Medical Insurance Early Warning and Review Method and System

Abstract

The present disclosure provides a medical insurance early warning and audit method and system, belonging to the technical field of data processing. The method includes: in response to identifying that there is a data update request in the target management system, obtaining the data source of the target database corresponding to the target management system, where the target management system is a system associated with the medical insurance early warning and audit system; extracting keywords from the data source of the target database to obtain a keyword group; matching the keyword group with the audit rule library to obtain a target audit rule that matches the data source of the target database, where the audit rule library is a rule library customized by the hospital; and giving an early warning for the data update request in the target management system based on the target audit rule. The present disclosure can give an early warning before and during the medical insurance audit to ensure the normal operation of the system.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of data processing, and more specifically, relates to a medical insurance early warning and audit method and system. Background Art

[0002] Currently, the procurement and use of a medical insurance early warning and audit system in a hospital is a relatively new management method. The cooperating third-party commercial medical information technology company generally modifies the hospital HIS system and embeds the medical insurance early warning and audit system in the HIS system. The intelligent audit engine of the audit system is used to audit the medical and charging behaviors of the hospital to achieve the purpose of medical insurance fund audit. However, there are significant application drawbacks in this mode. Generally, the medical insurance early warning and audit systems purchased by hospitals are semi-finished products and need to be continuously optimized and run-in during hospital operation to operate well. There is a problem of "mainly post-audit and insufficient pre-warning interception". For example, the long audit time for large medical insurance expenses affects clinical medical work, and the system is not convenient and cannot balance the patient's visit and the efficiency of medical insurance approval, etc., resulting in the system being unable to strictly implement the medical insurance audit policy, and even interfering with and affecting normal clinical diagnosis and treatment behaviors, and ultimately causing the problem that the medical insurance early warning and audit system cannot be used in the hospital. Summary of the Invention

[0003] The purpose of the present disclosure is to provide a medical insurance early warning and audit method and system that can give early warnings before and during medical insurance audits to ensure the normal operation of the system.

[0004] In the first aspect of the embodiments of the present disclosure, a medical insurance early warning and audit method is provided, including:

[0005] In response to identifying that there is a data update request in the target management system, obtain the data source of the target database corresponding to the target management system, where the target management system is a system associated with the medical insurance early warning and audit system;

[0006] Extract keywords from the data source of the target database to obtain a keyword group;

[0007] Match the keyword group with the audit rule library to obtain a target audit rule that matches the data source of the target database, where the audit rule library is a rule library customized by the hospital;

[0008] Give an early warning for the data update request in the target management system based on the target audit rule.

[0009] In the second aspect of the embodiments of the present disclosure, a medical insurance early warning and audit system is provided, including:

[0010] A data acquisition module, configured to obtain a data source of a target database corresponding to a target management system in response to identifying a data update request in the target management system, where the target management system is a system associated with a medical insurance early warning and audit system;

[0011] A feature extraction module, configured to extract keywords from the data source of the target database to obtain a keyword group;

[0012] A matching module, configured to match the keyword group with an audit rule library to obtain a target audit rule that matches the data source of the target database, where the audit rule library is a rule library customized by a hospital;

[0013] An early warning module, configured to give an early warning to a data update request in the target management system based on the target audit rule.

[0014] In a third aspect of the embodiments of the present disclosure, there is provided an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above-mentioned medical insurance early warning and audit method are implemented.

[0015] In a fourth aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium storing a computer program, where when the computer program is executed by a processor, the steps of the above-mentioned medical insurance early warning and audit method are implemented.

[0016] The beneficial effects of the medical insurance early warning and audit method and system provided by the embodiments of the present disclosure are as follows:

[0017] On the one hand, the present disclosure significantly improves the efficiency and accuracy of medical insurance data audit. By automatically extracting keyword groups and matching them with the audit rule library, the system can quickly determine applicable audit rules and give real-time warnings to data update requests. This not only reduces the workload of manual audit but also avoids the adverse effects caused by auditing after problems occur, thus ensuring the accuracy and compliance of medical insurance business data.

[0018] On the other hand, the present disclosure effectively reduces the loss of medical insurance funds and the risk of violations. Through the early warning mechanism, the system can timely detect potential data risks and violations, reminding relevant personnel to conduct further audits and processing. This helps hospitals promptly correct non-compliant medical insurance business operations, prevent the abuse and loss of medical insurance funds, and thus ensure the safety and sustainability of medical insurance funds. At the same time, the present disclosure also provides a more scientific and standardized medical insurance management means for hospitals, which helps to improve the medical insurance management level and service quality of hospitals. Description of the Drawings

[0019] To more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following will briefly introduce the accompanying drawings required for use in the embodiments or the description of the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present disclosure. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0020] Figure 1 It is a schematic flowchart of a medical insurance early warning and audit method provided by an embodiment of the present disclosure;

[0021] Figure 2 It is a schematic flowchart of the operation process of a medical insurance early warning and audit system provided by an embodiment of the present disclosure;

[0022] Figure 3 It is a management flowchart of a hospital for a medical insurance early warning and audit system provided by an embodiment of the present disclosure;

[0023] Figure 4 It is a structural block diagram of a medical insurance early warning and audit system provided by an embodiment of the present disclosure;

[0024] Figure 5 It is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Detailed implementation manners

[0025] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are presented to thoroughly understand the embodiments of the present disclosure. However, those skilled in the art should clearly understand that the present disclosure can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present disclosure.

[0026] To make the purpose, technical solutions, and advantages of the present disclosure clearer, the following will be described through specific embodiments in conjunction with the accompanying drawings.

[0027] Please refer to Figure 1 , Figure 1 It is a schematic flowchart of a medical insurance early warning and audit method provided by an embodiment of the present disclosure. The method includes:

[0028] S101: In response to identifying that there is a data update request in the target management system, obtain the data source of the target database corresponding to the target management system, where the target management system is a system associated with the medical insurance early warning and audit system.

[0029] In this embodiment, the target management system is a specific management system, which can be a medical information management system within a hospital (such as the HIS inpatient doctor's station, HIS inpatient nurse's station, emergency system, etc.), a medical insurance cost settlement system, etc.

[0030] The target management system can establish a connection with the medical insurance early warning and audit system through a data interface. The main function of the target management system is to manage various business data, and changes in these data may affect the medical insurance early warning and audit. For example, when a doctor prescribes a new examination item or adjusts the treatment plan for a patient, the target management system needs to record these changes and, when a data update request is made, pass the relevant information to the subsequent processing process to ensure that the medical insurance audit can make a judgment based on the latest information.

[0031] A data update request is a signal or instruction indicating that the data in the target management system needs to be updated. It can be triggered by the internal business logic of the system or by the operation of external users (such as medical staff, medical insurance administrators, etc.). For example, when medical staff modify the diagnosis result of a patient in the HIS inpatient doctor station system, the system will generate a data update request. Common triggering scenarios include, but are not limited to, medical staff prescribing drugs in the inpatient doctor station, emergency medical staff registering a patient's transfer of departments in the emergency system, and the timeout of medical insurance expense audit affecting the progress of subsequent processes.

[0032] The target database is the place where the target management system stores data. It contains various business data required for the system to run, and these data are organized and stored in a certain logical structure (such as the table structure of a relational database). For example, in a hospital's medical management system, the target database can contain multiple data tables such as a patient basic information table, a medical service record table, and a medical insurance reimbursement expense table.

[0033] The medical insurance early warning and audit system is used to conduct early warning and audit of medical insurance-related operations. It analyzes medical insurance data (such as medical expenses, whether treatment items comply with medical insurance policies, etc.) to detect potential medical insurance violation risks or unreasonable medical insurance reimbursement situations in advance and conducts audit processing. For example, it can check whether the treatment items provided by the hospital for patients are within the medical insurance reimbursement scope and whether the medical insurance reimbursement amount meets the specified ratio.

[0034] In this embodiment, the medical insurance early warning and audit system continuously monitors the target management system. Since the target management system and the medical insurance early warning and audit system are associated through a data interface, when a data update request in the target management system is identified, the medical insurance early warning and audit system can obtain the data source of the target database corresponding to the target management system. This data source contains various medical insurance-related business data stored in the target management system, the processing time nodes of each business, etc. By obtaining the data source, the medical insurance early warning and audit system can conduct early warning and audit of medical insurance business conditions according to preset rules and standards, so as to judge whether there are medical insurance violation risks or unreasonable reimbursement situations, thereby ensuring the compliance of medical insurance business and the safety of medical insurance funds.

[0035] S102: Extract keywords from the data source of the target database to obtain a keyword group.

[0036] In this embodiment, keyword extraction refers to automatically extracting words or phrases from text data that can represent the main content of the text. Common keyword extraction methods include statistical methods (such as the term frequency-inverse document frequency TF-IDF algorithm), machine learning-based methods (such as using neural network models for keyword prediction), clustering algorithm-based methods (such as using the K-Means clustering algorithm), and rule-based methods (such as extracting keywords according to specific part-of-speech tagging and grammar rules).

[0037] Keyword extraction helps improve the data processing efficiency and information retrieval ability, and helps the system quickly locate and obtain key information in a large amount of data.

[0038] A keyword group is a set of keywords extracted from the data source of the target database through keyword extraction technology. These keywords can reflect the main content and characteristics of the data source to a certain extent. For example, the keyword group extracted from the data source related to medical insurance reimbursement can include "medical insurance restriction conditions", "medical insurance reimbursement amount", "reimbursement process", "medical insurance catalog", etc.

[0039] The keyword group can be used as a concise representation form of the data source, facilitating further analysis and processing of the data. In practical applications, the keyword group can be used to build an index for quickly retrieving relevant data; it can also be used in data mining and machine learning tasks to help the model better understand the characteristics and patterns of the data.

[0040] S103: Match the keyword group with the audit rule library to obtain the target audit rule that matches the data source of the target database. The audit rule library is a rule library customized by the hospital.

[0041] In this embodiment, the audit rule library is a set of audit rules customized by the hospital according to factors such as its own business needs, medical insurance policy requirements, and management specifications. These rules are stored in a specific form and are used to judge whether the medical insurance-related data is compliant. Each rule in the rule library clearly stipulates the applicable conditions and corresponding processing methods. For example, when the audit time for a certain medical scenario is greater than 120s, directly prompt an error and inform the clinic that there is medical insurance violation content, and it is required to click back to modify and contact the hospital's medical insurance office staff for manual audit.

[0042] The content of the audit rule library is rich and diverse, covering rules in multiple aspects such as the medical insurance reimbursement process, reimbursement scope, drug usage specifications, and the reasonableness of medical service items. These rules can be set based on numerical ranges, such as the upper limit of reimbursement amount and the limit of drug dosage; they can also be set based on logical relationships, such as specific combinations that must be met for treatment items corresponding to a certain disease, etc.

[0043] Exemplarily, the audit rules in the audit rule library are arranged as follows:

[0044] Serial number - Rule instance - Audit content - Remarks

[0045] 1 - Items limited for children - Children and age - Violation

[0046] For example: Intravenous infusion (for children aged 6 and below), the limited age is 7 years;

[0047] Intravenous injection (for children aged 6 and below), the limited age is 7 years;

[0048] Doppler blood pressure monitoring for children is limited to 14 years old.

[0049] 2 - Items limited for a specific gender - Gender - Violation

[0050] For example: Tamsulosin hydrochloride sustained release capsules are limited for men

[0051] Fuke Qianjin capsules are limited for women

[0052] Clotrimazole vaginal suppositories are limited for women

[0053] 3 - Items limited for outpatient / inpatient use - Medical treatment method - Violation

[0054] For example: The general outpatient consultation fee is limited for outpatient use

[0055] The well-known expert consultation fee is limited for outpatient use

[0056] The inpatient consultation fee is limited for inpatient use

[0057] 4 - Upper limit of the daily charging quantity of an item - Daily charging quantity - Violation

[0058] For example: Coronary angiography is limited to 1 time

[0059] Anterior chamber puncture is limited to 1 time

[0060] Intrauterine packing is limited to 1 time

[0061] 5 - Upper limit of the daily charging amount of an item - Daily charging amount - Violation

[0062] For example: Coronary angiography is limited to 1 time

[0063] Anterior chamber puncture is limited to 1 time

[0064] Intrauterine packing is limited to 1 time

[0065] 6 - Consistency relationship between medical materials and surgical items - Whether there is an associated fee - Violation

[0066] For example: Functional dressings rely on general medical services, general examination and treatment, community health services and preventive care programs, other medical service items or burn dressing change;

[0067] Oral and maxillofacial general surgical materials rely on orthognathic surgery of the oral cavity, oral plastic surgery, oral and maxillofacial surgery treatment, oral trauma surgery, oral and maxillofacial general surgery, oral tumor surgery or oral implant surgery;

[0068] Vascular sheaths rely on transvascular interventional diagnosis and treatment, off - pump coronary artery bypass grafting, coronary artery bypass + valve replacement surgery, atrial septal defect repair or transcatheter aortic valve implantation.

[0069] 7 - Association relationship between main and subsidiary items - Whether there is an associated fee - Violation

[0070] For example: Grade I nursing - children (additional charge) relies on Grade I nursing

[0071] Clinical blood storage fee relies on ABO blood group identification (card method)

[0072] Monitoring during anesthesia - general anesthesia relies on general anesthesia

[0073] 8 - Association relationship between length of hospital stay and nursing level - Charge quantity and number of days - Violation

[0074] 9 - Association relationship between length of hospital stay and bed fee - Charge quantity and number of days - Violation

[0075] 10 - Association relationship between length of hospital stay and inpatient examination fee - Charge quantity and number of days - Violation

[0076] 11 - Mutually exclusive fees - Items with mutually exclusive relationship per day - Violation

[0077] For example: Intensive care nursing and special - grade nursing are charged repeatedly

[0078] Local infiltration anesthesia and local infiltration anesthesia (surface anesthesia) are charged repeatedly

[0079] Superficial mass resection and intraspinal anesthesia are charged repeatedly

[0080] The target audit rules are the specific audit rules that are determined by matching keyword groups with the audit rule library and are in line with the data sources of the target database. These rules are selected according to the characteristics of specific data sources and business scenarios and will be used to audit the data in the data source. For example, if the data source is about the reimbursement of medical insurance inpatient expenses, the target audit rule obtained by matching may be that "the reimbursement standard for nursing expenses in inpatient expenses shall not exceed [X] yuan per day".

[0081] In this embodiment, first, keywords are extracted from the data sources of the target database to obtain keyword groups that can highly summarize the core content of the data sources. Second, the extracted keyword groups are matched with the rules in the audit rule library. The matching process uses specific algorithms and logics. This algorithm can be a simple string match, that is, checking whether the keyword appears in the text description of the rule; it can also be a more complex semantic matching algorithm, considering the meaning of the keyword, context, and semantic relevance with the rule, etc. Finally, after the matching process, the system will screen out the rules with high relevance to the keyword groups from the audit rule library, and these rules are the target audit rules that match the data sources of the target database.

[0082] Determining the target audit rules through keyword groups can quickly and accurately find applicable audit rules for the data sources of the target database, improve the efficiency and accuracy of medical insurance data audit, and confirm the compliance of the hospital's medical insurance business. In case of non-compliance, early warnings can be issued in a timely manner.

[0083] S104: Issue a warning for the data update request in the target management system based on the target audit rules.

[0084] In this embodiment, warning refers to the process of checking and evaluating the data update request in the target management system according to the target audit rules and sending a prompt message to relevant personnel or systems when it is found that the data update may not conform to the rules. Warnings can be presented in various ways, such as a system pop-up prompt window, sending an email notification, generating a warning report, etc. For example, when the medical insurance reimbursement amount involved in the data update request exceeds the limit set by the target audit rules, the system will pop up a prompt window to inform the operator that there may be risks in this update.

[0085] The purpose of warning is to timely discover potential data risks and violations, remind relevant personnel to conduct further verification and processing, so as to ensure the accuracy and compliance of medical insurance business data and avoid possible losses of medical insurance funds and violation risks.

[0086] Exemplarily, refer to Figure 2 , Figure 2Schematic diagram of the operation process of the medical insurance early warning and audit system provided by an embodiment of the present disclosure. After a patient is admitted to the hospital, a doctor at the inpatient doctor's station issues medical orders. After the intelligent audit system monitors a data update request from the inpatient doctor's station, it obtains the corresponding personal information, medical diagnosis information, and medical orders of the patient for pre-audit. If the audited medical orders or medical expenses are unreasonable, a warning message will be sent to the doctor at the inpatient doctor's station, pointing out the unreasonable points and asking to reissue the medical orders. If both the medical orders and medical expenses are reasonable, the system will transfer to in-process monitoring. If the in-process monitoring is reasonable, it will transfer to post-event supervision. If the in-process supervision is unreasonable, a warning message will be sent to the department to remind the department to handle it and then transfer to post-event supervision. If the post-event supervision is reasonable, the patient can be arranged to transfer to another department or be discharged. If the post-event supervision by the system is unreasonable, it will intercept the violation and remind the relevant department. The relevant department will handle the violation matters, can issue a transfer application, and add the patient to the severe case white list.

[0087] As can be seen from the above, on the one hand, the present disclosure significantly improves the efficiency and accuracy of medical insurance data audit. By automatically extracting keyword groups and matching them with the audit rule library, the system can quickly determine the applicable audit rules and give real-time warnings for data update requests. This not only reduces the workload of manual audit but also avoids the adverse effects caused by auditing after problems occur, thus ensuring the accuracy and compliance of medical insurance business data.

[0088] On the other hand, the present disclosure effectively reduces the loss of medical insurance funds and the risk of violations. Through the early warning mechanism, the system can timely detect potential data risks and violations, reminding relevant personnel to conduct further audits and handling. This helps the hospital promptly correct non-compliant medical insurance business operations, prevent the abuse and loss of medical insurance funds, and thus ensure the safety and sustainability of medical insurance funds. At the same time, the present disclosure also provides a more scientific and standardized medical insurance management means for the hospital, which helps to improve the hospital's medical insurance management level and service quality.

[0089] In an embodiment of the present disclosure, the data sources of the target database include department information and diagnosis information.

[0090] Keyword extraction is performed on the data sources of the target database to obtain keyword groups, including:

[0091] The first keyword group is obtained by matching and extracting the department information based on the pre-constructed department classification keyword library, and the second keyword group is obtained by performing keyword extraction on the diagnosis information based on the clustering algorithm model.

[0092] The keyword group is obtained based on the first keyword group and the second keyword group.

[0093] In this embodiment, the pre-constructed department classification keyword library includes the standard names, abbreviations, aliases, and professional terms of various departments such as clinical departments, medical technology departments, and auxiliary departments. For example, for the "Department of Cardiology", its keywords include "Cardiology Department", "Department of Cardiovascular Diseases", "Department of Cardiac Medicine", and related disease diagnosis and treatment direction keywords such as "coronary heart disease", "arrhythmia", etc.; department information includes department name, special identifier of the department, etc.

[0094] Based on the pre-constructed department classification keyword library, the department information is matched and extracted to obtain the first keyword group, including:

[0095] Based on the pre-constructed department classification keyword library, keywords are extracted for each department in the department information, and a department-keyword association matrix is established. The matrix elements represent the association degree between different department keywords, and the association degree is obtained by analyzing the information between different departments in the historical medical insurance data.

[0096] The first keyword group is obtained according to the keywords corresponding to each department and the department-keyword association matrix.

[0097] Among them, the information between different departments in the historical medical insurance data includes the frequency of business cooperation, disease referral relationship, and the flow direction of medical insurance expenses, etc. During the medical insurance early warning and audit process, the department-keyword association matrix is used to comprehensively analyze complex diagnosis and treatment data involving multiple departments. If it is found that the association relationship between departments is significantly deviated from the historical pattern, an early warning can also be triggered to effectively monitor the rationality and standardization of medical insurance use between departments.

[0098] It can be concluded from the above that in this embodiment, by constructing a department classification keyword library and a department-keyword association matrix, the accurate extraction and association analysis of department information are realized, the accuracy and efficiency of keyword extraction are improved, which is helpful for the comprehensive analysis of complex diagnosis and treatment data in the medical insurance early warning and audit process, and effectively monitors the rationality and standardization of medical insurance use between departments.

[0099] In an embodiment of the present disclosure, keyword extraction is performed on the diagnosis information based on a clustering algorithm model to obtain a second keyword group, including:

[0100] Determine the data volume of the diagnosis information.

[0101] When the data volume of the diagnosis information is less than the first preset threshold, the elbow method is used to determine the K value of the clustering algorithm model, and the initial points of the clustering algorithm model are randomly generated;

[0102] Keyword extraction is performed on the diagnosis information based on the clustering algorithm model to obtain the second keyword group.

[0103] When the data volume of the diagnostic information is greater than or equal to a first preset threshold, a density-based spatial clustering algorithm is used to determine the number of clusters of the clustering algorithm model; the local density of each data point in the diagnostic information is calculated, and an initial point of the clustering algorithm model is determined based on the local density;

[0104] Keywords are extracted from the diagnostic information based on the clustering algorithm model to obtain a second keyword group.

[0105] In this embodiment, the clustering algorithm model is an unsupervised learning algorithm model that can divide the data points in the data set into different groups or clusters according to similarity, so that the data points in the same cluster have high similarity, while the data points between different clusters have large differences. The clustering algorithm model can mine the inherent structure and rules in the data by clustering the diagnostic information, and help extract the keywords that reflect the core content of the diagnostic information, so as to better understand and process the information.

[0106] Diagnostic information includes detailed information related to the diagnosis of the patient's condition, such as disease name, symptom description, diagnostic basis, test results and other text data. The second keyword group is a group of representative words or phrases obtained by extracting keywords from the diagnostic information using a clustering algorithm model. The second keyword group can summarize the main content of the diagnostic information and reflect the key features and key points of the diagnosis of the condition.

[0107] Data volume refers to the number of data points or the size of the text contained in the diagnostic information. In the present embodiment, data volume is an important basis for determining the clustering algorithm model parameters and initial points in which manner. Different data volumes require different processing methods to ensure the accuracy and efficiency of the clustering algorithm. Therefore, by determining the data volume of the diagnostic information, a more appropriate clustering strategy can be selected to obtain a better keyword extraction effect.

[0108] The first preset threshold is a pre-set data volume standard value, which is used to distinguish the size of the diagnostic information data volume, so as to decide to use different methods to process the clustering algorithm model. This threshold is set according to actual business needs and experience, and different types of hospitals may have different thresholds.

[0109] The elbow method is a method for determining the optimal number of clusters (K value) in a clustering algorithm. This method calculates the error (such as the sum of squared errors) of the clustering model under different K values and plots the K value and the error into a curve. The curve usually presents an elbow-like shape, and the K value corresponding to the inflection point (elbow point) of the curve is considered to be the optimal number of clusters. When the amount of diagnostic information data is less than the first preset threshold, the initial point of the clustering algorithm can be randomly determined as the initial center of each cluster.

[0110] The density-based spatial clustering algorithm is a clustering algorithm based on the density of data points. It determines the clustering structure by examining the density of data points in the vicinity. In this embodiment, when dealing with large-scale data, the density-based spatial clustering algorithm can more accurately reflect the data distribution, overcome the limitations of some traditional clustering algorithms (such as the K-Means algorithm) on the data shape and distribution, thus obtaining a more reasonable clustering result and providing a more reliable basis for keyword extraction.

[0111] In this embodiment, in the density-based spatial clustering algorithm, local density is used to determine the relationship between the core points of the clustering and the data points. Data points with higher local density are often regarded as the core of the clustering. By determining the initial points of the clustering algorithm model based on local density, the distribution characteristics of the data can be better reflected, improving the clustering effect and the accuracy of keyword extraction.

[0112] It can be concluded from the above that the present disclosure can flexibly select the clustering method according to the data volume of the diagnostic information. For small data volumes, the elbow method is used to determine the value of K, and for large data volumes, the density-based spatial clustering algorithm is used, thereby improving the accuracy and efficiency of keyword extraction and optimizing the processing flow of diagnostic information.

[0113] In an embodiment of the present disclosure, the audit rule library includes a drug audit rule library and a process audit rule library;

[0114] Matching the keyword group with the audit rule library to obtain a target audit rule that matches the data source of the target database, including:

[0115] Matching the keyword group with the drug audit rule library to obtain a first target audit rule that matches the data source of the target database;

[0116] Matching the keyword group with the process audit rule library to obtain a second target audit rule that matches the data source of the target database;

[0117] The first target audit rule and the second target audit rule constitute the target audit rule.

[0118] In this embodiment, the rules in the drug audit rule library are customized according to various factors such as medical insurance policies, drug usage specifications, and clinical treatment guidelines, and are used for prior warning of medical insurance services involving drugs. For example, the rules may include the target of the drug, the reimbursement scope of the drug (such as which drugs belong to Class A and Class B of medical insurance and which are self-paid drugs), the dosage limit of the drug (reasonable dosage for different diseases and different age groups of patients), the combination rules of drugs (which drugs can be used in combination and which are prohibited from being used in combination), etc.

[0119] Exemplarily, taking coenzyme Q10 as an example, this drug is restricted for emergency rescue use; after the patient is admitted to the hospital, the system monitors that the doctor at the inpatient doctor's station has issued a new medical order. The medical order contains a judgment on the patient's condition. When the patient meets the conditions (the system sets the emergency rescue diagnosis definitions: unconsciousness, coma, etc.; the system sets the emergency rescue cost definitions: major rescue, medium rescue, minor rescue costs, etc.), the drug can be prescribed if the conditions are met; if it does not conform to the system-set drug review rules, the system directly intercepts it in advance.

[0120] Exemplarily, taking ulinastatin as an example, this drug is restricted for use in acute pancreatitis and chronic relapsing pancreatitis; after the patient is admitted to the hospital, the system monitors that the doctor at the inpatient doctor's station has issued a new medical order. The medical order contains a judgment on the patient's condition and the prescribed drug. When the patient meets the conditions (the system sets the diagnosis definitions for acute pancreatitis and chronic relapsing pancreatitis: diagnosis keywords pancreatitis, pancreatic enlargement, pancreatic effusion; diagnosis codes K85, K86), the drug can be prescribed if the conditions are met; if it does not conform to the system-set drug review rules, the system directly intercepts it in advance.

[0121] Exemplarily, taking the injection of structured lipid emulsion (20%) / amino acid (16) / glucose (13%) as an example, this drug requires nutritional risk screening, and it is only payable for inpatients who are clearly at nutritional risk and cannot supplement sufficient nutrition through diet or "enteral nutrition agents". After the patient is admitted to the hospital, the system monitors that the doctor at the inpatient doctor's station has issued a new medical order. The medical order contains a judgment on the patient's condition and the prescribed drug. When the patient meets the conditions (the system sets that the patient first needs to issue a medical order for nutritional risk screening and nutritional assessment, and the drug can be prescribed when this item of medical order is reviewed), the drug can be prescribed if the conditions are met; if it does not conform to the system-set drug review rules, the system directly intercepts it in advance.

[0122] The process review rule library is a collection that contains the review rules for the medical insurance business process. These rules cover the time nodes of all links in the entire medical insurance business process from patient registration, medical treatment, examination, treatment to settlement. When patients perform operations in scenarios such as inpatient doctor workstations and inpatient nurse workstations for inpatient medical orders, inpatient charges, leaving the area / transferring departments, etc., due to a large number of medical insurance costs and charging items, there are situations of overlong review, and the continuous review by the system affects clinical medical work. By matching keyword groups with the process review rule library, a second target review rule that matches the data source of the target database is obtained; and then, based on the second target review rule, a warning is issued for the data update request in the target management system.

[0123] Exemplarily, when the system receives a data update request (status update), for the duration of a certain medical scenario review, the system determines whether to give an early warning by comparing it with a preset value. When the review duration is greater than 120s, the system directly prompts an error and informs the clinic that there is content of medical insurance violation, and the user needs to click "Return to Modify" and contact the staff of the hospital's medical insurance office for manual review. When the review duration is less than or equal to 120s, the system is set not to time out due to the review duration, and the system gives up the review and automatically skips it.

[0124] From the above, it can be concluded that in this embodiment, by constructing two major review rule bases for drugs and processes, accurate pre - warning of medical insurance services is achieved, effectively avoiding medical insurance violations, improving the review efficiency at the same time, ensuring the smooth progress of the medical process, and reducing the medical insurance risks and costs of hospitals and patients.

[0125] In one embodiment of the present disclosure, matching the keyword group with the drug review rule base to obtain the first target review rule that matches the data source of the target database includes:

[0126] Constructing a multi - dimensional rule index system based on the pharmacological effects, therapeutic uses, medical insurance reimbursement restrictions, and drug interaction information of drugs;

[0127] Matching the keyword group based on the multi - dimensional rule index system to obtain the first target review rule.

[0128] Among them, matching the keyword group based on the multi - dimensional rule index system includes:

[0129] Matching the keyword group based on the first formula, and the first formula is:

[0130]

[0131] Among them, represents the matching result; n is the number of dimensions in the rule index system; is the weight coefficient of the i - th dimension, indicating the importance of this dimension in the overall matching process; is the matching function of the i - th dimension, returning a matching value; P represents the keyword group, represents the data of the i - th dimension.

[0132] In this embodiment, a multi - dimensional rule index system is constructed based on the pharmacological effects, therapeutic uses, medical insurance reimbursement restrictions, and drug interaction information of drugs. When performing keyword group matching, using semantic analysis and ontology matching technologies, not only directly matches direct information such as drug names and dosage forms, but also deeply analyzes the association between the mechanism of action and applicable diseases of drugs and the keyword group.

[0133] For example, when the keyword group contains the name of a certain disease and symptom information, the rules related to the therapeutic drugs for this disease can be quickly located through the rule index system, and the drug interaction rules are combined to determine whether there are taboos among the multiple drugs used simultaneously. If there are taboos, corresponding strong warning signals will be triggered, and detailed drug substitution suggestions and medical bases will be provided at the same time.

[0134] In this embodiment, the first target audit rule is also obtained by matching the keyword group based on the first formula. In the first formula, R1 represents the matching result of the first target audit rule, and the magnitude or specific value range of its value can represent the degree of matching or whether the corresponding rule is triggered. For example, when R1 is greater than the first threshold, it means that the matching is successful and the relevant audit rule is triggered. n is the number of dimensions in the rule index system, such as including dimensions of drug name, pharmacological effect, treatment disease, etc. The weight coefficient can be determined according to factors such as historical data, key points of medical insurance policies, and medical expert experience. For example, for the dimensions corresponding to the drug categories under key medical insurance monitoring or key treatment diseases, their weights will be relatively high.

[0135] is the matching function of the i-th dimension, which accepts the keyword group P and the data of the i-th dimension as inputs and returns a matching value. This matching value can be a numerical value between 0 and 1, representing the degree of matching between the keyword group and the data of this dimension, where 0 means no match at all, and 1 means a complete match. For example, in the dimension of drug name, if the keyword group contains a vocabulary that is exactly the same as the drug name in the rule, then it returns 1. If there is a partial match or a certain association, a value between 0 and 1 is returned. In the dimension of pharmacological effect, the degree of matching between the vocabulary related to the pharmacological effect in the keyword group and the pharmacological effect defined in the rule can be judged through semantic analysis and knowledge graph technology, and the corresponding matching value is returned.

[0136] It can be concluded from the above that in this embodiment, by constructing a multi-dimensional rule index system and using the first formula for accurate matching, the efficient and accurate docking of the keyword group with the drug audit rule library is realized, effectively improving the intelligent level of medical insurance business audit and ensuring the safety and compliance of drug use.

[0137] In an embodiment of the present disclosure, matching the keyword group with the process audit rule library to obtain a second target audit rule that matches the data source of the target database includes:

[0138] Determine the process audit rule library by adopting a rule representation method based on the business process model;

[0139] Input the keyword group into the business process model for rule matching to obtain the second target audit rule.

[0140] In this embodiment, the system models the medical insurance reimbursement process, medical service provision process, etc., and converts the key links, participating roles, time nodes, data flow and other elements in the process into rule conditions. When matching the keyword group, the operation sequence of the department involved, the cost settlement stage, etc. are accurately matched with the process audit rule library to obtain the second target audit rule. If abnormal conditions such as missing process links, reversed order or time overdue are found, different levels of warnings are generated according to the severity of the abnormality, and traced back to the specific responsible links and personnel, providing strong support for process optimization and responsibility identification.

[0141] In this embodiment, in the rule representation of the business process model, a finite state machine (FSM) is used to model the medical insurance business process, and the medical insurance business process is divided into multiple state nodes, including but not limited to expense declaration, under review, approved, unapproved, expense settlement and other states. The transition between different states is triggered by preset conditions, which constitute part of the process review rule library. When the keyword group is input into the business process model for rule matching, the current state of the business process is determined based on the process-related information in the keyword group (such as the current process stage, operation time, operator information, etc.), and whether the state transition conditions are met based on the current state and other information in the keyword group. If not, the corresponding second target review rule is triggered. The second target review rule includes a detailed description of the illegal process state, the possible risk assessment, and the handling suggestions for the illegal state.

[0142] From the above, it can be concluded that this embodiment realizes intelligent monitoring of medical insurance business processes by constructing a business process model and a finite state machine, accurately matching keyword groups with process review rules, effectively warning of process anomalies, tracing responsibilities, providing strong support for process optimization and responsibility identification, and improving the efficiency and accuracy of medical insurance management.

[0143] In one embodiment of the present disclosure, an early warning is issued for a data update request in a target management system based on a target audit rule, including:

[0144] Rating the warning information according to the severity and risk level of the target audit rules;

[0145] Issue warnings based on the rated warning information.

[0146] Generate a unique identification code for each warning information, and store the warning information using the unique identification code as an index;

[0147] Update the audit rule base based on the warning information within the preset time period.

[0148] In this embodiment, the rating of the warning information is determined based on the second formula, and the second formula is:

[0149]

[0150] where S represents the severity level , the larger the value, the more serious the consequence after violating the target audit rule. R represents the risk level, , , the larger the value, the greater the possibility of violations. For example, r1 represents a low-risk situation where violations occur only under special circumstances; r l represents a high-risk situation where violations are likely to occur during normal operations. represents the rating of the warning information, , the larger the value, the more important the warning information and the higher the priority for processing. The rating of the warning information is determined according to the specific combination of the severity level and the risk level, which more intuitively reflects the importance differences in different situations.

[0151] In this embodiment, to facilitate the management and tracking of warning information, the system generates a unique identification code for each warning information. This identification code is like the "ID card" of the warning information, which can uniquely identify this warning information throughout the system and avoid confusion between different warning information. Storing the warning information using the unique identification code as an index means that in the database or other storage systems, this identification code can be used as the key basis for searching and positioning. Such a storage method can improve the storage and retrieval efficiency of data. When the detailed content of a certain warning information needs to be queried, the corresponding warning information record can be quickly found only through this unique identification code, which is convenient for subsequent system optimization and analysis.

[0152] It can be concluded from the above that in this embodiment, by classifying and grading the warning information according to the severity level and the risk level, relevant personnel can quickly clarify the importance of the warning; generating a unique identification code to store the warning information facilitates information management and traceability. Updating the audit rule library based on the preset duration of the warning information can continuously optimize the rules, improve the accuracy and efficiency of medical insurance audit, and ensure the compliance operation of medical insurance business.

[0153] In this embodiment, referring to Figure 3 , the management process of the hospital for the medical insurance warning audit system is as follows:

[0154] The review system feedbacks problems. If they are information system problems, such as network lag, slow review process, etc., the problems need to be sent to HIS engineers / intelligent review system engineers for troubleshooting. After the problems are solved, feedback to the clinic and the process is completed. If they are medical insurance policy problems, then it is necessary to check the policy documents and log in to the intelligent review system with the account password to make a judgment. For clinical charging problems, contact the department for handling and the process is completed. For the problem that the rules of the intelligent review system are not perfect, the medical insurance office maintains the rules, feedback to the clinic and the process is completed.

[0155] The medical insurance early warning review method corresponding to the above embodiment, Figure 4 is a structural block diagram of a medical insurance early warning review system provided by an embodiment of the present disclosure. For the convenience of description, only the parts related to the embodiments of the present disclosure are shown. Refer to Figure 4 The medical insurance early warning review system 20 includes: a data acquisition module 21, a feature extraction module 22, a matching module 23, and an early warning module 24.

[0156] Among them, the data acquisition module 21 is used to obtain the data source of the target database corresponding to the target management system in response to identifying that there is a data update request in the target management system, and the target management system is a system associated with the medical insurance early warning review system;

[0157] The feature extraction module 22 is used to extract keywords from the data source of the target database to obtain a keyword group;

[0158] The matching module 23 is used to match the keyword group with the review rule library to obtain a target review rule that matches the data source of the target database, and the review rule library is a rule library customized by the hospital;

[0159] The early warning module 24 is used to give an early warning to the data update request in the target management system based on the target review rule.

[0160] In an embodiment of the present disclosure, the data source of the target database includes department information and diagnosis information; the feature extraction module 22 is specifically used for:

[0161] Based on a pre-constructed department classification keyword library, match and extract the department information to obtain a first keyword group, and extract keywords from the diagnosis information based on a clustering algorithm model to obtain a second keyword group;

[0162] Based on the first keyword group and the second keyword group, obtain the keyword group.

[0163] In an embodiment of the present disclosure, the feature extraction module 22 is specifically used for:

[0164] Determine the data volume of the diagnosis information;

[0165] When the data volume of the diagnostic information is less than the first preset threshold, the elbow method is used to determine the K value of the clustering algorithm model, and the initial points of the clustering algorithm model are randomly generated;

[0166] Based on the clustering algorithm model, keyword extraction is performed on the diagnostic information to obtain a second keyword group;

[0167] When the data volume of the diagnostic information is greater than or equal to the first preset threshold, the density-based spatial clustering algorithm is used to determine the clustering number of the clustering algorithm model; calculate the local density of each data point in the diagnostic information, and determine the initial points of the clustering algorithm model based on the local density;

[0168] Based on the clustering algorithm model, keyword extraction is performed on the diagnostic information to obtain a second keyword group.

[0169] In an embodiment of the present disclosure, the audit rule library includes a drug audit rule library and a process audit rule library. The matching module 23 is specifically configured to:

[0170] Match the keyword group with the drug audit rule library to obtain a first target audit rule that matches the data source of the target database;

[0171] Match the keyword group with the process audit rule library to obtain a second target audit rule that matches the data source of the target database;

[0172] The first target audit rule and the second target audit rule constitute the target audit rule.

[0173] In an embodiment of the present disclosure, the matching module 23 is specifically configured to:

[0174] Construct a multi-dimensional rule index system according to the pharmacological effects, therapeutic uses, medical insurance reimbursement restrictions, and drug interaction information of drugs;

[0175] Match the keyword group based on the multi-dimensional rule index system to obtain a first target audit rule.

[0176] In an embodiment of the present disclosure, the matching module 23 is specifically configured to:

[0177] Match the keyword group based on the first formula, and the first formula is:

[0178]

[0179] Among them, represents the matching result; n is the number of dimensions in the rule index system; is the weight coefficient of the i-th dimension, indicating the importance of this dimension in the overall matching process; is the matching function of the i-th dimension, returning a matching value; P represents the keyword group, Represents the data of the i-th dimension.

[0180] In an embodiment of the present disclosure, the matching module 23 is specifically configured to:

[0181] Determine a process audit rule library by using a rule representation method based on a business process model;

[0182] Input the keyword group into the business process model for rule matching to obtain a second target audit rule.

[0183] In an embodiment of the present disclosure, the warning module 24 is specifically configured to:

[0184] Rate the warning information according to the severity level and risk level of the target audit rule;

[0185] Give a warning based on the rated warning information.

[0186] In an embodiment of the present disclosure, the warning module 24 is specifically further configured to:

[0187] Generate a unique identification code for each warning information, and store the warning information with the unique identification code as an index;

[0188] Update the audit rule library based on the warning information within a preset duration.

[0189] See Figure 5 , Figure 5 is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. As Figure 5 shown, the electronic device 300 in this embodiment may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The above-mentioned processors 301, input devices 302, output devices 303, and memories 304 communicate with each other through a communication bus 305. The memory 304 is used to store a computer program, and the computer program includes program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. Among them, the processor 301 is configured to call the program instructions to execute the functions of each module in the above-mentioned device embodiments, such as Figure 4 the functions of the modules 21 to 24 shown.

[0190] It should be understood that in the embodiments of the present disclosure, the so-called processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0191] The input device 302 may include a touchpad, a fingerprint acquisition sensor (for acquiring the fingerprint information and the direction information of the fingerprint of the user), a microphone, etc., and the output device 303 may include a display (such as an LCD), a speaker, etc.

[0192] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A part of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0193] In specific implementation, the processor 301, the input device 302, and the output device 303 described in the embodiments of the present disclosure may execute the implementation manners described in the first embodiment and the second embodiment of the medical insurance early warning and audit method provided by the embodiments of the present disclosure, and may also execute the implementation manner of the electronic device described in the embodiments of the present disclosure, which will not be elaborated herein.

[0194] In another embodiment of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, all or part of the processes in the methods of the above embodiments are implemented. It can also be completed by instructing relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0195] The computer-readable storage medium can be the internal storage unit of the electronic device in any of the foregoing embodiments, such as the hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device. Further, the computer-readable storage medium can also include both the internal storage unit and the external storage device of the electronic device. The computer-readable storage medium is used to store the computer program and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store the data that has been output or will be output.

[0196] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present disclosure.

[0197] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described electronic devices and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0198] In several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection between each other can be an indirect coupling or communication connection through some interfaces or units, and can also be in the form of electrical, mechanical or other connections.

[0199] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present disclosure.

[0200] In addition, in each embodiment of the present disclosure, each functional unit can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0201] The above is only the specific implementation manner of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present disclosure can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered by the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims

1. A medical insurance early warning and review method, characterized in that, Including: In response to identifying that there is a data update request in the target management system, obtain the data source of the target database corresponding to the target management system, where the target management system is a system associated with the medical insurance early warning and review system; Extract keywords from the data source of the target database to obtain a keyword group; match the keyword group with the drug review rule library to obtain a first target review rule that matches the data source of the target database; match the keyword group with the process review rule library to obtain a second target review rule that matches the data source of the target database; the process review rule library is a set of medical insurance business process review rules, and these rules include the time nodes of each link in the entire medical insurance business process from patient registration, consultation, examination, treatment to settlement; the first target review rule and the second target review rule constitute the target review rule; Among them, the process of matching the keyword group with the drug review rule library to obtain a first target review rule that matches the data source of the target database includes: Construct a multi-dimensional rule index system based on the pharmacological effects, therapeutic uses, medical insurance reimbursement restrictions, and drug interaction information of drugs; match the keyword group based on the multi-dimensional rule index system to obtain the first target review rule; The process of matching the keyword group based on the multi-dimensional rule index system includes: Match the keyword group based on the first formula, and the first formula is: Among them, represents the matching result; n is the number of dimensions in the rule index system; is the weight coefficient of the i-th dimension, indicating the importance of this dimension in the overall matching process; is the matching function of the i-th dimension, returning a matching value; P represents the keyword group, represents the data of the i-th dimension; The drug review rule library and the process review rule library are rule libraries customized by the hospital; Based on the target review rule, give an early warning for the data update request in the target management system.

2. The medical insurance early warning and review method according to claim 1, characterized in that, The data source of the target database includes department information and diagnosis information; The process of extracting keywords from the data source of the target database to obtain a keyword group includes: Match and extract the department information based on the pre-constructed department classification keyword library to obtain a first keyword group, and extract keywords from the diagnosis information based on the clustering algorithm model to obtain a second keyword group; Obtain the keyword group based on the first keyword group and the second keyword group.

3. The medical insurance early warning and review method according to claim 2, wherein The process of extracting keywords from the diagnosis information based on the clustering algorithm model to obtain a second keyword group includes: Determine the data volume of the diagnosis information; When the data volume of the diagnosis information is less than the first preset threshold, use the elbow method to determine the K value of the clustering algorithm model, and randomly generate the initial points of the clustering algorithm model; Extract keywords from the diagnosis information based on the clustering algorithm model to obtain a second keyword group; When the data volume of the diagnosis information is greater than or equal to the first preset threshold, use the density-based spatial clustering algorithm to determine the clustering number of the clustering algorithm model; calculate the local density of each data point in the diagnosis information, and determine the initial points of the clustering algorithm model based on the local density; Extract keywords from the diagnosis information based on the clustering algorithm model to obtain a second keyword group.

4. The medical insurance early warning and review method according to claim 1, characterized in that The process of matching the keyword group with the process review rule library to obtain a second target review rule that matches the data source of the target database includes: Determine the process audit rule library by using a rule representation method based on a business process model; Input the keyword group into the business process model for rule matching to obtain the second target audit rule.

5. The medical insurance early warning and review method according to claim 1, wherein The warning of the data update request in the target management system based on the target audit rule includes: Rate the warning information according to the severity level and risk level of the target audit rule; Give a warning based on the rated warning information.

6. The medical insurance early warning and review method according to claim 5, wherein It also includes: Generate a unique identification code for each warning information, and store the warning information with the unique identification code as the index; Update the audit rule library based on the warning information within a preset time period, and the audit rule library includes a drug audit rule library and a process audit rule library.

7. A medical insurance early warning and review system, characterized in that, It includes: A data acquisition module, which is used to respond to the identification of a data update request in the target management system, and acquire the data source of the target database corresponding to the target management system. The target management system is a system associated with the medical insurance warning audit system; A feature extraction module, which is used to extract keywords from the data source of the target database to obtain a keyword group; A matching module, which is used to match the keyword group with the drug audit rule library to obtain a first target audit rule that matches the data source of the target database; match the keyword group with the process audit rule library to obtain a second target audit rule that matches the data source of the target database; the process audit rule library is a set containing medical insurance business process audit rules, and these rules include the time nodes of each link in the entire medical insurance business process from patient registration, consultation, examination, treatment to settlement; the first target audit rule and the second target audit rule constitute the target audit rule; Among them, a multi-dimensional rule index system is constructed according to the pharmacological effects, therapeutic uses, medical insurance reimbursement restrictions, and drug interaction information of drugs; the keyword group is matched based on the multi-dimensional rule index system to obtain the first target audit rule; The matching of the keyword group based on the multi-dimensional rule index system includes: Match the keyword group based on the first formula, and the first formula is: Among them, represents the matching result; n is the number of dimensions in the rule index system; is the weight coefficient of the i-th dimension, indicating the importance of this dimension in the overall matching process; is the matching function of the i-th dimension, returning a matching value; P represents the keyword group, represents the data of the i-th dimension; the drug review rule library and the process review rule library are rule libraries customized by the hospital; A warning module, which is used to give a warning about the data update request in the target management system based on the target audit rule.

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