A medical insurance business index data storage and query method and system

Abstract

The application provides a medical insurance business index data storage and query method and system, acquires definition index classification, definition index modifier and definition index attribute, constructs an index model based on the acquired information, constructs an index storage model in a star model based on dimension modeling theory, acquires index calculation parameters corresponding to the index model, calculates and generates index data through matching calculation rules, updates the index data to a fact table, calculates the correlation between indexes through correlation analysis algorithms according to the indexes and index data, generates correlation relationship analysis results between the indexes, forms an index system, and extracts and queries data related to the instruction content from the fact table and corresponding data in the related dimension table according to the index model and the index system in response to a medical insurance business information query instruction. The application can make the number of data tables and the amount of single table data grow within a controllable range, and ensure clear data storage logic.

Description

Technical Field

[0001] This invention belongs to the field of information storage, specifically relating to a method and system for storing and querying medical insurance business indicators. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] The medical insurance field involves many factors such as the system construction at both the provincial and municipal levels, policy differences, and the wide and complex scope of business. The traditional model, based on the definition of codes, names, and classifications, allows each party to define various information of indicators according to their own understanding. This leads to chaotic definitions, difficulty in fully integrating various businesses, and an inability to achieve uniformity in indicator definitions and standards across cities and regions, as well as an inability to ensure the uniqueness of indicator definitions. Consequently, problems such as unclear indicator definitions, insufficient accuracy of indicator results, and the inability to classify, retrieve, and manage indicators have arisen.

[0004] It is evident that medical insurance business generates a wide variety of indicator dimensions, with different combinations producing numerous different indicators, making traditional data storage methods difficult to maintain and expand. To ensure accurate querying and viewing of medical insurance data, a necessary solution is to use appropriate modeling methods to keep the number of data tables and the amount of data per table within a controllable range, while maintaining clear data storage logic. This also facilitates the maintenance and expansion of medical insurance business data in the future, lowers the barrier to data use, and provides better support for data analysis. Summary of the Invention

[0005] To address the aforementioned problems, this invention proposes a method and system for storing and querying medical insurance business indicators. Based on a data warehouse model, this invention performs corresponding modeling and data storage applications through a defined medical insurance indicator system, enabling the number of data tables and the amount of data in a single table to grow within a controllable range, while ensuring clear data storage logic.

[0006] According to some embodiments, the present invention adopts the following technical solution:

[0007] A method for storing and querying medical insurance business indicator data includes the following steps:

[0008] Obtain the definition of indicator categories, indicator modifiers, and indicator attributes, and build an indicator model based on the obtained information;

[0009] Based on dimensional modeling theory, an indicator storage model is constructed using a star schema. The indicator storage model is centered on the fact table, and the relevant dimension tables are directly connected to the fact table.

[0010] Obtain the indicator calculation parameters corresponding to the indicator model, calculate and generate indicator data by matching calculation rules, and update the indicator data to the fact table;

[0011] Based on the indicators and the indicator data, the correlation between the indicators is calculated through the correlation analysis algorithm, generating the correlation analysis results between the various indicators, and forming an indicator system.

[0012] In response to a medical insurance business information query command, data related to the query command content, as well as corresponding data in the relevant dimension tables, are extracted from the fact table according to the aforementioned indicator model and indicator system.

[0013] As an alternative implementation method, the definition of indicators is classified according to their business segment, indicator type, statistical type, and evaluation type, specifically including:

[0014] Define the business segments of the indicators, summarize and categorize them according to the relevant business normative documents, and divide them into different business areas. The business segment to which the indicator belongs is determined by selecting candidate values ​​for the business segments.

[0015] Define indicator types, which are divided into atomic indicators and composite indicators;

[0016] Define the statistical type of the indicator, and distinguish between static and dynamic indicators based on the time range of the indicator being measured during the statistical analysis.

[0017] Define the evaluation type of the indicators, and classify the indicators into quantitative indicators and qualitative indicators according to the evaluation type.

[0018] As an alternative implementation, the definition of indicator modifiers is used to abstract the business scenario of the indicator, describe the business scenario other than the dimension, and uniformly encode the indicator. The specific process includes:

[0019] Define the time limit term for the indicator, which restricts the indicator to be counted within a set time period;

[0020] The object term that defines the indicator describes the thing or concept to which the indicator belongs;

[0021] Define the characteristic terms of the indicator to describe the distinctive features of the indicator;

[0022] Define the terms representing the indicator, which describe the set of valid values ​​for the indicator;

[0023] The indicator code and indicator name are formed by the permutation and combination of the above four types of modifiers, with the indicator code serving as the unique identifier of the indicator.

[0024] As an alternative implementation, defining indicator attributes includes defining the business attributes, technical attributes, and management attributes of the indicators. Tagging these indicator attributes specifically includes:

[0025] Define the business attribute information of the indicator. The business attributes describe the indicator based on business logic, including the business connotation of the indicator, the business category to which it belongs, and the related business action information.

[0026] Define the technical attributes of the indicator, and describe the technical information related to the indicator, including the data source, statistical scope, and storage location of the indicator;

[0027] Define the management attribute information of the indicator, and describe the information on the implementation, management and maintenance of the indicator, including the source business system of the indicator, the department and person in charge of the indicator, and the information on the indicator maintenance unit and maintenance personnel.

[0028] As an alternative implementation method, the process of constructing an indicator storage model using a star schema based on dimensional modeling theory includes:

[0029] By defining dimension information and attribute specifications, including dimension codes, dimension names, and dimension types, common dimension attributes are extracted from business reports and business systems. Dimensions are then created based on these attributes, and dimension tables are constructed.

[0030] The fact table is constructed by defining the fact table, combining dimensions, and assembling facts.

[0031] As an alternative implementation, the process of constructing a fact table by defining a fact table, combining dimensions, and assembling facts includes:

[0032] Categorize business facts, select facts whose business relevance exceeds the set value, include all indicators of the current business category in a fact table as much as possible, and select only facts related to the current business category. A fact table should only contain facts related to the current business.

[0033] Select dimensions that can describe business categories and combine them to form the dimensions of the fact table;

[0034] Select all facts that are relevant to the business category and whose dimensions are consistent with the dimensions of the created fact table to form a fact table;

[0035] Based on the needs of data analysis, the fact table is optimized by adding dimension attributes that are used more frequently than a set value during data analysis to the fact table as redundant dimensions, thereby reducing the number of table joins performed during statistical analysis.

[0036] As an alternative implementation, the process of calculating and generating indicator data by matching calculation rules and updating the indicator data to the fact table includes:

[0037] Obtain the indicator data calculation parameters, obtain the indicator data calculation trigger time based on the statistical frequency, execute the indicator data calculation task on a regular basis according to the trigger time, match the calculation rules, and obtain the indicator data calculation time range;

[0038] The time range of the indicator data is taken into the statistical calculation logic of the maintenance as a parameter, the calculation logic is executed in the data warehouse, the indicator result data is obtained, and the result data is updated to the fact table.

[0039] As an alternative implementation method, the process of calculating the correlation between indicators using a correlation analysis algorithm, generating correlation analysis results between various indicators, and forming an indicator system includes:

[0040] Set a minimum support threshold, calculate the support of itemsets composed of each indicator, and select itemsets with support greater than the minimum support threshold as frequent itemsets.

[0041] Association rules are generated based on frequent itemsets. Confidence scores are calculated, and association rules with confidence scores greater than the minimum confidence threshold are identified as strong association rules.

[0042] By analyzing the association rule results and repeatedly calculating frequent itemsets and association rules, the thresholds are continuously adjusted to find suitable minimum support and minimum confidence thresholds.

[0043] Discover the relationships corresponding to strong association rules and form an indicator system.

[0044] As an alternative implementation method, in response to a medical insurance business information query instruction, data related to the query instruction content is extracted from the fact table according to the aforementioned indicator model and indicator system, including:

[0045] The system retrieves the indicator code, query dimension list, condition dimension list, and calculation type parameters from the query command. Based on the indicator code, it determines the indicator to retrieve the data. Based on the query dimension list, it assembles the query fields of the query SQL. Based on the condition dimension list, it assembles the condition fields of the query SQL to query the indicator data query range. Based on the calculation type, it determines whether to query the summary value or detailed value, and whether to perform grouping and summarization. Finally, it executes the query to retrieve the indicator data and provides data support through a service.

[0046] As an alternative implementation, the following steps are also included:

[0047] The query results are recorded to form a data sharing record. Based on the statistical caliber of the indicators and the data sharing record, the indicator lineage is constructed. Specifically:

[0048] The statistical criteria are segmented based on lexical analysis, and then transformed into an abstract syntax tree with a traversable hierarchical structure through syntactic parsing. This process yields the source table and source fields of the indicators, forming the upstream dependencies of the indicators.

[0049] The correlation between atomic indicators is obtained based on the composite indicator calculation formula. The correlation between reports and indicators and data sharing records are used to establish the downstream dependency relationship of indicators. Based on the upstream and downstream dependency relationship of indicators, the complete lineage relationship of the indicator is finally formed.

[0050] A medical insurance business indicator data storage and query system, comprising:

[0051] The definition management module is configured to obtain definition indicator categories, definition indicator modifiers, and definition indicator attributes, and build indicator models based on the obtained information;

[0052] The modeling and storage module is configured to construct an indicator storage model based on dimensional modeling theory, using a star schema. The indicator storage model is centered on the fact table, and the relevant dimension tables are directly connected to the fact table.

[0053] The indicator data generation module is configured to obtain the indicator calculation parameters corresponding to the indicator model, calculate and generate indicator data by matching calculation rules, and update the indicator data to the fact table.

[0054] The indicator application module is configured to calculate the correlation between indicators based on the indicators and the indicator data through an association analysis algorithm, generate the association relationship analysis results between the various indicators, form an indicator system, and, in response to medical insurance business information query instructions, extract and query data related to the content of the query instruction from the fact table and the corresponding data in the relevant dimension table according to the indicator model and indicator system.

[0055] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the steps in the above method.

[0056] An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the steps in the method described above.

[0057] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0058] (1) By introducing indicator modifiers, classification, and attribute labeling, this invention can unify the encoding format of indicators, transforming meaningless codes into readable, ordered, and meaningful information. An indicator can be defined by selecting the corresponding phrases, reducing the difficulty of customizing names and codes. Furthermore, through the limitation of modifiers, both the indicator and its code maintain global uniqueness in the system. At the same time, the correlation between modifiers and indicator classification and statistical caliber further standardizes information maintenance, solving problems such as unclear indicator definitions, inability to guarantee uniqueness, inaccurate results due to inconsistent calibers, and inconvenience in indicator classification retrieval and management.

[0059] (2) This invention stores indicator data based on a star schema structure, which has a clear storage logic, keeps the number of data tables and the amount of data in a single table within a reasonable range, has high maintainability, and lowers the threshold for data use; by establishing globally unique dimensions through unified management of dimensions, it ensures dimension consistency, which facilitates cross-analysis and correlation analysis of different business indicator data in different business areas and different business indicators in the same business area when performing data analysis in the data warehouse; the fact table design is strongly related to the business process, and the single table has strong business correlation, high cohesion, and low coupling, which improves the efficiency of data warehouse data analysis.

[0060] (3) This invention is beneficial for the management and application of indicators and indicator data. Based on the data lineage established by statistical standards, the upstream and downstream of data can be traced through information flow, providing support for decision support, data quality repair, impact analysis, etc.; it can also construct an indicator history record table, which can make the historical version information traceable when the indicator changes.

[0061] (4) By constructing an indicator system, this invention can discover potential correlations between business processes, quickly and effectively supporting system applications, data visualization, and data mining and analysis. Indicator data is shared externally through data query services to improve data utilization and data sharing efficiency, ensuring data consistency and security. Compared to traditional operational databases, data warehouses have higher data capacity and data processing efficiency. By sharing data warehouse data tables externally, efficient data analysis can be supported, reducing data redundancy and repetitive work, and improving data management efficiency.

[0062] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0063] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0064] Figure 1 A flowchart of a method according to one embodiment;

[0065] Figure 2 This is a system structure diagram of one embodiment. Detailed Implementation

[0066] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0067] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0068] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0069] Where there is no conflict, the embodiments and features described in this application may be combined with each other.

[0070] Example 1

[0071] A method for storing and querying medical insurance business indicators, such as Figure 1 As shown, the specific steps are as follows:

[0072] Step 1: Build an indicator model by defining indicator categories, indicator modifiers, and indicator attributes.

[0073] Step 1 specifically includes:

[0074] Step (1-1): Define the indicator classification by categorizing the indicators according to their business segment, indicator type, statistical type, and evaluation type. The specific steps are as follows:

[0075] Step (1-1-1): Define the business segment of the indicator. According to each business segment, refer to the relevant business normative documents to summarize and classify different business areas. Determine the business segment to which the indicator belongs by selecting candidate values ​​of the business segment.

[0076] Step (1-1-2): Define the indicator type. The indicator type is divided into atomic indicators and composite indicators. Atomic indicators cannot be further divided in the business definition and have a clear business meaning. Composite indicators can be divided in the business definition. They are a set of calculated indicators formed by atomic indicators and certain calculation rules.

[0077] The calculation rules mentioned in this embodiment include year-on-year CPR, month-on-month LPR, SUM, SUBTRACT, MULT IPLY, DIVI IDE, and combinations of the aforementioned rules.

[0078] Step (1-1-3): Define the statistical type of the indicator. Based on the statistical type, distinguish between static and dynamic indicators according to the time range of the indicator being measured during the statistical analysis. The static indicator refers to the point-in-time number, which is a statistical indicator calculated using a unified standard within a certain period or a certain range at a point in time, and has a relative contrast.

[0079] Dynamic indicators are process numbers, referring to changes over a period of time. They are usually the result of tracking or monitoring certain data and are additive. The statistical type of an indicator is determined based on the time range of the statistical analysis and whether it is additive.

[0080] Step (1-1-4): Define the evaluation type of the indicator. According to the evaluation type, the indicators can be divided into quantitative indicators and qualitative indicators.

[0081] The quantitative indicators provided in this embodiment are relatively objective statistical indicators that can be directly described using numerical or statistical data, and can be quantified or calculated. Qualitative indicators refer to evaluation indicators that cannot be directly quantified but need to be quantified through other means. They are used to evaluate the merits and demerits of things. The evaluation type of the indicator is selected based on whether it can be quantified or calculated.

[0082] Taking the indicator "The cumulative number of hypertensive patients covered by the two-disease policy across years who have received benefits for outpatient chronic and special disease medications" as an example, this indicator falls under the business segment of Medical Insurance - Resident Benefits. This indicator cannot be further divided in its business definition, has a clear business meaning, and is an atomic indicator. The number of beneficiaries represents changes over a period of time, is additive, and is a dynamic indicator. The number of beneficiaries is an objective indicator that can be directly described by numerical values, can be calculated, and is a quantitative indicator.

[0083] Step (1-2): Define metric modifiers, abstract the business scenarios of the metrics, describe the business scenarios other than dimensions, and uniformly encode the metrics. The metric modifiers include four categories: time-limited terms, object terms, characteristic terms, and representation terms. These four categories of modifiers jointly describe the business scenarios of the metrics. Time-limited terms, object terms, and representation terms are mandatory for the metrics, while characteristic terms may not be specified for metrics in certain business scenarios. The specific steps are as follows:

[0084] Step (1-2-1): Define the time qualifier for the indicator, limiting its statistical scope to a specific time period, such as near real-time, daily, monthly, year-end cumulative, within the statistical period, or at the end of the statistical period. If the indicator is classified as a static indicator, only time qualifiers at the beginning and end of the statistical period can be defined, and variables such as start and end times cannot be used when maintaining the statistical scope. Based on the definition of the time qualifier, only one time qualifier can be defined for each indicator.

[0085] Step (1-2-2): Define the object terms of the indicator, which describe the thing or concept to which the indicator belongs, such as the participating unit. An indicator can be described by a combination of multiple object terms.

[0086] Step (1-2-3): Define the characteristic words of the indicator to describe the significant and distinctive features of the indicator, such as participation in insurance and payment. An indicator can be described by a combination of multiple characteristic words.

[0087] Step (1-2-4): Define the metric's descriptor, which describes the set of valid values ​​for the metric, such as quantity or total amount. When the descriptor and characteristic terms jointly describe a business metric, some overlap may occur. In this case, the parts of the characteristic terms that overlap with the descriptor can be deleted. Only one descriptor can be defined for a single metric.

[0088] In steps (1-2-5), the system generates indicator codes and indicator names based on the permutations and combinations of the above four types of modifiers, with the indicator code serving as the unique identifier of the indicator.

[0089] The unique identifier of an indicator is first verified by checking the combined indicator code and indicator name to see if there is an existing indicator with the same code and name in the indicator library. Then, the four types of modifiers for the indicator—time-limiting words, object words, characteristic words, and representation words—are checked in the indicator library to see if there are any identical indicators. If the indicator code, indicator name, and the four types of modifiers are completely identical, it means that the indicator already exists and cannot be defined further.

[0090] Taking the indicator "the cumulative number of hypertensive patients covered by the two-disease policy and the cumulative number of patients receiving treatment for the two diseases in outpatient chronic and special diseases" as an example, this indicator counts data from the beginning of the policy to the end of the period, so the time limit term is "cumulative (up to the end of the statistical period)". The object words describing the things or concepts to which the indicator belongs include "residents", "two diseases", "policy", "coverage", "hypertension", "patients", "treatment", and "outpatient chronic and special diseases". The characteristic words describing the significant and distinctive features of the indicator include "medication" and "treatment". The term representing the set of effective values ​​of the indicator is "number of people".

[0091] Step (1-3): Define the indicator attributes, including the business attributes, technical attributes, and management attributes of the indicators, and label the indicator attributes.

[0092] Step (1-3-1): Define the business attribute information of the indicator, describe the indicator based on business logic, including the business connotation of the indicator, the business category to which it belongs, and the related business action information.

[0093] Step (1-3-2): Define the technical attribute information of the indicator, describing the technical information related to the indicator, including the data source, statistical scope, and storage location of the indicator; among which, the data source describes the access layer data source of the original detailed data required to calculate the indicator data; the statistical scope describes the calculation logic of the indicator data aggregating from the access layer, and the statistical scope rules must match the time limit words defined in step (1-2-1); the storage location describes the specific fact table and fields in which the indicator result data is stored in the database.

[0094] Step (1-3-3): Define the management attribute information of the indicator, describe the information on the implementation, management and maintenance of the indicator, including the source business system of the indicator, the department responsible for the indicator, the person in charge of the indicator, the unit and personnel responsible for the indicator maintenance.

[0095] Steps (1-4): Establish an indicator version record table to store historical indicator version information and historical indicator dimension relationship information. Record the historical version of the indicator for operation processes such as indicator definition, modification, and cancellation. The historical version information of the indicator can be queried at any time to achieve traceability of the entire life cycle of the indicator.

[0096] Step 2: Based on dimensional modeling theory, construct an indicator storage model using a star schema, with the fact table as the center and related dimension tables directly connected to the fact table.

[0097] Step (2-1) Construct the dimension table: By defining dimension information and attribute specifications such as dimension code, dimension name, and dimension type, extract common dimension attributes from business reports and business systems, and establish dimensions based on the dimension attributes.

[0098] The dimension types include three categories: time dimension, geographic dimension, and general dimension. The time dimension includes commonly used dimensions such as year, half-year, quarter, month, and day to represent time information. The geographic dimension includes dimensions such as province, city, and county to represent geographic division information. The general dimension is other dimensions besides the time dimension and geographic dimension.

[0099] The construction of the dimension table also includes dimension uniqueness verification for duplicate dimensions or dimension values ​​based on naming, data type, and business meaning.

[0100] Dimension uniqueness verification involves performing exact and fuzzy matching between the dimension code, dimension description, and business meaning of the new dimension and the corresponding attribute information of each existing dimension. If a dimension with an exact match exists, the new dimension cannot be created. If a dimension with a fuzzy match exists, an approximate dimension will be indicated, and the administrator will decide whether to continue creating it.

[0101] The construction of dimension tables also includes establishing a dimension hierarchy structure, establishing hierarchical relationships for dimension attributes that can be associated hierarchically or in a one-to-many manner in the business, realizing data drill-down through the dimension hierarchy structure, and merging the hierarchy of dimension attributes into a single dimension through denormalization based on the star schema structure, reducing table joins and improving the performance of statistical analysis queries.

[0102] Step (2-2) Constructing the fact table: Construct the fact table by defining the fact table, combining dimensions, and assembling facts.

[0103] Step (2-2-1) Define a fact table, classify business facts, select facts that are highly relevant to the business, include as many indicators as possible for the current business category in a fact table, and select only facts that are related to the current business category. A fact table should only contain facts that are relevant to the current business.

[0104] Step (2-2-2) Combine dimensions: Select dimensions that can describe business categories and combine them to form the dimensions of the fact table. Combining dimensions can precisely define the business meaning represented by each row, ensuring that all facts in the fact table are stored at the same granularity.

[0105] Step (2-2-3) Construct a fact table by selecting all facts that are related to the business category and whose dimensions are consistent with the dimensions of the created fact table.

[0106] Step (2-2-4) optimizes the fact table according to the needs of data analysis. Commonly used dimension attributes in data analysis are added to the fact table as redundant dimensions to reduce the number of table joins in statistical analysis and improve the query efficiency of the fact table.

[0107] Taking the three indicators "Amount actually paid by individuals implementing the unified medical insurance system in the current period", "Total amount of contribution base for individuals implementing the unified medical insurance system", and "Total accumulated arrears at the end of the period for individuals implementing the unified medical insurance system" as examples, the business segment to which the above indicators belong is medical insurance - employee collection, and the dimensions defined are quarterly, city, unit type, and micro and small enterprises. Therefore, the above three indicators should be stored in the same fact table.

[0108] S3: Obtain indicator calculation parameters, calculate and generate indicator data by matching calculation rules, and update the indicator data to the fact table.

[0109] Step (3-1) Obtain the indicator data calculation parameters, obtain the indicator data calculation trigger time based on the statistical frequency, execute the indicator data calculation task at regular intervals according to the trigger time, match the calculation rules, and obtain the indicator data calculation time range.

[0110] The parameters for calculating the indicator data include: the indicator type defined in step (1-1-2), the statistical type defined in step (1-1-3), and the time-limiting term defined in step (1-2-1).

[0111] The specific matching and calculation rules include: First, determining the statistical type. If it is a static indicator, the date dimension parameter in the statistical scope is replaced based on the time-limiting keyword of the static indicator, and an execution statement is generated. If it is a dynamic indicator, the time-limiting keyword is determined. If the time-limiting keyword is a cumulative indicator from the beginning of the year to the end of the month, or from the beginning of the year to the end of the quarter, the date dimension parameter, query start date, and end date parameter in the statistical scope are replaced, and an execution statement is generated. If the time-limiting keyword is an indicator within the current month or year, no statistical scope parameter is replaced, and an execution statement is generated.

[0112] Step (3-2) calculates the time range based on the indicator data and inputs it into the statistical calculation logic maintained in step (1-3-2) as a parameter. The calculation logic is then executed in the data warehouse to obtain the indicator result data and update the result data to the fact table.

[0113] Taking the indicator of large-amount pooled payment for outpatient chronic and special diseases for employees as an example, the time modifier of this indicator is from the beginning of the year to the end of the quarter, the statistical type is a dynamic indicator, and the statistical scope includes three parameters: :dimdate, :startdate, and :enddate. Among them, the :dimdate parameter represents the current time dimension, and the :startdate and :enddate parameters represent the statistical start time and end time. The above parameter values ​​are obtained according to the indicator data generation time, and the statistical scope is generated. Based on the data warehouse specification, the statistical scope is converted into a query statement supported by the data warehouse, sent to the data warehouse for execution through the data warehouse interface, and the calculation results are obtained. Finally, the calculation results are stored in the defined fact table.

[0114] S4: Construct an indicator system based on indicators and indicator data, and provide services / applications such as indicator data sharing and indicator lineage.

[0115] The construction of the indicator system involves calculating the correlation between indicators based on the indicator classification, indicator attributes, and dimension types using a correlation analysis algorithm, generating correlation analysis results between various indicators, and thus forming the indicator system.

[0116] The specific steps are as follows:

[0117] First, a minimum support threshold is set, and the support of each item set composed of indicators is calculated. Item sets with support greater than the minimum support threshold are identified as frequent itemsets, as shown in Table 1. Second, association rules are generated based on these frequent itemsets, and their confidence scores are calculated. Association rules with confidence scores greater than the minimum confidence threshold are identified as strong association rules, as shown in Table 2. Then, the association rule results are analyzed. Through multiple calculations of frequent itemsets and association rules, the thresholds are continuously adjusted to find suitable minimum support and minimum confidence thresholds. The resulting strong association rules indicate a strong correlation between the indicators, and an indicator system is formed based on the correlations discovered in the rules.

[0118] Table 1

[0119]

[0120] Table 2

[0121]

[0122] The aforementioned indicator data can be shared externally through two methods: data query service and data warehouse data table.

[0123] Specifically:

[0124] Among them, the data query service is shared. It obtains indicator result data based on the query indicator code, query dimension list, condition dimension list, and calculation type parameters, providing support for data analysis and BI report generation. It determines the indicator to obtain data based on the indicator code, assembles the query fields of the query SQL based on the query dimension list, assembles the condition fields of the query SQL based on the condition dimension list to query the query range of indicator data, determines whether to query the summary value or detailed value based on the calculation type, whether to perform grouping and summarization, and finally executes the query to obtain indicator data, providing data support through the service.

[0125] Among them, data warehouse data tables are shared, and fact tables in dimensional modeling are synchronized to the data warehouse. By authorizing access permissions to fact tables in the data warehouse, authorized parties can freely query fact table data corresponding to the applied indicators and organize data query logic as needed for data analysis.

[0126] In some embodiments, the lineage of indicators can also be constructed based on the statistical caliber of the indicators and the records of external data sharing.

[0127] Specifically: First, the statistical criteria are segmented using lexical analysis. Second, a parser transforms the statistical criteria into an abstract syntax tree with a traversable hierarchical structure, thereby obtaining the source table and source fields of the indicators, forming the upstream dependencies of the indicators. Then, the association relationships of atomic indicators are obtained based on the composite indicator calculation formula, and the downstream dependencies of indicators are established based on the association relationships between reports and indicators, as well as externally shared records. Finally, based on the upstream and downstream dependencies of the indicators, the complete lineage of the indicator is formed.

[0128] Example 2

[0129] A data storage and query system for medical insurance business indicators, such as Figure 2 As shown, it includes a definition management module 101, a modeling storage module 102, an indicator data generation module 103, and an indicator application module 104.

[0130] The definition management module 101 is used to construct indicator models by defining indicator categories, defining indicator modifiers, and defining indicator attributes. Specifically, it includes the indicator category definition module 1011, the indicator modifier definition module 1012, the indicator attribute definition module 1013, and the lifecycle module 1014.

[0131] Define indicator classification module 1011, which is used to classify indicators according to their business segment, indicator type, statistical type, and evaluation type.

[0132] Define the indicator modifier module 1012, which is used to abstract the business scenarios of indicators, describe business scenarios other than dimensions, and uniformly encode indicators.

[0133] The indicator attribute definition module 1013 is used to define the business attributes, technical attributes, and management attributes of indicators, and to label the indicator attributes.

[0134] The full lifecycle module 1014 is used to establish an indicator version record table, store historical version information of indicators and historical indicator dimension relationship information, and realize the traceability of the entire lifecycle of indicators.

[0135] The modeling and storage module 102 is used to construct an indicator storage model based on dimensional modeling theory, using a star schema, with the fact table as the center and related dimension tables directly connected to the fact table. It includes a dimension construction module 1021 and a fact table construction module 1022.

[0136] The dimension module 1021 is used to extract common dimension attributes from business reports and business systems by defining dimension information and attribute specifications such as dimension codes, dimension names, and dimension types, and to create dimensions based on the dimension attributes.

[0137] The Fact Table Construction Module 1022 is used to construct a fact table by defining fact tables, combining dimensions, and assembling facts.

[0138] The indicator data generation module 103 is used to obtain indicator calculation parameters, calculate indicator data by matching calculation rules, and update the indicator data to the fact table. It includes a calculation time range module 1031 and an indicator data generation module 1032.

[0139] The calculation time range module 1031 is used to obtain the indicator data calculation parameters, obtain the indicator data calculation trigger time based on the statistical frequency, execute the indicator data calculation task at regular intervals according to the trigger time, match the calculation rules, and obtain the indicator data calculation time range.

[0140] The indicator data generation module 1032 is used to calculate the time range of the indicator data and input it into the statistical caliber calculation logic maintained in step (1-3-2) as a parameter, execute the calculation logic in the data warehouse, obtain the indicator result data, and update the result data to the fact table.

[0141] The indicator management application module 104 is used to construct indicator applications based on the indicators and indicator data generated by the definition management module, modeling storage module, and indicator data generation module. It includes the indicator system module 1041, the data sharing module 1042, and the data lineage module 1043.

[0142] The indicator system module 1041 is used to calculate the correlation between indicators based on the indicator classification, indicator attributes, and dimension type, and generate the correlation analysis results between various indicators.

[0143] The data sharing module 1042 is used to provide external sharing applications, which can be shared externally through two methods: data query service and data warehouse data table.

[0144] The data lineage module 1043 is used to construct the lineage relationship of indicators based on the statistical caliber of the indicators and the records of external data sharing.

[0145] Each module is configured to execute the corresponding steps in Example 1.

[0146] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0147] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0148] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0149] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0150] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made by those skilled in the art without creative effort within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for storing and querying medical insurance business indicator data, characterized in that, Includes the following steps: Obtain the definition of indicator categories, indicator modifiers, and indicator attributes, and build an indicator model based on the obtained information; Based on dimensional modeling theory, an indicator storage model is constructed using a star schema. The indicator storage model is centered on the fact table, and the relevant dimension tables are directly connected to the fact table. Obtain the indicator calculation parameters corresponding to the indicator model, calculate and generate indicator data by matching calculation rules, and update the indicator data to the fact table; Based on the indicators and the indicator data, the correlation between the indicators is calculated through the correlation analysis algorithm, generating the correlation analysis results between the various indicators, and forming an indicator system. In response to a medical insurance business information query command, data related to the query command content and corresponding data in the relevant dimension tables are extracted from the fact table according to the aforementioned indicator model and indicator system. The query results are recorded to form a data sharing record. Based on the statistical caliber of the indicators and the data sharing record, the indicator lineage is constructed. Specifically: Lexical analysis is used to segment the statistical criteria into words. Syntactic parsing is used to transform the statistical criteria into an abstract syntax tree with a traversable hierarchical structure, thereby obtaining the source table and source fields of the indicators and forming the upstream dependencies of the indicators. The correlation between atomic indicators is obtained based on the composite indicator calculation formula. The correlation between reports and indicators and data sharing records are used to establish the downstream dependency relationship of indicators. Based on the upstream and downstream dependency relationship of indicators, the complete lineage relationship of the indicator is finally formed.

2. The method for storing and querying medical insurance business indicators as described in claim 1, characterized in that, The defined indicator classification categorizes indicators according to their business segment, indicator type, statistical type, and evaluation type, specifically including: Define the business segments of the indicators, summarize and categorize them according to the relevant business normative documents, and divide them into different business areas. The business segment to which the indicator belongs is determined by selecting candidate values ​​for the business segments. Define indicator types, which are divided into atomic indicators and composite indicators; Define the statistical type of the indicator, and distinguish between static and dynamic indicators based on the time range of the indicator being measured during the statistical analysis. Define the evaluation type of the indicators, and classify the indicators into quantitative indicators and qualitative indicators according to the evaluation type.

3. The method for storing and querying medical insurance business indicators as described in claim 1, characterized in that, The defined metric modifiers are abstractions of the business scenarios of the metrics, describing business scenarios other than dimensions, and uniformly encoding the metrics. The specific process includes: Define the time limit term for the indicator, which restricts the indicator to be counted within a set time period; The object term that defines the indicator describes the thing or concept to which the indicator belongs; Define the characteristic terms of the indicator to describe the distinctive features of the indicator; Define the terms representing the indicator, which describe the set of valid values ​​for the indicator; The indicator code and indicator name are formed by the permutation and combination of the above four types of modifiers, with the indicator code serving as the unique identifier of the indicator.

4. The method for storing and querying medical insurance business indicators as described in claim 1, characterized in that, The defined indicator attributes include business attributes, technical attributes, and management attributes. The indicator attributes are labeled, specifically including: Define the business attribute information of the indicator. The business attributes describe the indicator based on business logic, including the business connotation of the indicator, the business category to which it belongs, and the related business action information. Define the technical attributes of the indicator, and describe the technical information related to the indicator, including the data source, statistical scope, and storage location of the indicator; Define the management attribute information of the indicator, and describe the information on the implementation, management and maintenance of the indicator, including the source business system of the indicator, the department and person in charge of the indicator, and the information on the indicator maintenance unit and maintenance personnel.

5. The method for storing and querying medical insurance business indicators as described in claim 1, characterized in that, The process of constructing an indicator storage model based on dimensional modeling theory and using a star schema includes: By defining dimension information and attribute specifications, including dimension codes, dimension names, and dimension types, common dimension attributes are extracted from business reports and business systems. Dimensions are then created based on these attributes, and dimension tables are constructed. Fact tables are constructed by defining fact tables, combining dimensions, and assembling facts. Alternatively, the process of constructing a fact table by defining the fact table, combining dimensions, and assembling facts includes: Categorize business facts, select facts whose business relevance exceeds the set value, include all indicators of the current business category in a fact table as much as possible, and select only facts related to the current business category. A fact table should only contain facts related to the current business. Select dimensions that can describe business categories and combine them to form the dimensions of the fact table; Select all facts that are relevant to the business category and whose dimensions are consistent with the dimensions of the created fact table to form a fact table; Based on the needs of data analysis, the fact table is optimized by adding dimension attributes that are used more frequently than a set value during data analysis to the fact table as redundant dimensions, thereby reducing the number of table joins performed during statistical analysis.

6. The method for storing and querying medical insurance business indicators as described in claim 1, characterized in that, The process of calculating and generating indicator data by matching calculation rules, and then updating the indicator data to the fact table includes: Obtain the indicator data calculation parameters, obtain the indicator data calculation trigger time based on the statistical frequency, execute the indicator data calculation task on a regular basis according to the trigger time, match the calculation rules, and obtain the indicator data calculation time range; The time range of the indicator data is taken into the statistical calculation logic of the maintenance as a parameter, the calculation logic is executed in the data warehouse, the indicator result data is obtained, and the result data is updated to the fact table.

7. The method for storing and querying medical insurance business indicators as described in claim 1, characterized in that, The process of calculating the correlation between indicators using correlation analysis algorithms, generating correlation analysis results between various indicators, and forming an indicator system includes: Set a minimum support threshold, calculate the support of itemsets composed of each indicator, and itemsets with support greater than the minimum support threshold are considered frequent itemsets. Association rules are generated based on frequent itemsets. Confidence scores are calculated, and association rules with confidence scores greater than the minimum confidence threshold are identified as strong association rules. By analyzing the association rule results and repeatedly calculating frequent itemsets and association rules, the thresholds are continuously adjusted to find suitable minimum support and minimum confidence thresholds. Discover the relationships corresponding to strong association rules and form an indicator system.

8. The method for storing and querying medical insurance business indicators as described in claim 1, characterized in that, In response to a medical insurance business information query command, data related to the query command content is extracted from the fact table according to the aforementioned indicator model and indicator system, including: The system retrieves the indicator code, query dimension list, condition dimension list, and calculation type parameters from the query command. Based on the indicator code, it determines the indicator to retrieve the data. Based on the query dimension list, it assembles the query fields of the query SQL. Based on the condition dimension list, it assembles the condition fields of the query SQL to query the indicator data query range. Based on the calculation type, it determines whether to query the summary value or detailed value, and whether to perform grouping and summarization. Finally, it executes the query to retrieve the indicator data and provides data support through a service.

9. A data storage and query system for medical insurance business indicators, characterized in that, include: The definition management module is configured to obtain definition indicator categories, definition indicator modifiers, and definition indicator attributes, and build indicator models based on the obtained information; The modeling and storage module is configured to construct an indicator storage model based on dimensional modeling theory, using a star schema. The indicator storage model is centered on the fact table, and the relevant dimension tables are directly connected to the fact table. The indicator data generation module is configured to obtain the indicator calculation parameters corresponding to the indicator model, calculate and generate indicator data by matching calculation rules, and update the indicator data to the fact table. The indicator application module is configured to calculate the correlation between indicators based on the indicators and the indicator data through an association analysis algorithm, generate the correlation analysis results between various indicators, form an indicator system, and, in response to medical insurance business information query instructions, extract data related to the query instruction content from the fact table and the corresponding data from the relevant dimension table according to the indicator model and indicator system. The query results are recorded to form a data sharing record. Based on the statistical caliber of the indicators and the data sharing record, the indicator lineage is constructed. Specifically: Lexical analysis is used to segment the statistical criteria into words. Syntactic parsing is used to transform the statistical criteria into an abstract syntax tree with a traversable hierarchical structure, thereby obtaining the source table and source fields of the indicators and forming the upstream dependencies of the indicators. The correlation between atomic indicators is obtained based on the composite indicator calculation formula. The correlation between reports and indicators and data sharing records are used to establish the downstream dependency relationship of indicators. Based on the upstream and downstream dependency relationship of indicators, the complete lineage relationship of the indicator is finally formed.

Images