. The average single hospitalization amount can be obtained by dividing the total hospitalization amount by the total number of hospitalizations. For example, if diabetes is complicated by lactic acidosis, the total amount of hospitalization is 100,000, and the total number of hospitalizations is 20, then the average single hospitalization for the disease is 100,000 / 20 = 5,000 yuan / time.

The average amount of single hospitalization for each disease refers to the average amount of single hospitalization for a certain disease. The average single hospitalization amount for different diseases is different. Therefore, the average single hospitalization amount for each disease type is calculated based on the first subset corresponding to each disease type.

Optionally, in order to ensure more accurate data and compatibility with accidental accidents, the medical data of a single hospitalization amount higher / lower than the first preset value may be marked in the first subset corresponding to the disease type Come out and further verify, at the same time, calculate the average single hospitalization amount of the disease based on the remaining medical data.

Step S317, compare the average single hospitalization amount of each disease type with the corresponding preset amount, obtain the difference between the average single hospitalization amount of each disease type and the corresponding preset amount, and calculate the The ratio of the amount difference to the corresponding preset amount to obtain the amount abnormality factor corresponding to each disease type;

The preset amount is calculated based on the average single hospitalization amount of the same class and the same disease in the medical institutions of the same class that have been reviewed. For example, they belong to the specialty of children, and the same disease includes the main diagnosis and the auxiliary diagnosis. The preset amount can be a range of values.

The amount abnormality factor refers to the parameter used to identify the abnormal degree of the average single hospitalization amount. In this embodiment, the ratio of the difference between the average single hospitalization amount of each disease type and the corresponding preset amount and the corresponding preset amount is used as the amount abnormality factor corresponding to each disease type. For example, if the preset amount of diabetes complicated with lactic acidosis is 10,000 yuan / time, and the average single hospitalization amount of the target hospital is 5,000 yuan / time, the difference is 5000 yuan / time, according to the preset amount, the average single The hospitalization amount and the amount difference are calculated as the amount abnormality factor. The amount abnormality factor in the above example is (10000-5000) / 10000. If the average amount of a single hospitalization is relatively small, it may be shared with another hospitalization, that is, there may be decomposition of hospitalization.

When the amount abnormality factor <0 or = 1, it means that the average single hospitalization amount of the corresponding disease type is not abnormal. When the amount abnormality factor <1, it means there is an abnormality. The smaller the value, the greater the degree of abnormality.

Step S318, when the amount abnormality factor corresponding to any disease type is greater than the third preset threshold, the first subset corresponding to the disease type meets the abnormal characteristics.

The third preset threshold refers to a value preset by the user and can be adjusted according to actual conditions.

Optionally, after calculating the amount abnormality factor corresponding to each disease type, the abnormal score of the target hospital may be calculated according to the amount abnormality factor corresponding to each disease type. In the overall assessment of the target hospital, the abnormal amount of money corresponding to each disease type is integrated to calculate the abnormal score of the target hospital. Optionally, the sum of all abnormal amounts of money can be used as the abnormal score of the target hospital; The degree of instability of the disease type (the disease with large individual difference) is assigned a reference value, and the sum of the product of the reference value corresponding to each disease type and the amount abnormality factor is used as the abnormal score of the target hospital; it can also be based on the amount abnormality factor and the number of days The abnormal factor and the frequency abnormal factor calculate the abnormal score of the target hospital.

In this embodiment, the average single hospitalization amount of each disease type is obtained by calculation, and the average single hospitalization amount of each disease type of the target hospital is compared with the average single hospitalization amount of the same type and type of medical institution (ie. Preset amount) to compare, identify the medical data in the target hospital's medical data that does not meet the usual situation, and characterize the degree of abnormality by the amount abnormality factor, when the amount abnormality factor is greater than the third preset threshold, the corresponding disease type The first sub-set is determined to satisfy the abnormal features and realize the identification of abnormal medical data.

Further, based on the above embodiment, as shown in FIG. 4, in the fifth embodiment of the medical data abnormality recognition method of the present application, when the preset dimension is a personal dimension, the step S20 includes:

Step S22: Group the medical data to be tested according to the insured persons to obtain a second subset corresponding to each insured person;

Take the insured as the unit to obtain the medical data to be tested corresponding to each insured in the medical data to be tested, including the type of treatment, identification of the treatment item, time of diagnosis, diagnosis result, medication situation, and amount of treatment , Amount of medication, etc. The medical data set to be tested corresponding to each insured person is the second subset.

The step S30 includes:

Step S320, extracting the consultation time corresponding to each piece of medical data in the second sub-collection, and calculating the interval between adjacent consultations to obtain the adjacent medical data in the second sub-collection;

The identification terminal can obtain the visit time of each visit from the medical data, wherein each visit generates a corresponding piece of medical data, which is arranged in order of the visit time, and then calculates the adjacent visit interval for arranging the adjacent medical data.

In one embodiment, the identification terminal obtains the adjacent visit intervals of all adjacent medical data in the second subset; in another embodiment, the second subset is regrouped according to the disease type to obtain the third subset, and the third Each piece of medical data in the three sub-collections is arranged according to the order of visit time, and the adjacent visit interval of the adjacent hospital visits of the same disease type is calculated.

Step S321: Obtain a preset time interval threshold, calculate a time difference between the adjacent consultation interval and the time interval threshold, calculate a ratio of the time difference to the time interval threshold, and obtain each item in the second subset The interval anomaly factor corresponding to medical data;

Obtain a preset time interval threshold and determine whether the adjacent medical visit interval corresponding to each piece of medical data is less than the time interval threshold. If it is less, it indicates that there may be suspicious decomposition of hospitalization in this piece of medical data. In one embodiment, the recognition terminal may calculate the difference between the adjacent visit interval and the time interval threshold, and determine the interval anomaly factor corresponding to each piece of medical data according to the difference. When the adjacent visit interval is less than the time interval threshold, the two The greater the difference is, the greater the interval anomaly factor. When the adjacent visit interval is greater than or equal to the time interval threshold, the greater the difference between the two, the smaller the interval anomaly factor.

Because the adjacent visit interval involves at least two pieces of medical data, in this embodiment, each piece of medical data corresponds to at least one interval anomaly factor, for example, [1, 2], [2, 3], etc., where the number refers to a single parameter The serial number of the insured ’s hospitalization times is arranged in chronological order. For the medical data corresponding to the second hospitalization, there are at least two interval anomaly factors, which are the interval anomaly factor and the second hospitalization for the second hospitalization and the first hospitalization, respectively. Abnormal interval factor between the second hospitalization and the third hospitalization.

Step S322, when the interval abnormality factor corresponding to the medical data in the second subset is greater than the fourth preset threshold, the medical data meets the abnormality feature.

The fourth preset threshold refers to a value preset by the user and can be adjusted according to actual conditions.

In this embodiment, by extracting the visit time corresponding to each piece of medical data in the second subset, the neighboring visit interval of the neighboring medical data in the second subset is calculated; the preset time interval threshold is obtained, and the The time difference between the adjacent visit interval and the time interval threshold, calculating the ratio of the time difference to the time interval threshold to obtain the interval anomaly factor corresponding to each piece of medical data in the second subset; in the second When the interval anomaly factor corresponding to the medical data in the subset is greater than the fourth preset threshold, the medical data meets the anomaly characteristics, because for the same insured person, if the interval between adjacent hospitalizations is too short, there may be decomposition hospitalization Suspected, by calculating the adjacent medical interval of adjacent medical data, and screening out the medical data with a large degree of suspiciousness based on the interval abnormality factor corresponding to each medical data, it can accurately determine whether the medical data contains suspicious decomposition of hospitalization behavior, effective Prevent fraudulent behavior and reduce loss of public interest.

Further, the step after the step S321 includes:

Step S323: Extract the diagnosis result corresponding to each piece of medical data in the second subset, and determine whether there is adjacent medical data corresponding to a similar diagnosis result in the second subset;

The diagnosis result refers to the doctor's judgment on the cause of the insured person's illness. For example, the diagnosis is diabetic complicated with lactic acidosis. The diagnosis result includes the main diagnosis and the auxiliary diagnosis.

The similar diagnosis results in this embodiment include the same diagnosis results and similar diagnosis results. A diagnosis result database can be established in advance, and the same diagnosis results and similar diagnosis results with different names can be stored in the diagnosis result database to determine the diagnosis of adjacent medical data. When the result is similar to the diagnosis result, it can be judged by querying the diagnosis result database.

Step S323, if there is adjacent medical data corresponding to a similar diagnosis result in the second subset, the time difference between the adjacent visit interval of the adjacent medical data and the time interval threshold is obtained, and the time difference and The ratio of the time interval threshold to obtain the interval anomaly factor corresponding to the adjacent medical data;

If two or more adjacent medical data correspond to similar diagnosis results, the two or more adjacent medical data have a great possibility of decomposition and hospitalization, and then further obtain the two or more adjacent medical data to correspond together The interval anomaly factor determines whether the two or more adjacent medical data have an abnormal time (if there is an abnormality, the size of the abnormality).

Step S324, when the interval anomaly factor corresponding to the adjacent medical data is greater than the fifth preset threshold, the medical data meets the anomaly characteristics.

When the interval anomaly factor corresponding to the adjacent medical data is greater than the fifth preset threshold, the possibility that the adjacent medical data is decomposed into inpatient medical data is further increased. Because if the medical treatment interval corresponding to the adjacent medical data is small and the diagnosis results are the same or similar, the adjacent medical data is likely to be the data for decomposing hospitalization.

In this embodiment, by extracting the diagnosis result corresponding to each piece of medical data in the second subset, it is determined whether there is adjacent medical data corresponding to a similar diagnosis result in the second subset; if there is in the second subset Adjacent medical data corresponding to similar diagnosis results, the time difference between the adjacent medical interval of the adjacent medical data and the time interval threshold is obtained, and the ratio of the time difference to the time interval threshold is calculated to obtain the adjacent The interval anomaly factor corresponding to the medical data; when the interval anomaly factor corresponding to the adjacent medical data is greater than the fifth preset threshold, the medical data satisfies the abnormal feature, that is, the diagnosis result of the adjacent medical data and the interval between adjacent visits As a reference parameter for judging whether the adjacent medical data meets the abnormal characteristics, it can more accurately identify the decomposition of hospitalization behavior, effectively prevent fraudulent insurance behavior, and reduce the loss of public interest.

Further, based on the foregoing embodiment, in the sixth embodiment of the medical data abnormality recognition method of the present application, the step S30 includes:

Step S325: Extract the diagnosis and treatment items corresponding to each piece of medical data in the second sub-collection, and compare the diagnosis and treatment items corresponding to each adjacent consultation with the reference diagnosis and treatment item set respectively to obtain the corresponding diagnosis and treatment items and reference diagnosis and treatment for each adjacent treatment The coincidence ratio of the item set, and calculate the average coincidence ratio of all adjacent visits to obtain the continuity of the adjacent treatment items;

After the identification terminal arranges each inpatient consultation according to the time sequence of the consultation, it can obtain the identification of the treatment items included in the adjacent inpatient consultations, and compare the corresponding treatment items of the adjacent consultations with the reference set of treatment items, Obtain the coincidence ratio of the corresponding treatment items of the adjacent consultations and the set of reference treatment items, and calculate the average coincidence ratio of all the adjacent consultations to obtain the continuity of the treatment items of the adjacent consultations. A database of diagnosis and treatment specifications corresponding to various diagnosis results can be established in advance, including the diagnosis and treatment items (identification) corresponding to each diagnosis result. Because the treatment of a disease often follows a certain clinical / treatment path, therefore, the corresponding diagnosis results The diagnosis and treatment items are arranged in sequence based on a certain clinical / treatment path.

Comparing the corresponding diagnosis and treatment items of the adjacent consultations with the reference diagnosis and treatment item sets respectively to obtain the coincidence ratio of the corresponding diagnosis and treatment items of the adjacent consultations and the reference diagnosis and treatment item set, and calculating the average coincidence ratio of all adjacent visits to obtain the corresponding When the continuity of the diagnosis and treatment items of the neighboring visits is obtained, the target diagnosis result corresponding to the neighboring visits is obtained, the diagnosis specification database is queried, and the reference diagnosis item set corresponding to the target diagnosis result is obtained from the specification database. Compare the corresponding diagnosis and treatment items of the adjacent consultations with the reference diagnosis and treatment item sets respectively to obtain the coincidence ratio of the corresponding diagnosis and treatment items of the adjacent consultations and the reference diagnosis and treatment item set, that is, what proportion of the corresponding diagnosis and treatment items of the adjacent treatments The diagnosis and treatment items are in the reference diagnosis and treatment item set, specifically: the diagnosis and treatment items in the reference diagnosis and treatment item set account for the proportion of the adjacent diagnosis and treatment items, and the average coincidence ratio of all adjacent visits is calculated, and the average coincidence ratio is that of the adjacent consultations Continuity of diagnosis and treatment items. For example, the nth visit is the n + 1 visit as a neighboring visit, the nth visit is A, B, C, and the n + 1 visit is D, E, F, G, H , The reference diagnosis and treatment item set is [A, B, C, D, E, F, G], then the coincidence ratio of the diagnosis and treatment items corresponding to the nth visit and the reference diagnosis and treatment item set is 100%, corresponding to the n + 1th visit The coincidence ratio of the diagnosis and treatment items and the reference diagnosis and treatment item set is 80%, then the average coincidence ratio of all adjacent visits is (100% + 80%) / 2 = 90%.

Step S326, when the continuity is greater than the sixth preset threshold, the corresponding medical data meets abnormal characteristics.

The sixth preset threshold refers to a value preset by the user and can be adjusted according to actual conditions. In the above example, the sixth preset threshold may be 45%.

Optionally, before step S325 in this embodiment, it may further include a judgment based on the similarity between the adjacent medical intervals of the adjacent medical data and the diagnosis results, specifically including:

Extracting the diagnosis result corresponding to each piece of medical data in the second sub-set to determine whether there is adjacent medical data corresponding to a similar diagnosis in the second sub-set; if there is a corresponding similar diagnosis in the second sub-set Adjacent medical data, the time difference between the adjacent medical interval of the adjacent medical data and the time interval threshold is obtained, and the ratio of the time difference to the time interval threshold is calculated to obtain the corresponding Interval anomaly factor; if the interval anomaly factor corresponding to the adjacent medical data is greater than the fifth preset threshold, extract the diagnosis and treatment items corresponding to each piece of medical data in the second subset, and separate the corresponding diagnosis and treatment items of the adjacent treatments Compare with the reference diagnosis and treatment item set respectively to obtain the coincidence ratio of the corresponding diagnosis and treatment items of the adjacent consultation and the reference diagnosis and treatment item set, and calculate the average coincidence ratio of all adjacent consultations to obtain the continuity of the adjacent treatment items; When the continuity is greater than the sixth preset threshold, the corresponding medical data meets abnormal characteristics. Among them, the related description of the similarity between the adjacent medical visits of the adjacent medical data and the similarity of the diagnosis results has been described in the fifth embodiment, and will not be repeated here. The interval between adjacent visits based on adjacent medical data, the similarity of diagnosis results, and the continuity of the diagnosis and treatment items of adjacent visits can improve the accuracy of identifying abnormal medical data, reduce false positives, and increase the flexibility of abnormal detection.

In this embodiment, by analyzing the continuity of the diagnosis and treatment items of adjacent visits, when the continuity is greater than the sixth preset threshold, the corresponding medical data satisfies the abnormal characteristics, and the judgment on the decomposition of the hospitalization behavior can be realized based on the clinical treatment path, which is effective Prevent fraudulent behavior and reduce loss of public interest.

In addition, the present application also provides a medical data abnormality recognition device corresponding to each step of the foregoing medical data abnormality recognition method.

Referring to FIG. 5, FIG. 5 is a schematic diagram of functional modules of the first embodiment of the medical data abnormality recognition device of the present application.

In this embodiment, the medical data abnormality identification device of the present application includes:

The obtaining module 10 is configured to obtain the target hospital corresponding to the abnormality detection request and the medical data to be detected of the target hospital when the abnormality detection request is detected;

A grouping module 20, configured to group the medical data to be detected according to a preset dimension to obtain a grouped subset;

An anomaly analysis module 30, configured to analyze each of the subsets based on a preset rule corresponding to a preset dimension, and determine whether there is target medical data satisfying anomalous characteristics in the medical data to be detected;

The abnormal marking module 40 is configured to mark the target medical data abnormally if there is target medical data satisfying abnormal characteristics in the medical data to be detected.

Further, the medical data abnormality identification device includes:

The grouping module 20 is further configured to group the medical data to be tested according to the disease type when the preset dimension is the hospital dimension, to obtain a first subset corresponding to each disease type;

The first calculation module is used to obtain the total number of hospitalizations and the total number of hospitalization days of each disease type from the first subset corresponding to each disease type, and calculate and obtain each disease based on the total number of hospitalizations and the total number of hospitalization days of each disease type The average number of hospital stays per species; compare the average number of hospital stays for each disease with the preset days corresponding to each disease to obtain the difference between the average number of hospital stays for each disease and the number of days corresponding to the preset days , Calculate the ratio of the difference between the number of days of each disease type and the corresponding preset number of days to obtain the abnormal factor of the number of days corresponding to each disease type;

The first abnormality determination module is configured to: when the abnormality factor of the number of days corresponding to any disease type is greater than the first preset threshold, the first subset corresponding to the disease type meets the abnormality feature.

Further, the medical data abnormality identification device includes:

The second calculation module is used to obtain the number of insured persons and the total number of hospitalizations of each disease type from the first sub-collection corresponding to each disease type, and calculate and obtain the correspondence of each disease type based on the number of insured persons of each disease type and the total number of hospitalizations The average number of hospitalizations per person for each patient; compare the average number of hospitalizations per person for each disease type with the corresponding preset number of times to obtain the difference between the average number of hospitalizations per person for each disease type and the corresponding preset number of times to calculate each disease type The ratio of the difference between the number of times and the corresponding preset number of times to obtain the abnormal factor of the number of times corresponding to each disease type;

The second abnormality determination module is configured to: when the abnormality factor of the number of times corresponding to any disease type is greater than the second preset threshold, the first subset corresponding to the disease type satisfies abnormal characteristics.

Further, the medical data abnormality identification device includes:

The third calculation module is used to obtain the total number of hospitalizations and total hospitalization amount of each disease type from the first subset corresponding to each disease type, and calculate and obtain each disease based on the total number of hospitalizations and total hospitalization amount of each disease type The average single hospitalization amount of each kind; compare the average single hospitalization amount of each disease with the corresponding preset amount to obtain the difference between the average single hospitalization amount of each disease and the corresponding preset amount, and calculate each The ratio of the amount difference of the disease type to the corresponding preset amount to obtain the amount abnormality factor corresponding to each disease type;

The third abnormality determination module is configured to: when the amount abnormality factor corresponding to any disease type is greater than the third preset threshold, the first subset corresponding to the disease type meets abnormal characteristics.

Further, the medical data abnormality identification device includes:

The grouping module 20 is further configured to group the medical data to be tested according to the insured persons when the preset dimension is an individual dimension to obtain a second subset corresponding to each insured person;

The fourth calculation module is used for extracting the consultation time corresponding to each piece of medical data in the second subset, calculating the adjacent consultation interval for obtaining the adjacent medical data in the second subset; obtaining the preset time interval threshold Calculate the time difference between the adjacent consultation interval and the time interval threshold, calculate the ratio of the time difference and the time interval threshold to obtain the interval abnormality factor corresponding to each piece of medical data in the second subset;

The fourth abnormality determination module is configured to: when the interval abnormality factor corresponding to the medical data in the second subset is greater than the fourth preset threshold, the medical data meets the abnormality feature.

Further, the medical data abnormality identification device includes:

The similarity judgment module is used to extract the diagnosis result corresponding to each piece of medical data in the second subset and determine whether there is adjacent medical data corresponding to the similar diagnosis result in the second subset;

The first obtaining module is used to obtain the time difference between the adjacent medical visit interval of the adjacent medical data and the time interval threshold if there is adjacent medical data corresponding to similar diagnosis results in the second subset The ratio of the time difference to the time interval threshold to obtain an interval anomaly factor corresponding to the adjacent medical data;

The fifth abnormality determination module is configured to: when the interval abnormality factor corresponding to the adjacent medical data is greater than a fifth preset threshold, the medical data meets abnormal characteristics.

Further, the medical data abnormality identification device includes:

The fifth calculation module is used for extracting the diagnosis and treatment items corresponding to each piece of medical data in the second sub-collection, respectively comparing the diagnosis and treatment items corresponding to the adjacent treatments with the reference diagnosis and treatment item set, and obtaining the corresponding treatments for the adjacent treatments The coincidence ratio of the item and the reference diagnosis and treatment item set, and calculate the average coincidence ratio of all adjacent consultations to obtain the continuity of the adjacent treatment items;

The sixth abnormality determination module is configured to: when the continuity is greater than the sixth preset threshold, the corresponding medical data meets abnormal characteristics.

The present application also proposes a computer-readable storage medium on which computer-readable instructions are stored. The computer-readable storage medium may be a non-volatile readable storage medium. The computer-readable storage medium may be the memory 201 in the medical data abnormality recognition terminal of FIG. 1, or may be a ROM (Read-Only Memory, read-only memory) / RAM (Random Access Memory (random access memory), at least one of magnetic disks and optical disks, the computer-readable storage medium includes several instructions to make a terminal device (which may be a mobile phone, computer, server, network device) with a processor Or the medical data abnormality identification terminal in the embodiments of the present application, etc.) execute the methods described in the embodiments of the present application.

It should be noted that in this article, the terms "include", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or service that includes a series of elements includes not only those elements , And include other elements not explicitly listed, or include elements inherent to this process, method, item, or service. Without more restrictions, the element defined by the sentence "include one ..." does not exclude that there are other identical elements in the process, method, article, or server that includes the element.

The sequence numbers of the above embodiments of the present application are for description only, and do not represent the advantages and disadvantages of the embodiments.

Through the description of the above embodiments, those skilled in the art can clearly understand that the methods in the above embodiments can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware, but in many cases the former is better Implementation.

The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by the description and drawings of this application, or directly or indirectly used in other related technical fields , The same reason is included in the scope of patent protection of this application.

Claims (20)

1.  A medical data abnormality identification method, characterized in that the medical data abnormality identification method includes the following steps:

   When an abnormality detection request is detected, the target hospital corresponding to the abnormality detection request and the medical data to be detected of the target hospital are obtained;

   Group the medical data to be tested according to a preset dimension to obtain a grouped subset;

   Analyzing each of the subsets based on a preset rule corresponding to a preset dimension to determine whether there is target medical data satisfying abnormal characteristics in the medical data to be detected;

   If there is target medical data satisfying abnormal characteristics in the medical data to be detected, the target medical data is marked abnormally.
2. The medical data abnormality identification method according to claim 1, wherein when the preset dimension is a hospital dimension, the medical data to be detected is grouped according to the preset dimension to obtain a grouped subset The steps include:

   Grouping the medical data to be detected according to disease types to obtain a first subset corresponding to each disease type;

   The step of analyzing each of the subsets based on a preset rule corresponding to a preset dimension to determine whether there is target medical data satisfying abnormal characteristics in the medical data to be detected includes: The

   From the first sub-collection corresponding to each disease type, obtain the total number of hospitalizations and the total number of hospitalization days of each disease type, and calculate the average number of single hospitalization days of each disease type based on the total number of hospitalizations and total hospitalization days of each disease type ;

   Compare the average number of days of each hospitalization with the preset days corresponding to each disease type to obtain the difference between the average number of days of each hospitalization and the number of days corresponding to the preset number of days, and calculate the The ratio of the difference between the number of days and the corresponding preset number of days to obtain the abnormal factor of the number of days corresponding to each disease type;

   When the abnormality factor for the number of days corresponding to any disease type is greater than the first preset threshold, the first subset corresponding to the disease type satisfies the abnormality feature.
3. The medical data abnormality identification method according to claim 2, wherein each of the subsets is analyzed based on a preset rule corresponding to a preset dimension to determine whether there is a target satisfying the abnormal feature in the medical data to be detected The steps of medical data include:

   From the first sub-collection corresponding to each disease type, obtain the number of insured persons and the total number of hospitalizations of each disease type, and calculate the average number of hospitalizations per person corresponding to each disease type based on the number of insured persons of each disease type and the total number of hospitalizations;

   Compare the average number of hospitalizations per person for each disease type with the corresponding preset number of times to obtain the difference between the number of average hospitalizations per person for each disease type and the corresponding preset number of times, and calculate the difference between the number of times for each disease type and the corresponding The ratio of the preset number of times to obtain the abnormal factor of the number of times corresponding to each disease type;

   When the abnormality factor of the number of times corresponding to any disease type is greater than the second preset threshold, the first subset corresponding to the disease type meets the abnormal characteristics.
4. The medical data abnormality identification method according to claim 2, wherein each of the subsets is analyzed based on a preset rule corresponding to a preset dimension to determine whether there is a target satisfying the abnormal feature in the medical data to be detected The steps of medical data include:

   From the first sub-collection corresponding to each disease type, obtain the total number of hospitalizations and total hospitalization amount of each disease type, and calculate the average single hospitalization amount of each disease type based on the total number of hospitalizations and total hospitalization amount of each disease type ;

   Compare the average single hospitalization amount of each disease type with the corresponding preset amount, obtain the difference between the average single hospitalization amount of each disease type and the corresponding preset amount, and calculate the amount difference and Corresponding to the ratio of the preset amount, the amount abnormality factor corresponding to each disease type is obtained;

   When the amount abnormality factor corresponding to any disease type is greater than the third preset threshold, the first subset corresponding to the disease type meets the abnormal characteristics.
5. The medical data abnormality identification method according to claim 1, wherein when the preset dimension is a personal dimension, the medical data to be detected is grouped according to the preset dimension to obtain a grouped subset The steps include:

   Group the medical data to be tested according to the insured persons to obtain a second sub-set corresponding to each insured person;

   The step of analyzing each of the subsets based on a preset rule corresponding to a preset dimension to determine whether there is target medical data satisfying abnormal characteristics in the medical data to be detected includes:

   Extracting the consultation time corresponding to each piece of medical data in the second sub-set, and calculating the adjacent consultation interval of the adjacent medical data in the second sub-set;

   Obtain a preset time interval threshold, calculate the time difference between the adjacent visit interval and the time interval threshold, calculate the ratio of the time difference to the time interval threshold, and obtain each medical data correspondence in the second subset The interval anomaly factor;

   When the interval abnormality factor corresponding to the medical data in the second subset is greater than the fourth preset threshold, the medical data satisfies the abnormality feature.
6. The medical data abnormality identification method according to claim 5, wherein the acquiring a preset time interval threshold value, calculating the time difference between the adjacent visit interval and the time interval threshold value, calculating the time difference and the The ratio of the time interval threshold to obtain the interval anomaly factor corresponding to each piece of medical data in the second subset includes:

   Extracting the diagnosis result corresponding to each piece of medical data in the second subset, and judging whether there is adjacent medical data corresponding to similar diagnosis results in the second subset;

   If there is adjacent medical data corresponding to a similar diagnosis result in the second subset, the time difference between the adjacent medical interval of the adjacent medical data and the time interval threshold is obtained, and the time difference and the time interval are calculated The ratio of the threshold to obtain the interval anomaly factor corresponding to the adjacent medical data;

   When the interval anomaly factor corresponding to the adjacent medical data is greater than the fifth preset threshold, the medical data meets the anomaly characteristics.
7. The medical data abnormality identification method according to claim 5, wherein each of the subsets is analyzed based on a preset rule corresponding to a preset dimension to determine whether there is a target satisfying the abnormal feature in the medical data to be detected The steps of medical data include:

   Extracting the diagnosis and treatment items corresponding to each piece of medical data in the second sub-collection, comparing the corresponding diagnosis and treatment items of the adjacent treatments with the reference diagnosis and treatment item sets respectively, and obtaining the corresponding diagnosis and treatment items of the adjacent diagnosis and reference treatment item sets The coincidence ratio, and calculate the average coincidence ratio of all adjacent visits to obtain the continuity of the adjacent treatment items;

   When the continuity is greater than the sixth preset threshold, the corresponding medical data meets abnormal characteristics.
8. A medical data abnormality identification device, characterized in that the medical data abnormality identification device includes:

   An obtaining module, configured to obtain the target hospital corresponding to the abnormal detection request and the medical data to be detected of the target hospital when the abnormal detection request is detected;

   A grouping module, configured to group the medical data to be detected according to a preset dimension to obtain a grouped subset;

   An anomaly analysis module, configured to analyze each of the subsets based on a preset rule corresponding to a preset dimension, and determine whether there is target medical data satisfying an abnormal feature in the medical data to be detected;

   The abnormal marking module is configured to mark the target medical data abnormally if there is target medical data satisfying abnormal characteristics in the medical data to be detected.
9. The medical data abnormality recognition device according to claim 8, wherein the grouping module is further configured to group the medical data to be detected according to the disease type when the preset dimension is a hospital dimension to obtain The first sub-collection corresponding to each disease type;

   The grouping module includes:

   The first calculation module is used to obtain the total number of hospitalizations and the total number of hospitalization days of each disease type from the first subset corresponding to each disease type, and calculate and obtain each disease based on the total number of hospitalizations and the total number of hospitalization days of each disease type The average number of hospital stays per species; compare the average number of hospital stays for each disease with the preset days corresponding to each disease to obtain the difference between the average number of hospital stays for each disease and the number of days corresponding to the preset days , Calculate the ratio of the difference between the number of days of each disease type and the corresponding preset number of days to obtain the abnormal factor of the number of days corresponding to each disease type;

   The first abnormality determination module is configured to: when the abnormality factor of the number of days corresponding to any disease type is greater than the first preset threshold, the first subset corresponding to the disease type meets the abnormality feature.
10. The medical data abnormality identification device according to claim 9, wherein the abnormality analysis module comprises:

    The second calculation module is used to obtain the number of insured persons and the total number of hospitalizations of each disease type from the first sub-collection corresponding to each disease type, and calculate and obtain the correspondence of each disease type based on the number of insured persons of each disease type and the total number of hospitalizations The average number of hospitalizations per person for each patient; compare the average number of hospitalizations per person for each disease type with the corresponding preset number of times to obtain the difference between the average number of hospitalizations per person for each disease type and the corresponding preset number of times to calculate each disease type The ratio of the difference between the number of times and the corresponding preset number of times to obtain the abnormal factor of the number of times corresponding to each disease type;

    The second abnormality determination module is configured to: when the abnormality factor of the number of times corresponding to any disease type is greater than the second preset threshold, the first subset corresponding to the disease type satisfies abnormal characteristics.
11. The medical data abnormality identification device according to claim 9, wherein the abnormality analysis module comprises:

    The third calculation module is used to obtain the total number of hospitalizations and total hospitalization amount of each disease type from the first subset corresponding to each disease type, and calculate and obtain each disease based on the total number of hospitalizations and total hospitalization amount of each disease type The average single hospitalization amount of each kind; compare the average single hospitalization amount of each disease with the corresponding preset amount to obtain the difference between the average single hospitalization amount of each disease and the corresponding preset amount, and calculate each The ratio of the amount difference of the disease type to the corresponding preset amount to obtain the amount abnormality factor corresponding to each disease type;

    The third abnormality determination module is configured to: when the amount abnormality factor corresponding to any disease type is greater than the third preset threshold, the first subset corresponding to the disease type meets abnormal characteristics.
12. The medical data abnormality identification device according to claim 8, wherein the grouping module is further configured to group the medical data to be detected according to the insured person when the preset dimension is an individual dimension, Obtain the second sub-collection corresponding to each participant;

    The grouping module includes:

    The fourth calculation module is used for extracting the consultation time corresponding to each piece of medical data in the second subset, calculating the adjacent consultation interval for obtaining the adjacent medical data in the second subset; obtaining the preset time interval threshold Calculate the time difference between the adjacent consultation interval and the time interval threshold, calculate the ratio of the time difference and the time interval threshold to obtain the interval abnormality factor corresponding to each piece of medical data in the second subset;

    The fourth abnormality determination module is configured to: when the interval abnormality factor corresponding to the medical data in the second subset is greater than the fourth preset threshold, the medical data meets the abnormality feature.
13. The medical data abnormality identification device according to claim 12, wherein the grouping module further comprises:

    The similarity judgment module is used to extract the diagnosis result corresponding to each piece of medical data in the second subset and determine whether there is adjacent medical data corresponding to the similar diagnosis result in the second subset;

    The first obtaining module is used to obtain the time difference between the adjacent medical visit interval of the adjacent medical data and the time interval threshold if there is adjacent medical data corresponding to similar diagnosis results in the second subset The ratio of the time difference to the time interval threshold to obtain an interval anomaly factor corresponding to the adjacent medical data;

    The fifth abnormality determination module is configured to: when the interval abnormality factor corresponding to the adjacent medical data is greater than a fifth preset threshold, the medical data meets abnormal characteristics.
14. The medical data abnormality identification device according to claim 12, wherein the grouping module comprises:

    The fifth calculation module is used for extracting the diagnosis and treatment items corresponding to each piece of medical data in the second sub-collection, respectively comparing the diagnosis and treatment items corresponding to the adjacent treatments with the reference diagnosis and treatment item set, and obtaining the corresponding treatments for the adjacent treatments The coincidence ratio of the item and the reference diagnosis and treatment item set, and calculate the average coincidence ratio of all adjacent consultations to obtain the continuity of the adjacent treatment items;

    The sixth abnormality determination module is configured to: when the continuity is greater than the sixth preset threshold, the corresponding medical data meets abnormal characteristics.
15. A medical data abnormality identification terminal, characterized in that the medical data abnormality identification terminal includes a processor, a memory, and computer readable instructions stored on the memory and executable by the processor, wherein the computer When the readable instructions are executed by the processor, the following steps are implemented:

    When an abnormality detection request is detected, the target hospital corresponding to the abnormality detection request and the medical data to be detected of the target hospital are obtained;

    Group the medical data to be tested according to a preset dimension to obtain a grouped subset;

    Analyzing each of the subsets based on a preset rule corresponding to a preset dimension to determine whether there is target medical data satisfying abnormal characteristics in the medical data to be detected;

    If there is target medical data satisfying abnormal characteristics in the medical data to be detected, the target medical data is marked abnormally.
16. The medical data abnormality recognition terminal according to claim 15, wherein when the preset dimension is a hospital dimension, the medical data to be detected is grouped according to the preset dimension to obtain a grouped subset The steps include:

    Grouping the medical data to be detected according to disease types to obtain a first subset corresponding to each disease type;

    The step of analyzing each of the subsets based on a preset rule corresponding to a preset dimension to determine whether there is target medical data satisfying abnormal characteristics in the medical data to be detected includes: The

    From the first sub-collection corresponding to each disease type, obtain the total number of hospitalizations and the total number of hospitalization days of each disease type, and calculate the average number of single hospitalization days of each disease type based on the total number of hospitalizations and total hospitalization days of each disease type ;

    Compare the average number of days of each hospitalization with the preset days corresponding to each disease type to obtain the difference between the average number of days of each hospitalization and the number of days corresponding to the preset number of days, and calculate the The ratio of the difference between the number of days and the corresponding preset number of days to obtain the abnormal factor of the number of days corresponding to each disease type;

    When the abnormality factor for the number of days corresponding to any disease type is greater than the first preset threshold, the first subset corresponding to the disease type satisfies the abnormality feature.
17. The medical data abnormality identification terminal according to claim 15, wherein when the preset dimension is a personal dimension, the medical data to be detected is grouped according to the preset dimension to obtain a grouped subset The steps include:

    Group the medical data to be tested according to the insured persons to obtain a second sub-set corresponding to each insured person;

    The step of analyzing each of the subsets based on a preset rule corresponding to a preset dimension to determine whether there is target medical data satisfying abnormal characteristics in the medical data to be detected includes:

    Extracting the consultation time corresponding to each piece of medical data in the second sub-set, and calculating the adjacent consultation interval of the adjacent medical data in the second sub-set;

    Obtain a preset time interval threshold, calculate the time difference between the adjacent visit interval and the time interval threshold, calculate the ratio of the time difference to the time interval threshold, and obtain each medical data correspondence in the second subset The interval anomaly factor;

    When the interval abnormality factor corresponding to the medical data in the second subset is greater than the fourth preset threshold, the medical data satisfies the abnormality feature.
18. A computer-readable storage medium, characterized in that computer-readable instructions for medical data abnormality recognition are stored on the computer-readable storage medium, and when the computer-readable instructions are executed by a processor, the following steps are implemented:

    When an abnormality detection request is detected, the target hospital corresponding to the abnormality detection request and the medical data to be detected of the target hospital are obtained;

    Group the medical data to be tested according to a preset dimension to obtain a grouped subset;

    Analyzing each of the subsets based on a preset rule corresponding to a preset dimension to determine whether there is target medical data satisfying abnormal characteristics in the medical data to be detected;

    If there is target medical data satisfying abnormal characteristics in the medical data to be detected, the target medical data is marked abnormally.
19. The computer-readable storage medium of claim 18, wherein when the preset dimension is a hospital dimension, the medical data to be tested is grouped according to the preset dimension to obtain a grouped subset The steps include:

    Grouping the medical data to be detected according to disease types to obtain a first subset corresponding to each disease type;

    The step of analyzing each of the subsets based on a preset rule corresponding to a preset dimension to determine whether there is target medical data satisfying abnormal characteristics in the medical data to be detected includes: The

    From the first sub-collection corresponding to each disease type, obtain the total number of hospitalizations and the total number of hospitalization days of each disease type, and calculate the average number of single hospitalization days of each disease type based on the total number of hospitalizations and total hospitalization days of each disease type ;

    Compare the average number of days of each hospitalization with the preset days corresponding to each disease type to obtain the difference between the average number of days of each hospitalization and the number of days corresponding to the preset number of days, and calculate the The ratio of the difference between the number of days and the corresponding preset number of days to obtain the abnormal factor of the number of days corresponding to each disease type;

    When the abnormality factor for the number of days corresponding to any disease type is greater than the first preset threshold, the first subset corresponding to the disease type satisfies the abnormality feature.
20. The computer-readable storage medium of claim 18, wherein when the preset dimension is a personal dimension, the medical data to be tested is grouped according to the preset dimension to obtain a grouped subset The steps include:

    Group the medical data to be tested according to the insured persons to obtain a second sub-set corresponding to each insured person;

    The step of analyzing each of the subsets based on a preset rule corresponding to a preset dimension to determine whether there is target medical data satisfying abnormal characteristics in the medical data to be detected includes:

    Extracting the consultation time corresponding to each piece of medical data in the second sub-set, and calculating the adjacent consultation interval of the adjacent medical data in the second sub-set;

    Obtain a preset time interval threshold, calculate the time difference between the adjacent visit interval and the time interval threshold, calculate the ratio of the time difference to the time interval threshold, and obtain each medical data correspondence in the second subset The interval anomaly factor;

    When the interval abnormality factor corresponding to the medical data in the second subset is greater than the fourth preset threshold, the medical data satisfies the abnormality feature.