A hospital procurement contract automated auditing method and system

Abstract

The application provides a hospital procurement contract automatic auditing method and system, the method comprises the following steps: obtaining a digital procurement contract; identifying the procurement content in the digital procurement contract based on the type of the goods in the hospital procurement, obtaining the type information of the goods in the procurement content, dividing the type of the digital procurement contract based on the type information of the goods, and automatically classifying; based on the type of the digital procurement contract, matching the preset rule engine of the contract type to automatically audit the digital procurement contract, detecting whether the procurement contract meets the compliance regulations, and obtaining the detection result; risk prediction is performed on the abnormally marked contract terms and data points, and a risk prediction result is obtained; the application automatically audits the procurement contract, reduces the time and effort of manual auditing of the procurement contract, improves the consistency of the audit based on the preset rule engine, predicts the risk of the procurement contract, ensures the compliance of the procurement contract, and improves the overall procurement management level.

Description

Technical Field

[0001] This application relates to the field of automated auditing technology for procurement contracts, specifically a method and system for automated auditing of hospital procurement contracts. Background Technology

[0002] Traditionally, the processing and auditing of hospital procurement contracts rely primarily on manual labor. Auditors need to read each contract clause individually, classify and assess them, a process that is not only time-consuming and labor-intensive but also prone to errors due to human factors.

[0003] As hospitals expand their procurement scale and increase the variety of procured items, traditional manual auditing methods are no longer sufficient to meet hospitals' requirements for audit efficiency and accuracy, and are ill-suited to the complexity and diversity of the environment.

[0004] While automated auditing technologies exist, different types of contracts may involve varying regulatory requirements and approval processes. For example, the procurement of medical equipment may require compliance with medical device regulations, while the procurement of pharmaceuticals must follow relevant regulations such as the Drug Administration Law. Furthermore, regulatory requirements and approval processes may differ across regions. These differences necessitate that automated auditing systems accurately identify and adapt to various regulatory requirements and approval processes to ensure the legality and compliance of audit work. However, existing auditing systems may have limitations in handling these differences. Summary of the Invention

[0005] To overcome the aforementioned problems in the existing technology, this application provides a method and system for automated auditing of hospital procurement contracts, which adopts the following technical solution:

[0006] Firstly, this application provides an automated auditing method for hospital procurement contracts, including:

[0007] Obtain digital procurement contracts;

[0008] Based on the types of items procured by the hospital, the procurement content in the digital procurement contract is identified, the type information of the items in the procurement content is obtained, the contract type is classified based on the type information of the items, and automatic classification is performed.

[0009] Based on the contract type of the digital procurement contract, a preset rule engine matching the contract type automatically audits the digital procurement contract to detect whether the procurement contract meets compliance requirements and obtains the detection results;

[0010] Perform risk prediction on contract terms and data points marked with anomalies, and obtain the risk prediction results.

[0011] Furthermore, the acquisition of the digital procurement contract includes: when the acquired procurement contract is a paper contract, scanning the paper contract to obtain a digital procurement contract. The specific steps are: based on a preset scanning device, image acquisition is performed on the paper contract to obtain a scanned version of the paper contract; text is extracted from the scanned version using text recognition technology; the scanned file is obtained and named to obtain the digital procurement contract.

[0012] Furthermore, based on the types of goods procured by the hospital, the procurement content in the digital procurement contract is identified, the type information of the goods mentioned in the procurement content is obtained, and the contract type is classified according to the type information of the goods, and automatically categorized. Specific content includes:

[0013] Determine the classification criteria for item types, obtain the attributes of each item type, design a database structure based on the item types and attributes, and input the item types and attributes into the database;

[0014] Key feature items related to the type of goods in digital procurement contracts were extracted using natural language processing technology.

[0015] The extracted key features are compared with the item types in the database. When the similarity value exceeds a preset threshold, the digital procurement contract is matched with the corresponding item type in the database, and the item type of the digital procurement contract is obtained.

[0016] Based on the types of goods in the digital procurement contract and the predefined contract type rules, the contract type of the digital procurement contract is determined.

[0017] Based on the determined contract type, the digital procurement contract is moved to the corresponding category directory, and the database record is updated to mark the category information of the digital procurement contract.

[0018] Furthermore, the types of items procured by the hospital include medical equipment contracts, pharmaceutical contracts, consumable contracts, laboratory equipment contracts, repair and maintenance contracts, and office supply contracts.

[0019] Furthermore, based on the contract type of the digital procurement contract, a preset rule engine matching the contract type automatically audits the digital procurement contract to detect whether the procurement contract meets compliance requirements and obtains the detection results. Specific content includes:

[0020] Develop audit rules for each contract type;

[0021] Configure the parameter information in the audit rules for each contract type; define logical operations for the audit rules for each contract type;

[0022] Based on the parameter information corresponding to the digital procurement contract, relevant parameters are extracted from the digital procurement contract;

[0023] Map the relevant parameters to the audit rules corresponding to the digital procurement contract to obtain the mapping parameters;

[0024] The mapping parameters are input into the preset rule engine, and logical operations are performed. The rule engine detects each contract clause and data point based on the corresponding audit rules to determine whether each contract clause and data point in the mapping parameters meets the predefined logical operations.

[0025] Contract terms and data points that do not meet the predefined logical operations are marked as exceptions.

[0026] Furthermore, the mapping parameters are input into a preset rule engine to perform logical operations. The rule engine detects each contract clause and data point based on the corresponding audit rules to determine whether each contract clause and data point in the mapping parameters meets the predefined logical operations. The preset rule engine also includes dynamic updates based on changes in laws, regulations, and hospital policies.

[0027] Furthermore, the mapping parameters are input into a preset rule engine to perform logical operations. The rule engine detects each contract clause and data point based on the corresponding audit rules to determine whether each contract clause and data point in the mapping parameters meets the predefined logical operations. The preset rule engine also includes dynamic updates based on changes in laws, regulations, and hospital policies.

[0028] Furthermore, the step of performing risk prediction on the contract terms and data points marked with anomalies and obtaining the risk prediction results includes:

[0029] Identify the relevant variables affecting contract risk, obtain the dependencies between the relevant variables, construct a Bayesian network, and estimate the conditional probability distribution of each variable in the Bayesian network;

[0030] The contract terms and data points marked with anomalies are used as input to a Bayesian network to update the confidence of relevant nodes in the network; the risk probability of the contract terms and data points marked with anomalies is obtained through inference using the Bayesian network.

[0031] The output of the Bayesian network is interpreted to transform the risk probability into an actionable risk prediction, generating a risk prediction report for the digital procurement contract, which includes a risk score, risk level, and risk mitigation measures.

[0032] Secondly, this application also provides an automated auditing system for hospital procurement contracts, used to implement an automated auditing method for hospital procurement contracts, the system comprising:

[0033] The digital procurement contract acquisition module is used to acquire digital procurement contracts.

[0034] The automatic contract classification module is used to identify the procurement content in the digital procurement contract based on the types of items procured by the hospital, obtain the item type information of the procurement content, classify the contract type of the digital procurement contract based on the item type information, and automatically classify it.

[0035] The detection result acquisition module is used to automatically audit the digital procurement contract based on the contract type of the digital procurement contract, match the preset rule engine of the corresponding contract type, detect whether the procurement contract meets the compliance requirements, and obtain the detection result;

[0036] The risk prediction result acquisition module is used to perform risk prediction on contract terms and data points marked with anomalies and obtain risk prediction results.

[0037] Thirdly, this application provides an electronic device, comprising:

[0038] One or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when executed by the device, cause the device to perform the method as described in the first aspect.

[0039] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to perform the method described in the first aspect.

[0040] Fifthly, this application provides a computer program that, when executed by a computer, performs the method described in the first aspect.

[0041] In one possible design, the program in the fifth aspect can be stored wholly or partially on a storage medium packaged with the processor, or it can be stored wholly or partially on a memory not packaged with the processor.

[0042] This application has the following beneficial effects:

[0043] 1. This application identifies the procurement content in the digital procurement contract based on the types of items procured by the hospital, obtains the item type information of the procurement content, classifies the digital procurement contract into contract types based on the item type information, and automatically categorizes them; this improves the efficiency of contract management, ensures that contracts are quickly archived according to type, and facilitates subsequent auditing and management.

[0044] 2. This application automatically audits the digital procurement contract by matching the contract type with a preset rule engine of the corresponding contract type, detecting whether the procurement contract meets compliance requirements, and obtaining the detection results. This application ensures the consistency of the audit by using a preset rule engine to automate the audit process, thereby improving the speed and accuracy of hospital procurement contract audits.

[0045] 3. This application automatically audits the digital procurement contracts by matching the contract type with a preset rule engine of the corresponding contract type, so that different types of contracts adopt different rule engines, which meets the requirements of different types of contracts for different laws, regulations and hospital policies.

[0046] 4. This application is dynamically updated based on changes in laws, regulations, and hospital policies, enabling automated auditing to adapt to changes in a timely manner and improving the efficiency and accuracy of auditing.

[0047] 5. This application obtains risk prediction results by performing risk prediction on contract terms and data points marked with anomalies; through risk prediction, this application can identify and warn of potential risks in contracts in advance, and take timely measures to control risks. Attached Figure Description

[0048] Figure 1 This is an exemplary system architecture diagram to which embodiments of this application can be applied;

[0049] Figure 2 This is a flowchart of the automated auditing method for hospital procurement contracts according to an embodiment of this application;

[0050] Figure 3 This is a schematic diagram of the automatic contract classification process in an embodiment of this application;

[0051] Figure 4 This is a schematic diagram of the process for obtaining detection results according to an embodiment of this application;

[0052] Figure 5 This is a schematic diagram of the process for obtaining risk prediction results according to an embodiment of this application;

[0053] Figure 6 This is a system flowchart of an embodiment of this application;

[0054] Figure 7 This is a schematic diagram of a computer device according to an embodiment of this application. Detailed Implementation

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0056] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0057] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0058] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0059] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0060] Terminal devices 101, 102, and 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc.

[0061] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal devices 101, 102, and 103.

[0062] It should be noted that the automated auditing method for hospital procurement contracts provided in this application is generally executed by a server / terminal device, and correspondingly, the automated auditing system for hospital procurement contracts is generally set up in the server / terminal device.

[0063] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0064] Continue to refer to Figure 2 The figure shows a flowchart of an automated auditing method for hospital procurement contracts according to this application. The method includes the following steps:

[0065] Step 201: Obtain the digital procurement contract.

[0066] In one possible implementation, when the obtained purchase contract is a paper contract, the paper contract is scanned to obtain a digital purchase contract. The specific steps are as follows: based on a preset scanning device, the paper contract is image-captured to obtain a scanned version of the paper contract; text in the scanned version is extracted using text recognition technology; the scanned file is obtained and named to obtain a digital purchase contract.

[0067] In one possible implementation, the scanning device is a document scanning tool.

[0068] In one possible implementation, during the storage of the digital procurement contract, fields such as contract name, contract number, and date are constructed to facilitate the retrieval of the digital procurement contract.

[0069] Step 202: Based on the types of items procured by the hospital, identify the procurement content in the digital procurement contract, obtain the item type information of the procurement content, classify the contract type of the digital procurement contract based on the item type information, and automatically categorize it.

[0070] Continue to refer to Figure 3 , Figure 3 This is a flowchart of a specific embodiment of step 202, the specific steps of which include:

[0071] Step 301: Determine the classification criteria for item types, obtain the attributes of each item type, design a database structure based on the item types and attributes, and input the item types and attributes into the database.

[0072] Step 302: Extract key feature items related to the type of goods from the digital procurement contract based on natural language processing technology; such as the name, specifications, and model of the goods.

[0073] Step 303: Calculate the similarity between the extracted key features and the item types in the database. When the similarity value exceeds a preset threshold, the digital procurement contract is matched with the corresponding item type in the database, and the item type of the digital procurement contract is obtained.

[0074] Step 304: Determine the contract type of the digital procurement contract based on the types of goods in the digital procurement contract and the predefined contract type rules.

[0075] Step 305: Automatically categorize digital procurement contracts based on contract type.

[0076] Specifically, based on the determined contract type, the digital procurement contract is moved to the corresponding category directory, and the database record is updated to mark the category information of the digital procurement contract.

[0077] In one possible implementation, the types of items procured by the hospital include medical equipment contracts, pharmaceutical contracts, consumable contracts, laboratory equipment contracts, repair and maintenance contracts, and office supplies contracts.

[0078] Step 203: Based on the contract type of the digital procurement contract, the preset rule engine of the corresponding contract type is matched to automatically audit the digital procurement contract, detect whether the procurement contract meets the compliance requirements, and obtain the detection result.

[0079] Continue to refer to Figure 4 , Figure 4 This is a flowchart of a specific embodiment of step 203, and the specific steps include:

[0080] Step 401: Develop audit rules for each contract type; based on laws and regulations, industry standards, and hospital policies, develop audit rules for each contract type.

[0081] Step 402: Configure the parameter information in the audit rules for each contract type; define logical operations for the audit rules for each contract type. The parameter information includes, for example, the upper limit of the amount, the delivery date, and the quality standards; the logical operations include "less than or equal to", "inclusive", and "not equal to", such as "the contract amount must be less than or equal to the budget amount".

[0082] Step 403: Extract relevant parameters from the digital procurement contract based on the parameter information corresponding to the digital procurement contract.

[0083] In this embodiment, a text analysis algorithm can be used to identify key parameters of the contract.

[0084] Step 404: Map the relevant parameters to the audit rules corresponding to the digital procurement contract to obtain the mapping parameters.

[0085] Step 405: Input the mapping parameters into the preset rule engine and perform logical operations. The rule engine detects each contract clause and data point based on the corresponding audit rules to determine whether each contract clause and data point in the mapping parameters meets the predefined logical operations.

[0086] Step 406: Mark contract terms and data points that do not meet the predefined logical operations as exceptions.

[0087] Taking steps 401-406 as an example, assume the hospital's procurement contract type is medical equipment. Construct audit rules: For medical equipment contracts, the audit rule might be: "The equipment procurement price shall not exceed 110% of the market average price"; Configure audit rule parameters: Rule parameters may include "contract price," "market average price," and "price cap percentage"; Define logical operations: The logical operation is: "contract price" ≤ "market average price" × 1.10; Extract relevant parameters: Extract the "contract price" from the medical equipment procurement contract as ¥100,000; Map parameters: Map the extracted "contract price" parameter to the audit rule, determining the "market average price" as ¥90,000; Execute rule engine detection: The rule engine compares the "contract price" with the "market average price" and the "price cap percentage": Detection result: ¥100,000 > ¥90,000 × 1.10; Anomaly labeling: Because the contract price exceeds the predefined price cap, the rule engine labels the price clause of this contract as an anomaly.

[0088] In one possible implementation, automated auditing of the digital contract includes reviewing terms for consistency, verifying prices, checking delivery terms, and confirming payment terms.

[0089] In one possible implementation, the mapping parameters are input into a preset rule engine to perform logical operations. The rule engine detects each contract clause and data point based on the corresponding audit rules to determine whether each contract clause and data point in the mapping parameters meets the predefined logical operations. The preset rule engine also includes dynamic updates based on changes in laws, regulations, and hospital policies.

[0090] In one possible implementation, the laws and regulations can be national laws and regulations, or local laws and regulations if the hospital is based on local laws and regulations.

[0091] Step 204: Perform risk prediction on the contract terms and data points marked with anomalies, and obtain the risk prediction results.

[0092] Continue to refer to Figure 5 , Figure 5 This is a flowchart of a specific embodiment of step 204, and the specific steps are as follows:

[0093] Step 501: Identify the relevant variables affecting contract risk, obtain the dependencies between the relevant variables, construct a Bayesian network, and estimate the conditional probability distribution of each variable in the Bayesian network;

[0094] Step 502: Use the contract terms and data points marked with anomalies as input to the Bayesian network to update the confidence of relevant nodes in the network; use the Bayesian network to perform inference to obtain the risk probability of the contract terms and data points marked with anomalies.

[0095] Step 503: Interpret the output of the Bayesian network, transform the risk probability into an actionable risk prediction, and generate a risk prediction report for the digital procurement contract, wherein the risk prediction report includes risk score, risk level and risk mitigation measures.

[0096] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0097] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0098] Continue to refer to Figure 6 The automated auditing system for hospital procurement contracts described in this embodiment includes:

[0099] Digital Procurement Contract Acquisition Module 601 is used to acquire digital procurement contracts;

[0100] The automatic contract classification module 602 is used to identify the procurement content in the digital procurement contract based on the types of items procured by the hospital, obtain the item type information of the procurement content, classify the contract type of the digital procurement contract based on the item type information, and automatically classify it.

[0101] The detection result acquisition module 603 is used to automatically audit the digital procurement contract based on the contract type of the digital procurement contract, match the preset rule engine of the corresponding contract type, detect whether the procurement contract meets the compliance requirements, and obtain the detection result;

[0102] The risk prediction result acquisition module 604 is used to perform risk prediction on contract terms and data points marked with anomalies and obtain risk prediction results.

[0103] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 7 , Figure 7 This is a basic structural block diagram of the computer device in this embodiment.

[0104] The computer device 7 includes a memory 7a, a processor 7b, and a network interface 7c that are interconnected via a system bus. It should be noted that only the computer device 7 with components 7a-7c is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0105] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0106] The memory 7a includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 7a may be an internal storage unit of the computer device 7, such as the hard disk or memory of the computer device 7. In other embodiments, the memory 7a may also be an external storage device of the computer device 7, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 7. Of course, the memory 7a may also include both the internal storage unit and its external storage device of the computer device 7. In this embodiment, the memory 7a is typically used to store the operating system and various application software installed on the computer device 7, such as the program code of a hospital procurement contract automated auditing method. In addition, the memory 7a can also be used to temporarily store various types of data that have been output or will be output.

[0107] In some embodiments, the processor 7b may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 7b is typically used to control the overall operation of the computer device 7. In this embodiment, the processor 7b is used to run program code stored in the memory 7a or process data, for example, to run the program code of the automated auditing method for hospital procurement contracts.

[0108] The network interface 7c may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 7 and other electronic devices.

[0109] This application also provides another embodiment, namely, a non-volatile computer-readable storage medium storing a program for an automated auditing method for hospital procurement contracts, which can be executed by at least one processor to perform the steps of the automated auditing method for hospital procurement contracts as described above.

[0110] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0111] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A method for automated auditing of hospital procurement contracts, characterized in that, include: Obtain digital procurement contracts; Based on the types of items procured by the hospital, the procurement content in the digital procurement contract is identified, the type information of the items in the procurement content is obtained, the contract type is classified based on the type information of the items, and automatic classification is performed. Based on the contract type of the digital procurement contract, a preset rule engine matching the contract type automatically audits the digital procurement contract to detect whether the procurement contract meets compliance requirements and obtains the detection results; Perform risk prediction on contract terms and data points marked with anomalies, and obtain the risk prediction results.

2. The automated auditing method for hospital procurement contracts according to claim 1, characterized in that, The process of obtaining a digital procurement contract includes: when the procurement contract is a paper contract, scanning the paper contract to obtain a digital procurement contract. The specific steps are: using a preset scanning device, capturing an image of the paper contract to obtain a scanned version of the paper contract; extracting the text from the scanned version using text recognition technology; obtaining the scanned file and naming it to obtain the digital procurement contract.

3. The automated auditing method for hospital procurement contracts according to claim 1, characterized in that, The process involves identifying the procurement content in the digital procurement contract based on the types of goods procured by the hospital, obtaining information on the types of goods mentioned in the procurement content, classifying the digital procurement contract into contract types based on the item type information, and automatically categorizing them. Specific details include: Determine the classification criteria for item types, obtain the attributes of each item type, design a database structure based on the item types and attributes, and input the item types and attributes into the database; Key feature items related to the type of goods in digital procurement contracts were extracted using natural language processing technology. The extracted key features are compared with the item types in the database. When the similarity value exceeds a preset threshold, the digital procurement contract is matched with the corresponding item type in the database, and the item type of the digital procurement contract is obtained. Based on the types of goods in the digital procurement contract and the predefined contract type rules, the contract type of the digital procurement contract is determined. Based on the determined contract type, the digital procurement contract is moved to the corresponding category directory, and the database record is updated to mark the category information of the digital procurement contract.

4. The automated auditing method for hospital procurement contracts according to claim 3, characterized in that, The types of items procured by the hospital include medical equipment contracts, drug contracts, consumable contracts, laboratory equipment contracts, repair and maintenance contracts, and office supply contracts.

5. The automated auditing method for hospital procurement contracts according to claim 1, characterized in that, The digital procurement contract, based on its contract type, is automatically audited by a preset rule engine that matches the contract type. This audit checks whether the procurement contract meets compliance requirements and obtains the audit results. Specifically, this includes: Develop audit rules for each contract type; Configure the parameter information in the audit rules for each contract type; define logical operations for the audit rules for each contract type; Based on the parameter information corresponding to the digital procurement contract, relevant parameters are extracted from the digital procurement contract; Map the relevant parameters to the audit rules corresponding to the digital procurement contract to obtain the mapping parameters; The mapping parameters are input into the preset rule engine, and logical operations are performed. The rule engine detects each contract clause and data point based on the corresponding audit rules to determine whether each contract clause and data point in the mapping parameters meets the predefined logical operations. Contract terms and data points that do not meet the predefined logical operations are marked as exceptions.

6. The automated auditing method for hospital procurement contracts according to claim 5, characterized in that, The mapping parameters are input into a preset rule engine to perform logical operations. The rule engine detects each contract clause and data point based on the corresponding audit rules to determine whether each contract clause and data point in the mapping parameters meets the predefined logical operations. The preset rule engine also includes dynamic updates based on changes in laws, regulations, and hospital policies.

7. The automated auditing method for hospital procurement contracts according to claim 1, characterized in that, The process of performing risk prediction on contract terms and data points marked with anomalies, and obtaining the risk prediction results, includes: Identify the relevant variables affecting contract risk, obtain the dependencies between the relevant variables, construct a Bayesian network, and estimate the conditional probability distribution of each variable in the Bayesian network; The contract terms and data points marked with anomalies are used as input to a Bayesian network to update the confidence of relevant nodes in the network; the risk probability of the contract terms and data points marked with anomalies is obtained through inference using the Bayesian network. The output of the Bayesian network is interpreted to transform the risk probability into an actionable risk prediction, generating a risk prediction report for the digital procurement contract, which includes a risk score, risk level, and risk mitigation measures.

8. An automated auditing system for hospital procurement contracts, used to implement the automated auditing method for hospital procurement contracts according to claims 1-7, characterized in that, include: The digital procurement contract acquisition module is used to acquire digital procurement contracts. The automatic contract classification module is used to identify the procurement content in the digital procurement contract based on the types of items procured by the hospital, obtain the item type information of the procurement content, classify the contract type of the digital procurement contract based on the item type information, and automatically classify it. The detection result acquisition module is used to automatically audit the digital procurement contract based on the contract type of the digital procurement contract, match the preset rule engine of the corresponding contract type, detect whether the procurement contract meets the compliance requirements, and obtain the detection result; The risk prediction result acquisition module is used to perform risk prediction on contract terms and data points marked with anomalies and obtain risk prediction results.

9. An electronic device, characterized in that, include: One or more processors; Memory; And one or more computer programs, wherein the one or more computer programs are stored in the memory, the one or more computer programs including instructions that, when executed by the device, cause the device to perform the steps of the automated auditing method for hospital procurement contracts as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when run on a computer, causes the computer to perform the steps of the automated auditing method for hospital procurement contracts as described in any one of claims 1 to 7.

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