AI-based intelligent correction processing method for hospital electronic medical records

Abstract

This invention relates to the field of intelligent error correction technology, specifically an AI-based intelligent error correction method for hospital electronic medical records. The method includes the following steps: receiving multi-source data input from hospital electronic medical records; performing standardized preprocessing and noise removal on the electronic medical record data; completing dimensional matching of text, test reports, and treatment records through a multi-modal data alignment mechanism to obtain a standardized full dataset of electronic medical records. This invention achieves dimensional alignment and accurate extraction of core elements from electronic medical record text, test reports, and treatment records through multi-source data standardized preprocessing and a medical terminology weighting algorithm, improving the efficiency of medical record data standardization and the accuracy of element identification. It employs a large-scale medical model combined with a medical knowledge graph for semantic parsing and logical reasoning, enabling deep identification of data errors, logical conflicts, and non-standard treatment issues in medical records, thus expanding the scope of error correction.

Description

Technical Field

[0001] This invention relates to the field of intelligent error correction technology, specifically to an AI-based intelligent error correction method for hospital electronic medical records. Background Technology

[0002] Electronic medical records (EMRs) are the core carrier of hospital information management, encompassing multi-dimensional medical data such as patient basic information, medical records, medication information, and laboratory indicators. The accuracy of this data directly affects clinical treatment safety and medical quality control. Currently, the entry and management of EMRs in hospitals commonly suffer from problems such as data format errors, missing fields, conflicting diagnostic and treatment logic, unidentified medication contraindications, and non-compliance with clinical standards, which can easily lead to medical errors and data management risks. However, existing electronic medical record error correction methods generally have significant technical shortcomings: First, they rely heavily on manual verification and correction, resulting in low processing efficiency, difficulty in meeting the real-time processing needs of massive amounts of medical record data, and susceptibility to errors due to human negligence; second, traditional error correction methods only employ a single rule matching approach, failing to incorporate semantic understanding and clinical knowledge reasoning in the medical field, thus failing to identify logical errors in the relationships between treatment records, medication information, and laboratory indicators, limiting the scope of error correction; third, they lack a hierarchical verification mechanism and have not constructed a collaborative verification system for data formats, medical logic, and clinical standards. The system suffers from several shortcomings: First, the accuracy of error correction is insufficient. Second, the technology of combining medical-specific AI models with knowledge graphs is not adopted, resulting in low accuracy in recognizing medical terminology and treatment constraints, making it unsuitable for complex scenarios such as multi-departmental, special disease, and emergency medical records. Third, the error correction process lacks a closed-loop iteration mechanism, making it impossible to continuously optimize error correction rules and model performance based on clinical feedback, and the error correction effect is difficult to improve steadily in the long term. Fourth, the error correction results lack standardized output and traceability management capabilities, making it impossible to efficiently connect with hospital HIS, EMR, and other systems, resulting in insufficient data management standardization.

[0003] In summary, an AI-based intelligent error correction method for hospital electronic medical records needs to be proposed to solve the above problems. Summary of the Invention

[0004] The purpose of this invention is to provide an AI-based intelligent error correction method for hospital electronic medical records, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: This invention proposes an AI-based intelligent error correction method for hospital electronic medical records, comprising the following steps: S1. Receive multi-source data input from hospital electronic medical records, perform standardized preprocessing and noise removal on the electronic medical record data, and complete dimensional matching of text, test reports, and treatment records through a multimodal data alignment mechanism to obtain the normalized full dataset of electronic medical records; S2. A large-scale model specifically designed for the medical field is used to perform multi-dimensional semantic analysis on the standardized electronic medical record data, extracting the core elements of the medical record, including basic patient information, diagnosis and treatment records, medication information, test indicators and medical logical constraints, and generating a structured intermediate representation of medical semantics. S3. Based on medical semantic structured intermediate representation, construct an electronic medical record error correction rule base and medical knowledge graph, and generate an initial error correction candidate set through rule matching and knowledge reasoning, which includes data error items, logical conflict items, and non-standard diagnosis and treatment items. S4. Establish a three-tiered verification system for medical data error correction. Through data format verification, medical logic verification, and clinical standard verification, the initial error correction candidate set is verified in layers to generate accurate error correction results and anomalies to be confirmed. S5. Automatically correct the accurate error correction results, push the unconfirmed abnormal items to the medical staff terminal for manual verification, complete the medical record correction after the verification is passed, and update the error correction rule library if the verification fails. S6. Record the entire process parameters and verification results of medical record error correction, and continuously iterate and optimize the error correction model and rule base based on clinical feedback and error correction accuracy to achieve dynamic updates of the intelligent error correction process.

[0006] Preferably, the implementation process of step S1 is as follows: It provides multi-source data access interfaces for electronic medical record texts, test reports, and medical records, and performs format conversion, redundant data removal, and abnormal character cleaning on the input data. Core medical data elements are extracted using a medical terminology weighting algorithm, as shown in equations (1) and (2): (1); (2); The weight threshold is set to 0.65; terms exceeding the threshold are considered core medical terms. λ is the clinical department matching coefficient, ranging from 0.75 to 1.25. μ is the data type association coefficient, ranging from 0.85 to 1.15. For medical records lacking core medical data elements, the missing information is obtained through targeted data completion requests, and standardized data items are automatically completed based on the medical standard dataset.

[0007] Preferably, the implementation process of step S2 is as follows: Based on the term weights calculated in step S1, the following is filtered. The terminology is used as candidate medical elements, and the core medical record element set is determined through medical element classification. For each core medical record element, semantically related medical data are associated as attributes and medical constraint information is extracted; The diagnostic and treatment relationship between elements is inferred by calculating the medical logical confidence level, as shown in equation (3): (3); The value ranges from 0 to 1. Determined to be a strong association, It was determined to be a weak association. Determined to be unrelated; The final output is a JSON-formatted medical semantic structured intermediate representation containing patient information, medical records, medications, and test results.

[0008] Preferably, the implementation process of step S3 is as follows: The medical semantic structured intermediate representation is matched field by field with the electronic medical record error correction rule base to identify data format errors and missing field items; Map core medical record elements to a medical knowledge graph, and use graph reasoning to identify diagnostic and treatment logic conflicts and medication contraindications; The priority of error correction is determined by scoring the necessity of error correction, as shown in equation (4): (4); An initial set of error correction candidates is generated based on the matching and reasoning results, and the error type and associated medical evidence are labeled.

[0009] Preferably, the implementation process of step S4 is as follows: Perform data format validation to verify whether the data type, length, and value range of medical record fields conform to medical data standards; Perform medical logic verification to check the clinical logic consistency between medical records, medication information, and test indicators; Perform clinical standard verification to confirm that the medical record content complies with the national medical institution medical record management standards and clinical diagnosis and treatment guidelines; The accurate location of anomalies is achieved by calculating the extended attribute matching degree, as shown in equation (5): (5); The value ranges from 0 to 1. This item is identified as an outlier; δ is the weighting coefficient, with a value of 0.45. Accurate error correction results and anomalies to be confirmed are generated based on the stratified verification results.

[0010] Preferably, the implementation process of step S5 is as follows: Automatically correct low-risk errors in the accurate error correction results and record the correction trajectory simultaneously; The abnormal items to be confirmed are pushed to the corresponding department's medical staff according to the error risk level, and the abnormal content and related medical evidence are displayed; Receive verification feedback from medical staff. If the verification is successful, the medical record is corrected. If the verification fails, the feedback information is extracted and the error correction rule base is updated. After the correction is completed, a corrected version of the electronic medical record is generated, retaining the association mapping between the original medical record and the corrected record.

[0011] Preferably, the implementation process of the large-scale medical model used in step S2 is as follows: A large-scale medical model with a semantic understanding accuracy of ≥92% and a single medical record processing speed of ≤2 seconds was selected as the base model. Based on a fine-tuned model using a corpus of hospital electronic medical records, the objectives are: accuracy of medical record element recognition ≥96%, constraint extraction accuracy ≥93%, and logical inference accuracy ≥91%. Model performance is optimized by employing model quantization and knowledge distillation techniques, with a single full medical record processing time of ≤25 seconds; a collaborative mechanism between the main model and the sub-specialty models is established, and the accuracy of analysis is improved through result fusion. We import national clinical practice guidelines and drug contraindications dictionaries, and regularly update medical knowledge and treatment rules.

[0012] Preferably, the implementation process of the medical data triple verification engine in step S4 is as follows: Configure the verification rules for data format, medical logic, and clinical standards. The rules include trigger conditions, verification results, and priority levels from 1 to 10. It has a built-in national medical record management standard and clinical diagnosis and treatment guideline library, automatically applies standard verification rules, and pushes the confirmation to the medical staff when there are rule conflicts. It handles special verification scenarios such as multi-departmental joint diagnosis and treatment, special diseases, and emergency medical records; it provides verification rule templates and a visual configuration interface, and supports hot loading of rules and recording of change logs.

[0013] Preferably, the medical record correction output and archiving process in step S5 is as follows: Generate corrected electronic medical record files, error correction reports, and anomaly verification records; Generates medical record data access permission configuration files and correction log archives, supporting integration with hospital HIS and EMR systems; The system generates clinical data quality inspection reports based on the error correction results and supports exporting them to medical standard formats; it also enables medical record version management and error correction data traceability, and supports multi-condition querying of error correction records.

[0014] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention achieves dimensional alignment and precise extraction of core elements from electronic medical record text, test reports, and treatment records through multi-source data standardization preprocessing and medical terminology weighting algorithms, improving the efficiency of medical record data organization and the accuracy of element identification. It employs a medical-specific large-scale model combined with a medical knowledge graph for semantic parsing and logical reasoning, enabling deep identification of data errors, logical conflicts, and non-standard treatment issues in medical records, expanding the scope of error correction, and constructing a triple collaborative verification system encompassing data format, medical logic, and clinical standards. Through hierarchical verification and precise anomaly location algorithms, it significantly improves the accuracy of electronic medical record error correction and reduces the rate of missed errors. The invention adopts a collaborative processing mode of automatic correction and manual verification, balancing the rapid correction of low-risk errors. This system handles clinical confirmation of high-risk anomalies, balancing error correction efficiency with treatment safety. The main model and specialized sub-models work in synergy, combining model quantization and distillation techniques to optimize performance, enabling efficient processing of single medical records. It adapts to the real-time error correction needs of hospitals with massive medical records, establishing a full-process parameter recording and closed-loop iteration mechanism. Based on clinical feedback, it continuously optimizes the error correction model and rule base, maintaining the long-term applicability and accuracy of the error correction method. The error correction results are output in a standardized manner and support integration with hospital HIS and EMR systems. It also includes medical record version management and error correction traceability functions, improving the standardization and traceability of electronic medical record data management. Validation rules are optimized for special scenarios such as multi-departmental joint diagnosis and treatment, special diseases, and emergency medical records, adapting to the complex clinical treatment environment of hospitals and expanding the application scope of the method. Attached Figure Description

[0015] Figure 1 This is a flowchart of the AI-based intelligent error correction method for hospital electronic medical records according to the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] For examples, please refer to Figure 1 In practical applications, this invention proposes an AI-based intelligent error correction method for hospital electronic medical records, specifically including the following steps: S1. Receive multi-source data input from hospital electronic medical records, perform standardized preprocessing and noise removal on the electronic medical record data, and complete dimensional matching of text, test reports, and treatment records through a multimodal data alignment mechanism to obtain the normalized full dataset of electronic medical records; In a specific embodiment, it should also be noted that the implementation process of step S1 is as follows: It provides multi-source data access interfaces for electronic medical record texts, test reports, and medical records, and performs format conversion, redundant data removal, and abnormal character cleaning on the input data. Core medical data elements are extracted using a medical terminology weighting algorithm, as shown in equations (1) and (2): (1); (2); The weight threshold is set to 0.65; terms exceeding the threshold are considered core medical terms. λ is the clinical department matching coefficient, ranging from 0.75 to 1.25. μ is the data type association coefficient, ranging from 0.85 to 1.15. For medical records lacking core medical data elements, the missing information is obtained through targeted data completion requests, and standardized data items are automatically completed based on the medical standard dataset.

[0018] S2. A large-scale model specifically designed for the medical field is used to perform multi-dimensional semantic analysis on the standardized electronic medical record data, extracting the core elements of the medical record, including basic patient information, diagnosis and treatment records, medication information, test indicators and medical logical constraints, and generating a structured intermediate representation of medical semantics. In a specific embodiment, it should also be noted that the implementation process of step S2 is as follows: Based on the term weights calculated in step S1, the following is filtered. The terminology is used as candidate medical elements, and the core medical record element set is determined through medical element classification. For each core medical record element, semantically related medical data are associated as attributes and medical constraint information is extracted; The diagnostic and treatment relationship between elements is inferred by calculating the medical logical confidence level, as shown in equation (3): (3); The value ranges from 0 to 1. Determined to be a strong association, It was determined to be a weak association. Determined to be unrelated; The final output is a JSON-formatted medical semantic structured intermediate representation containing patient information, medical records, medications, and test results.

[0019] In a specific embodiment, it should also be noted that the implementation process of the large-scale medical model used in step S2 is as follows: A large-scale medical model with a semantic understanding accuracy of ≥92% and a single medical record processing speed of ≤2 seconds was selected as the base model. Based on a fine-tuned model using a corpus of hospital electronic medical records, the objectives are: accuracy of medical record element recognition ≥96%, constraint extraction accuracy ≥93%, and logical inference accuracy ≥91%. Model performance is optimized by employing model quantization and knowledge distillation techniques, with a single full medical record processing time of ≤25 seconds; a collaborative mechanism between the main model and the sub-specialty models is established, and the accuracy of analysis is improved through result fusion. We import national clinical practice guidelines and drug contraindications dictionaries, and regularly update medical knowledge and treatment rules.

[0020] S3. Based on medical semantic structured intermediate representation, construct an electronic medical record error correction rule base and medical knowledge graph, and generate an initial error correction candidate set through rule matching and knowledge reasoning, which includes data error items, logical conflict items, and non-standard diagnosis and treatment items. In a specific embodiment, it should also be noted that the implementation process of step S3 is as follows: The medical semantic structured intermediate representation is matched field by field with the electronic medical record error correction rule base to identify data format errors and missing field items; Map core medical record elements to a medical knowledge graph, and use graph reasoning to identify diagnostic and treatment logic conflicts and medication contraindications; The priority of error correction is determined by scoring the necessity of error correction, as shown in equation (4): (4); An initial set of error correction candidates is generated based on the matching and reasoning results, and the error type and associated medical evidence are labeled.

[0021] S4. Establish a three-tiered verification system for medical data error correction. Through data format verification, medical logic verification, and clinical standard verification, the initial error correction candidate set is verified in layers to generate accurate error correction results and anomalies to be confirmed. In a specific embodiment, it should also be noted that the implementation process of step S4 is as follows: Perform data format validation to verify whether the data type, length, and value range of medical record fields conform to medical data standards; Perform medical logic verification to check the clinical logic consistency between medical records, medication information, and test indicators; Perform clinical standard verification to confirm that the medical record content complies with the national medical institution medical record management standards and clinical diagnosis and treatment guidelines; The accurate location of anomalies is achieved by calculating the extended attribute matching degree, as shown in equation (5): (5); The value ranges from 0 to 1. This item is identified as an outlier; δ is the weighting coefficient, with a value of 0.45. Accurate error correction results and anomalies to be confirmed are generated based on the stratified verification results.

[0022] In a specific embodiment, it should also be noted that the implementation process of the medical data triple verification engine in step S4 is as follows: Configure the verification rules for data format, medical logic, and clinical standards. The rules include trigger conditions, verification results, and priority levels from 1 to 10. It has a built-in national medical record management standard and clinical diagnosis and treatment guideline library, automatically applies standard verification rules, and pushes the confirmation to the medical staff when there are rule conflicts. Handles special verification scenarios such as multi-departmental joint diagnosis and treatment, special diseases, and emergency medical records; provides verification rule templates and a visual configuration interface, supports hot loading of rules and recording of change logs. S5 automatically corrects accurate error correction results, pushes unconfirmed abnormal items to the medical staff for manual verification, completes medical record correction after verification, and updates the error correction rule library if verification fails. In a specific embodiment, it should also be noted that the implementation process of step S5 is as follows: Automatically correct low-risk errors in the accurate error correction results and record the correction trajectory simultaneously; The abnormal items to be confirmed are pushed to the corresponding department's medical staff according to the error risk level, and the abnormal content and related medical evidence are displayed; Receive verification feedback from medical staff. If the verification is successful, the medical record is corrected. If the verification fails, the feedback information is extracted and the error correction rule base is updated. After the correction is completed, a corrected version of the electronic medical record is generated, retaining the association mapping between the original medical record and the corrected record.

[0023] In a specific embodiment, it should also be noted that the medical record correction output and archiving process in step S5 is as follows: Generate corrected electronic medical record files, error correction reports, and anomaly verification records; Generates medical record data access permission configuration files and correction log archives, supporting integration with hospital HIS and EMR systems; Generates clinical data quality inspection reports based on error correction results, supports exporting to medical standard formats; enables medical record version management and error correction data traceability, and supports multi-condition query of error correction records; S6. Record the entire process parameters and verification results of medical record error correction, and continuously iterate and optimize the error correction model and rule base based on clinical feedback and error correction accuracy to achieve dynamic updates of the intelligent error correction process.

[0024] Through the above steps, this invention achieves dimensional alignment and precise extraction of core elements from electronic medical record texts, test reports, and treatment records by using multi-source data standardization preprocessing and medical terminology weighting algorithms, thereby improving the efficiency of medical record data organization and the accuracy of element identification. By using a large-scale model specifically designed for the medical field and combining it with a medical knowledge graph for semantic parsing and logical reasoning, it can deeply identify data errors, logical conflicts, and non-standard diagnosis and treatment issues in medical records, and expand the scope of error correction. A three-tiered collaborative verification system integrating data format, medical logic, and clinical standards is constructed. Through layered verification and anomaly detection algorithms, the accuracy of electronic medical record error correction is significantly improved, and the error and missed detection rate is reduced. The system adopts a collaborative processing mode of automatic correction and manual verification, which takes into account both the rapid processing of low-risk errors and the clinical confirmation of high-risk abnormalities, thus balancing error correction efficiency and treatment safety. The main model and the sub-specialty models work together, and the performance is optimized by combining model quantization and distillation techniques to achieve efficient processing of a single medical record and meet the real-time error correction needs of hospitals with massive medical records. Establishing a full-process parameter recording and closed-loop iteration mechanism allows for continuous optimization of the error correction model and rule base based on clinical feedback, maintaining the long-term applicability and accuracy of the error correction method. The error correction results are output in a standardized manner and support integration with hospital HIS and EMR systems. They are accompanied by medical record version management and error correction traceability functions to improve the standardization and traceability of electronic medical record data management. The verification rules are optimized for special scenarios such as multidisciplinary joint diagnosis and treatment, special diseases, and emergency medical records, to adapt to the complex clinical diagnosis and treatment environment of hospitals and expand the application scope of the method.

[0025] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An AI-based intelligent error correction method for hospital electronic medical records, characterized in that, Includes the following steps: S1. Receive multi-source data input from hospital electronic medical records, perform standardized preprocessing and noise removal on the electronic medical record data, and complete dimensional matching of text, test reports, and treatment records through a multimodal data alignment mechanism to obtain the normalized full dataset of electronic medical records; S2. A large-scale model specifically designed for the medical field is used to perform multi-dimensional semantic analysis on the standardized electronic medical record data, extracting the core elements of the medical record, including basic patient information, diagnosis and treatment records, medication information, test indicators and medical logical constraints, and generating a structured intermediate representation of medical semantics. S3. Based on medical semantic structured intermediate representation, construct an electronic medical record error correction rule base and medical knowledge graph, and generate an initial error correction candidate set through rule matching and knowledge reasoning, which includes data error items, logical conflict items, and non-standard diagnosis and treatment items. S4. Establish a three-tiered verification system for medical data error correction. Through data format verification, medical logic verification, and clinical standard verification, the initial error correction candidate set is verified in layers to generate accurate error correction results and anomalies to be confirmed. S5. Automatically correct the accurate error correction results, push the unconfirmed abnormal items to the medical staff terminal for manual verification, complete the medical record correction after the verification is passed, and update the error correction rule library if the verification fails. S6. Record the entire process parameters and verification results of medical record error correction, and continuously iterate and optimize the error correction model and rule base based on clinical feedback and error correction accuracy to achieve dynamic updates of the intelligent error correction process.

2. The AI-based intelligent error correction method for hospital electronic medical records according to claim 1, characterized in that, The implementation process of step S1 is as follows: It provides multi-source data access interfaces for electronic medical record texts, test reports, and medical records, and performs format conversion, redundant data removal, and abnormal character cleaning on the input data. Core medical data elements are extracted using a medical terminology weighting algorithm, as shown in equations (1) and (2): （1）； （2）； The weight threshold is set to 0.65; terms exceeding the threshold are considered core medical terms. λ is the clinical department matching coefficient, ranging from 0.75 to 1.

25. μ is the data type association coefficient, ranging from 0.85 to 1.

15. For medical records lacking core medical data elements, the missing information is obtained through targeted data completion requests, and standardized data items are automatically completed based on the medical standard dataset.

3. The AI-based intelligent error correction method for hospital electronic medical records according to claim 2, characterized in that, The implementation process of step S2 is as follows: Based on the term weights calculated in step S1, the following is filtered. The terminology is used as candidate medical elements, and the core medical record element set is determined through medical element classification. For each core medical record element, semantically related medical data are associated as attributes and medical constraint information is extracted; The diagnostic and treatment relationship between elements is inferred by calculating the medical logical confidence level, as shown in equation (3): （3）； The value ranges from 0 to 1. Determined to be a strong association, It was determined to be a weak association. Determined to be unrelated; The final output is a JSON-formatted medical semantic structured intermediate representation containing patient information, medical records, medications, and test results.

4. The AI-based intelligent error correction method for hospital electronic medical records according to claim 3, characterized in that, The implementation process of step S3 is as follows: The medical semantic structured intermediate representation is matched field by field with the electronic medical record error correction rule base to identify data format errors and missing field items; Map core medical record elements to a medical knowledge graph, and use graph reasoning to identify diagnostic and treatment logic conflicts and medication contraindications; The priority of error correction is determined by scoring the necessity of error correction, as shown in equation (4): （4）； An initial set of error correction candidates is generated based on the matching and reasoning results, and the error type and associated medical evidence are labeled.

5. The AI-based intelligent error correction method for hospital electronic medical records according to claim 4, characterized in that, The implementation process of step S4 is as follows: Perform data format validation to verify whether the data type, length, and value range of medical record fields conform to medical data standards; Perform medical logic verification to check the clinical logic consistency between medical records, medication information, and test indicators; Perform clinical standard verification to confirm that the medical record content complies with the national medical institution medical record management standards and clinical diagnosis and treatment guidelines; The precise location of anomalies is achieved by calculating the extended attribute matching degree, as shown in equation (5): （5）； The value ranges from 0 to 1. This item was identified as an anomaly. δ is the weighting coefficient, with a value of 0.45; Accurate error correction results and anomalies to be confirmed are generated based on the stratified verification results.

6. The AI-based intelligent error correction method for hospital electronic medical records according to claim 5, characterized in that, The implementation process of step S5 is as follows: Automatically correct low-risk errors in the accurate error correction results and record the correction trajectory simultaneously; The abnormal items to be confirmed are pushed to the corresponding department's medical staff according to the error risk level, and the abnormal content and related medical evidence are displayed; Receive verification feedback from medical staff. If the verification is successful, the medical record is corrected. If the verification fails, the feedback information is extracted and the error correction rule base is updated. After the correction is completed, a corrected version of the electronic medical record is generated, retaining the association mapping between the original medical record and the corrected record.

7. The AI-based intelligent error correction method for hospital electronic medical records according to claim 6, characterized in that, The implementation process of the large-scale medical model used in step S2 is as follows: A large-scale medical model with a semantic understanding accuracy of ≥92% and a single medical record processing speed of ≤2 seconds was selected as the base model. Based on a fine-tuned model using a corpus of hospital electronic medical records, the objectives are: accuracy of medical record element recognition ≥96%, constraint extraction accuracy ≥93%, and logical inference accuracy ≥91%. Model performance is optimized by employing model quantization and knowledge distillation techniques, with a processing time of ≤25 seconds for a single full medical record; a collaborative mechanism between the main model and the sub-specialty models is established, and the accuracy of analysis is improved through result fusion. We import national clinical practice guidelines and drug contraindications dictionaries, and regularly update medical knowledge and treatment rules.

8. The AI-based intelligent error correction method for hospital electronic medical records according to claim 7, characterized in that, The implementation process of the medical data triple verification engine in step S4 is as follows: Configure the verification rules for data format, medical logic, and clinical standards. The rules include trigger conditions, verification results, and priority levels from 1 to 10. It has a built-in national medical record management standard and clinical diagnosis and treatment guideline library, automatically applies standard verification rules, and pushes the confirmation to the medical staff when there are rule conflicts. Handling special verification scenarios involving multidisciplinary joint diagnosis and treatment, special diseases, and emergency medical records; It provides verification rule templates and a visual configuration interface, and supports hot loading of rules and recording of change logs.

9. The AI-based intelligent error correction method for hospital electronic medical records according to claim 8, characterized in that, The medical record correction output and archiving process in step S5 is as follows: Generate corrected electronic medical record files, error correction reports, and anomaly verification records; Generates medical record data access permission configuration files and correction log archives, supporting integration with hospital HIS and EMR systems; Generate clinical data quality inspection reports based on error correction results, and support exporting them to medical standard formats; It enables medical record version management and error correction data traceability, and supports multi-condition querying of error correction records.

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