A medical insurance internal rule intelligent checking and self-examination and self-correction method and system

Abstract

This application discloses a method and system for intelligent verification and self-inspection of internal medical insurance rules. The method includes: loading and parsing initial internal medical insurance rules from a target database; receiving target requirements from target personnel, configuring and managing the initial internal medical insurance rules, obtaining target internal medical insurance rules, and updating them to the target database; calling the target internal medical insurance rules to verify the target medical insurance data and obtaining verification results; constructing a mutually exclusive rule base based on the negative list of medical insurance in the verification results, performing data mining analysis and self-inspection of the target medical insurance data, and obtaining data analysis and self-inspection results; and generating a visualized internal medical insurance rule analysis report based on the verification results and the analysis and self-inspection results. This achieves efficient and accurate verification and intelligent early warning of medical insurance rules, adapts to policy changes, and facilitates traceability and record-keeping.

Description

Technical Field

[0001] This application relates to the field of medical data analysis technology, and in particular to a method and system for intelligent verification and self-checking of internal rules of medical insurance. Background Technology

[0002] With the continuous improvement of the medical security system, medical insurance management faces increasingly complex challenges. The effective implementation of internal medical insurance rules is crucial for controlling medical expenses and preventing the abuse of medical insurance funds.

[0003] Currently, medical institutions primarily rely on manual review and simple system verification for medical insurance management. These methods suffer from low efficiency, error-proneness, and incomplete coverage. Using commercial rule engines (such as Drools) presents challenges, including complex configuration, high learning costs, and difficulties in integrating with medical systems. In particular, traditional engines lack the flexibility and scalability to handle dynamically changing medical insurance rules. Furthermore, with the continuous updating and increasing complexity of medical insurance policies, traditional manual review methods are no longer sufficient to meet the needs of medical insurance management.

[0004] In summary, existing verification methods and systems for medical insurance management have the following shortcomings: Manual review is inefficient: Traditional manual review methods are not only time-consuming and labor-intensive, but also prone to inconsistencies and inaccuracies in review results due to human factors. Poor system verification flexibility: Existing system verification methods often have fixed rules, making it difficult to adjust flexibly according to rapid changes in medical insurance policies, resulting in a significant reduction in verification effectiveness; Incomplete coverage: Due to the complexity and diversity of medical insurance rules, traditional methods are difficult to fully cover all rules and policy requirements, which can easily lead to omissions and violations. Lack of intelligent analysis: Existing technologies lack in-depth mining and intelligent analysis of medical insurance data, making it difficult to discover potential medical insurance risks and management loopholes; Integration is difficult: Interfacing with HIS / EMR systems requires complex interface development. Summary of the Invention

[0005] The main purpose of this application is to provide a method and system for intelligent verification and self-checking of internal rules of medical insurance, which can at least solve one of the problems existing in the verification methods and systems of medical insurance management, such as low efficiency of manual review, poor system verification flexibility, incomplete coverage, lack of intelligent analysis and great difficulty in integration.

[0006] To achieve the above objectives, this application proposes a method for intelligent verification and self-checking of internal rules for medical insurance, comprising the following steps: Load and parse the initial medical insurance internal rules from the target database; Receive target requirements from target personnel, configure and manage initial medical insurance internal rules, obtain target medical insurance internal rules and update them to the target database; Call the target medical insurance internal rules to verify the target medical insurance data and obtain the medical insurance data verification result; Based on the negative list of medical insurance in the medical insurance data verification results, a mutual exclusion rule base is constructed, and the target medical insurance data is mined, analyzed and self-checked to obtain the medical insurance data analysis and self-check results. Based on the results of medical insurance data verification and the results of medical insurance data analysis and self-inspection, a visualized internal medical insurance rule analysis report is generated.

[0007] In one embodiment, a method for loading and parsing initial medical insurance internal rules from a target database includes: From the target relational database storing medical insurance rules, the initial internal medical insurance rules are loaded using the condition-action pair design pattern, and the logical combinations in the initial internal medical insurance rules are parsed based on the data container.

[0008] In one embodiment, a method for receiving target needs from target individuals, configuring and managing initial medical insurance internal rules, obtaining target medical insurance internal rules, and updating the target database includes: Receive rule configuration management requests from hospital medical insurance management personnel via a Winform visual interface; Based on the rule configuration management requirements, the initial medical insurance internal rules are configured and managed, including enabling medical insurance internal rules, disabling medical insurance internal rules, configuring project details and setting trigger conditions, to obtain the target medical insurance internal rules. The target medical insurance internal rules are synchronized and updated to the target relational database via the WCF interface.

[0009] In one embodiment, a method for invoking the target medical insurance internal rules to verify the target medical insurance data and obtain the medical insurance data verification result includes: The internal rules of the target medical insurance system are invoked to perform multi-dimensional verification on the target medical insurance data, including consistency between the number of medical orders and the number of billing orders, matching between departments and items, repetition of items within the time range, and whether the number of billing orders exceeds the number of days of hospitalization. The multi-dimensional verification results corresponding to each target medical insurance data are summarized and analyzed in real time, and negative medical insurance data that failed the verification are screened out. Based on negative medical insurance data, error reports, correction suggestions, and a negative list of medical insurance data are generated as the results of medical insurance data verification.

[0010] In one embodiment, a mutually exclusive rule base is constructed based on the medical insurance negative list in the medical insurance data verification results. The target medical insurance data is then analyzed and self-checked to obtain the medical insurance data analysis and self-check results, including: Analyze the negative list of medical insurance and extract a list of mutually exclusive items from it; By combining automatic matching with manual review, a combination of self-inspection and self-correction items corresponding to the target hospital's in-hospital items is generated; Retrieve electronic medical records and historical medical orders and expenses from the HIS system's business center database via service interfaces; Based on electronic medical records, historical medical orders, and expenses, obtain and cache the historical data of the target patient. Retrieve the current medical orders of the target patient and retrieve cached historical data; Based on current medical orders, historical data, a list of mutually exclusive items, and a combination of self-checked and self-corrected items, mutual exclusion checks are performed on target patients, violations are recorded, and mutual exclusion reports are generated. The results of medical insurance data analysis and self-checking and self-correction, which include the verification conclusions, violation records, and mutual exclusion reports, are obtained.

[0011] In one embodiment, a method for performing item mutual exclusion verification on target patients based on current medical orders, historical data, a list of mutually exclusive items, and a combination of self-checked and self-corrected items, recording mutual exclusion violations and generating mutual exclusion reports, and obtaining medical insurance data analysis and self-checking and self-correction results including verification conclusions, violation records, and mutual exclusion reports, includes: Based on the list of mutually exclusive items extracted from the medical insurance negative list and the combination of items for self-inspection and self-correction within the hospital, multiple mutually exclusive item pairs, including the first item and the second item, were identified to be verified. Based on the medical insurance code or hospital code corresponding to the first item in the current mutually exclusive item pair, check whether the first item exists in the current medical order. If it does not exist, end the verification of the current mutually exclusive item pair and return the normal verification result. If the first item exists in the current medical order, then check whether the second item exists in the current medical order based on the medical insurance code or hospital code of the second item. If it exists, immediately return the mutual exclusion result, record the mutual exclusion violation information and generate a mutual exclusion report. If the second item is not in the current medical order, then query the historical data to see if the second item exists based on the medical insurance code or hospital code of the second item. If it exists, immediately return the mutual exclusion result, record the mutual exclusion violation information and generate a mutual exclusion report. If it does not exist, end the inspection of the current mutual exclusion item pair and return the normal verification result. Repeat the query process for the current mutually exclusive item pair to perform mutual exclusion verification on the next mutually exclusive item pair, until all mutually exclusive item pairs have been verified.

[0012] Furthermore, to achieve the above objectives, this application also proposes an intelligent verification and self-checking system for internal medical insurance rules, used to execute any of the methods described above, including: The pre- and in-process rules engine module for medical insurance is used to load and parse the initial internal rules of medical insurance from the target database. The medical insurance rule configuration and management module is used to receive target requirements from target personnel, configure and manage initial medical insurance internal rules, obtain target medical insurance internal rules and update them to the target database; The pre- and in-process verification module for medical insurance data is used to call the internal rules of the target medical insurance system to verify the target medical insurance data and obtain the verification results. The intelligent analysis and self-inspection module for medical insurance data is used to call the internal rules of the target medical insurance system to perform mining analysis and self-inspection of the target medical insurance data, and obtain the results of medical insurance data analysis and self-inspection. The report generation and export module is used to generate visualized internal medical insurance rule analysis reports based on the results of medical insurance data verification and medical insurance data analysis and self-inspection.

[0013] In one embodiment, the medical insurance pre- and in-process rule engine module uses .NET's Datatable as a data container.

[0014] In one embodiment, the medical insurance rule configuration and management module includes a client interface, which is developed based on the Winform technology stack. The backend of the client interface uses WCF to provide a web service interface to interact with the medical insurance pre-event and in-event rule engine module.

[0015] In one embodiment, the report generation and export module is configured to use the reporting tool ActiveReports, combined with preset report templates and styles, to generate a visual medical insurance internal rule analysis report.

[0016] This application provides a method and system for intelligent verification and self-checking of internal medical insurance rules. The method replaces traditional manual review and simple system verification by automatically loading and parsing medical insurance rules, batch verifying medical insurance data, and intelligently mining and analyzing data. This significantly reduces labor costs and avoids inconsistencies and inaccuracies caused by human factors. It supports loading and parsing initial internal medical insurance rules, as well as dynamic configuration, management and updates based on needs, which can quickly respond to the updates and increasing complexity of medical insurance policies and solve the shortcomings of existing systems with fixed rules and insufficient flexibility. By combining in-depth analysis of medical insurance rule verification and mutual exclusion rule base, it covers multi-dimensional compliance requirements of medical insurance data, making up for the problems of incomplete rule coverage and easy omission of violations in traditional methods; By constructing a mutually exclusive rule base to conduct in-depth mining of medical insurance data, potential medical insurance risks and management loopholes can be accurately identified, triggering self-inspection and self-correction processes, thus addressing the shortcomings of existing technologies that lack intelligent analysis. Compared to commercial rule engines that are complex to configure and have high learning costs, this method offers intuitive rule configuration, clear system interaction logic, and adapts to the data interaction needs of medical insurance data and related business systems, alleviating the problem of high integration difficulty in traditional systems. Generates a visual analysis report that includes verification results and self-inspection and rectification status, fully recording the entire process of rule configuration, data verification, and problem correction, facilitating problem tracking and responsibility definition, and standardizing medical insurance management processes. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating an embodiment of the intelligent verification and self-checking method for internal rules of medical insurance in this application; Figure 2 This is a flowchart illustrating the mutual exclusion verification process provided in an embodiment of the intelligent verification and self-checking method for internal rules of medical insurance in this application. Figure 3 This is a schematic diagram of a structural embodiment of the intelligent verification and self-inspection system for internal rules of medical insurance provided in this application.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance. Example

[0023] This application provides a method for intelligent verification and self-checking of internal rules in medical insurance, referring to... Figure 1 This includes the following steps: Step S1: Load and parse the initial medical insurance internal rules from the target database; Step S2: Receive target requirements from target personnel, configure and manage the initial medical insurance internal rules, obtain the target medical insurance internal rules and update them to the target database; Step S3: Call the target medical insurance internal rules to verify the target medical insurance data and obtain the medical insurance data verification result; Step S4: Construct a mutually exclusive rule base based on the medical insurance negative list in the medical insurance data verification results, perform mining analysis and self-checking on the target medical insurance data, and obtain the medical insurance data analysis and self-checking results; Step S5: Based on the medical insurance data verification results and the medical insurance data analysis and self-inspection results, generate a visualized medical insurance internal rule analysis report.

[0024] In this embodiment, the target database refers to a relational database (such as SQL Server) that stores medical insurance-related rules, configuration information and business data. It is both the storage carrier of the initial internal medical insurance rules and the persistent storage medium after the target internal medical insurance rules are updated, and it also supports data interaction with the HIS / EMR system. The initial internal medical insurance rules are a set of original medical insurance compliance rules pre-entered based on medical insurance policy requirements, medical insurance negative lists, etc., covering basic rules such as medical order verification, item matching, and mutual exclusion constraints. They serve as the initial basis for subsequent rule configuration and management and have not been customized by the hospital. The target medical insurance internal rules are personalized compliance rules adapted to the actual business of the hospital after the initial medical insurance internal rules are configured by the hospital's medical insurance management personnel through a visual interface (enable / disable, adjust trigger conditions, etc.). They will be updated to the target database in real time and serve as the direct execution basis for medical insurance data verification. The medical insurance negative list is a list of medical insurance violations and non-compliant situations that failed the verification of medical insurance data. The mutual exclusion list is a set of mutually exclusive medical insurance items (such as dynamic electroencephalography and electroencephalography video monitoring) intelligently extracted from the medical insurance negative list. It is the basic source data of the mutual exclusion rule base and clarifies the combination of non-compliant items that need to be verified. The self-inspection and self-correction project portfolio is a hospital-wide version of the mutual exclusion verification list formed after the mutual exclusion list of projects is "automatically matched (linked with medical insurance code and hospital code) and manually reviewed". It is linked with the hospital's exclusive code and business specifications and can be directly used for compliance verification of medical insurance data within the hospital. The mutual exclusion rule base is a special rule base formed by integrating medical insurance compliance requirements with the mutual exclusion list of items and the combination of self-inspection and self-correction items as the core. It supports the intelligent analysis module to carry out mutual exclusion verification of items and covers key rules such as coding matching logic and data verification dimensions. The medical insurance data verification results are a collection of results generated after the medical insurance data has been verified in multiple dimensions. They include verification pass / fail indicators, negative medical insurance data, detailed error reports, correction suggestions, and a negative list of medical insurance data. This is the basis for subsequent self-inspection and self-correction and report generation. The results of medical insurance data analysis and self-inspection are formed by in-depth mining of medical insurance data (including current medical orders and cached historical data) based on a mutual exclusion rule base. The results include mutual exclusion violation records, mutual exclusion reports, risk warnings and correction processes and results, so as to achieve the identification of potential violations and closed-loop rectification.

[0025] This implementation method replaces traditional manual review and simple system verification by automatically loading and parsing medical insurance rules, batch verifying medical insurance data, and intelligently mining and analyzing data. This significantly reduces labor costs and avoids inconsistencies and inaccuracies caused by human factors. It supports loading and parsing initial internal medical insurance rules, as well as dynamic configuration, management and updates based on needs, which can quickly respond to the updates and increasing complexity of medical insurance policies and solve the shortcomings of existing systems with fixed rules and insufficient flexibility. By combining in-depth analysis of medical insurance rule verification and mutual exclusion rule base, it covers multi-dimensional compliance requirements of medical insurance data, making up for the problems of incomplete rule coverage and easy omission of violations in traditional methods; By constructing a mutually exclusive rule base to conduct in-depth mining of medical insurance data, potential medical insurance risks and management loopholes can be accurately identified, triggering self-inspection and self-correction processes, thus addressing the shortcomings of existing technologies that lack intelligent analysis. Compared to commercial rule engines that are complex to configure and have high learning costs, this method offers intuitive rule configuration, clear system interaction logic, and adapts to the data interaction needs of medical insurance data and related business systems, alleviating the problem of high integration difficulty in traditional systems. Generates a visual analysis report that includes verification results and self-inspection and rectification status, fully recording the entire process of rule configuration, data verification, and problem correction, facilitating problem tracking and responsibility definition, and standardizing medical insurance management processes.

[0026] In one optional implementation, step S1, loading and parsing the initial medical insurance internal rules from the target database, includes: From the target relational database storing medical insurance rules, the initial internal medical insurance rules are loaded using the condition-action pair design pattern, and the logical combinations in the initial internal medical insurance rules are parsed based on the data container.

[0027] Of course, .NET's Datatable can also be used as a data container. By combining the conditional-action pair design pattern with .NET's Datatable data container, this implementation method enables dynamic loading and real-time updates of medical insurance rules, which can quickly respond to iterative changes in medical insurance policies and completely solve the defects of fixed rules and insufficient flexibility in traditional systems. On the other hand, it can efficiently parse complex logical combinations (AND / OR / NOT) and calculation column requirements in rules, fully compatible with pre-approval and in-process supervision scenarios of various hospital businesses, greatly improve the adaptability of rule execution, and avoid the problems of complex configuration and high learning cost of commercial rule engines, thus reducing the threshold for system use.

[0028] In one optional implementation, step S2, receiving target requirements from target personnel, configuring and managing initial medical insurance internal rules, obtaining target medical insurance internal rules, and updating the target database, includes: Step S201: Receive rule configuration management requests sent by hospital medical insurance management personnel through the Winform visual interface; Step S202: According to the rule configuration management requirements, complete the configuration and management of the initial medical insurance internal rules, including enabling medical insurance internal rules, disabling medical insurance internal rules, configuring project details and setting trigger conditions, to obtain the target medical insurance internal rules; Step S203: Synchronize and update the target medical insurance internal rules to the target relational database via the WCF interface.

[0029] Using this implementation method, the visual interface based on the Winform technology stack is intuitive and easy to use, allowing hospital medical insurance management personnel to quickly complete operations such as enabling and disabling rules, configuring project details and setting trigger conditions without a complex technical background. This significantly reduces the learning cost and operational difficulty of rule configuration and solves the problems of low efficiency and error-proneness in traditional manual configuration. Meanwhile, by linking with the target relational database (such as SQL Server) through the WCF interface, the target medical insurance rules can be updated in real time, ensuring the security and consistency of rule storage. The interface interaction logic is adapted to the data flow requirements of the medical system, alleviating the pain point of the difficulty in traditional system integration.

[0030] In one optional implementation, step S3, invoking the target medical insurance internal rules to verify the target medical insurance data and obtain the medical insurance data verification result, includes: Step S301: Call the target medical insurance internal rules to perform multi-dimensional verification on the target medical insurance data, including consistency between the number of medical orders and the billing quantity, matching between departments and items, repetition of items within the time range, and billing quantity exceeding the number of hospitalization days; Step S302: Real-time summary and analysis of the multi-dimensional verification results corresponding to each target medical insurance data, and screening out the negative medical insurance data that failed the verification; Step S303: Based on the negative medical insurance data, generate an error report, correction suggestions, and a negative list of medical insurance data as the verification results of the medical insurance data.

[0031] By adopting this implementation method, the core compliance requirements of medical insurance rules are fully covered through multi-dimensional verification such as consistency of the number of medical orders and matching of departmental items, making up for the shortcomings of traditional verification methods that are incomplete and prone to overlooking violation scenarios. Real-time aggregation and analysis of verification results can quickly filter out negative medical insurance data. Combined with automatically generated detailed error reports, actionable correction suggestions, and a negative list of medical insurance data, this not only allows for precise identification of violations but also guides staff in efficient correction, reducing the cost of human judgment and improving the compliance of medical insurance data. The generated negative list for medical insurance provides a precise core basis for the subsequent construction of a mutually exclusive rule base, realizing a closed-loop process of verification and analysis.

[0032] Further, in step S4, a mutually exclusive rule base is constructed based on the negative list of medical insurance in the medical insurance data verification results. The target medical insurance data is then analyzed and self-checked to obtain the results of the medical insurance data analysis and self-check, including: Step S401: Analyze the medical insurance negative list and extract a list of mutually exclusive items from the medical insurance negative list; Step S402: Generate a combination of self-inspection and self-correction items corresponding to the target hospital's in-hospital items through automatic matching and manual review; Step S403: Retrieve electronic medical records and historical medical orders and expenses from the HIS system's business center database via a service interface; Step S404: Based on electronic medical records, historical medical orders, and costs, obtain and cache the historical data of the target patient; Step S405: Obtain the current medical orders of the target patient and retrieve cached historical data; Step S406: Based on the current medical orders, historical data, item mutual exclusion list, and self-inspection and self-correction item combination, perform item mutual exclusion verification on the target patient, record mutual exclusion violations and generate mutual exclusion reports, and obtain medical insurance data analysis and self-inspection and self-correction results that include verification conclusions, violation records, and mutual exclusion reports.

[0033] This implementation method extracts a list of mutually exclusive items based on the medical insurance negative list, and generates a combination of items for in-hospital self-inspection and self-correction through automatic matching and manual review. This ensures that the mutually exclusive rules not only comply with the requirements of official medical insurance policies, but also adapt to the hospital's internal coding system and actual business practices, thus solving the problem of the disconnect between general rules and in-hospital scenarios. By pulling historical data through service interfaces and caching it, the running speed of mutual exclusion verification for projects is greatly improved, avoiding efficiency losses caused by repeated data retrieval. Combining full-cycle verification of current medical orders and cached historical data, mutual exclusion violations across time dimensions can be accurately identified, making up for the shortcomings of traditional verification that only focuses on current data and misses potential risks. Violation information is recorded simultaneously and mutual exclusion reports are generated, making the self-inspection and self-correction process traceable, facilitating the definition of responsibility and process optimization, and further strengthening the security management of medical insurance funds.

[0034] Furthermore, step S406, based on current medical orders, historical data, a list of mutually exclusive items, and a combination of self-checked and self-corrected items, performs item mutual exclusion verification on the target patient, records mutual exclusion violations, generates mutual exclusion reports, and obtains medical insurance data analysis and self-checking and self-correction results including verification conclusions, violation records, and mutual exclusion reports. This method includes: Step S4061: Based on the mutually exclusive list of items extracted from the medical insurance negative list and the combination of self-inspection and self-correction items in the hospital, determine the multiple mutually exclusive item pairs to be verified, including the first item and the second item. Step S4062: Based on the medical insurance code or hospital code corresponding to the first item in the current mutually exclusive item pair, check whether the first item exists in the current medical order. If it does not exist, end the verification of the current mutually exclusive item pair and return the normal verification result. Step S4063: If the first item exists in the current medical order, then according to the medical insurance code or hospital code of the second item, check whether the second item exists in the current medical order. If it exists, immediately return the mutual exclusion result, synchronously record the mutual exclusion violation information and generate a mutual exclusion report. Step S4064: If the second item is not in the current medical order, query the historical data to see if the second item exists based on the medical insurance code or hospital code of the second item. If it exists, immediately return the mutual exclusion result, record the mutual exclusion violation information and generate a mutual exclusion report. If it does not exist, end the check of the current mutual exclusion item pair and return the normal verification result. Step S4065: Repeat the above query process for the current mutual exclusion pair, and perform mutual exclusion verification for the next mutual exclusion pair until all mutual exclusion pairs have been verified.

[0035] This implementation method uses a dual-coding matching logic that combines medical insurance codes with hospital codes to adapt to the actual scenario of hospital data management, ensuring the accuracy of identifying mutually exclusive items and avoiding misjudgments or omissions due to coding mismatches. By designing a cyclical check using multiple mutually exclusive items, we can achieve complete and thorough checking of all mutual exclusion rules, thus solving the problem of incomplete coverage in traditional checks. The verification process proceeds in the order of current data → historical data, which is logically rigorous and meets the actual needs of medical insurance supervision. It not only ensures verification efficiency, but also accurately captures potential mutually exclusive violations. Simultaneously recording violation information and generating mutually exclusive reports allows for direct linking of verification results with rectification basis, improving the efficiency of self-inspection and self-correction, and strengthening the standardization of medical insurance management.

[0036] The first and second items are only for distinguishing items in a mutually exclusive item pair. In one optional implementation, a mutually exclusive item pair includes dynamic EEG (item A) and EEG video monitoring fees (item B), and the corresponding mutual exclusion verification process is as follows: Figure 2 As shown. Example

[0037] This application also proposes an embodiment of an intelligent verification and self-checking system for internal medical insurance rules, used to execute the method in embodiment 1, such as... Figure 3 As shown, it includes: The pre- and in-process rules engine module for medical insurance is used to load and parse the initial internal rules of medical insurance from the target database. The medical insurance rule configuration and management module is used to receive target requirements from target personnel, configure and manage initial medical insurance internal rules, obtain target medical insurance internal rules and update them to the target database; The pre- and in-process verification module for medical insurance data is used to call the internal rules of the target medical insurance system to verify the target medical insurance data and obtain the verification results. The intelligent analysis and self-inspection module for medical insurance data is used to call the internal rules of the target medical insurance system to perform mining analysis and self-inspection of the target medical insurance data, and obtain the results of medical insurance data analysis and self-inspection. The report generation and export module is used to generate visualized internal medical insurance rule analysis reports based on the results of medical insurance data verification and medical insurance data analysis and self-inspection.

[0038] In one optional implementation, the medical insurance pre- and in-process rule engine module uses .NET's Datatable as the data container; Using this implementation method, the conditional expression parsing engine can be built efficiently, supporting complex logic combinations and calculation column functions, without relying on third-party commercial engines, thus reducing system procurement and maintenance costs. At the same time, it is fully compatible with the pre-approval and in-process supervision needs of various businesses, ensuring the comprehensiveness and adaptability of rule execution, and solving the problems of insufficient scalability and disconnection from medical business of traditional engines.

[0039] In one optional implementation, the medical insurance rule configuration and management module includes a client interface, which is developed based on the Winform technology stack. The backend of the client interface uses WCF to provide a web service interface to interact with the medical insurance pre-event and in-event rule engine module.

[0040] By adopting this implementation method, the visual design of the Winform client interface lowers the operational threshold for administrators and improves the efficiency of rule configuration; The backend WCF interface enables efficient interaction with the rule engine module, ensuring stable and real-time data transmission. This guarantees seamless integration of rule configuration and execution, avoids delays in rule activation due to interface compatibility issues, and further optimizes the workflow continuity of medical insurance management.

[0041] In one optional implementation, the report generation and export module is configured to use the reporting tool ActiveReports, combined with preset report templates and styles, to generate a visual medical insurance internal rule analysis report.

[0042] Using this implementation method, with the help of the mature ActiveReports reporting tool and custom templates, a multi-dimensional visual report can be generated, which includes verification pass rate, error type distribution, correction status, risk warning, etc., to intuitively present the effectiveness and potential risks of medical insurance management. It supports PDF / Excel format export, printing, and scheduled automatic sending functions, meeting the diverse needs of hospitals for cross-departmental transfer, archiving, and regulatory reporting, and improving the transparency and efficiency of medical insurance management.

[0043] Therefore, when using the above system implementation method to automatically generate error reports, the preset report templates and styles include basic violation information, detailed violation description, supporting data, and risk level labeling; Among them, the basic information on violations is obtained by automatically capturing the unique identifier of the violation data (such as doctor's order ID, patient ID, billing number), the time of the violation, the department involved, the operator, and the data source system (HIS / EMR), to ensure that the problem can be accurately located; The violation details are clearly marked in advance the violation type, such as inconsistency between the number of medical orders and the billed quantity, mismatch between department and item, duplicate items within the time range, billed quantity exceeding the number of days of hospitalization, and mutually exclusive item violations. The corresponding medical insurance rule number and the original text of the rule are further cited to quantify the difference in violation data, such as "the number of medical orders is 5, the billed quantity is 8, the difference is 3" or "the number of days of hospitalization is 7, and the billed item 'daily nursing' reaches 10 times". Supporting data refers to automatically attaching screenshots / field information of the original data related to the violation, such as key fields of medical orders, fragments of departmental item matching tables, examples of historical similar compliant data, and synchronously linking potential risk extensions identified by the intelligent analysis module. For example, "This type of violation has occurred 5 times in our department in the past 30 days, and the billing process needs to be verified." Risk level labeling refers to automatically classifying violations into levels (general / moderate / serious) based on the severity of the violation, such as whether it involves a large loss of medical insurance funds, whether it is a high-frequency violation, or whether it touches on the core clauses of the medical insurance negative list. Serious violations are marked with a "priority handling" label.

[0044] It can call the verification logs of the rules engine module (including the basis for violation judgment and data comparison results) to synchronize basic information of violation data, related business data and the original text of medical insurance rules from the SQL Server database; based on the ActiveReports reporting tool, it presets multi-dimensional report templates (categorized by violation type, department and time period), and the system automatically matches the corresponding template according to the violation scenario to populate real-time verification data and analysis results; it supports real-time visualization (pop-up window / dedicated report page in Winform client interface), and provides batch export of PDF / Excel format and printing of single violation reports. The exported file is automatically named, such as "20240520-Internal Medicine-Item Mutual Exclusion Violation Report.xlsx", which is convenient for archiving and cross-departmental circulation.

[0045] The correction suggestions can be precise according to different scenarios, and actionable correction solutions can be generated for different types of violations, avoiding general statements; For example, in scenario 1 (quantity inconsistency violation): it is recommended to "verify the medical order records and billing data, and adjust the billing quantity to match the number of medical orders (current number of medical orders: X, billing quantity: Y, it is recommended to correct it to X); if it is a duplicate entry of multiple billings, it can be handled through the system's 'batch cancellation of duplicate billing' function"; Scenario 2 (Department and Project Mismatch): It is recommended that "the current department 'XX Department' does not have the authority to carry out 'XX Project'. It can be changed to the compliant department 'YY Department', or the non-compliant project can be deleted and replaced with the compliant project 'ZZ Project' (refer to the hospital's self-inspection and self-correction project combination number: XXX)". Scenario 3 (Item Mutual Exclusion Violation): It is recommended to "cancel any item in the mutual exclusion item pair in the current / historical data (mutual exclusion items: A / B, item A is currently billed, it is recommended to delete item B or replace it with a non-mutual exclusion alternative item C), for details please refer to rule X of the medical insurance negative list"; Scenario 4 (Billing Exceeds Hospitalization Days): It is recommended to "adjust the number of billing cycles based on the actual number of hospitalization days (X days). The excess (current billing X+N times) can be deleted in batches through the 'Billing Cycle Correction' function. After correction, it is necessary to resubmit for verification." In addition, each suggestion is accompanied by compliance evidence, such as "According to medical insurance rule number XXX: the number of billing for inpatient items shall not exceed the actual number of days of hospitalization"; and a link to similar violation amendment examples is attached, such as "Refer to the surgical similar violation correction record 20240518", which allows users to click to view details.

[0046] Establish a "Violation Type - Correction Suggestion" mapping table in the SQL Server database, link the medical insurance rule base with the hospital's self-inspection and self-correction project combination, preset correction schemes, operation paths and compliance basis for each scenario, and support manual maintenance and updates through the rule configuration module (Winform interface) to complete the construction of the suggestion template library; Based on the specific characteristics of the violation data, such as the violation type, the project involved, and the department, the system calls the rule engine module to parse the corresponding rules, matches basic suggestions from the suggestion template library, and then combines the department permissions, project adaptation relationships, patient hospitalization cycle and other data synchronized in real time by the HIS system to dynamically optimize the suggestion details, such as automatically filling in the name of the current compliant department and the list of replaceable projects, so as to achieve real-time adaptation and generation. Correction suggestions are displayed in conjunction with error reports on the Winform client interface. Clicking on a suggestion will take you to the system's "Data Correction" function page, which supports one-click application of suggestions, such as automatically filling in the corrected data. After correction, the system will automatically trigger a second verification, recording the comparison of data before and after correction and the verification results to achieve interactive support.

[0047] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for intelligent verification and self-checking of internal rules in medical insurance, characterized in that, Includes the following steps: Load and parse the initial medical insurance internal rules from the target database; Receive target requirements from target personnel, configure and manage initial medical insurance internal rules, obtain target medical insurance internal rules and update them to the target database; Call the target medical insurance internal rules to verify the target medical insurance data and obtain the medical insurance data verification result; Based on the negative list of medical insurance in the medical insurance data verification results, a mutual exclusion rule base is constructed, and the target medical insurance data is mined, analyzed and self-checked to obtain the medical insurance data analysis and self-check results. Based on the results of medical insurance data verification and the results of medical insurance data analysis and self-inspection, a visualized internal medical insurance rule analysis report is generated.

2. The intelligent verification and self-checking method for internal rules of medical insurance as described in claim 1, characterized in that, Methods for loading and parsing initial medical insurance internal rules from the target database include: From the target relational database storing medical insurance rules, the initial internal medical insurance rules are loaded using the condition-action pair design pattern, and the logical combinations in the initial internal medical insurance rules are parsed based on the data container.

3. The intelligent verification and self-checking method for internal rules of medical insurance as described in claim 1, characterized in that, The method for receiving target requirements from target personnel, configuring and managing initial medical insurance internal rules, obtaining target medical insurance internal rules, and updating the target database includes: Receive rule configuration management requests from hospital medical insurance management personnel via a Winform visual interface; Based on the rule configuration management requirements, the initial medical insurance internal rules are configured and managed, including enabling medical insurance internal rules, disabling medical insurance internal rules, configuring project details and setting trigger conditions, to obtain the target medical insurance internal rules. The target medical insurance internal rules are synchronized and updated to the target relational database via the WCF interface.

4. The intelligent verification and self-checking method for internal rules of medical insurance as described in claim 1, characterized in that, Methods for invoking the target medical insurance internal rules to verify the target medical insurance data and obtain the medical insurance data verification results include: The internal rules of the target medical insurance system are invoked to perform multi-dimensional verification on the target medical insurance data, including consistency between the number of medical orders and the number of billing orders, matching between departments and items, repetition of items within the time range, and whether the number of billing orders exceeds the number of days of hospitalization. The multi-dimensional verification results corresponding to each target medical insurance data are summarized and analyzed in real time, and negative medical insurance data that failed the verification are screened out. Based on negative medical insurance data, error reports, correction suggestions, and a negative list of medical insurance data are generated as the results of medical insurance data verification.

5. The intelligent verification and self-checking method for internal rules of medical insurance as described in claim 4, characterized in that, Based on the negative list of medical insurance in the medical insurance data verification results, a mutual exclusion rule base is constructed. The target medical insurance data is then analyzed and self-checked to obtain the results of medical insurance data analysis and self-checking, including: Analyze the negative list of medical insurance and extract a list of mutually exclusive items from it; By combining automatic matching with manual review, a combination of self-inspection and self-correction items corresponding to the target hospital's in-hospital items is generated; Retrieve electronic medical records and historical medical orders and expenses from the HIS system's business center database via service interfaces; Based on electronic medical records, historical medical orders, and expenses, obtain and cache the historical data of the target patient. Retrieve the current medical orders of the target patient and retrieve cached historical data; Based on current medical orders, historical data, a list of mutually exclusive items, and a combination of self-checked and self-corrected items, mutual exclusion checks are performed on target patients, violations are recorded, and mutual exclusion reports are generated. The results of medical insurance data analysis and self-checking and self-correction, which include the verification conclusions, violation records, and mutual exclusion reports, are obtained.

6. The intelligent verification and self-checking method for internal rules of medical insurance as described in claim 5, characterized in that, Based on current medical orders, historical data, a list of mutually exclusive items, and a combination of self-checked and self-corrected items, methods are used to verify the mutual exclusivity of items for target patients, record violations, and generate mutual exclusivity reports. These methods yield medical insurance data analysis and self-checking and self-correction results that include verification conclusions, violation records, and mutual exclusivity reports. Based on the list of mutually exclusive items extracted from the medical insurance negative list and the combination of items for self-inspection and self-correction within the hospital, multiple mutually exclusive item pairs, including the first item and the second item, were identified to be verified. Based on the medical insurance code or hospital code corresponding to the first item in the current mutually exclusive item pair, check whether the first item exists in the current medical order. If it does not exist, end the verification of the current mutually exclusive item pair and return the normal verification result. If the first item exists in the current medical order, then check whether the second item exists in the current medical order based on the medical insurance code or hospital code of the second item. If it exists, immediately return the mutual exclusion result, record the mutual exclusion violation information and generate a mutual exclusion report. If the second item is not in the current medical order, then query the historical data to see if the second item exists based on the medical insurance code or hospital code of the second item. If it exists, immediately return the mutual exclusion result, record the mutual exclusion violation information and generate a mutual exclusion report. If it does not exist, end the inspection of the current mutual exclusion item pair and return the normal verification result. Repeat the query process for the current mutually exclusive item pair to perform mutual exclusion verification on the next mutually exclusive item pair, until all mutually exclusive item pairs have been verified.

7. A smart verification and self-checking system for internal rules of medical insurance, used to execute the method as described in any one of claims 1 to 6, characterized in that, include: The pre- and in-process rules engine module for medical insurance is used to load and parse the initial internal rules of medical insurance from the target database. The medical insurance rule configuration and management module is used to receive target requirements from target personnel, configure and manage initial medical insurance internal rules, obtain target medical insurance internal rules and update them to the target database; The pre- and in-process verification module for medical insurance data is used to call the internal rules of the target medical insurance system to verify the target medical insurance data and obtain the verification results. The intelligent analysis and self-inspection module for medical insurance data is used to call the internal rules of the target medical insurance system to perform mining analysis and self-inspection of the target medical insurance data, and obtain the results of medical insurance data analysis and self-inspection. The report generation and export module is used to generate visualized internal medical insurance rule analysis reports based on the results of medical insurance data verification and medical insurance data analysis and self-inspection.

8. The intelligent verification and self-checking system for internal medical insurance rules as described in claim 7, characterized in that: The medical insurance pre- and in-process rule engine module uses .NET's Datatable as the data container.

9. The intelligent verification and self-checking system for internal medical insurance rules as described in claim 7, characterized in that: The medical insurance rule configuration and management module includes a client interface, which is developed based on the Winform technology stack. The backend of the client interface uses WCF to provide a web service interface to interact with the medical insurance pre-event and in-event rule engine module.

10. The intelligent verification and self-checking system for internal medical insurance rules as described in claim 7, characterized in that: The report generation and export module is configured to use the reporting tool ActiveReports, combined with preset report templates and styles, to generate a visual medical insurance internal rule analysis report.

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