Healthcare data processing method and device, electronic equipment and storage medium

Abstract

The application discloses a medical insurance data processing method and device, electronic equipment and storage medium, and relates to the technical field of computers. A specific embodiment of the method comprises: in response to a processing request, obtaining the data table identifier of the medical insurance data table in the processing request to query the field information of the fields included in the medical insurance data table; based on the field information, determining the first field set belonging to the statistical dimension type and the second field set belonging to the statistical index type; combining the fields in the first field set to obtain each dimension field combination to calculate the statistical result corresponding to each dimension field combination; obtaining the query condition in the query request to obtain the query condition including the dimension field combination and the second field; querying the statistical result corresponding to the query condition including the dimension field combination, and extracting the data corresponding to the second field to return the extracted data. The embodiment can solve the problem of low database performance caused by the complex table structure of the database in the prior art.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method, apparatus, electronic device, and storage medium for processing medical insurance data. Background Technology

[0002] In medical insurance data processing scenarios, analysis of medical insurance data from different perspectives is required based on varying business needs. Therefore, data statistics for various indicators need to be performed from different dimensions according to these business analysis requirements. Medical insurance data is typically stored in a database as tables. Upon receiving query requests from different business needs, the database performs statistical analysis based on these requests and creates new table structures to store the statistical data. However, due to the massive volume and complex structure of medical insurance data, numerous business analysis needs may arise. Consequently, a large number of table structures need to be created in the database, leading to complex table structures and low performance. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a method, apparatus, electronic device and storage medium for processing medical insurance data, which can solve the problem that the complex table structure of the database in the prior art leads to low database performance.

[0004] To achieve the above objectives, according to one aspect of the present invention, a method for processing medical insurance data is provided.

[0005] An embodiment of the present invention provides a method for processing medical insurance data, comprising: responding to a processing request, obtaining a data table identifier of a medical insurance data table in the processing request, and querying field information of fields included in the medical insurance data table; invoking a preset classification model, and based on the field information, determining a first set of fields belonging to a statistical dimension type and a second set of fields belonging to a statistical indicator type; combining the fields in the first set of fields to obtain various dimension field combinations, and calculating statistical results corresponding to each dimension field combination based on the medical insurance data table; receiving a query request, obtaining query conditions in the query request, and matching them with the dimension field combinations and the second set of fields to determine that the query conditions include dimension field combinations and a second field; querying the statistical results corresponding to the dimension field combinations included in the query conditions, and extracting data corresponding to the second field to return the extracted data.

[0006] In one embodiment, the step of invoking a preset classification model to determine, based on the field information, a first set of fields belonging to the statistical dimension type and a second set of fields belonging to the statistical indicator type includes:

[0007] Invoke the first preset classification model, and based on the field types in the field information, determine the first set of fields belonging to the statistical dimension type and the second set of fields belonging to the statistical indicator type;

[0008] And / or,

[0009] The second preset classification model is invoked, and based on the field names in the field information, the first set of fields belonging to the statistical dimension type and the second set of fields belonging to the statistical indicator type are determined.

[0010] In yet another embodiment, the fields in the first field set are combined to obtain the field combinations for each dimension, including:

[0011] In the medical insurance data table, extract the field value of each field in the first field set, and calculate the storage space corresponding to the field value of each field in the first field set;

[0012] The fields in the first set of fields are arranged and combined to generate undetermined field combinations. Based on the storage space of the field value corresponding to each field, the storage space of the field value corresponding to each undetermined field combination is determined, and it is determined whether the sum of the storage spaces of the field values ​​corresponding to each undetermined field combination is greater than a preset threshold.

[0013] If not, then each of the pending field combinations is determined as a dimension field combination; if yes, the target number of fields in the first field set are deleted to obtain a new first field set, and the above steps of generating pending field combinations and judging are repeated until the sum of the storage space of the field values ​​corresponding to each new pending field combination is not greater than the preset threshold, and all fields included in the first field set are determined as a dimension field combination, and each new pending field combination is determined as a dimension field combination.

[0014] In yet another embodiment, the step of deleting a target number of fields from the first field set to obtain a new first field set includes:

[0015] From the first set of fields, select the target number of fields with the largest storage space corresponding to the field value, and delete them from the first set of fields to obtain a new first set of fields.

[0016] In yet another embodiment, in the medical insurance data table, extracting the field value of each field in the first field set includes:

[0017] For each field in the first set of fields, the data set corresponding to the field in the medical insurance data table is deduplicated to determine the field value of the deduplicated data set.

[0018] In yet another embodiment, after calculating the statistical result corresponding to each combination of the dimension fields, the method further includes:

[0019] Based on the data table identifier, the field identifiers of the fields included in the dimension field combination, and the field identifiers of the fields included in the second field set, an identifier for each statistical result is generated to store the correspondence between the identifiers of the statistical results and the statistical results.

[0020] In yet another embodiment, it further includes:

[0021] Obtain the query hit count for each statistical result to determine whether the query hit count is less than a preset threshold;

[0022] If yes, delete the statistical results; otherwise, do not perform any operation on the statistical results.

[0023] To achieve the above objectives, according to another aspect of the present invention, a medical insurance data processing apparatus is provided.

[0024] An embodiment of the present invention provides a medical insurance data processing apparatus comprising: a response unit, configured to respond to a processing request by acquiring a data table identifier of a medical insurance data table in the processing request, and querying field information of fields included in the medical insurance data table; a determination unit, configured to invoke a preset classification model, and based on the field information, determine a first set of fields belonging to a statistical dimension type and a second set of fields belonging to a statistical indicator type; a calculation unit, configured to combine the fields in the first set of fields to obtain combinations of dimension fields, and calculate statistical results corresponding to each combination of dimension fields based on the medical insurance data table; an acquisition unit, configured to receive a query request, acquire query conditions in the query request, and match them with the combination of dimension fields and the second set of fields to determine that the query conditions include a combination of dimension fields and a second field; and a query unit, configured to query the statistical results corresponding to the combination of dimension fields included in the query conditions, extract data corresponding to the second field, and return the extracted data.

[0025] To achieve the above objectives, according to another aspect of the present invention, an electronic device is provided.

[0026] An electronic device according to an embodiment of the present invention includes: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the medical insurance data processing method provided in the embodiment of the present invention.

[0027] To achieve the above objectives, according to another aspect of the present invention, a computer-readable medium is provided.

[0028] An embodiment of the present invention provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the medical insurance data processing method provided in the embodiment of the present invention.

[0029] One embodiment of the above invention has the following advantages or beneficial effects: In this embodiment, by classifying the fields in the medical insurance data table, a first set of fields belonging to statistical dimension types and a second set of fields belonging to statistical indicators are obtained. Then, the fields in the first set of fields can be combined to determine the dimension field combinations, and then the statistical results of each dimension field combination are calculated and stored. That is, all possible query results of the medical insurance data table can be obtained and stored. Thus, after receiving a query request, the corresponding data can be directly retrieved and returned based on the dimension field combinations and second fields included in the query request. In this embodiment, for the medical insurance data table, fields of statistical dimension types and fields belonging to statistical indicator types can be automatically determined. Then, based on the field combinations of statistical dimension types and the fields of statistical indicator types, possible business analysis needs can be calculated and the statistical results can be directly stored. Thus, after receiving a query request, the corresponding data can be directly retrieved and returned from the stored statistical results without having to create a new table structure for each query result, thereby reducing the creation of table structures in the database, reducing redundancy, and improving database performance.

[0030] The further effects of the aforementioned unconventional alternative methods will be explained below in conjunction with specific implementation methods. Attached Figure Description

[0031] The accompanying drawings are provided to better understand the invention and are not intended to unduly limit the scope of the invention. Wherein:

[0032] Figure 1 This is a schematic diagram of a system architecture for a medical insurance data processing system according to an embodiment of the present invention;

[0033] Figure 2 This is a schematic diagram of a main process of a medical insurance data processing method according to an embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram of another main process of the medical insurance data processing method according to an embodiment of the present invention;

[0035] Figure 4 This is a schematic diagram of the main units of a medical insurance data processing device according to an embodiment of the present invention;

[0036] Figure 5 This is yet another exemplary system architecture diagram to which embodiments of the present invention can be applied;

[0037] Figure 6This is a schematic diagram of the structure of a computer system suitable for implementing embodiments of the present invention. Detailed Implementation

[0038] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0039] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0040] This invention provides a medical insurance data processing system that can be used for medical insurance data processing scenarios.

[0041] Figure 1 The image shows a medical insurance data processing system according to an embodiment of the present invention. Figure 1 As shown, the medical insurance data processing system includes a database (external database) and an indicator analysis engine. The database stores the medical insurance data to be processed, such as business data and medical insurance data, providing the data source for the indicator analysis engine. The indicator analysis engine analyzes the medical insurance data in the database, determines the corresponding dimension fields and indicator fields, and combines the dimension fields with the indicator fields to calculate statistical results that meet the needs of various business analyses. These statistical results are then stored. The medical insurance data processing system can also receive query requests from external business applications. These requests are used to retrieve the medical insurance data required for business analysis. The query requests can include query conditions, and the medical insurance data processing system can match the query results from the stored statistical results based on the query conditions and return them.

[0042] In this embodiment of the invention, the indicator analysis engine can be divided into an indicator analysis logic layer and a data storage and calculation layer. The indicator analysis logic layer can be used for task scheduling, dimensional analysis, and query parsing. The data storage and calculation layer can provide data calculation and storage for the indicator analysis logic layer. The data storage and calculation layer can be built using components in the Hadoop ecosystem plus ClickHouse to support the calculation and storage of large amounts of data and improve system query performance. In the data storage and calculation layer, data can be periodically synchronized from the data source to Hive using methods such as ETL for processing in this embodiment of the invention. The statistical data in the indicator analysis engine can be stored using a columnar data storage database, i.e., using ClickHouse, to provide more powerful query functions.

[0043] This invention provides a method for processing medical insurance data, which can be achieved by... Figure 1 The medical insurance data processing system shown executes the following, such as Figure 2 As shown, the method includes:

[0044] S201: In response to the processing request, obtain the data table identifier of the medical insurance data table in the processing request, so as to query the field information of the fields included in the medical insurance data table.

[0045] The processing request can be automatically triggered by the medical insurance data processing system or can be input from outside. The processing request can include the data table identifier of the medical insurance data table corresponding to the medical insurance data to be processed. Therefore, the data packet identifier can be obtained from the processing request, and then the field information of each field in the medical insurance data table can be queried based on the data table identifier.

[0046] Specifically, field information can include field name and field type, and field type can include numeric, character, and date types.

[0047] It should be noted that this step can utilize Hive to retrieve medical insurance data tables. These tables can be related to various aspects of medical insurance settlement and reimbursement, such as the settlement information table. This table can include information about medical visits and settlement amounts. Visit information can include data related to medical institutions, insured individuals, visit dates, and medical behavior. Settlement amount information can include medical expenses, medical insurance expenses, out-of-pocket expenses, and various subsidies. Let the table identifier for the medical data table be tk_setl_info. Taking Table 1 as an example, it contains information about the fields included in the medical data table.

[0048] Table 1

[0049]

[0050] In Table 1, NUMBER indicates that the field type is numeric, DATE indicates that the field type is date, and VARCHAR indicates that the field type is character.

[0051] S202: Call the preset classification model and, based on the field information, determine the first set of fields belonging to the statistical dimension type and the second set of fields belonging to the statistical indicator type.

[0052] When performing statistical analysis on data in a data table, the statistical conditions typically include statistical dimension types and statistical indicator types. Statistical indicators usually refer to data that needs to be calculated. For example, if we need to calculate the total sales in location A, then location A is the statistical dimension, and sales amount is the statistical indicator. Therefore, the field type corresponding to the statistical indicator is usually numeric, and calculations are required. Thus, in this embodiment of the invention, a pre-trained classification model can be used to classify the data in the data table based on corresponding fields, determining which fields in the data table correspond to statistical dimension types (which can be called dimension fields) and which fields correspond to statistical indicator types (which can be called indicator fields). This allows for the determination of the first set of fields belonging to the statistical dimension types and the second set of fields belonging to the statistical indicator types.

[0053] Specifically, in this embodiment of the invention, fields can be classified by field type and / or field name.

[0054] Classifying fields by field type can be performed as follows: call the first preset classification model, and based on the field type in the field information, determine the first set of fields belonging to the statistical dimension type and the second set of fields belonging to the statistical indicator type.

[0055] Since date and character fields are typically statistical dimension types, while numeric fields are statistical indicator types, this step allows us to pre-train a first preset classification model to determine the first set of fields belonging to statistical dimension types and the second set of fields belonging to statistical indicator types in the data table based on the field types.

[0056] Classifying fields by field name can be performed as follows: call the second preset classification model, and based on the field names in the field information, determine the first set of fields belonging to the statistical dimension type and the second set of fields belonging to the statistical indicator type.

[0057] In this step, a keyword set for statistical dimension type fields can be pre-established, which may include keywords such as amount, cost, number of days, number, quantity, etc., to train the second preset classification model. Then, this step can first segment the fields in the data table, and then input the segmented fields into the trained second preset classification model to determine the first set of fields belonging to the statistical dimension type and the second set of fields belonging to the statistical indicator type.

[0058] It should be noted that when determining the first and second field sets simultaneously through field type and field information, the results of the two methods can be combined to determine the final first and second field sets. In this case, if a field belongs to both the first and second field sets, it is determined as a statistical dimension type, that is, it belongs to the first field set.

[0059] S203: Combine the fields in the first set of fields to obtain the field combinations of each dimension, and calculate the statistical results corresponding to each field combination based on the medical insurance data table.

[0060] The first set of fields can include multiple fields, which can be combined in numerical arrangements to obtain dimension field combinations. For example, if the first set of fields has three fields, such as A, B, and C, then the dimension field combinations can include ABC, AB, AC, BC, A, B, C, and empty, totaling 8 combinations. Since the number of dimension field combinations is 2^3 = 8, this can cover all business analysis needs of the medical insurance data table, as shown in Formula 1 and Formula 2.

[0061]

[0062]

[0063] In Formula 1 and Formula 2, n represents the number of fields included in the first field set, m represents the number of dimension fields included in the dimension field combination, and m is an integer greater than or equal to 0 and less than or equal to n.

[0064] In this step, after determining the combination of dimension fields, the statistical results for each combination of dimension fields can be calculated. This includes the data for each field in the second field set corresponding to each dimension field combination, and the statistical results are stored as data columns. A corresponding statistical identifier can be generated for each statistical result. The statistical identifier can be generated based on the data table identifier, the identifiers of the fields included in the dimension field combination, and the identifiers of the fields included in the second field set.

[0065] It should be noted that, in this embodiment of the invention, the statistical identifier generation method can be as follows: concatenating the data table identifier, the identifiers of the fields included in the dimension field combination, and the identifiers of the fields included in the second field set; then, transcoding the concatenated result using a preset algorithm (such as base64) to obtain a statistical identifier of uniform length; and storing the correspondence between the concatenated result and the statistical identifier for data querying. For example, in this embodiment of the invention, the data table identifier, the identifiers of the fields included in the dimension field combination, and the second field identifier can be concatenated using "|". Taking the data table identifier tk_doc_info and the fields org and dept, which belong to the statistical dimension type, as an example, the corresponding concatenation result is: tk_doc_info|org|dept, and the transcoded statistical identifier can be: dGtfZG9jX2luZm98b3JnfGRlcHQ. In this embodiment of the invention, the data table identifier, the identifiers of the fields included in the dimension field combination, the second field identifier, the concatenation result, and the statistical identifier corresponding to the statistical result can be determined as the source data of the statistical result and stored. In this embodiment of the invention, calculating each statistical result can initiate a corresponding Spark computation task, i.e., the SQL query to be performed, such as `select org,dept,count(id),sum(amt)from tk_doc_info group by org,dept`. Here, the fields appearing in the first part of the `select` statement and in the `group by` clause are the fields (org, dept) in the dimension field combination, and the field containing the function is the second field. After Spark computation in this way, the calculated statistical results can be directly written to the corresponding ClickHouse table, i.e., the `dGtfZG9jX2luZm98b3JnfGRlcHQ` table, for storage.

[0066] S204: Receive a query request, obtain the query conditions in the query request, match them with the combination of dimension fields and the set of second fields, and conclude that the query conditions include the combination of dimension fields and the second field.

[0067] The query request can be sent by an external business application and includes query conditions, such as the identifier of the medical insurance data table to be queried, the identifier of the indicator field to be queried and the query logic (pass-in expression, such as count(id), sum(amt)), the identifier of the dimension field to be statistically analyzed (such as org, dept, setl_date), the date range to be queried (which can be unlimited, usually including start and end dates), and other auxiliary conditions (such as sorting logic, pagination parameters, etc.). In this step, the combination of dimension fields and the second field included can be determined through the query conditions.

[0068] For example, taking the medical data table shown in Table 1 as an example, the query conditions in the received query request are: 2 statistical indicators (sum(MEDFEE_SUMAMT), sum(OWNPAY_AMT)), 3 statistical dimension fields (POOLAREA_CODE, FIXMEDINS_CODE, SETL_DATE), and the query dates are from January 1, 2020 to February 1, 2020. Based on the above query conditions, the included dimension fields can be matched as a combination of the fields POOLARA_CODE, FIXMEDINS_CODE, and SETL_DATE.

[0069] S205: Query the statistical results corresponding to the combination of dimension fields according to the query conditions, extract the data corresponding to the second field, and return the extracted data.

[0070] After determining the combination of dimension fields included in the query request, the statistical results calculated based on S203 can be used to retrieve the corresponding statistical results. Then, the data corresponding to the second field of the query conditions can be extracted from the data and returned to the external business application.

[0071] It should be noted that the query request can also include the identifier of the medical insurance data table to indicate which medical insurance data table is being queried, and further query the corresponding statistical results.

[0072] In this embodiment of the invention, since the fewer fields included in a dimension field combination, the larger the amount of data in its corresponding statistical results, and the more comprehensive the medical insurance data included, this embodiment of the invention can also divide each dimension field combination into levels based on the number of fields included in the dimension field combination. The fewer fields included in a dimension field combination, the higher the corresponding level. For example, suppose the first field set includes the fields: org, dept, doctor, and setl_date. The dimension field combinations that can be combined and their corresponding levels are shown in Table 2.

[0073] Table 2

[0074]

[0075] In this step, when determining the statistical results corresponding to the query conditions, we can first determine the number of fields belonging to the statistical dimension type included in the query conditions. This allows us to determine the hierarchy of dimension field combinations and further query the included dimension field combinations. If no corresponding dimension field combination is found at the hierarchy corresponding to the query conditions, we can look for dimension field combinations that include the dimension fields in the query conditions at the next higher level and return their corresponding statistical results as the statistical results corresponding to the query conditions. This situation can be called indirect query hit.

[0076] It should be noted that after each query request is received, the corresponding processing data can be stored, such as the query conditions in the query request, whether the query hit, etc.

[0077] In this embodiment of the invention, the stored statistical results can also be optimized periodically (e.g., monthly). Since some dimension field combinations are not required for business analysis, this embodiment can obtain the query hit count for each statistical result to determine if the hit count is less than a preset threshold. If the hit count is less than the preset threshold, it indicates that the dimension field combination is rarely queried, so the statistics and results can be deleted to free up storage space. The preset threshold can be set according to requirements, for example, it can be set to 0.

[0078] It should be noted that for some query requests that are indirectly hit, the statistical results of the combination of dimension fields included in the query request can be calculated and stored so that they can be directly hit in subsequent queries.

[0079] In this embodiment of the invention, for the medical insurance data table, fields of statistical dimension type and fields of statistical indicator type can be automatically determined. Then, based on the fields of statistical dimension type and statistical indicator type, possible business analysis needs can be calculated, and the statistical results can be directly stored. In this way, when a request is received, the corresponding data can be directly retrieved from the stored statistical results and returned, without having to create a new table structure for each query result. This reduces the creation of table structures in the database, lowers costs, and improves database performance.

[0080] It should be noted that although the combination of all fields in the first field set can meet all the needs of business analysis, the storage space of the field values ​​corresponding to these combination of dimensions is usually quite large, easily exceeding the available storage space. For example, if the medical insurance data table contains 1GB of data (generally on the order of one month's medical insurance data, as medical insurance data is usually imported and statistically analyzed monthly, we will use a monthly example), and there are 10 fields belonging to the statistical dimension, with their corresponding field values ​​accounting for 80% of the total data volume (i.e., 0.8GB), then on average, each field value belonging to the statistical dimension occupies 0.08GB of storage space (in actual scenarios, the storage space occupied by each field value is usually not evenly distributed). Using Formula 2, these 10 field combinations can yield 2^10 = 1024 possible combination of dimensions, as shown in Table 3, which displays the storage space occupied by the field values ​​corresponding to each dimension combination.

[0081] Table 3

[0082]

[0083] The total storage space required for the statistical results of each dimension field combination can be calculated by multiplying the number of fields in the dimension field combination by 0.08G and the number of dimension field combinations. As shown in Table 3, the total storage space calculated reaches 400+GB. If the available storage space is less than this total calculated storage space, it is impossible to store the statistical results. Therefore, the dimension field combination needs to be optimized.

[0084] The following is combined Figure 2 The illustrated embodiment provides a detailed explanation of the method for determining the combination of dimension fields in step S210 of this embodiment of the invention, such as... Figure 3 As shown, the method includes:

[0085] S301: In the medical insurance data table, extract the field value of each field in the first field set, and calculate the storage space of the corresponding field value of each field in the first field set.

[0086] In the medical insurance data table, each field corresponds to a set of data, which may contain duplicate data. Therefore, the data set can be deduplicated, and the deduplicated data set represents the field value. Once the field value is determined, the required storage space for storing that field value can be calculated.

[0087] S302: Arrange and combine the fields in the first field set to generate undetermined field combinations, and determine the storage space of the field values ​​corresponding to each undetermined field combination based on the storage space of the field values ​​corresponding to each field.

[0088] Once the storage space for the field value corresponding to each field is determined, the storage space occupied by each combination of undetermined fields can be determined. However, in real-world scenarios, storage space is usually limited. Therefore, after determining the storage space for the field value corresponding to each combination of undetermined fields, it is also necessary to determine whether the storage space can meet the requirements.

[0089] S303: Determine whether the sum of the storage space of the corresponding field values ​​of each undetermined field combination is greater than a preset threshold. If yes, proceed to step S304; otherwise, determine each undetermined field combination as a dimension field combination.

[0090] If the sum of the storage space for the field values ​​corresponding to each combination of undetermined fields is not greater than a preset threshold, then the storage space is sufficient, and each combination of undetermined fields can be designated as a dimension field combination. If the sum of the storage space for the field values ​​corresponding to each combination of undetermined fields is greater than the preset threshold, then optimization of the combination of undetermined fields is necessary.

[0091] S304: Delete the target number of fields from the first field set to obtain a new first field set.

[0092] In this embodiment of the invention, the optimization of the desired field combination essentially involves discarding fields, that is, deleting the target number of fields from the first field set, thereby reducing the number of desired field combinations and thus reducing the required storage space. The principle for discarding fields is generally the principle of discarding the least valuable dimension.

[0093] Specifically, the larger the storage space required for a field's corresponding field value, the larger the storage space required for the combination of dimension fields that include that field. These fields can be called high-cardinality dimension fields, and the value of high-cardinality dimensions is usually lower than that of low-cardinality dimensions.

[0094] The field values ​​of the high-cardinality dimension field corresponding to the dimension field combination are close to the original base dimension dimension (a dimension field combination composed of all fields in the first field set). Its query performance is not superior, but it consumes a lot of resources. Furthermore, the statistical results of the high-cardinality dimension field after grouping the data by are basically the same as the statistical results corresponding to the original base dimension dimension field combination. Therefore, in this step, we can filter the target number of fields with the largest storage space corresponding to the field values ​​from the first field set and delete them from the first field set to obtain a new first field set.

[0095] It should be noted that since the optimization of dimension field combinations is usually carried out step by step, the value of all target quantities is usually 1.

[0096] S305: Arrange and combine the fields in the new first field set to generate new undetermined field combinations, and determine the storage space of the field values ​​corresponding to each new undetermined field combination based on the storage space of the field values ​​corresponding to each field.

[0097] After generating the new first set of fields, the storage space for the field values ​​corresponding to each new combination of fields to be determined can be determined based on the principle of step S302.

[0098] S306: Determine whether the sum of the storage space of the corresponding field values ​​of each new undetermined field combination is greater than the preset threshold. If yes, proceed to step S304; otherwise, proceed to S307.

[0099] After deleting fields from the original first field set, the reduction in storage space for the sum of the storage space of the corresponding field values ​​in the undetermined field combination can be calculated based on Formula 3.

[0100] S*2^(n-2)+T*2^(n-1)–T (3)

[0101] In Formula 3, T is the storage space for the field value corresponding to the field to be deleted, S is the sum of the storage space for the field values ​​corresponding to each field in the new first field set, and n is the number of fields in the original first field set.

[0102] S307: Determine all fields included in the first set of fields as a dimension field combination, and determine each new undetermined field combination as a dimension field combination.

[0103] In this step, since the combination of dimension fields that includes all fields in the original first field set is the original base dimension field combination, it includes the most comprehensive dimension fields and all of them need to be retained in order to achieve query hits.

[0104] Taking the original first field set, which includes fields A, B, C, and D, as an example, the storage space for the corresponding field values ​​is 1GB, 2GB, 3GB, and 4GB, respectively. Step S302 yields the undetermined field combinations, and the storage space size for the field values ​​of these combinations is shown in Table 4. Field D is a high-radix field, so it is deleted during optimization. The optimized result is shown in edge 5. Before optimizing D, the space occupied was 80GB; after optimizing D, the space occupied by all combinations of ABC is 28GB, a reduction of 52GB.

[0105] Table 4

[0106] Dimension field combination Space occupied (GB) ABCD 10 ABC, ABD, ACD, BCD 30 AB, AC, AD, BC, BD, CD 30 A, B, C, D 10

[0107] Table 5

[0108] Dimension field combination Space occupied (GB) ABCD 10 AB, AC, BC 12 A, B, C 6

[0109] In this embodiment of the invention, for the medical insurance data table, fields of statistical dimension type and fields of statistical indicator type can be automatically determined. Then, based on the fields of statistical dimension type and statistical indicator type, possible business analysis needs can be calculated, and the statistical results can be directly stored. In this way, when a request is received, the corresponding data can be directly retrieved from the stored statistical results and returned, without having to create a new table structure for each query result. This reduces the creation of table structures in the database, lowers costs, and improves database performance.

[0110] In this embodiment of the invention, when a business application has new analytical needs for certain medical insurance data, only the field identifiers included in the query request need to be adjusted to perform the corresponding data query, without the need to redevelop the requirements, simplifying the operation process and improving the efficiency of data query. Furthermore, it maximizes resource utilization, generating dimension combinations that can cover all business analysis scenarios, reducing the production of redundant dimensions, greatly improving flexibility, and eliminating the need to passively create data tables and develop indicator calculation SQL. Simultaneously, at the business level, based on this indicator analysis solution, a business indicator configuration system is built. When users submit indicator analysis requirements, it can achieve rapid delivery through configuration without iterative development, providing a rapid response capability for business expansion.

[0111] To address the problems existing in the prior art, embodiments of the present invention provide a medical insurance data processing device 400, such as... Figure 4 As shown, the device 400 includes:

[0112] The response unit 401 is used to respond to the processing request, obtain the data table identifier of the medical insurance data table in the processing request, and query the field information of the fields included in the medical insurance data table.

[0113] The determining unit 402 is used to call a preset classification model and, based on the field information, determine a first set of fields belonging to the statistical dimension type and a second set of fields belonging to the statistical indicator type.

[0114] The calculation unit 403 is used to combine the fields in the first field set to obtain the field combinations of each dimension, so as to calculate the statistical results corresponding to each field combination of the dimension based on the medical insurance data table.

[0115] The acquisition unit 404 is used to receive a query request, acquire the query conditions in the query request, match them with the dimension field combination and the second field set, and conclude that the query conditions include the dimension field combination and the second field.

[0116] The query unit 405 is used to query the statistical results corresponding to the combination of dimension fields included in the query conditions, extract the data corresponding to the second field, and return the extracted data.

[0117] It should be understood that the manner in which embodiments of the present invention are implemented is different from the implementation method. Figure 2 The embodiments shown are the same and will not be described again here.

[0118] In one implementation of this invention, the determining unit 402 is specifically used for:

[0119] Invoke the first preset classification model, and based on the field types in the field information, determine the first set of fields belonging to the statistical dimension type and the second set of fields belonging to the statistical indicator type;

[0120] And / or,

[0121] The second preset classification model is invoked, and based on the field names in the field information, the first set of fields belonging to the statistical dimension type and the second set of fields belonging to the statistical indicator type are determined.

[0122] In another implementation of this invention, the computing unit 403 is specifically used for:

[0123] In the medical insurance data table, extract the field value of each field in the first field set, and calculate the storage space corresponding to the field value of each field in the first field set;

[0124] The fields in the first set of fields are arranged and combined to generate undetermined field combinations. Based on the storage space of the field value corresponding to each field, the storage space of the field value corresponding to each undetermined field combination is determined, and it is determined whether the sum of the storage spaces of the field values ​​corresponding to each undetermined field combination is greater than a preset threshold.

[0125] If not, then each of the pending field combinations is determined as a dimension field combination; if yes, the target number of fields in the first field set are deleted to obtain a new first field set, and the above steps of generating pending field combinations and judging are repeated until the sum of the storage space of the field values ​​corresponding to each new pending field combination is not greater than the preset threshold, and all fields included in the first field set are determined as a dimension field combination, and each new pending field combination is determined as a dimension field combination.

[0126] In another implementation of this invention, the computing unit 403 is specifically used for:

[0127] From the first set of fields, select the target number of fields with the largest storage space corresponding to the field value, and delete them from the first set of fields to obtain a new first set of fields.

[0128] In another implementation of this invention, the computing unit 403 is specifically used for:

[0129] For each field in the first set of fields, the data set corresponding to the field in the medical insurance data table is deduplicated to determine the field value of the deduplicated data set.

[0130] In another implementation of the present invention, the apparatus further includes:

[0131] The generation unit is used to generate an identifier for each statistical result based on the data table identifier, the field identifiers of the fields included in the dimension field combination, and the field identifiers of the fields included in the second field set, so as to store the correspondence between the identifiers of the statistical results and the statistical results.

[0132] In another implementation of the present invention, the apparatus further includes:

[0133] The deletion unit is used to obtain the number of query hits for each statistical result, and to determine whether the number of query hits is less than a preset threshold; if so, the statistical result is deleted; if not, no operation is performed on the statistical result.

[0134] It should be understood that the manner in which embodiments of the present invention are implemented is different from the implementation method. Figure 2The embodiments shown are the same and will not be described again here.

[0135] In this embodiment of the invention, for the medical insurance data table, fields of statistical dimension type and fields of statistical indicator type can be automatically determined. Then, based on the fields of statistical dimension type and statistical indicator type, possible business analysis needs can be calculated, and the statistical results can be directly stored. In this way, when a request is received, the corresponding data can be directly retrieved from the stored statistical results and returned, without having to create a new table structure for each query result. This reduces the creation of table structures in the database, lowers costs, and improves database performance.

[0136] According to embodiments of the present invention, an electronic device and a readable storage medium are also provided.

[0137] An electronic device according to an embodiment of the present invention includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to perform the medical insurance data processing method provided in the embodiment of the present invention.

[0138] Figure 5 An exemplary system architecture 500 is shown, which can be applied to the medical insurance data processing method or medical insurance data processing apparatus of the present invention.

[0139] like Figure 5 As shown, system architecture 500 may include terminal devices 501, 502, and 503, a network 504, and a server 505. Network 504 serves as the medium for providing communication links between terminal devices 501, 502, and 503 and server 505. Network 504 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0140] Users can use terminal devices 501, 502, and 503 to interact with server 505 via network 504 to receive or send messages, etc. Various client applications can be installed on terminal devices 501, 502, and 503.

[0141] Terminal devices 501, 502, and 503 can be, but are not limited to, smartphones, tablets, laptops, and desktop computers, etc.

[0142] Server 505 can be a server that provides various services. The server can analyze and process data such as received product information query requests, and feed back the processing results (such as product information - just an example) to the terminal device.

[0143] It should be noted that the medical insurance data processing method provided in this embodiment of the invention is generally executed by server 505, and correspondingly, the medical insurance data processing device is generally set in server 505.

[0144] It should be understood that Figure 5 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0145] The following is for reference. Figure 6 It shows a schematic diagram of the structure of a computer system 600 suitable for implementing embodiments of the present invention. Figure 6 The computer system shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0146] like Figure 6 As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 602 or programs loaded from storage section 608 into random access memory (RAM) 603. The RAM 603 also stores various programs and data required for the operation of the system 600. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0147] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.

[0148] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit (CPU) 601, it performs the functions defined above in the system of this invention.

[0149] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0150] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a unit, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0151] The units described in the embodiments of the present invention can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor can be described as including a response unit, a determination unit, a calculation unit, an acquisition unit, and a comparison unit. The names of these units do not necessarily limit the specific unit; for example, a response unit can also be described as a "unit that processes request-response functions."

[0152] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs, which, when executed by the device, cause the device to perform the medical insurance data processing method provided by the present invention.

[0153] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for processing medical insurance data, characterized in that, include: In response to a processing request, the data table identifier of the medical insurance data table in the processing request is obtained in order to query the field information of the fields included in the medical insurance data table; Invoke the preset classification model, and based on the field information, determine the first set of fields belonging to the statistical dimension type and the second set of fields belonging to the statistical indicator type; The process of combining fields in the first field set to obtain dimensional field combinations includes: extracting the field value of each field in the first field set from the medical insurance data table, calculating the storage space of the corresponding field value of each field in the first field set; arranging and combining fields in the first field set to generate undetermined field combinations, determining the storage space of the corresponding field value of each undetermined field combination based on the storage space of the corresponding field value of each field, and determining whether the sum of the storage spaces of the corresponding field values ​​of each undetermined field combination is greater than a preset threshold; if not, then each undetermined field combination is determined as a dimensional field combination; if so, deleting a target number of fields in the first field set to obtain a new first field set, and repeating the above steps of generating undetermined field combinations and determining the threshold until the sum of the storage spaces of the corresponding field values ​​of each new undetermined field combination is not greater than the preset threshold, determining all fields included in the first field set as a dimensional field combination, and determining each new undetermined field combination as a dimensional field combination; Based on the medical insurance data table, calculate the statistical results corresponding to each combination of the dimension fields; Receive a query request, obtain the query conditions in the query request, match them with the dimension field combination and the second field set, and conclude that the query conditions include the dimension field combination and the second field. The query conditions include the statistical results corresponding to the combination of dimension fields. The data corresponding to the second field is extracted and returned.

2. The method according to claim 1, characterized in that, The step of calling the preset classification model, based on the field information, to determine a first set of fields belonging to the statistical dimension type and a second set of fields belonging to the statistical indicator type, includes: Invoke the first preset classification model, and based on the field types in the field information, determine the first set of fields belonging to the statistical dimension type and the second set of fields belonging to the statistical indicator type; And / or, The second preset classification model is invoked, and based on the field names in the field information, the first set of fields belonging to the statistical dimension type and the second set of fields belonging to the statistical indicator type are determined.

3. The method according to claim 1, characterized in that, The step of deleting a target number of fields from the first field set to obtain a new first field set includes: From the first set of fields, select the target number of fields with the largest storage space corresponding to the field value, and delete them from the first set of fields to obtain a new first set of fields.

4. The method according to claim 1, characterized in that, In the medical insurance data table, the field values ​​of each field in the first set of fields are extracted, including: For each field in the first set of fields, the data set corresponding to the field in the medical insurance data table is deduplicated to determine the field value of the deduplicated data set.

5. The method according to claim 1, characterized in that, After calculating the statistical results corresponding to each combination of the dimension fields, the method further includes: Based on the data table identifier, the field identifiers of the fields included in the dimension field combination, and the field identifiers of the fields included in the second field set, an identifier for each statistical result is generated to store the correspondence between the identifiers of the statistical results and the statistical results.

6. The method according to claim 1, characterized in that, Also includes: Obtain the query hit count for each statistical result to determine whether the query hit count is less than a preset threshold; If so, delete the statistical results. If not, no action will be taken on the statistical results.

7. A medical insurance data processing device, characterized in that, include: A response unit is used to respond to a processing request by obtaining the data table identifier of the medical insurance data table in the processing request, so as to query the field information of the fields included in the medical insurance data table. The determining unit is used to call a preset classification model and, based on the field information, determine a first set of fields belonging to the statistical dimension type and a second set of fields belonging to the statistical indicator type; The calculation unit is used to combine fields in the first field set to obtain field combinations for each dimension, including: extracting the field value of each field in the first field set from the medical insurance data table, calculating the storage space of the field value corresponding to each field in the first field set; arranging and combining the fields in the first field set to generate undetermined field combinations, determining the storage space of the field value corresponding to each undetermined field combination based on the storage space of the field value corresponding to each field, and determining whether the sum of the storage spaces of the field values ​​corresponding to each undetermined field combination is greater than a preset threshold; if not, determining each undetermined field combination as a dimension field combination; if so, deleting a target number of fields in the first field set to obtain a new first field set, and repeating the above steps of generating undetermined field combinations and determining the threshold until the sum of the storage spaces of the field values ​​corresponding to each new undetermined field combination is not greater than the preset threshold, determining all fields included in the first field set as a dimension field combination, and determining each new undetermined field combination as a dimension field combination; and calculating the statistical result corresponding to each dimension field combination based on the medical insurance data table. The acquisition unit is used to receive a query request, acquire the query conditions in the query request, match them with the dimension field combination and the second field set, and conclude that the query conditions include the dimension field combination and the second field. The query unit is used to query the statistical results corresponding to the combination of dimension fields included in the query conditions, extract the data corresponding to the second field, and return the extracted data.

8. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-6.

9. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-6.

Images