Electronic nursing medical record safety management system based on block chain

Abstract

The invention discloses an electronic nursing medical record safety management system based on a block chain, and relates to the technical field of medical informatization. The system comprises a user management module, a block chain encryption module, a data consistency verification module, a data integrity verification module, a data analysis and evaluation module, a data transmission module and a data management module which are connected through electric signals. And the user management module adopts an identity authentication technology of a block chain to realize registration, login, authority distribution and logout functions of user information. According to the electronic nursing medical record safety management system based on the block chain, through the data consistency verification module and the data integrity verification module, the consistency and integrity of the electronic nursing medical record data are ensured, so that the system can discover the inconsistency and inintegrity of the data in time, corresponding correction measures are taken, data errors are reduced, and the safety of the electronic nursing medical record safety management system is improved. And data quality is improved.

Description

Technical Field

[0001] The present invention relates to the field of medical information technology, and in particular to a blockchain-based electronic nursing medical record security management system. Background Art

[0002] With the rapid development of medical informatization, electronic medical records, as one of the important medical information resources, are directly related to the quality and efficiency of medical care through their management and utilization. However, medical institutions currently generally adopt decentralized medical record management systems, which makes it difficult to share patient information between different institutions. This not only increases medical costs, but also affects the continuity and quality of medical services. Blockchain technology, with its decentralization, immutability, and high security, provides a new approach to solving problems in electronic medical record management.

[0003] During the data transmission and storage process of electronic nursing medical records, data transmission errors and insufficient integrity are prone to occur, which in turn leads to defects in the application of electronic nursing medical record data. Therefore, how to conduct a comprehensive verification and analysis of the consistency and integrity of electronic nursing medical record data and evaluate the application risk of the data is the problem we need to solve. To this end, this paper proposes an electronic nursing medical record security management system based on blockchain. Summary of the invention

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: a blockchain-based electronic nursing medical record security management system, the system includes a user management module, a blockchain encryption module, a data consistency verification module, a data integrity verification module, a data analysis and evaluation module, a data transmission module and a data management module, wherein the modules are connected by electrical signals;

[0005] The user management module adopts the identity authentication technology of blockchain to realize the functions of user information registration, login, authority allocation and logout, ensure the authenticity and security of user identity, and support multi-role access control function to meet the access needs of different users to medical record data;

[0006] The blockchain encryption module encrypts medical information based on the distributed storage and encryption algorithm of the blockchain to ensure the security of electronic nursing medical records during transmission and storage, prevent data from being tampered with or leaked, and thus ensure the authenticity and integrity of medical record data;

[0007] The data consistency verification module verifies the consistency of the electronic nursing medical record data, compares heterogeneous data, and analyzes whether there are differences and contradictions between the data. Once data inconsistency is found, an alarm is immediately issued and corresponding corrective measures are taken, thereby reducing the failure rate of data consistency verification;

[0008] The data integrity verification module is used to verify the integrity of the electronic nursing medical record data, analyze the proportion of data errors through integrity verification, ensure that the data is not lost or damaged during transmission and storage, and mark the medical record data with integrity verification errors and remind relevant personnel to handle them to reduce the integrity verification error rate;

[0009] The data analysis and evaluation module is used to comprehensively analyze and evaluate the electronic nursing medical record data, and to evaluate and warn of the application risks of the data in combination with the data consistency and integrity verification results, so as to provide support for medical decision-making;

[0010] The data transmission module adopts the distributed transmission technology of blockchain to transmit electronic nursing medical record data, ensure the security and reliability of data during transmission, support multi-channel transmission and breakpoint resume function of data, so as to improve the efficiency and stability of data transmission;

[0011] The data management module is used to store electronic nursing medical record data. By utilizing the distributed storage characteristics of blockchain, it can achieve secure storage and efficient access to data, support data backup and recovery functions to prevent data loss or damage from affecting medical work, and record the operation log of electronic nursing medical record data. By recording the creation, modification, and deletion operation records of data, it can trace and audit data operation behaviors, which is of great significance for discovering and handling problems in the data operation process.

[0012] Preferably, the user management module specifically includes:

[0013] Enter the user's basic information and registration information, including name, gender, date of birth, ID number, fill in the user's address, and select the user role (for determining user permissions when querying information later), and verify the entered user's basic information and registration information to ensure their authenticity and completeness;

[0014] After the user's basic information and registration information are verified, the registration information is encrypted and stored on the blockchain, and a blockchain address is created to generate a public-private key pair. The private key is kept safe by the user, and the public key is used to identify the user on the blockchain.

[0015] The system packages the user's public key and registration information into a transaction and broadcasts it to the blockchain network. It uses the nodes in the blockchain network to verify the transaction. After confirmation, the transaction is packaged into a block and added to the blockchain to complete the registration.

[0016] The user enters the username and password to log in. The system verifies the username and password and requests the user's public key. The user signs the login request with the private key, and the system verifies the validity of the signature with the public key.

[0017] After successful login, the system assigns access rights according to the user's role and responsibilities, associates the permission information with the user's public key, and records it on the blockchain. The user's permission changes are recorded on the blockchain by creating new transactions, ensuring the transparency and traceability of permission changes.

[0018] Different user roles are defined according to the user's role and responsibilities, namely doctors, nurses, administrators, and patients, and permission sets are preset for each role to define the scope of data access and the type of operation. The user's blockchain address is associated with his role, and permissions are assigned based on the address and role. When a user tries to access data, the system checks his address and role to ensure that his request meets the permission requirements.

[0019] Preferably, the blockchain encryption module specifically includes:

[0020] Export the original data of electronic nursing medical records from the hospital's electronic nursing medical record system, and pre-process the original data to remove redundant information and format the data to ensure the accuracy and consistency of the data;

[0021] By utilizing the distributed storage characteristics of blockchain technology, the pre-processed original data of electronic nursing medical records is dispersedly stored on multiple nodes. Each node stores a copy of the data, and the consensus algorithm of blockchain is used between nodes to maintain data consistency and immutability.

[0022] Apply encryption algorithms on the nodes to encrypt the pre-processed raw data, where the encryption algorithms include symmetric encryption algorithms (AES), asymmetric encryption algorithms (RSA) and hash algorithms;

[0023] During the encryption process, a hierarchical key management method is adopted to manage the master key and the data encryption key separately. The master key is used to encrypt and decrypt the data encryption key, and the data encryption key is used to actually encrypt and decrypt the data. The keys are properly managed to ensure the security of the data.

[0024] Preferably, in the data consistency verification module, the specific process of data consistency verification includes:

[0025] Further analyze the preprocessed electronic nursing medical record data, perform data consistency verification, and set the data consistency verification standard based on the consistency ratio, numerical difference and timestamp mismatch indicators of data verification;

[0026] Use data comparison technology to compare different source data one by one, combine the set data verification indicators and standards, calculate the data consistency verification index, and quantify the data consistency verification failure rate;

[0027] Analyze the calculated data consistency check index, identify differences and contradictions between data, count the cases where data consistency check fails, and analyze the changing trend of failure rate;

[0028] Once data inconsistency is found, the alarm mechanism is triggered immediately, and relevant personnel are notified via email, SMS, or instant messaging, and appropriate corrective measures are taken, such as data correction or update, to reduce the failure rate of data consistency verification;

[0029] Record all data consistency verification activities, including verification time, verification results and measures taken, and generate a data consistency report based on the recorded verification activities and data consistency verification index. The report includes the overall situation of data consistency, detailed analysis of differences and contradictions, and the implementation effect of corrective measures, etc., to provide support for data management and decision-making.

[0030] Preferably, in the data integrity verification module, the specific process of data integrity verification includes:

[0031] Receive pre-processed electronic nursing medical record data from different sources, use the CRC cyclic redundancy check algorithm to check the electronic nursing medical record data, calculate the check value of the data, and compare it with the check value of the original data. If the check value does not match, it is considered that the data is lost or damaged during transmission or storage, and then determine the erroneous data with integrity check abnormality;

[0032] According to the verification results, determine the integrity verification error rate index, and calculate the integrity verification index by combining the ratio of the number of erroneous data to the total number of data. Analyze the integrity verification index to understand the proportion of data errors, and then evaluate the effect of data integrity verification, including the accuracy and efficiency of the verification algorithm;

[0033] Conduct detailed analysis of data with integrity check errors to determine the cause and type of the error, identify the specific location of data corruption, such as a single character, field, or entire record, and mark the erroneous data, add a marking field to the data set, and record the error type and location for subsequent processing and tracking;

[0034] Use email, SMS, and instant messaging to send integrity check error data and related information to relevant personnel so that they can handle and correct them in a timely manner. Also, continuously monitor the integrity check error rate and evaluate the effectiveness of the data integrity check module.

[0035] Based on the monitoring results, continuously optimize and improve data integrity verification algorithms and processes to reduce the integrity verification error rate.

[0036] Preferably, the calculation formula of the data consistency check index is:

[0037] ;

[0038] in, is the data consistency check index, It is The numerical difference of the data points, is the mean of all numerical differences, is the standard deviation of all numerical differences, is the total number of data points, is the ratio of successful timestamp matches, is the total number of timestamps, is the adjustment factor, which indicates the influence of timestamp matching on the consistency index;

[0039] The calculation formula of the integrity check index is:

[0040] ;

[0041] in, is the integrity check index, is the number of erroneous data, is the total amount of data, is the adjustment parameter, which represents the standard deviation of the proportion of erroneous data, is the baseline value, indicating the average value of the proportion of erroneous data.

[0042] Preferably, in the data analysis and assessment module, the data application risk assessment and early warning process includes:

[0043] Integrate the analysis results of the electronic nursing medical record data by the data consistency check module and the data integrity check module, extract the electronic nursing medical record data with abnormalities, that is, the data that failed the consistency check or the integrity check was wrong, and integrate them to obtain the abnormal data set;

[0044] Combine historical data to analyze the severity of abnormal electronic nursing medical record data in abnormal data sets, evaluate the possible impact of abnormal data on medical decision-making, patient treatment, etc., and then analyze the application risk of data and determine the risk level of abnormal data, which are slight risk level, medium risk level and severe risk level;

[0045] Combine the calculation results of the data consistency check index and the integrity check index, and evaluate the data update frequency to understand the timeliness and reliability of the data, and comprehensively calculate the data risk assessment coefficient to further evaluate the application risk of the data;

[0046] Based on the data risk assessment coefficient, the corresponding evaluation thresholds are set for each risk level of abnormal data, and the data of each risk level is analyzed in detail to identify potential risk points and impacts, analyze possible causes of data errors, error types, and the specific impacts that erroneous data may have on patient treatment, medical decision-making, etc.;

[0047] Based on the analysis results, early warning information is issued to relevant personnel (medical staff, data managers) to remind them to pay attention to and deal with potential data risks, and a detailed data report is generated, including data consistency test results, integrity check results, risk assessment coefficients and risk level information.

[0048] Preferably, the calculation formula of the data risk assessment coefficient is:

[0049] ;

[0050] in, is the data risk assessment coefficient, It is the data consistency check index, which reflects the result of data consistency check. It is the integrity check index, which reflects the result of data integrity check. is the data update frequency, which indicates how frequently the data is updated. , , is the weight coefficient, which is used to adjust the influence of different indicators on risk assessment. The value range of is between 0 and 1;

[0051] Get the value of the data risk assessment coefficient and the preset assessment threshold of the risk level to match the three risk levels, which are:

[0052] Minor risk level: ;

[0053] Medium risk level: ;

[0054] Severe risk level: ;

[0055] in, is the data risk assessment coefficient, is the lower threshold corresponding to the slight risk level and the upper threshold corresponding to the medium risk level, is the lower threshold corresponding to the medium risk level and the upper threshold corresponding to the severe risk level, , .

[0056] Preferably, in the data transmission module, the process of transmitting electronic nursing medical record data includes:

[0057] Use the blockchain encryption module to package and encrypt the electronic nursing medical record data in the prescribed format in preparation for transmission;

[0058] According to the network topology and node status, nodes are screened for data transmission, with priority given to nodes with short distance, high bandwidth and good stability. Multiplexing technology is used to divide data into multiple data packets, which are transmitted in parallel through selected nodes to increase the transmission speed.

[0059] During the data transmission process, if there is a network interruption or other transmission errors, the system automatically detects the breakpoint and uses the blockchain's breakpoint resume function to continue transmission from the breakpoint after the network is restored, instead of resending all the data;

[0060] After the data transmission is completed, the receiver reassembles the received data blocks into the original format and verifies the received data to ensure that the data has not been tampered with or damaged during the transmission process. After verifying that the data is correct, the receiver sends a confirmation message to the sender, indicating that the data has been successfully received.

[0061] Preferably, the data management module specifically includes:

[0062] Utilizing the distributed storage characteristics of blockchain, verified and encrypted electronic nursing medical record data is dispersedly stored on multiple nodes in the blockchain network;

[0063] Perform data synchronization operations on the stored electronic nursing medical record data so that each node can save the latest copy of the data, and back up the data regularly to different storage media or nodes to prevent data loss or damage;

[0064] Implement access control based on user roles, permission levels, and data sensitivity, so that authorized users can access electronic nursing medical record data through the blockchain network. During the access process, the system performs identity authentication and permission checks to ensure that only legitimate users can access the data;

[0065] When data operations occur, the logging mechanism is automatically triggered to capture and record all operations on electronic nursing medical record data;

[0066] Store the collected log information in a secure log database and adopt appropriate encryption and protection measures to prevent the log information from being tampered with or accessed without authorization. Back up the log data regularly and store it in a cloud environment to prevent data loss or damage.

[0067] Audit log data regularly and use log analysis tools to analyze log data to identify potential data operation problems or security threats;

[0068] For any problems or abnormal behaviors found during data operation, an alarm message will be issued and relevant personnel will be notified to handle the situation.

[0069] The present invention provides a blockchain-based electronic nursing medical record security management system, which has the following beneficial effects:

[0070] 1. The blockchain-based electronic nursing medical record security management system ensures the consistency and integrity of electronic nursing medical record data through data consistency verification modules and data integrity verification modules, so that the system can promptly detect data inconsistencies and incompleteness, thereby taking corresponding corrective measures to reduce data errors and improve data quality.

[0071] 2. The blockchain-based electronic nursing medical record security management system compares data from different sources, calculates the consistency check index, analyzes whether there are differences and contradictions between the data, quantifies the failure rate of data consistency check, and analyzes the proportion of data errors by calculating the integrity check index to ensure that the data is not lost or damaged during transmission and storage, thereby further improving the integrity and availability of the data.

[0072] 3. The blockchain-based electronic nursing medical record security management system calculates the data risk assessment coefficient by comprehensively analyzing the data consistency check index, integrity check index and data update frequency, and then evaluates the application risk of the data, divides the corresponding evaluation thresholds for each risk level of abnormal data, and conducts detailed analysis of the data at each risk level to identify potential risk points and impacts, so that medical institutions can identify and respond to data risks in a timely manner, and take measures to prevent or mitigate the impact of data problems on patient treatment and medical decisions. In addition, the data reports generated by the system provide support for data management and decision-making, helping medical institutions optimize data management processes and improve the quality of medical services and patient safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 This is a module structure diagram of a blockchain-based electronic nursing medical record security management system of the present invention;

[0074] Figure 2 This is a flow chart of data consistency verification of the present invention;

[0075] Figure 3 It is a flow chart of data integrity verification of the present invention;

[0076] Figure 4 This is a flow chart for the assessment and early warning of data application risks of the present invention. DETAILED DESCRIPTION

[0077] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for the purpose of illustration and description, and are not intended to be exhaustive or to limit the present invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention, and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

[0078] The first embodiment, as Figures 1 to 3 As shown, the present invention provides a technical solution: an electronic nursing medical record security management system based on blockchain, the system includes a user management module, a blockchain encryption module, a data consistency verification module, a data integrity verification module, a data analysis and evaluation module, a data transmission module and a data management module, wherein the modules are connected by electrical signals;

[0079] The user management module uses blockchain identity authentication technology to realize user information registration, login, authority allocation and logout functions, ensure the authenticity and security of user identity, and support multi-role access control functions to meet the access needs of different users to medical record data. Enter the user's basic information and registration information, including name, gender, date of birth, ID number, fill in the user address and select the user role (for subsequent user authority judgment when querying information), and verify the entered user basic information and registration information to ensure their authenticity and integrity. After the user's basic information and registration information are verified, the registration information is encrypted and stored on the blockchain, and a blockchain address is created to generate a public-private key pair. The private key is safely kept by the user, and the public key is used to identify the user identity on the blockchain. The system packages the user's public key and registration information into a transaction, broadcasts it to the blockchain network, and uses the nodes in the blockchain network to verify the transaction. After confirmation, the transaction is packaged into a block and added to the blockchain to complete the registration. The user enters the username and password to log in. The system verifies the username and password and requests the user's public key. The user signs the login request with the private key. The system verifies the validity of the signature through the public key. After successful login, the system assigns access rights according to the user's role and responsibilities, associates the permission information with the user's public key, and records it on the blockchain. The user's permission changes are recorded on the blockchain by creating new transaction records to ensure the transparency and traceability of permission changes. Different user roles are defined according to the user's role and responsibilities, namely doctors, nurses, administrators, and patients, and permission sets are preset for each role to define the data range and operation type to be accessed. The user's blockchain address is associated with his role, and permissions are assigned according to the address and role. When a user tries to access data, the system checks his address and role to ensure that his request meets the permission requirements.

[0080] The blockchain encryption module encrypts medical information based on the distributed storage and encryption algorithm of the blockchain to ensure the security of electronic nursing medical records during transmission and storage, prevent data from being tampered with or leaked, and thus ensure the authenticity and integrity of medical record data. The original data of the electronic nursing medical record is exported from the hospital's electronic nursing medical record system, and the original data is pre-processed to remove redundant information and format the data to ensure data accuracy and consistency. The distributed storage characteristics of blockchain technology are used to store the pre-processed original data of the electronic nursing medical record in multiple nodes. Each node stores a copy of the data, and the nodes maintain data consistency and immutability through the consensus algorithm of the blockchain. The encryption is applied on the nodes. The encryption algorithm encrypts the pre-processed raw data, including symmetric encryption algorithm (AES), asymmetric encryption algorithm (RSA) and hash algorithm. The symmetric encryption algorithm can quickly encrypt and decrypt data and is suitable for scenarios with large amounts of data; the asymmetric encryption algorithm is used to ensure the security of data transmission, such as digital signatures and key exchange; the hash algorithm is used to generate a unique identifier for the data (such as a hash value) to verify the integrity and non-tampering of the data. In the encryption process, the master key and the data encryption key are managed separately in a hierarchical key management manner. The master key is used to encrypt and decrypt the data encryption key, and the data encryption key is used to actually encrypt and decrypt the data. The keys are properly managed to ensure the security of the data.

[0081] The data consistency verification module verifies the consistency of the electronic nursing medical record data, compares heterogeneous data, and analyzes whether there are differences and contradictions between the data. Once data inconsistency is found, an alarm is immediately issued and corresponding corrective measures are taken to reduce the failure rate of data consistency verification. The pre-processed electronic nursing medical record data is further analyzed to perform data consistency verification. Based on the consistency ratio, numerical difference and timestamp mismatch indicators of data verification, the standard for data consistency verification is set. The consistency ratio sets an acceptable consistency ratio threshold of 0.1. When the calculated consistency ratio is lower than this threshold, the data consistency is considered acceptable. A tolerance range of ±5% for numerical differences is determined. When the numerical difference between different source data exceeds this range, it is considered that the data is inconsistent. A time window of ±1 minute for timestamp matching is set. When the timestamp difference between different source data exceeds this time window, it is considered that the timestamp Mismatch, use data comparison technology to compare different source data one by one, combine the set data verification indicators and standards, calculate the data consistency verification index, quantify the data consistency verification failure rate, analyze the calculated data consistency verification index, identify the differences and contradictions between the data, count the failures of statistical consistency verification, and analyze the changing trend of the failure rate. Once data inconsistency is found, immediately trigger the alarm mechanism, notify relevant personnel by email, SMS, instant messaging, and take corresponding corrective measures, such as data correction or update, to reduce the data consistency verification failure rate, record all data consistency verification activities, including verification time, verification results and measures taken, and generate a data consistency report based on the recorded verification activities and data consistency verification index. The report contains the overall situation of data consistency, detailed analysis of differences and contradictions, and the implementation effect of corrective measures, etc., to provide support for data management and decision-making;

[0082] Furthermore, the calculation formula of the data consistency check index is:

[0083] ;

[0084] in, is the data consistency check index, It is The numerical difference of the data points, is the mean of all numerical differences, is the standard deviation of all numerical differences, is the total number of data points, is the ratio of successful timestamp matches, is the total number of timestamps, is the adjustment factor, which indicates the influence of timestamp matching on the consistency index. The value range is between 0 and 1. A value of 1 indicates that the data is completely consistent, and a value of 0 indicates that the data is completely inconsistent. As the numerical difference decreases and the timestamp matching ratio increases, The value of tends to 1, indicating that the data consistency is better, and vice versa. The value of tends to 0, indicating that the data consistency is worse;

[0085] The data integrity check module is used to check the integrity of the electronic nursing medical record data, analyze the proportion of data errors through integrity check, ensure that the data is not lost or damaged during transmission and storage, mark the medical record data with integrity check errors and remind relevant personnel to handle them to reduce the integrity check error rate, receive pre-processed electronic nursing medical record data from different sources, use the CRC cyclic redundancy check algorithm to check the electronic nursing medical record data, calculate the data check value, and compare it with the check value of the original data. If the check value does not match, it is considered that the data has been lost or damaged during transmission or storage, and then determine the erroneous data with abnormal integrity check. According to the check result, determine the integrity check error rate index, and calculate it in combination with the ratio of the number of erroneous data to the total number of data. Integrity check index: Analyze the integrity check index to understand the proportion of data errors and then evaluate the effectiveness of data integrity check, including the accuracy and efficiency of the check algorithm. Perform a detailed analysis of the data with integrity check errors to determine the cause and type of the error, identify the specific location of data damage, such as a single character, field, or entire record, and mark the erroneous data. Add a marker field to the data set to record the error type and location for subsequent processing and tracking. Use email, SMS, and instant messaging to send integrity check error data and related information to relevant personnel so that they can handle and correct them in a timely manner. Continue to monitor the integrity check error rate, evaluate the effectiveness of the data integrity check module, and continuously optimize and improve the data integrity check algorithm and process based on the monitoring results to reduce the integrity check error rate.

[0086] Furthermore, the calculation formula of the integrity check index is:

[0087] ;

[0088] in, is the integrity check index, is the number of erroneous data, is the total amount of data, is the adjustment parameter, which represents the standard deviation of the proportion of erroneous data, is the benchmark value, indicating the average value of the proportion of erroneous data. The value range is between 0 and 1. A value of 1 indicates that the data is completely consistent, and a value of 0 indicates that the data is completely inconsistent. As the number of erroneous data increases, The value of tends to 0, indicating that the data integrity is worse. On the contrary, as the number of erroneous data decreases, The value of tends to 1, indicating better data integrity;

[0089] The data analysis and evaluation module is used to conduct comprehensive analysis and evaluation of electronic nursing medical record data, and to evaluate and warn of data application risks based on the results of data consistency and integrity verification, so as to provide support for medical decision-making;

[0090] The data transmission module uses the distributed transmission technology of blockchain to transmit electronic nursing medical record data, ensuring the security and reliability of data during transmission. It supports multi-channel data transmission and breakpoint resume functions to improve the efficiency and stability of data transmission.

[0091] The data management module is used to store electronic nursing medical record data. By utilizing the distributed storage characteristics of blockchain, it can achieve secure storage and efficient access to data, support data backup and recovery functions to prevent data loss or damage from affecting medical work, and record the operation log of electronic nursing medical record data. By recording the creation, modification, and deletion of data operations, it can trace and audit data operation behaviors, which is of great significance for discovering and handling problems in the data operation process.

[0092] The second embodiment is based on the first embodiment. Figure 4 As shown in the figure, in the data analysis and assessment module, the assessment and early warning process of data application risks includes:

[0093] Integrate the analysis results of the electronic nursing medical record data by the data consistency check module and the data integrity check module, extract the electronic nursing medical record data with abnormalities, that is, the data that failed the consistency check or the integrity check was wrong, integrate them to obtain the abnormal data set, and analyze the severity of the abnormal electronic nursing medical record data in the abnormal data set in combination with the historical data, evaluate the possible impact of the abnormal data on medical decision-making, patient treatment, etc., and then analyze the application risk of the data, and determine the risk level of the abnormal data, which are respectively minor risk level, medium risk level and severe risk level. The minor risk level includes some small data inconsistencies or omissions, which have little impact on medical decision-making. The medium risk level involves some errors or omissions in key data, which may have a certain impact on medical decision-making. The severe risk level may involve issues related to patient safety or treatment effectiveness. Key data errors or missing, combined with the calculation results of data consistency check index and integrity check index, and evaluate the data update frequency to understand the timeliness and reliability of the data, comprehensively calculate the data risk assessment coefficient, and then evaluate the application risk of the data. Based on the data risk assessment coefficient, the corresponding evaluation threshold is divided for each risk level of abnormal data, and the data of each risk level is analyzed in detail to identify potential risk points and impacts, analyze the possible causes of data errors, error types, and the specific impacts that erroneous data may have on patient treatment, medical decision-making, etc. According to the analysis results, early warning information is issued to relevant personnel (medical staff, data managers) to remind them to pay attention to and deal with potential data risks, and generate detailed data reports, including data consistency test results, integrity check results, risk assessment coefficients and risk level information;

[0094] Furthermore, the calculation formula of the data risk assessment coefficient is:

[0095] ;

[0096] in, is the data risk assessment coefficient, It is the data consistency check index, which reflects the result of data consistency check. It is the integrity check index, which reflects the result of data integrity check. is the data update frequency, which indicates how frequently the data is updated. , , is the weight coefficient, which is used to adjust the influence of different indicators on risk assessment. The value range is between 0 and 1, with a value of 1 indicating that the data is completely risk-free and a value of 0 indicating that the data risk is extremely high. and The increase of (that is, the better the data consistency and integrity check results are) and The reduction of (that is, the lower the frequency of data update), The value of tends to 1, indicating that the data risk is lower, and vice versa. The value of tends to 0, indicating higher data risk;

[0097] Get the value of the data risk assessment coefficient and the preset assessment threshold of the risk level to match the three risk levels, which are:

[0098] Minor risk level: ;

[0099] Medium risk level: ;

[0100] Severe risk level: ;

[0101] in, is the data risk assessment coefficient, is the lower threshold corresponding to the slight risk level and the upper threshold corresponding to the medium risk level, is the lower threshold corresponding to the medium risk level and the upper threshold corresponding to the severe risk level, , ;

[0102] In the data transmission module, the process of electronic nursing medical record data transmission includes:

[0103] Use the blockchain encryption module to package and encrypt the electronic nursing medical record data in the prescribed format in preparation for transmission to ensure the security of the data during transmission, including using symmetric encryption, asymmetric encryption or hash algorithm to protect the data. According to the network topology and node status, select nodes for data transmission, give priority to nodes with short distance, high bandwidth and good stability, and use multiplexing technology to divide the data into multiple data packets, which are transmitted in parallel through selected nodes to increase the transmission speed. During the data transmission process, if a network interruption or other transmission error occurs, the system automatically detects the breakpoint and uses the breakpoint resumption function of the blockchain to continue transmission from the breakpoint after the network is restored, instead of resending all the data. After the data transmission is completed, the receiver reassembles the received data blocks into the original format and verifies the received data to ensure that the data has not been tampered with or damaged during the transmission process. After verifying that the data is correct, the receiver sends a confirmation message to the sender, indicating that the data has been successfully received;

[0104] The data management module specifically includes:

[0105] Utilizing the distributed storage characteristics of blockchain, the verified and encrypted electronic nursing medical record data is dispersedly stored on multiple nodes in the blockchain network. Data synchronization operations are performed on the stored electronic nursing medical record data so that each node saves the latest copy of the data and backs up the data. The data is regularly backed up to different storage media or nodes to prevent data loss or damage. Access control is implemented based on user roles, authority levels, and data sensitivity, so that authorized users can access electronic nursing medical record data through the blockchain network. During the access process, the system performs identity authentication and authority checks to ensure that only legitimate users can access the data. When data operations occur, the logging mechanism is automatically triggered to capture and record all operations on the electronic nursing medical record data, including But not limited to the creation, modification, and deletion of data, etc., record the specific operation time, operation content, operation results, and operator information, store the collected log information in a secure log database, and adopt appropriate encryption and protection measures to prevent log information from being tampered with or unauthorized access, regularly back up log data and store it in a cloud environment to prevent data loss or damage, regularly audit log data, and use log analysis tools to analyze log data to identify potential data operation problems or security threats, issue alarm information for problems or abnormal behaviors discovered during data operations, and notify relevant personnel to handle them. Relevant personnel can take appropriate measures based on the alarm information to solve the problem or prevent the situation from further deteriorating.

[0106] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without creative work should fall within the scope of protection of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention are implemented according to the conventional means in the field unless otherwise specified and limited.

Claims

1. A blockchain-based electronic nursing medical record security management system, characterized in that: The system includes a user management module, a blockchain encryption module, a data consistency check module, a data integrity check module, a data analysis and evaluation module, a data transmission module and a data management module, wherein the modules are connected by electrical signals; The user management module uses blockchain identity authentication technology to implement user information registration, login, authority allocation and logout functions; The blockchain encryption module performs encryption operations on medical information based on the distributed storage and encryption algorithm of the blockchain; The data consistency verification module verifies the consistency of the electronic nursing medical record data, compares heterogeneous data, and analyzes whether there are differences and contradictions between the data; The data integrity verification module is used to verify the integrity of the electronic nursing medical record data; The data analysis and evaluation module is used to comprehensively analyze and evaluate the electronic nursing medical record data, and to evaluate and warn of the application risks of the data in combination with the data consistency and integrity verification results; The data transmission module adopts the distributed transmission technology of blockchain to transmit electronic nursing medical record data; The data management module is used to store electronic nursing medical record data and record the operation log of the electronic nursing medical record data.

2. According to the blockchain-based electronic nursing medical record security management system of claim 1, it is characterized by: The user management module specifically includes: Enter the user's basic information and registration information, including name, gender, date of birth, ID number, fill in the user's address, select the user role, and verify the entered user's basic information and registration information; After the user's basic information and registration information are verified, the registration information is encrypted and stored on the blockchain, and a blockchain address is created to generate a public-private key pair. The private key is kept safe by the user, and the public key is used to identify the user on the blockchain. The system packages the user's public key and registration information into a transaction and broadcasts it to the blockchain network. It uses the nodes in the blockchain network to verify the transaction. After confirmation, the transaction is packaged into a block and added to the blockchain to complete the registration. The user enters the username and password to log in. The system verifies the username and password and requests the user's public key. The user signs the login request with the private key, and the system verifies the validity of the signature with the public key. After successful login, the system assigns access rights based on the user’s role and responsibilities, associates the permission information with the user’s public key, and records it on the blockchain; Different user roles are defined according to the user's role and responsibilities, namely doctors, nurses, administrators, and patients, and permission sets are preset for each role, defining the scope of data access and the type of operation, and associating the user's blockchain address with his or her role.

3. According to claim 2, a blockchain-based electronic nursing medical record security management system is characterized by: The blockchain encryption module specifically includes: Export the original data of electronic nursing medical records from the hospital's electronic nursing medical record system and pre-process the original data; By utilizing the distributed storage characteristics of blockchain technology, the pre-processed raw data of electronic nursing medical records are dispersedly stored on multiple nodes; Applying an encryption algorithm on the node to encrypt the pre-processed original data, wherein the encryption algorithm includes a symmetric encryption algorithm, an asymmetric encryption algorithm and a hash algorithm; During the encryption process, a hierarchical key management method is adopted to manage the master key and the data encryption key separately. The master key is used to encrypt and decrypt the data encryption key, and the data encryption key is used to actually encrypt and decrypt the data.

4. According to claim 3, a blockchain-based electronic nursing medical record security management system is characterized by: In the data consistency verification module, the specific process of data consistency verification includes: Further analyze the preprocessed electronic nursing medical record data, perform data consistency verification, and set the data consistency verification standard based on the consistency ratio, numerical difference and timestamp mismatch indicators of data verification; Use data comparison technology to compare different source data one by one, combine the set data verification indicators and standards, calculate the data consistency verification index, and quantify the data consistency verification failure rate; Analyze the calculated data consistency check index, identify differences and contradictions between data, count the cases where data consistency check fails, and analyze the changing trend of failure rate; Once data inconsistency is found, the alarm mechanism is triggered immediately, and relevant personnel are notified via email, SMS, or instant messaging, and appropriate corrective measures are taken; Record all data consistency verification activities, including verification time, verification results and measures taken, and generate a data consistency report based on the recorded verification activities and data consistency verification index.

5. According to claim 4, a blockchain-based electronic nursing medical record security management system is characterized by: In the data integrity verification module, the specific process of data integrity verification includes: Receive pre-processed electronic nursing medical record data from different sources, use the CRC cyclic redundancy check algorithm to check the electronic nursing medical record data, calculate the check value of the data, and compare it with the check value of the original data. If the check value does not match, it is considered that the data is lost or damaged during transmission or storage, and then determine the erroneous data with integrity check abnormality; According to the verification results, determine the integrity verification error rate index, and calculate the integrity verification index by combining the ratio of the number of erroneous data to the total number of data. Analyze the integrity verification index to understand the proportion of data errors and then evaluate the effect of data integrity verification. Conduct detailed analysis of data with integrity check errors to determine the cause and type of the error, identify the specific location of data corruption, and mark the erroneous data. Add a marker field to the data set to record the error type and location. Use email, SMS, and instant messaging to send integrity check error data and related information to relevant personnel, and continuously monitor the integrity check error rate to evaluate the effectiveness of the data integrity check module; Based on the monitoring results, continuously optimize and improve data integrity verification algorithms and processes to reduce the integrity verification error rate.

6. According to the blockchain-based electronic nursing medical record security management system of claim 5, it is characterized by: The calculation formula of the data consistency check index is: ； in, is the data consistency check index, It is The numerical difference of the data points, is the mean of all numerical differences, is the standard deviation of all numerical differences, is the total number of data points, is the ratio of successful timestamp matches, is the total number of timestamps, is the adjustment factor, which indicates the influence of timestamp matching on the consistency index; The calculation formula of the integrity check index is: ； in, is the integrity check index, is the number of erroneous data, is the total amount of data, is the adjustment parameter, which represents the standard deviation of the proportion of erroneous data, is the baseline value, indicating the average value of the proportion of erroneous data.

7. According to claim 6, a blockchain-based electronic nursing medical record security management system is characterized by: In the data analysis and assessment module, the data application risk assessment and early warning process includes: Integrate the analysis results of the electronic nursing medical record data by the data consistency check module and the data integrity check module, extract the electronic nursing medical record data with abnormalities, that is, the data that failed the consistency check or the integrity check was wrong, and integrate them to obtain the abnormal data set; Combine historical data to analyze the severity of abnormal electronic nursing medical record data in the abnormal data set, and then analyze the application risk of the data, and determine the risk level of the abnormal data, which are slight risk level, medium risk level and severe risk level; Combine the calculation results of the data consistency check index and the integrity check index, and evaluate the data update frequency to understand the timeliness and reliability of the data, and comprehensively calculate the data risk assessment coefficient to further evaluate the application risk of the data; Based on the data risk assessment coefficient, the corresponding evaluation thresholds are set for each risk level of abnormal data, and the data of each risk level is analyzed in detail to identify potential risk points and impacts; Based on the analysis results, early warning information is issued to relevant personnel to remind them to pay attention to and deal with potential data risks, and a detailed data report is generated, including data consistency check results, integrity check results, risk assessment coefficients and risk level information.

8. According to claim 7, a blockchain-based electronic nursing medical record security management system is characterized by: The calculation formula of the data risk assessment coefficient is: ； in, is the data risk assessment coefficient, It is the data consistency check index, which reflects the result of data consistency check. It is the integrity check index, which reflects the result of data integrity check. is the data update frequency, which indicates how frequently the data is updated. , , is the weight coefficient, which is used to adjust the influence of different indicators on risk assessment. The value range of is between 0 and 1; Get the value of the data risk assessment coefficient and the preset assessment threshold of the risk level to match the three risk levels, which are: Minor risk level: ; Medium risk level: ; Severe risk level: ; in, is the data risk assessment coefficient, is the lower threshold corresponding to the slight risk level and the upper threshold corresponding to the medium risk level, is the lower threshold corresponding to the medium risk level and the upper threshold corresponding to the severe risk level, , .

9. According to claim 8, a blockchain-based electronic nursing medical record security management system is characterized by: In the data transmission module, the process of electronic nursing medical record data transmission includes: Use the blockchain encryption module to package and encrypt the electronic nursing medical record data in the prescribed format in preparation for transmission; According to the network topology and node status, nodes are selected for data transmission, and multiplexing technology is used to divide the data into multiple data packets for parallel transmission through the selected nodes; During the data transmission process, if there is a network interruption or other transmission errors, the system will automatically detect the breakpoint and use the blockchain's breakpoint resume function to continue the transmission from the breakpoint after the network is restored; After the data transmission is completed, the receiver reassembles the received data blocks into the original format and verifies the received data. After verifying that the data is correct, the receiver sends a confirmation message to the sender, indicating that the data has been successfully received.

10. According to claim 9, a blockchain-based electronic nursing medical record security management system is characterized by: The data management module specifically includes: Utilizing the distributed storage characteristics of blockchain, verified and encrypted electronic nursing medical record data is dispersedly stored on multiple nodes in the blockchain network; Perform data synchronization operations on the stored electronic nursing medical record data so that each node saves the latest data copy and backs up the data; Implement access control based on user roles, permission levels, and data sensitivity, so that authorized users can access electronic nursing records data through the blockchain network; When data operations occur, the logging mechanism is automatically triggered to capture and record all operations on electronic nursing medical record data; Store the collected log information in a secure log database, back up the log data regularly, and store it in a cloud environment; Audit log data regularly and use log analysis tools to analyze log data to identify potential data operation problems or security threats; For any problems or abnormal behaviors found during data operation, an alarm message will be issued and relevant personnel will be notified to handle the situation.

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