Automatic electronic medical record generating and sharing system for dental grading diagnosis and treatment

Abstract

The invention relates to the technical field of dental diagnosis and treatment systems, and discloses a dental grading diagnosis and treatment automatic electronic medical record generation and sharing system. A medical record data acquisition module of the system integrates diagnosis and treatment data of different levels of medical institutions and historical treatment information of patients to generate a preliminary medical record data set; the grading diagnosis and treatment judgment module analyzes the illness state based on the preliminary data set, divides diagnosis and treatment levels and corresponding medical institution types, and determines referral necessity and priority; the electronic medical record automatic generation module extracts key information and standardized terms, matches medical record formats of corresponding levels, integrates structured data and unstructured clinical notes, and generates a complete electronic medical record document; the data sharing interaction module establishes a cross-mechanism data transmission channel, controls medical record access authority and modifies records, and realizes information sharing; the authority management control module sets access authority levels of different user roles, records operation logs and access traces, and checks abnormal access behaviors.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of dental diagnosis and treatment system, in particular to a dental hierarchical diagnosis and treatment automatic electronic medical record generation and sharing system. BACKGROUND

[0002] In the current dental medical system, different levels of medical institutions bear differentiated diagnosis and treatment responsibilities. Primary medical institutions mostly deal with routine cases such as dental caries filling and periodontal basic treatment, while tertiary hospitals focus on complex root canal treatment and maxillofacial surgery. This division of labor mode should form an efficient diagnosis and treatment network, but in actual operation, the diagnosis and treatment data between different levels of institutions are always relatively fragmented. When patients are transferred to higher-level hospitals after treatment in primary medical institutions, their previous diagnosis and treatment information is often transferred in the form of paper medical records, scattered electronic documents or oral descriptions, some of which lack key data and have ambiguous records, making it difficult for doctors in higher-level hospitals to fully understand the evolution of the disease.

[0003] The promotion and application of electronic medical records should alleviate this dilemma, but existing electronic medical record systems are mostly developed independently by single institutions, with significant differences in data format and terminology standards. The medical record templates commonly used by primary institutions focus on basic symptom recording, while the templates used by higher-level hospitals contain a large number of specialized examination parameters, making it impossible to directly associate the diagnosis and treatment information of the same patient in different systems. Doctors need to manually enter patient information, examination results, treatment plans and other content when generating medical records, which not only consumes a lot of time, but also easily leads to data errors and omissions due to human operation. Some medical institutions still retain the method of scanning and archiving handwritten medical records, and unstructured text information cannot be recognized by other institutions' systems, further exacerbating the obstacles to data circulation.

[0004] During the promotion of hierarchical diagnosis and treatment policy, the rationality and timeliness of referral have become a major problem. Due to the lack of a unified disease assessment standard, primary doctors often over-referral or delay referral due to insufficient information when determining whether to refer. After receiving the referral patients, higher-level hospitals need to arrange various examinations, which not only increases the economic burden of patients, but also wastes medical resources. This information asymmetry can also lead to poor treatment plan continuity, such as the lack of awareness of the basic treatment carried out by primary institutions by higher-level hospitals, leading to repeated treatment or treatment interruption.

[0005] The security and standardization of data sharing links cannot be ignored. Existing cross-institutional data transmission relies on non-special channels such as email and USB, and there is a risk of tampering and leakage of data during transmission. Some medical institutions simplify the process by setting up a rough access permission for medical records, and administrative staff, medical staff and trainees use the same permission account. Once information is leaked, it is difficult to trace the responsibility. There is also a lack of effective measures to protect patient privacy, and non-related personnel can freely access medical record content, which violates medical ethics and data security regulations. These problems jointly restrict the further promotion of dental hierarchical diagnosis and treatment, resulting in low efficiency of medical resource allocation and poor patient experience. SUMMARY

[0006] The purpose of the present application is to provide an automatic electronic medical record generation and sharing system for dental hierarchical diagnosis and treatment to solve the problems raised in the background art.

[0007] To achieve the above purpose, the present application provides an automatic electronic medical record generation and sharing system for dental hierarchical diagnosis and treatment, which comprises:

[0008] A medical record data acquisition module collects diagnosis and treatment data and medical record templates of different levels of medical institutions according to patient basic information and dental diagnosis and treatment records, integrates patient historical medical information and current symptom description, and generates a preliminary medical record data set;

[0009] A hierarchical diagnosis and treatment determination module analyzes the severity of the patient's condition and treatment needs based on the preliminary medical record data set, divides the diagnosis and treatment level and the corresponding medical institution type, determines the necessity and priority order of referral, and generates a hierarchical diagnosis and treatment plan;

[0010] An electronic medical record automatic generation module extracts key information and standardized terminology from patient diagnosis and treatment data based on the hierarchical diagnosis and treatment plan, matches the medical record format requirements of the corresponding level, integrates structured data and unstructured clinical notes, and generates a complete electronic medical record document;

[0011] A data sharing interaction module based on the complete electronic medical record document establishes a data transmission channel between different levels of medical institutions, verifies the security and integrity of data transmission, controls medical record access permissions and modification records, and realizes cross-institutional medical record information sharing;

[0012] A permission management control module sets the access permission level of different user roles based on the cross-institutional medical record information sharing, records user operation logs and data access traces, and audits abnormal access behavior and permission change requests.

[0013] Preferably, the preliminary medical record dataset includes a patient basic information summary, a historical diagnosis and treatment record abstract, and a current symptom classification result, the hierarchical diagnosis and treatment plan includes a disease severity classification, a recommended treatment institution type, and a referral priority ranking, the complete electronic medical record document includes a structured diagnosis and treatment data table, an unstructured clinical note text, and a standardized terminology annotation result, and the cross-institution medical record information sharing includes a data transmission encryption protocol, an access permission control list, and a medical record modification operation record, and the permission management control result includes a user role permission level table, an operation log audit report, and an abnormal access behavior alarm.

[0014] Preferably, the preliminary medical record dataset acquisition step specifically includes:

[0015] receiving patient treatment records and examination reports at various medical institutions, extracting patient basic information and dental disease diagnosis results, integrating historical diagnosis and treatment data in chronological order, and forming a patient diagnosis and treatment timeline;

[0016] analyzing the patient diagnosis and treatment timeline, classifying current symptoms and past medical history according to the relevance of symptom descriptions and examination results, and establishing a symptom and diagnosis correspondence table;

[0017] According to the symptom and diagnosis correspondence table, combining a pre-set medical record template structure, filling in patient basic information, historical diagnosis and treatment abstract, and current symptom details, and generating a preliminary medical record dataset.

[0018] Preferably, the hierarchical diagnosis and treatment plan acquisition step specifically includes:

[0019] According to the disease description and examination indicators in the preliminary medical record dataset, referring to the dental disease classification standard, evaluating the emergency and complexity of the patient's condition, and determining the preliminary diagnosis and treatment level;

[0020] Through the preliminary diagnosis and treatment level, combining the diagnosis and treatment ability range of various medical institutions, matching the appropriate treatment institution type, and determining whether cross-level referral is needed;

[0021] Using the judgment result of whether cross-level referral is needed, combining the patient's geographical location and the medical institution resource load situation, ranking the referral priority, and generating a hierarchical diagnosis and treatment plan.

[0022] Preferably, the complete electronic medical record document acquisition step specifically includes:

[0023] extracting patient disease classification results and target treatment institution types from the hierarchical diagnosis and treatment plan, and determining corresponding electronic medical record templates and data field requirements;

[0024] According to the electronic medical record template and data field requirements, key diagnosis and treatment information is screened from the preliminary medical record data set, converted into standardized medical terminology, and supplemented with structured data tables;

[0025] The structured data tables and unstructured clinical notes are integrated, the content order and format are adjusted according to the medical record writing specifications, and a complete electronic medical record document is generated.

[0026] Preferably, the implementation steps of the cross-institutional medical record information sharing are specifically:

[0027] Based on the content sensitivity level of the complete electronic medical record document, a corresponding encryption algorithm is selected to encrypt the data, and an encrypted medical record data package is generated;

[0028] The qualifications and data access permissions of the receiving medical institution are verified, a secure transmission channel is established, the encrypted medical record data package is transmitted, and the data integrity is verified;

[0029] The access time, operation type and user identity of the medical record data are recorded, a shared operation log is generated, and the log is synchronized to the permission management control module.

[0030] Preferably, the obtaining step of the permission management control result is specifically:

[0031] User access records are extracted from the operation log of the cross-institutional medical record information sharing, compared with a preset role permission level table, and access behaviors beyond the permission range are identified;

[0032] The access behaviors beyond the permission range are analyzed, combined with user historical operation habits and data sensitivity, to determine whether they are abnormal access, and an abnormal behavior analysis report is generated;

[0033] According to the abnormal behavior analysis report, the permission freezing or audit notification is automatically triggered, the user role permission level table is updated, and the permission management control result is generated.

[0034] Preferably, it further includes a medical record data updating module, which receives the diagnosis and treatment results and subsequent medical records of medical institutions based on the cross-institutional medical record information sharing, compares the time stamp and content version in the original medical record data, integrates the new diagnosis and treatment information, and updates the complete electronic medical record document.

[0035] Preferably, the updating step of the medical record data updating module is specifically:

[0036] The subsequent diagnosis and treatment data uploaded by medical institutions at all levels are monitored, the time identifier and content summary in the data are extracted, and the historical records in the complete electronic medical record document are compared;

[0037] If there is time-lagged new diagnosis and treatment information, verify the legality and integrity of the data source, analyze the structured data and unstructured notes in the new information;

[0038] Integrate the new information into the electronic medical record document of the corresponding patient, update the data version number and modification record, and synchronize to the data sharing interaction module.

[0039] Preferably, it also includes a diagnosis and treatment data analysis module, which is based on the complete electronic medical record document and cross-institutional medical record information sharing, statistics the diagnosis and treatment frequency and treatment success rate of different levels of medical institutions, analyzes the distribution of dental disease types and regional correlation, and generates a hierarchical diagnosis and treatment effect evaluation report;

[0040] The acquisition step of the hierarchical diagnosis and treatment effect evaluation report is specifically:

[0041] Extract patient diagnosis and treatment results and medical institution level information from the complete electronic medical record document and cross-institutional medical record information sharing, and classify and statistically analyze the diagnosis and treatment times and the number of successful cases according to disease types and treatment methods;

[0042] Analyze the diagnosis and treatment times and the number of successful cases, calculate the treatment success rate and the average diagnosis and treatment cycle of different levels of medical institutions, and establish a correlation model between diagnosis and treatment effect and institution level;

[0043] According to the correlation model, combined with regional distribution data, identify the disease high-incidence types and diagnosis and treatment resource matching situation in different regions, and generate a hierarchical diagnosis and treatment effect evaluation report.

[0044] Compared with the prior art, the beneficial effects of the present application are:

[0045] The system breaks through the diagnosis and treatment data barriers between different levels of medical institutions through the medical record data acquisition module. This module can actively collect diagnosis and treatment records of different institutions such as primary clinics, secondary hospitals, and tertiary hospitals, and integrate the patient's past medical history information and current symptom description to form a coherent and complete preliminary medical record data set. This means that doctors do not need to rely on patient recall or scattered paper records when seeing patients, and can quickly obtain the patient's entire diagnosis and treatment trajectory from the first visit to the present, including details such as the treatment plan received, drug reactions, test results, etc., making diagnosis and treatment decisions more in line with the actual situation of the patient.

[0046] The hierarchical diagnosis and treatment determination module is based on the analysis of the preliminary medical record data set. The diagnosis and treatment level division is more targeted to the matching of institutions. This module does not simply make judgments based on the severity of symptoms, but considers the complexity of treatment needs, the patient's physical condition, the technical ability of primary institutions, and other factors to determine whether referral is needed and the priority of referral. For example, for early caries patients, the system will directly match primary medical institutions; for complex repair cases involving multiple teeth, it will accurately point to higher-level hospitals with corresponding technical equipment, avoiding the blindness caused by incomplete information in previous referrals, and allowing the functions of medical institutions at all levels to be fully utilized.

[0047] The electronic medical record automatic generation module changes the cumbersome mode of traditional medical record generation. The module extracts key information from diagnosis and treatment data, such as tooth number, treatment procedure, and drug dosage, and converts it into standardized terminology while automatically adjusting the document structure according to the format requirements of corresponding level medical institutions. Structured examination data and unstructured clinical notes by doctors are integrated in this process, generating electronic medical records that contain standardized digital information and retain doctors' personalized analysis of the disease. Doctors do not need to manually format or repeatedly modify the format, reducing rework caused by format inconsistencies, allowing doctors to focus more on patient diagnosis and treatment.

[0048] The data sharing and interaction module builds a cross-institution transmission channel, making medical record information flow more stable. This module is not a simple file transfer tool, but uses a special data transmission protocol to encrypt medical record data before transmission and check data integrity in real time during transmission to ensure that the information received by the recipient is identical to that sent by the sender. At the same time, the module controls access behavior of different institutions in detail, such as primary institutions can only view treatment recommendations from higher-level hospitals, but cannot modify core diagnosis and treatment records generated by higher-level hospitals. This two-way constraint makes cross-institutional data sharing efficient and orderly.

[0049] The permission management control module fundamentally regulates the order of medical record information access. This module sets different levels of access permissions for different user roles, such as attending physicians, nurses, administrative staff, and visiting doctors. For example, nurses can only view patient basic information and nursing records, but cannot access surgical plans and other core content. At the same time, the module records the time, user, and operation content of each data access in detail. If there is abnormal access behavior, such as downloading a large number of medical records outside working hours, the system will automatically issue a warning and suspend the relevant account permissions. This full-process traceability protects patient privacy and clearly defines the responsibility of each link, ensuring that medical record information use is always within a controllable range. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1The working principle diagram of the dental hierarchical diagnosis and treatment automatic electronic medical record generation and sharing system described in the application;

[0051] Figure 2 The generation flowchart of the preliminary medical record data set;

[0052] Figure 3 The generation flowchart of the complete electronic medical record document;

[0053] Figure 4 The implementation flowchart of the cross-institution medical record information sharing. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the application.

[0055] Please refer to Figure 1 The application provides a dental hierarchical diagnosis and treatment automatic electronic medical record generation and sharing system, which comprises a medical record data collection module, a hierarchical diagnosis and treatment determination module, an electronic medical record automatic generation module, a data sharing interaction module, and a permission management control module. The specific implementation steps are as follows:

[0056] The medical record data collection module collects diagnosis and treatment data and medical record templates of different levels of medical institutions according to patient basic information and dental diagnosis and treatment records, integrates patient historical medical information and current symptom descriptions, and generates a preliminary medical record data set. The hierarchical diagnosis and treatment determination module analyzes the severity of the patient's condition and the treatment demand based on the preliminary medical record data set, divides the diagnosis and treatment level and the corresponding medical institution type, determines the necessity and priority order of referral, and generates a hierarchical diagnosis and treatment plan. The electronic medical record automatic generation module extracts key information and standardized terms in the patient's diagnosis and treatment data based on the hierarchical diagnosis and treatment plan, matches the medical record format requirements of the corresponding level, integrates structured data and unstructured clinical notes, and generates a complete electronic medical record document. The data sharing interaction module establishes a data transmission channel between different levels of medical institutions based on the complete electronic medical record document, verifies the security and integrity of data transmission, controls medical record access permissions and modification records, and realizes cross-institution medical record information sharing. The permission management control module sets the access permission level of different user roles based on the cross-institution medical record information sharing, records user operation logs and data access traces, audits abnormal access behavior and permission change requests.

[0057] Embodiment 1: Please refer to Figure 2The preliminary medical record data set includes a summary of patient basic information, a summary of historical diagnosis and treatment records, and a classification result of current symptoms. The hierarchical diagnosis and treatment plan consists of disease severity classification, recommended treatment institution type, and referral priority ranking. The complete electronic medical record document covers structured diagnosis and treatment data tables, unstructured clinical note texts, and standardized term annotation results. Cross-institutional medical record information sharing involves data transmission encryption protocols, access permission control lists, and medical record modification operation records. The permission management control results include user role permission level tables, operation log audit reports, and abnormal access behavior alerts.

[0058] The process of obtaining the preliminary medical record data set is as follows: receiving patient records and examination reports from various medical institutions, including community clinics, secondary hospitals, and tertiary hospitals, covering outpatient medical records, dental panoramic films, dental films, periodontal probing reports, and laboratory examination results. From these data, patient basic information is extracted, including name, gender, date of birth, ID number, contact number, home address, medical insurance type and number, and other identification information. Dental disease diagnosis results are also extracted, including diagnosis name, diagnosis date, diagnosing physician, and diagnosis basis. The extracted historical diagnosis and treatment data are sorted in chronological order, with the first visit time as the starting point, and subsequent visit records arranged in sequence to form a coherent patient diagnosis and treatment timeline. The timeline clearly presents information such as the name of each visit institution, department, attending physician, main symptoms, examination items, diagnosis results, treatment measures, and treatment effects.

[0059] Analyzing the patient diagnosis and treatment timeline, natural language processing techniques are used to analyze the symptom description text in the diagnosis and treatment records, extracting key symptom words such as "gum bleeding", "tooth pain", and "tooth loosening". These symptoms are then associated with corresponding examination results. For example, the "gum bleeding" symptom is matched with periodontal probing examination data such as periodontal pocket depth and attachment loss, to determine the association between symptoms and examination results. Based on this association, current symptoms and past medical history are classified to distinguish between symptoms that are currently occurring and those that have been cured or alleviated in the past, and a symptom-diagnosis correspondence table is established. In this correspondence table, each symptom corresponds to one or more possible diagnosis results, as well as the frequency of symptom occurrence, duration, aggravating or alleviating factors, and other supporting factors for the diagnosis.

[0060] According to the correspondence table of symptoms and diagnosis, combined with the preset medical record template structure, a preliminary medical record data set is generated. The preset medical record template includes fixed information columns, such as patient basic information column, historical diagnosis and treatment summary column, current symptom detail column, examination result summary column, diagnosis conclusion column, etc. According to the template requirements, the patient's basic information is completely filled into the basic information column; the information in the historical diagnosis and treatment timeline is refined to form a historical diagnosis and treatment summary, briefly outlining the patient's past medical treatment, main diagnosis and treatment experience, and filling in the historical diagnosis and treatment summary column; according to the symptom classification result, the manifestation, occurrence time, development and change of the current symptoms are described in detail, and filled into the current symptom detail column; at the same time, the sorted examination results and the preliminary diagnosis conclusion based on the correspondence table are filled into the corresponding columns, and finally a preliminary medical record data set containing all the above information is formed.

[0061] In example 2, referring to Figure 3 and Figure 4 , the hierarchical diagnosis and treatment scheme is obtained as follows: according to the disease description and examination index in the preliminary medical record data set, referring to the dental disease grading standard, the emergency degree and complexity of the patient's condition are evaluated, and the preliminary diagnosis and treatment level is determined. The disease description covers the patient's self-reported symptom manifestations, such as the nature of tooth pain (dull pain, sharp pain, jumping pain), onset time (persistent, intermittent), pain intensity, inducing factors (cold and heat stimulation, chewing food, night aggravation, etc.), and objective manifestations observed in oral examination, such as gum swelling, bleeding, tooth loosening, size of dental caries, pulpitis signs, etc. The examination index includes the depth of dental caries, the range of periapical lesion, the degree of alveolar bone resorption shown by dental X-ray, the depth of periodontal pocket, the amount of attachment loss, the degree of gingival recession in periodontal examination, and the data of inflammation indicators in laboratory examination. In the dental disease grading standard, the basis for classification of different levels is clearly defined, for example, mild condition corresponds to mild symptoms, low and localized abnormality of examination index, and less impact on daily life; moderate condition corresponds to obvious symptoms, moderate abnormality of examination index, and may affect normal eating or sleep; severe condition corresponds to severe symptoms, high and extensive abnormality of examination index, and may be accompanied by systemic symptoms or severe dysfunction. According to these information, the patient's condition is classified into the corresponding preliminary diagnosis and treatment level.

[0062] By the initial diagnosis and treatment level, combined with the diagnosis and treatment ability range of medical institutions at all levels, the appropriate types of medical institutions are matched to determine whether cross-level referral is needed. There are differences in the diagnosis and treatment ability range of medical institutions at all levels. For example, community health service centers can usually handle basic diagnosis and treatment projects such as mild tooth filling, simple tooth cleaning, and baby tooth extraction; secondary hospitals can carry out root canal treatment, complex tooth extraction, and periodontal basic treatment; tertiary hospitals have the ability to carry out complex diagnosis and treatment projects such as dental implantation, orthognathic surgery, complex root canal re-treatment, and oral and maxillofacial tumor treatment. If the treatment needs corresponding to the initial diagnosis and treatment level are within the diagnosis and treatment ability range of the current medical institution, it is recommended to continue treatment in the institution; if it is beyond the diagnosis and treatment ability of the current institution, it is determined that cross-level referral is needed.

[0063] Using the judgment result of whether cross-level referral is needed, combined with the geographical location of the patient and the resource load situation of the medical institution, the referral priority is sorted, and the graded diagnosis and treatment plan is generated. The geographical location of the patient takes the permanent address or the current location as the benchmark to calculate the distance and traffic accessibility to the target medical institution; the resource load situation of the medical institution includes the current number of patients in the department of the target institution, the doctor's outpatient schedule, the use rate of beds or diagnosis chairs, and other data. When sorting the referral priority, the factors such as the severity of the disease, the distance, and the resource load are considered comprehensively to form a clear referral order suggestion, and finally the severity of the disease classification, the recommended medical institution type, and the referral priority sorting are integrated to form a complete graded diagnosis and treatment plan.

[0064] The process of obtaining a complete electronic medical record document is as follows: extracting the patient's disease classification result and the target medical institution type from the graded diagnosis and treatment plan to determine the corresponding electronic medical record template and data field requirements. The electronic medical record templates corresponding to the target medical institutions at different levels are different, for example, the template of community health service centers focuses on basic information and simple diagnosis and treatment records, including patient basic information, chief complaint, present illness history, simple examination results, diagnosis, treatment plan, and other fields; the template of tertiary hospitals is more detailed, adding fields such as specialized examination items, detailed description of imaging examination, differential diagnosis, complex treatment plan, and multidisciplinary consultation records.

[0065] According to the electronic medical record template and data field requirements, the key diagnosis and treatment information is screened from the preliminary medical record data set, converted into standardized medical terminology, and supplemented with structured data tables. During the screening process, for each data field in the template, the corresponding information is extracted from the preliminary medical record data set, such as the "chief complaint" field extracting the patient's main symptom description, and the "current history" field extracting the development process of the current symptoms. The standardized medical terminology conversion refers to the "Medical Terms" issued by the National Health Commission, the "International Classification of Diseases (ICD-10)" oral part and dental specialty terminology standards, and converts non-standardized descriptions into unified terms, such as converting "tooth flesh swollen" to "gingival swelling" and "tooth hole" to "tooth decay". The structured data table is made according to the template format, including the patient basic information table, examination result summary table, treatment item detail table, etc., and the screened and converted information is filled into the corresponding table cells to ensure clear and orderly data.

[0066] The structured data table and unstructured clinical notes are integrated, the content order and format are adjusted according to the medical record writing specifications, and a complete electronic medical record document is generated. The unstructured clinical notes include the doctor's diagnosis and treatment ideas, analysis of the disease, communication records with the patient, and post-treatment precautions and instructions. According to the medical record writing specifications, the content order of the electronic medical record document is as follows: patient basic information, chief complaint, current history, past history, personal history, family history, physical examination (including oral specialty examination), auxiliary examination, diagnosis, treatment plan, treatment process record, medical advice, etc. Format adjustment includes uniform font size, setting paragraph spacing, bolding or numbering key information, etc. to make the document meet the format requirements of medical documents, and finally form a complete electronic medical record document containing structured diagnosis and treatment data tables, unstructured clinical notes, and standardized term annotation results.

[0067] The implementation process of cross-institutional medical record information sharing is as follows: based on the content sensitivity level of the complete electronic medical record document, the corresponding encryption algorithm is selected to encrypt the data, and the encrypted medical record data package is generated. The content sensitivity level is divided according to the impact of information leakage, for example, patient basic identity information, past medical history, special disease diagnosis, etc. belong to high sensitive content; routine examination results, general treatment records, etc. belong to moderate sensitive content; public health education information, basic diagnosis and treatment process description, etc. belong to low sensitive content. Different encryption algorithms are used for different sensitive levels, high sensitive content uses RSA asymmetric encryption algorithm, the key length is set to 2048 bits; moderate sensitive content uses SM4 symmetric encryption algorithm, the key length is 128 bits; low sensitive content uses AES-128 encryption algorithm. During encryption processing, the electronic medical record document is first processed in blocks, each block is 1024 bytes, each block of data is encrypted, and the block number and check value are attached, and all encrypted blocks are integrated into an encrypted medical record data package. The data package header includes patient unique identifier, encryption algorithm identifier, encryption timestamp and other metadata.

[0068] The qualifications and data access rights of the receiving medical institution are verified, a secure transmission channel is established, the encrypted medical record data package is transmitted, and the data integrity is verified. When verifying the qualifications of the receiving party, check the effectiveness of the medical institution business license number, diagnosis and treatment subject range, information system security level protection record certificate and other documents provided by the receiving party, and cross-verify through the national health committee website database. When verifying the data access rights, query the access rights control list, which records the medical record data types, patient range and operation rights (such as read-only, modifiable, downloadable, etc.) that each medical institution can access. Only when the receiving party is in the list and the rights match, data transmission is allowed. The secure transmission channel uses transmission layer encryption based on TLS1.3 protocol, certificate authentication is used to ensure the identity authenticity of the communication parties, session key is automatically negotiated during channel establishment process, and all data is encrypted through session key during transmission process. After transmission is completed, SHA-256 hash algorithm is used to calculate the received encrypted medical record data package to get the actual hash value, and compare it with the original hash value provided by the sender. If they are consistent, it is confirmed that the data is complete, otherwise it is required to be retransmitted.

[0069] The access time, operation type and user identity of the recorded medical record data are recorded to generate a shared operation log, which is synchronized to the permission management control module. The access time is accurate to milliseconds, recording the starting and ending time points of the operation; the operation type includes specific behaviors such as viewing, downloading, printing, modifying, deleting, etc.; the user identity includes the name, work number, department, position and digital certificate number of the operator. The shared operation log adopts a tamper-proof chain storage structure, each record contains the hash value of the previous record, ensuring that the log content cannot be modified arbitrarily, and the log is synchronized to the database of the permission management control module in real time after generation.

[0070] The access behavior beyond the permission range is identified by comparing the user access record extracted from the cross-institutional medical record information sharing operation log with the preset role permission level table. The preset role permission level table divides users into roles such as system administrator, department director, attending physician, resident physician, nurse, and intern, each role corresponding to a specific permission range, for example, a system administrator can access all medical record data and configure permissions, an attending physician can access the complete medical record of his own patients and modify it, and a nurse can only view the patient's basic information and nursing-related records. The extracted user access record includes user role, operation object, operation content, etc. By comparing each item with the role permission level table, if the user performs an operation not allowed in the permission table, such as a nurse modifying the diagnosis result in the medical record, it is marked as an access behavior beyond the permission range.

[0071] Analyze the access behavior beyond the permission range, combine the user's historical operation habits and the sensitivity of the data to determine whether it is an abnormal access, and generate an abnormal behavior analysis report. User historical operation habits are obtained by analyzing the operation log of the past 3 months, including common operation types, access time, access frequency, etc.; the sensitivity of the data is the classification of high, medium and low sensitivity as described above. When analyzing, calculate the deviation degree of the behavior from the user's historical habits, the deviation degree formula is:

[0072]

[0073] Where D is the deviation degree, T is the operation time, T0 is the average time of the same operation of the user, W t is the operation time weight (value 0.3), F is the operation frequency, F0 is the average frequency of the same operation of the user, W f is the operation frequency weight (value 0.3), S is the data sensitivity coefficient (high sensitivity takes 1.0, medium sensitivity takes 0.5, and low sensitivity takes 0.2), W sThe data-sensitive weight is 0.4. When the D value is greater than 0.7, it is judged as abnormal access. The abnormal behavior analysis report contains the specific time of abnormal access, user information, operation content, deviation calculation process and judgment basis, etc.

[0074] According to the abnormal behavior analysis report, the authority freezing or audit notification is automatically triggered, the user role authority level table is updated, and the authority management control result is generated. For serious abnormal access with D value greater than 0.9, the system automatically freezes all access permissions of the user, and sends an emergency alert to the information security department of the institution to which the user belongs; for general abnormal access with 0.7≤D≤0.9, send an audit notification to the department director's mailbox, and require manual review within 24 hours. After the authority freezing or audit processing is completed, the processing result is recorded in the change record of the user role authority level table, including processing time, processor, processing measures and reason explanation, finally the user role authority level table, operation log audit report, abnormal access behavior alarm and other information are integrated to form the authority management control result.

[0075] In embodiment 4, the system includes a medical record data update module, which relies on the cross-institution medical record information sharing mechanism to receive feedback of patient diagnosis and treatment results and subsequent visit records from medical institutions at all levels. These records cover re-visit records, subsequent treatment plans, examination reports, rehabilitation conditions and other contents of patients in different institutions, such as re-examination results after filling of dental caries uploaded by community health service centers, postoperative evaluation reports of root canal treatment provided by secondary hospitals, and healing conditions of implant surgery feedback by tertiary hospitals. The module identifies new diagnosis and treatment information by comparing the time stamp and content version in the original medical record data, and integrates it into the patient's electronic medical record to realize dynamic update of complete electronic medical record documents.

[0076] The updating step of the medical record data updating module is specifically as follows: First, monitor the subsequent diagnosis and treatment data uploaded by medical institutions at all levels. The monitoring range covers community clinics, secondary hospitals, tertiary hospitals and all other institutions connected to the system. Through the system interface, real-time capture of newly uploaded data is achieved, including but not limited to outpatient medical records, inpatient medical records, imaging examination reports (such as dental films and oral CT), laboratory test results (such as inflammation index detection), treatment operation records (such as root canal treatment steps and restoration installation status), etc. From these data, time identifiers and content summaries are extracted. Time identifiers include the actual execution time of the diagnosis and treatment operation, the time the data is uploaded to the system, and the time the record is generated, etc., accurate to the minute; the content summary is a brief summary of the diagnosis and treatment data, such as "On June 15, 2024, the patient returned for a follow-up visit due to left posterior tooth pain, and the examination found that the filling was intact, and the percussion pain was negative." "On July 3, 2024, full mouth scaling was performed, and gum bleeding was reduced." Compare the extracted time identifiers with the historical records in the complete electronic medical record document. The time stamps in the historical records include the time of each diagnosis and treatment operation, the time of the last update of the medical record, etc. Through the judgment of the time sequence, it is determined whether there is new diagnosis and treatment information.

[0077] If there is new diagnosis and treatment information after the time, that is, the time identifier of the subsequent diagnosis and treatment data is later than the latest time recorded in the electronic medical record, the legality and integrity of the data source are verified. When verifying the legality of the data source, check whether the uploading institution is a system-authorized cooperative medical institution and whether the uploading user has the corresponding operation authority (such as a registered doctor or nurse of the institution). This can be achieved by verifying the digital certificate of the institution and the identity token of the user. When verifying the data integrity, check whether the new information contains necessary elements such as diagnosis and treatment time, doctor on duty, diagnosis and treatment project, examination result, treatment measure, and patient reaction, to ensure that the information is not missing any key. Then analyze the structured data and unstructured notes in the new information. Structured data includes information that can be directly filled into a form, such as treatment duration, drug name and dosage used, specific values of examination indicators (such as periodontal pocket depth 2mm), etc.; unstructured notes include the doctor's diagnosis and treatment ideas, analysis of the patient's condition, communication content with the patient (such as informing the patient of the treatment risks and the patient's informed consent), special situation records (such as the patient's allergy to a certain anesthetic drug), etc.

[0078] The new information is integrated into the electronic medical record document of the corresponding patient. During the integration process, the newly added medical records are added to the patient's medical timeline in chronological order, ensuring the continuity of historical records. For example, if a patient had a caries examination at a community hospital in March and received root canal treatment at a secondary hospital in April, the record of root canal treatment is added after the March examination record. For structured data, fill in the corresponding table fields in the electronic medical record, such as "root canal treatment length 22mm" in the "length" column of the "treatment details" table; for unstructured notes, add them to the clinical note section in paragraph form and maintain the same format as the original notes. After integration, update the data version number and modification record, and the version number is incremented in the form of "major version number.minor version number", such as from V2.1 to V2.2, and the modification record notes the time, update content, operator, data source institution, etc. information of this update. Finally, the updated complete electronic medical record document is synchronized to the data sharing and interaction module to ensure that other authorized medical institutions can access the latest medical information when accessing the patient's medical record, realizing real-time synchronization of medical record data across institutions. For example, when a patient is transferred from a community hospital to a tertiary hospital, doctors at the tertiary hospital can view the electronic medical record containing the latest community medical records through the system, providing comprehensive information for subsequent treatment.

[0079] Example 5: The system includes a medical data analysis module that is based on complete electronic medical record documents and cross-institutional medical information sharing data to carry out multi-dimensional data analysis. By analyzing the medical data of different levels of medical institutions, the frequency of medical treatment of various institutions is calculated, that is, the total number of patients treated and the number of various dental diseases treated in a certain period of time; At the same time, the success rate of treatment is calculated, that is, the proportion of cases that achieve the expected effect after treatment in the total number of treatment cases. In addition, the distribution of dental disease types in different regions is analyzed to explore the correlation between disease occurrence and regional factors, and finally a hierarchical medical treatment effect evaluation report is formed.

[0080] The steps for obtaining the hierarchical diagnosis and treatment effect evaluation report are as follows: first, extract key information from complete electronic medical record documents and cross-institutional medical record information sharing, including the diagnosis and treatment results of patients and the level information of the medical institutions. The diagnosis and treatment results cover cure, symptom relief, no significant improvement, etc. The medical institution level information clearly distinguishes community health service centers, secondary hospitals, tertiary hospitals, etc. After extracting the information, classify and count according to disease types and treatment methods. Disease types include common dental diseases such as dental caries, pulpitis, periodontitis, wisdom tooth pericoronitis, malocclusion, tooth defect, and oral mucosa disease. Treatment methods include specific diagnosis and treatment means such as dental filling, root canal treatment, dental cleaning, tooth extraction, tooth correction, denture repair, and implant surgery. For each disease type and corresponding treatment method, count the number of diagnoses and treatments in different levels of medical institutions, i.e. the total number of visits for that disease using that treatment method; at the same time, count the number of successful cases, i.e. the number of cases where the patient's symptoms disappear, function recovers or achieves the preset treatment goal after treatment.

[0081] On this basis, analyze the number of diagnoses and treatments and the number of successful cases, and calculate the treatment success rate and average diagnosis and treatment cycle of different levels of medical institutions. The treatment success rate is obtained by the ratio of the number of successful cases to the number of diagnoses and treatments, and the average diagnosis and treatment cycle is the total time from the first visit to the end of treatment divided by the number of diagnoses and treatments for that type of disease. Through these data, an association model between diagnosis and treatment effect and institution level is established, which shows the effect difference of different levels of medical institutions in handling various diseases, such as the performance of community health service centers in basic diagnosis and treatment projects such as dental filling and basic dental cleaning, the effect of secondary hospitals in medium difficulty projects such as root canal treatment and complex tooth extraction, and the achievements of tertiary hospitals in complex projects such as implant surgery and orthodontic treatment.

[0082] Combined with regional distribution data, further analyze the matching of disease high-incidence types and diagnosis and treatment resources in different regions. Regional distribution data includes the source distribution of patients in provinces, cities and counties, and resource information such as the number of medical institutions, beds and doctors in different regions. By comparing the statistical results of disease types in different regions, identify the high-incidence diseases in each region, such as high incidence of children's dental caries in a certain region, and more cases of periodontitis in the elderly in a certain region. At the same time, match the treatment needs of high-incidence diseases with the diagnosis and treatment resources in the region, and check whether the number of medical institutions with corresponding treatment capacity in the region can meet the needs of patients, for example, the number of secondary hospitals capable of complex wisdom tooth extraction is insufficient in a region with high incidence of wisdom tooth pericoronitis, resulting in long-distance transfer of patients to tertiary hospitals. Integrate these analysis results to form a hierarchical diagnosis and treatment effect evaluation report containing disease type distribution, diagnosis and treatment effect of different levels of medical institutions, and regional resource matching situation, etc. The report clearly presents the statistical results and correlation of various data, providing reference information for the optimization of hierarchical diagnosis and treatment system.

[0083] It has to be noted that, in the present document, the terms "first", "second", etc. merely serve to identify a subject or action, without necessarily requiring or implying any such actual relationship or order between such subjects or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0084] While embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, combinations, and variations of the embodiments can be undertaken without departing from the spirit and scope of the present application, which is defined by the appended claims and their equivalents.

Claims

1. An automated electronic medical record generation and sharing system for tiered dental care, characterized in that, The system includes: The medical record data acquisition module collects medical data and medical record templates from different levels of medical institutions based on the patient's basic information and dental treatment records. It integrates the patient's historical medical information and current symptom descriptions to generate a preliminary medical record dataset. The tiered diagnosis and treatment determination module, based on the preliminary medical record dataset, analyzes the severity of the patient's condition and treatment needs, classifies the diagnosis and treatment levels and corresponding medical institution types, determines the necessity and priority of referrals, and generates a tiered diagnosis and treatment plan. The electronic medical record automatic generation module, based on the aforementioned hierarchical diagnosis and treatment plan, extracts key information and standardized terminology from patient diagnosis and treatment data, matches the corresponding level of medical record format requirements, integrates structured data and unstructured clinical notes, and generates a complete electronic medical record document. The data sharing and interaction module is based on the complete electronic medical record document, establishes a data transmission channel between medical institutions at different levels, verifies the security and integrity of data transmission, controls medical record access permissions and modification records, and realizes cross-institutional medical record information sharing. The access control module, based on the cross-institutional medical record information sharing, sets access permission levels for different user roles, records user operation logs and data access traces, and reviews abnormal access behavior and permission change requests.

2. The automated electronic medical record generation and sharing system for dental hierarchical diagnosis and treatment according to claim 1, characterized in that, The preliminary medical record dataset includes a summary of basic patient information, a summary of historical medical records, and current symptom classification results. The tiered medical treatment plan includes severity grading, recommended types of medical institutions, and referral priority ranking. The complete electronic medical record document includes structured medical data tables, unstructured clinical notes, and standardized terminology annotation results. The cross-institutional medical record information sharing includes a data transmission encryption protocol, an access control list, and medical record modification operation records. The access control results include a user role permission level table, operation log audit reports, and abnormal access behavior alerts.

3. The automated electronic medical record generation and sharing system for dental hierarchical diagnosis and treatment according to claim 2, characterized in that, The specific steps for obtaining the preliminary medical record dataset are as follows: Receive patients' medical records and examination reports from medical institutions at all levels, extract patients' basic information and dental disease diagnosis results, integrate historical treatment data in chronological order, and form a patient treatment timeline; Analyze the patient's diagnosis and treatment timeline, classify current symptoms and past medical history based on the correlation between symptom description and examination results, and establish a correspondence table between symptoms and diagnoses; Based on the symptom-diagnosis correspondence table and the preset medical record template structure, the patient's basic information, historical treatment summary and current symptom details are filled in to generate a preliminary medical record dataset.

4. The automated electronic medical record generation and sharing system for dental hierarchical diagnosis and treatment according to claim 3, characterized in that, The specific steps for obtaining the tiered medical service plan are as follows: Based on the description of the patient's condition and examination indicators in the preliminary medical record dataset, and referring to the dental disease grading standards, the urgency and complexity of the patient's condition are assessed to determine the preliminary treatment level. Based on the initial diagnosis and treatment level, combined with the diagnosis and treatment capabilities of medical institutions at each level, a suitable type of medical institution is matched to determine whether a cross-level referral is necessary. Using the results of the judgment on whether cross-level referral is needed, combined with the patient's geographical location and the resource load of the medical institution, the referral priority is ranked and a tiered medical treatment plan is generated.

5. The automated electronic medical record generation and sharing system for dental hierarchical diagnosis and treatment according to claim 4, characterized in that, The specific steps for obtaining the complete electronic medical record document are as follows: Extract the patient's condition classification results and the type of the target medical institution from the aforementioned hierarchical diagnosis and treatment plan, and determine the corresponding electronic medical record template and data field requirements; Based on the electronic medical record template and data field requirements, key diagnostic and treatment information is filtered from the preliminary medical record dataset, converted into standardized medical terminology, and supplemented with structured data tables; By integrating the structured data tables and unstructured clinical notes, and adjusting the content order and format according to medical record writing standards, a complete electronic medical record document is generated.

6. The automated electronic medical record generation and sharing system for dental hierarchical diagnosis and treatment according to claim 5, characterized in that, The specific steps for implementing cross-institutional medical record information sharing are as follows: Based on the content sensitivity level of the complete electronic medical record document, the corresponding encryption algorithm is selected to encrypt the data and generate an encrypted medical record data package; Verify the qualifications and data access permissions of the receiving medical institution, establish a secure transmission channel, transmit encrypted medical record data packets, and verify data integrity; Record the access time, operation type, and user identity of medical record data, generate a shared operation log, and synchronize it to the access control module.

7. The automated electronic medical record generation and sharing system for dental hierarchical diagnosis and treatment according to claim 6, characterized in that, The specific steps for obtaining the permission management control results are as follows: User access records are extracted from the operation logs of the cross-institutional medical record information sharing, compared with the preset role permission level table, and access behaviors that exceed the permission scope are identified. Analyze the access behavior that exceeds the scope of permissions, combine the user's historical operation habits and data sensitivity to determine whether it is abnormal access, and generate an abnormal behavior analysis report; Based on the abnormal behavior analysis report, the system automatically triggers permission freeze or audit notification, updates the user role permission level table, and generates permission management control results.

8. The automated electronic medical record generation and sharing system for dental hierarchical diagnosis and treatment according to claim 7, characterized in that, It also includes a medical record data update module, which, based on the cross-institutional medical record information sharing, receives the diagnosis and treatment results and subsequent medical records fed back by medical institutions, compares the timestamps and content versions in the original medical record data, integrates the newly added diagnosis and treatment information, and updates the complete electronic medical record document.

9. The automated electronic medical record generation and sharing system for dental hierarchical diagnosis and treatment according to claim 8, characterized in that, The specific update steps of the medical record data update module are as follows: Monitor the patient's subsequent diagnosis and treatment data uploaded by medical institutions at all levels, extract the time stamps and content summaries from the data, and compare them with the historical records in the complete electronic medical record document; If there is new medical information with a later time, verify the legality and completeness of the data source, and parse the structured data and unstructured notes in the new information; The newly added information is integrated into the corresponding patient's electronic medical record document, the data version number and modification record are updated, and synchronized to the data sharing and interaction module.

10. The automated electronic medical record generation and sharing system for dental hierarchical diagnosis and treatment according to claim 1, characterized in that, It also includes a diagnosis and treatment data analysis module, which, based on the complete electronic medical record document and cross-institutional medical record information sharing, statistically analyzes the frequency of diagnosis and treatment and the success rate of treatment in different levels of medical institutions, analyzes the distribution of dental disease types and their regional correlation, and generates a hierarchical diagnosis and treatment effect evaluation report. The specific steps for obtaining the hierarchical medical treatment effectiveness evaluation report are as follows: Extract patient diagnosis and treatment results and medical institution level information from the complete electronic medical record document and cross-institutional medical record information sharing, and classify and count the number of diagnosis and treatment and the number of successful cases according to disease type and treatment method; By analyzing the number of treatments and successful cases, the treatment success rate and average treatment cycle of medical institutions at different levels are calculated, and a correlation model between treatment effectiveness and institution level is established. Based on the aforementioned correlation model and combined with regional distribution data, the matching status of high-incidence disease types and medical resources in different regions is identified, and a hierarchical medical treatment effectiveness evaluation report is generated.

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