Diagnostic multidimensional analysis method, system, equipment and medium

Abstract

The invention discloses a diagnostic multi-dimensional analysis method, system and device and a medium. By determining the mapping relation between the target medical information and the evidence-based node and evaluating the execution result of the clinical path based on the evidence-based evidence, the method is beneficial to reducing the influence of personal experience and subjective judgment of doctors on a diagnosis and treatment scheme and improving the scientificity and rationality of diagnosis and treatment decisions. For each piece of diagnosis information in the target medical information, if a corresponding target clinical path exists, the diagnosis information can be obtained according to the mapping relation between the target medical information and evidence-based nodes, the target medical information and evidence-based evidences corresponding to at least one evidence-based node contained in the target clinical path. And the execution condition of the target clinical pathway of the diagnosis information is evaluated, so that problems possibly occurring in the execution process can be found and solved in time, the execution process of the clinical pathway is optimized, the quality and efficiency of medical services are improved, and scientific, objective and traceable evidence chain support can be provided for diagnosis selection.

Description

Technical Field

[0001] The present application relates to the field of medical diagnosis selection and deep learning technology, and in particular to a diagnostic multidimensional analysis method, system, device and medium. Background Art

[0002] In the current medical environment, the writing of medical records and the selection of diagnoses have a crucial impact on patients' treatment decisions and the allocation of medical resources. For outpatient and inpatient medical records in hospitals, different departments and doctors need to follow corresponding writing specifications, including reporting field standards, such as the requirements for reporting the main diagnosis and other diagnosis. These specifications ensure the consistency and comparability of medical records and provide basic data for subsequent diagnosis and treatment and medical management.

[0003] However, in actual operation, due to the complexity and diversity of medical record content, the choice of diagnosis is often affected by multiple factors. The diagnosis on the front page of the medical record usually focuses on reflecting the difficulty of the disease, while the medical insurance list focuses more on solving patient problems and the consumption of medical resources. This difference makes the selection of diagnostic codes and names more complicated.

[0004] Traditional diagnosis selection methods often rely on doctors' experience and subjective judgment, lacking scientific and objective basis. In addition, with the deepening of medical payment reforms such as DRG (Disease Related Group) and DRP (Disease Point Payment), hospitals and doctors are facing increasing economic pressure. This pressure may cause clinical departments to tend to choose high-weighted and high-score options when selecting diagnoses in order to obtain more medical insurance payments, rather than truly reflecting the patient's actual condition and treatment needs.

[0005] To solve this problem, existing technical methods usually rely on program logic, OCR recognition and other technologies to obtain medical record data sources from hospital information systems, and extract the main diagnosis and other diagnoses of the corresponding medical records according to established rules. However, although this method can automatically extract diagnostic information from medical records, it still has shortcomings in the scientificity and objectivity of diagnostic selection. In particular, when the doctor's subjective judgment or writing habits do not conform to the rules, it may lead to deviations in diagnostic information, which in turn affects subsequent diagnosis and treatment decisions and medical resource allocation.

[0006] Therefore, there is an urgent need for a method that can scientifically and objectively analyze the rationality of diagnosis, associate diagnosis with actual clinical treatment, and provide a traceable chain of evidence to support diagnostic selection. Summary of the invention

[0007] The present application provides a diagnostic multidimensional analysis method, system, device and medium to address the problem that the scientificity and objectivity of existing diagnostic options are still insufficient, and subsequent diagnosis and treatment decisions and medical resource allocation are easily affected by doctors' subjective factors.

[0008] In a first aspect, the present application provides a diagnostic multidimensional analysis method, the method comprising:

[0009] Preprocessing the acquired medical record data to be analyzed, and extracting target medical information from the preprocessed medical record data to be analyzed; wherein the preprocessing includes structured processing and terminology standardization, and the target medical information includes diagnostic information;

[0010] Determine a mapping relationship between the target medical information and the evidence-based node, and map the diagnostic information in the mapping relationship to the International Classification of Diseases code;

[0011] For each diagnostic information in the target medical information, if it is determined from the pre-configured clinical pathways that there is a target clinical pathway corresponding to the diagnostic information, then the execution result of the target clinical pathway is determined according to the mapping relationship, the target medical information, and the evidence-based evidence corresponding to at least one evidence-based node contained in the target clinical pathway; wherein the execution result includes one or more of the following: the clinical pathway type adopted, whether the at least one evidence-based node is executed, the execution result corresponding to the at least one evidence-based node, the weight corresponding to the at least one evidence-based node, the extracted data source path corresponding to the at least one evidence-based node, the node score corresponding to the at least one evidence-based node, and the final score of the target clinical pathway; if it is determined from the pre-configured clinical pathways that there is no target clinical pathway corresponding to the diagnostic information, then it is determined that the clinical pathway of the diagnostic information is missing;

[0012] According to the target medical information and the evidence-based results corresponding to each diagnostic information, the evidence-based archiving information is determined and saved; wherein the evidence-based result of any diagnostic information is the execution result of the diagnostic information, or the clinical pathway of the diagnostic information is lost.

[0013] In a second aspect, the present application also provides a diagnostic multidimensional analysis system, the system comprising:

[0014] A data extraction unit, used to pre-process the acquired medical record data to be analyzed, and extract target medical information from the pre-processed medical record data to be analyzed; wherein the pre-processing includes structured processing and terminology standardization, and the target medical information includes diagnostic information;

[0015] A determination unit, used to determine a mapping relationship between the target medical information and the evidence-based node, and map the diagnosis information in the mapping relationship to an International Classification of Diseases code;

[0016] A processing unit, for determining, for each diagnostic information in the target medical information, an execution result of the target clinical pathway according to the mapping relationship, the target medical information, and the evidence-based evidence respectively corresponding to at least one evidence-based node contained in the target clinical pathway, if it is determined from the pre-configured clinical pathways that there is a target clinical pathway corresponding to the diagnostic information; wherein the execution result includes one or more of the following: the clinical pathway type adopted, whether the at least one evidence-based node is executed, the execution result respectively corresponding to the at least one evidence-based node, the weight respectively corresponding to the at least one evidence-based node, the extracted data source path respectively corresponding to the at least one evidence-based node, the node score respectively corresponding to the at least one evidence-based node, and the final score of the target clinical pathway; if it is determined from the pre-configured clinical pathways that there is no target clinical pathway corresponding to the diagnostic information, then determining that the clinical pathway of the diagnostic information is missing;

[0017] A storage unit is used to determine and save evidence-based archiving information based on the target medical information and the evidence-based results corresponding to each diagnostic information; wherein the evidence-based result of any diagnostic information is the execution result of the diagnostic information, or the clinical pathway of the diagnostic information is lost.

[0018] In a third aspect, the present application provides a computer device, the computer device comprising a processor, the processor being configured to implement the steps of the diagnostic multidimensional analysis method as described above when executing a computer program stored in a memory.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, which implements the steps of the diagnostic multidimensional analysis method as described above when executed by a processor.

[0020] The beneficial effects of this application are as follows:

[0021] 1. By preprocessing the medical record data to be analyzed, including structured processing and terminology standardization, this process ensures that medical record data from different sources and in different formats can be processed and analyzed in a unified and standardized manner, avoiding the results of the analysis of the medical record data to be affected by the doctor's writing habits.

[0022] 2. By determining the mapping relationship between target medical information and evidence-based nodes, and evaluating the execution results of clinical pathways based on evidence-based evidence, this method helps reduce the impact of doctors' personal experience and subjective judgment on diagnosis and treatment plans, and improves the scientificity and rationality of diagnosis and treatment decisions.

[0023] 3. For each diagnostic information in the target medical information, if there is a corresponding target clinical pathway, the execution of the target clinical pathway of the diagnostic information can be evaluated based on the mapping relationship between the target medical information and the evidence-based node, and the evidence-based evidence corresponding to the target medical information and at least one evidence-based node contained in the target clinical pathway. This helps to promptly discover and resolve problems that may arise during the execution process, optimize the execution process of the clinical pathway, improve the quality and efficiency of medical services, and provide scientific, objective, and traceable evidence chain support for diagnostic selection.

[0024] 4. Clinical pathway management based on evidence-based medicine can more accurately evaluate the cost-effectiveness of different treatment options, thereby helping hospitals and decision makers to allocate medical resources more rationally. At the same time, by reducing unnecessary medical interventions and waste, it can also reduce the medical burden of patients and improve the overall efficiency of medical services.

[0025] 5. By recording and preserving evidence-based archived information, including the execution results of clinical pathways, the weights and scores of evidence-based nodes, and data source paths, this process enhances the transparency of diagnosis and treatment decisions and makes the medical process traceable and auditable. This not only helps to improve medical quality, but also provides valuable data support for subsequent medical research and quality improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0027] Figure 1 A schematic diagram of a diagnostic multidimensional analysis process provided in an embodiment of the present application;

[0028] Figure 2 A schematic diagram of the structure of a functional module provided in an embodiment of the present application;

[0029] Figure 3 A schematic diagram of the structure of a diagnostic multidimensional analysis system provided in an embodiment of the present application;

[0030] Figure 4 It is a structural schematic diagram of a computer device provided in an optional embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0032] In order to improve the scientificity and objectivity of diagnostic selection and avoid the influence of doctors' subjective factors on subsequent diagnosis and treatment decisions and medical resource allocation, the present application provides a diagnostic multidimensional analysis method, system, equipment and medium.

[0033] Embodiment 1:

[0034] The present application provides a diagnostic multidimensional analysis method, Figure 1 A schematic diagram of a diagnostic multidimensional analysis process provided in an embodiment of the present application, the process includes:

[0035] S101: preprocessing the acquired medical record data to be analyzed, and extracting target medical information from the preprocessed medical record data to be analyzed; wherein the preprocessing includes structured processing and terminology standardization, and the target medical information includes diagnosis information.

[0036] In the present application, the diagnostic multidimensional analysis method is applied to a computer device, which may be an intelligent terminal, such as a computer, a robot, etc., or a server, such as an application server, a business server, etc.

[0037] Medical record data in medical systems are usually stored in different data sources, such as hospital information systems, electronic medical record systems, etc. In order to integrate medical record data from different data sources, improve the efficiency and accuracy of medical data analysis, and thus support better clinical decision-making, medical research or public health policy formulation, in this application, the computer equipment used for diagnostic multidimensional analysis can be connected to different data sources to obtain medical record data to be analyzed from different data sources, so that subsequent medical data analysis can be performed based on the medical record data to be analyzed. For example, the original medical record data (recorded as medical record data to be analyzed) can be obtained from a hospital information system, an electronic medical record system or other medical data warehouse through a data extraction module. These medical record data to be analyzed may include the patient's personal information, medical history, test results, doctor's orders, diagnosis, etc.

[0038] In general, the content of the medical record data to be analyzed is filled in according to the doctor's subjective judgment and writing habits, which may lead to problems such as inconsistent formats and non-standardized terms in the medical record data to be analyzed, thereby affecting the analysis of the medical record data to be analyzed. Based on this, in the present application, the medical record data to be analyzed can be preprocessed after being obtained. Among them, the preprocessing includes structured processing and terminology standardization. For structured processing, the unstructured text medical record data can be converted into a structured or semi-structured data format. Exemplarily, the key information fields in the medical record data to be analyzed are identified, such as basic information of the patient, chief complaint, current medical history, past history, physical examination, auxiliary examination, diagnosis, treatment, etc., and these key information fields are organized according to a predetermined data structure to facilitate subsequent more efficient parsing and processing of data. For terminology standardization, the medical terms in the medical record data to be analyzed can be standardized to ensure that the same concept has a consistent expression in different medical record data to be analyzed, so as to reduce the understanding deviation caused by inconsistent use of terms and improve the accuracy of data analysis. Exemplarily, it can be achieved by establishing a medical terminology library and using natural language processing technology to perform term identification and mapping.

[0039] Optionally, the preprocessing may also include data cleaning, such as removing noise data (such as incorrectly entered text, irrelevant blank lines, etc.) and processing missing values ​​(such as using the mean, median to fill or directly deleting records with missing values).

[0040] For large-scale medical record data to be analyzed, directly analyzing the entire data set may consume a lot of computing resources and time. And there may be a lot of redundant information in the entire data set, which interferes with the judgment of diagnostic information. Based on this, in the present application, target medical information can be extracted from the pre-processed medical record data to be analyzed, so as to significantly improve the processing speed and responsiveness of the system and optimize the user experience by reducing the amount of data to be processed. For example, for free text, natural language processing technology, such as named entity recognition, semantic understanding large model, etc., can be used, and the data extraction module can be used to identify and extract target medical information from the pre-processed medical record data to be analyzed; for structured data, the data extraction module can directly extract relevant information from the pre-processed medical record data to be analyzed through SQL query or other programming methods. Among them, the target diagnosis and treatment information includes at least diagnostic information, so as to perform subsequent analysis based on the target diagnosis and treatment information.

[0041] S102: Determine a mapping relationship between the target medical information and the evidence-based node, and map the diagnostic information in the mapping relationship to the International Classification of Diseases code.

[0042] In order to ensure the objectivity and scientificity of the diagnostic selection, in this application, at least one clinical pathway corresponding to each diagnostic information is pre-configured to guide the diagnostic selection through the clinical pathway. Among them, any clinical pathway contains each evidence-based node contained in the diagnostic information and the evidence-based evidence corresponding to each evidence-based node. Exemplarily, the evidence-based knowledge module of the computer device builds a dynamic knowledge base by integrating and updating clinical guidelines, evidence-based medical literature and best practices. The knowledge base not only contains standardized clinical pathways, but also records the evidence-based basis related to each evidence-based node to ensure that all diagnosis and treatment options have a clear scientific basis.

[0043] In a possible implementation, the clinical pathway includes a public latest clinical pathway, which integrates the latest clinical practice guidelines and evidence-based medicine research results and records the corresponding versions, and is the latest clinical pathway generally recognized by the public.

[0044] In order to protect the security of the public latest clinical pathway, in this application, manual modification and updating of the data in the public latest clinical pathway may be prohibited.

[0045] In another possible implementation, the evidence-based knowledge module allows the adapted hospital to configure a personalized clinical pathway, and the evidence-based nodes in the clinical pathway support import and modification. For example, it supports the import and modification of key evidence-based nodes such as initial diagnosis and screening (medical history collection, physical examination, preliminary diagnosis), diagnosis (laboratory examination, imaging examination, diagnosis confirmation), treatment (drug therapy, surgery, rehabilitation therapy), follow-up and management (regular examination, adjustment of treatment plan, long-term management). In other words, the clinical pathway maintained in the evidence-based knowledge module also includes a custom imported clinical pathway, which supports co-construction and maintenance, and is reviewed and maintained by professionals.

[0046] In one example, the at least one evidence-based node includes a diagnostic basis, a rehabilitation assessment and program, a treatment program, a standard hospital stay, examination items during hospitalization, and a resource node. The diagnostic basis is a scientific basis for determining the patient's disease state or health status, usually based on a comprehensive analysis of multiple means such as clinical manifestations, laboratory tests, and imaging examinations to ensure the accuracy and timeliness of the diagnosis, provide a solid foundation for subsequent treatment, and avoid misdiagnosis and mistreatment. The rehabilitation assessment and program is used to comprehensively evaluate the patient's functional status, quality of life, psychological state, etc. at different stages of treatment or surgery, and formulate a personalized rehabilitation plan for the patient's rehabilitation needs, including rehabilitation training, life guidance, psychological support, etc., to help doctors and rehabilitation therapists understand the patient's rehabilitation progress and existing problems, formulate or adjust personalized rehabilitation plans, allocate medical resources, and improve rehabilitation effects. The treatment plan is used for a treatment plan based on the diagnosis results and the patient's specific conditions, including drug therapy, surgical treatment, physical therapy, and other means to ensure the pertinence and effectiveness of treatment measures, reduce uncertainty and risks in the treatment process, improve treatment effects, and shorten the course of the disease. The standard hospitalization day is a reasonable hospitalization time range set for a specific disease or treatment process to optimize the allocation of medical resources, avoid over-medicalization or under-medicalization, reduce the cost and time of hospitalization for patients, and improve the turnover rate of hospital beds. The examination items during hospitalization are necessary examinations to monitor the condition, evaluate the treatment effect or find potential complications, so as to timely find and deal with problems, adjust the treatment plan, ensure the safety and effectiveness of treatment, and improve patient satisfaction and medical quality. The resource node refers to the various resources involved in the medical service process, including human resources, material resources and information resources, so as to avoid excessive examinations and excessive consumption.

[0047] In order to facilitate the analysis of the target medical information in combination with the clinical pathway, in the present application, after the target medical information is obtained based on the above embodiment, the mapping relationship between the target medical information and the evidence-based node can be determined to correspond the collected medical information to the specific evidence-based node in the clinical pathway. Through this mapping relationship, it is not only helpful to quickly locate the patient's current diagnosis and treatment stage, but also to analyze whether the diagnosis and treatment process meets the requirements of the clinical pathway and whether there are any variations or deficiencies. Exemplarily, after obtaining the target medical information, the data extraction module can match and map the target medical information with the evidence-based nodes in the clinical pathway through a preset algorithm or rule engine.

[0048] In a possible implementation, when configuring the data required for the medical record data to be analyzed and the target medical information to be extracted, it can be determined according to the type of evidence-based node to ensure that the mapping relationship between the target medical information and the evidence-based node can be accurately determined later.

[0049] For example, if the evidence-based nodes include diagnostic basis, rehabilitation assessment and plan, treatment plan, standard hospital stay, resource node, examination items during hospitalization and resource node, then the medical record data to be analyzed includes medical history collection and initial diagnosis records, examination results, treatment records, rehabilitation records, checklist records, and discharge records; the target medical information includes the medical history information in the medical history collection and initial diagnosis records, the results of laboratory tests and imaging tests in the examination results, the drug treatment, surgical records and other treatment measures in the treatment records, the time, content, and operation methods of each stage in the rehabilitation record, the summary name and quantity of the three types in the checklist record, and the discharge time and diagnosis in the discharge record; wherein the three types include medicines, diagnosis and treatment, and consumables. "Medical history information in medical history collection and initial diagnosis records" is used to match the evidence-based node "Diagnostic basis", "Time, content, and operation methods of each stage in rehabilitation records" are used to match the evidence-based node "Rehabilitation assessment and plan", "Drug treatment, surgical records and other treatment measures in treatment records" are used to match "Treatment plan", "Laboratory test and imaging test results in examination results" are used to match the evidence-based node "Examination items during hospitalization", "Three-type summary name and quantity in inventory records" are used to match the evidence-based node "Resource node", and "Discharge time and diagnosis in discharge records" are used to match the evidence-based node "Standard hospitalization day".

[0050] In order to facilitate subsequent matching, in this application, after obtaining the mapping relationship, the diagnostic information in the mapping relationship can be mapped to the International Classification of Diseases code. For the mapping of diagnostic information to the International Classification of Diseases (ICD) code, the ICD code is an internationally used disease classification method that can provide a unified classification standard for diagnostic information. By mapping diagnostic information to the ICD code, the comparability and interoperability of diagnostic information between different hospitals and different regions can be achieved, and it is also convenient to analyze based on the ICD code.

[0051] S103: For each diagnostic information in the target medical information, if it is determined from the pre-configured clinical pathways that there is a target clinical pathway corresponding to the diagnostic information, then the execution result of the target clinical pathway is determined according to the mapping relationship, the target medical information, and the evidence-based evidence corresponding to at least one evidence-based node contained in the target clinical pathway; wherein the execution result includes one or more of the following: the clinical pathway type adopted, whether the at least one evidence-based node is executed, the execution result corresponding to the at least one evidence-based node, the weight corresponding to the at least one evidence-based node, the extracted data source path corresponding to the at least one evidence-based node, the node score corresponding to the at least one evidence-based node, and the final score of the target clinical pathway; if it is determined from the pre-configured clinical pathways that there is no target clinical pathway corresponding to the diagnostic information, then it is determined that the clinical pathway of the diagnostic information is missing.

[0052] After obtaining the mapping relationship based on the above embodiment, the target medical information, the mapping relationship and the pre-configured clinical pathway can be used to provide image support for the diagnostic conclusion of the clinical department, and standardize the hospital-side diagnosis and treatment behavior and medical resource consumption. Exemplarily, the target medical information of a patient is analyzed, especially the diagnostic information therein. The diagnostic information will be used to match with various pre-configured clinical pathways. Then, for each diagnostic information in the target medical information, the diagnostic information is matched with the standard diagnostic information corresponding to various clinical pathways. If it is determined that there is matching standard diagnostic information, it means that there is a target clinical pathway corresponding to the diagnostic information in the pre-configured clinical pathways, and the clinical pathway corresponding to the standard diagnostic information is determined as the target clinical pathway; if it is determined that there is no matching standard diagnostic information, it is determined that there is no target clinical pathway corresponding to the diagnostic information in the pre-configured clinical pathways.

[0053] If it is determined from the pre-configured clinical pathways that there is a target clinical pathway corresponding to the diagnostic information, the medical decision is further evaluated and executed according to the evidence-based nodes contained in the target clinical pathway. Exemplarily, for at least one evidence-based node in the target clinical pathway, according to the mapping relationship determined in the above embodiment, the key data corresponding to the evidence-based node is determined from the target medical data, and the node execution result corresponding to the evidence-based node is determined according to the evidence-based evidence and key data corresponding to the evidence-based node. Among them, the node execution result includes whether the evidence-based node is executed, the execution result of the evidence-based node, the source path of the extracted data of the evidence-based node, and the node score of the evidence-based node. Then, based on the node execution results corresponding to the at least one evidence-based node, the execution result of the target clinical pathway is determined. Among them, the execution result includes one or more of the following: the type of clinical pathway adopted, whether the at least one evidence-based node is executed, the execution result corresponding to the at least one evidence-based node, the weight corresponding to the at least one evidence-based node, the source path of the extracted data corresponding to the at least one evidence-based node, the node score corresponding to the at least one evidence-based node, and the final score of the target clinical pathway. Among them, the clinical pathway types adopted include public latest clinical pathways and custom imported clinical pathways, and the weights corresponding to the at least one evidence-based node can be pre-configured according to the importance of each evidence-based node in the target clinical pathway.

[0054] If it is determined that a target clinical pathway corresponding to the diagnosis information exists from among the pre-configured clinical pathways, it is determined that the clinical pathway of the diagnosis information is missing.

[0055] In a possible implementation, the final score is determined according to the weights corresponding to the at least one evidence-based node, the node scores corresponding to the at least one evidence-based node, and the number of the at least one evidence-based node. Exemplarily, for at least one evidence-based node included in the target clinical pathway, the product of the node score corresponding to each evidence-based node and its corresponding weight is calculated. Subsequently, these products are accumulated to obtain the sum of the products of all evidence-based nodes. Finally, the final score is determined by dividing the sum by the total number of evidence-based nodes (i.e., the number of at least one evidence-based node).

[0056] For example, the formula for the final score can be expressed as follows:

[0057]

[0058] Among them, i is the i-th evidence-based node, Factor i is the weight of the i-th evidence-based node, Score iis the node score of the ith evidence-based node, EvidenceNodeCount is the number of all evidence-based nodes in the target clinical pathway, and ClinicEvidenceChainScore is the final score.

[0059] In a possible implementation, if the pre-configured clinical pathways include a public latest clinical pathway and a custom imported clinical pathway, the public latest clinical pathway will be given priority when determining the target clinical pathway because the public latest clinical pathway represents the best practice, that is, the matching priority of the public latest clinical pathway is higher than the matching priority of the custom imported clinical pathway. In this way, it can be ensured that patients can obtain treatment plans based on the latest research and consensus.

[0060] In one example, after obtaining the target medical information, before determining the target clinical pathways corresponding to each diagnostic information in the target medical information from the pre-configured clinical pathways, the method further includes:

[0061] The various diagnostic information contained in the target medical information are merged and deduplicated.

[0062] In order to ensure the accuracy and efficiency of subsequent clinical pathway matching, after the target medical information is extracted, the diagnostic information contained in the target medical information can be further merged and deduplicated. The merging process is mainly a process for integrating diagnostic information that may have similar or repeated expressions. Since medical information may come from different systems or doctors, they may use slightly different terms to describe the same diagnosis. Therefore, the purpose of the merging process is to unify these essentially the same but differently expressed diagnostic information into a standardized entry. For example, "acute bronchitis" and "bronchitis, acute" are different in expression, but they essentially refer to the same disease. During the merging process, the system will identify these similar entries and merge them into a unified diagnostic information. Exemplarily, the merging process can be implemented based on a preset medical terminology library or algorithm that can identify the semantic similarity or equivalence between terms. Deduplication is the process of deleting duplicate diagnostic information that may appear in the target medical information. Due to various reasons (such as system errors, human errors, etc.), the same diagnostic information may be recorded multiple times in the medical records of the same patient. If deduplication is not performed, this duplicate information will not only increase the complexity and time cost of subsequent processing, but may also mislead the choice of clinical pathways. Exemplary deduplication can be achieved by comparing the unique identifiers (such as ICD-10 codes) of each diagnostic information or the content itself. For diagnostic information with the same unique identifier or exactly the same content, the system will treat it as a duplicate and delete it, leaving only one entry.

[0063] S104: Determine and save the evidence-based archiving information according to the target medical information and the evidence-based results corresponding to each diagnostic information; wherein the evidence-based result of any diagnostic information is the execution result of the diagnostic information, or the clinical pathway of the diagnostic information is lost.

[0064] Based on the above embodiments, evidence-based archived information can be determined and saved based on the evidence-based results corresponding to the target medical information and each diagnostic information to improve the quality, efficiency and safety of medical services. For example, the evidence-based results corresponding to the target medical information and each diagnostic information are saved according to the pre-configured format requirements. For another example, based on the pre-configured archived data types, the basic data of each archived data type is determined from the target medical information, and then the basic data and the evidence-based results corresponding to each diagnostic information are saved according to the pre-configured format requirements.

[0065] In a possible implementation, the method further includes:

[0066] For each diagnostic information in the target medical information, if the execution result corresponding to the diagnostic information is saved and the final score corresponding to the diagnostic information is lower than the pre-configured score threshold, it is determined that the diagnostic information has a low correlation with the target clinical pathway corresponding to the diagnostic information.

[0067] For diagnostic information that already has execution results, the association between the diagnostic information and the target clinical pathway can be evaluated, thereby helping medical workers better understand and optimize the clinical decision-making process.

[0068] Specifically, for each diagnostic information in the target medical information, if the execution result corresponding to the diagnostic information is saved, the final score of the diagnostic information can be further analyzed. Compare this final score with the pre-configured score threshold. This score threshold is set based on clinical experience and / or statistical data to determine to what extent a diagnostic information conforms to or deviates from the expected clinical pathway effect. If the final score of the diagnostic information is lower than this score threshold, it can be determined that the correlation between the diagnostic information and the target clinical pathway is low. In this way, it can help the medical team identify those diagnostic information that perform poorly in actual applications, and then analyze the reasons, which may be problems with the diagnosis itself, deviations in the execution process, or individual differences in patients. Based on such analysis, the medical team can adjust the clinical pathway, optimize the diagnostic strategy, or develop personalized treatment plans for specific patients to improve overall medical quality and patient satisfaction.

[0069] In one example, the medical record data to be analyzed includes medical record data for which evidence-based archive information has not been established, and medical record data for which evidence-based archive information has been established and for which a complaint request has been received. In the present application, for the medical record data to be analyzed that has not undergone diagnostic multidimensional analysis, that is, the medical record data for which evidence-based archive information has not been established, the diagnostic multidimensional analysis method provided in the present application can be used for processing. Of course, for the medical record data to be analyzed that has undergone diagnostic multidimensional analysis, that is, the medical record data for which evidence-based archive information has been established, the diagnostic multidimensional analysis method provided in the present application can also be used again for processing under the appeal of the hospital. Exemplarily, if the hospital has objections to a medical record data for which evidence-based archive information has been established, for example, the content in a medical record data is input incorrectly, etc., the complaint request can be input through the complaint configuration interface provided by the computer device. The complaint request can carry the modified data. After receiving the complaint request through the complaint configuration interface, the diagnostic multidimensional analysis method provided in the present application can be used again for processing.

[0070] The beneficial effects of this application are as follows:

[0071] 1. By preprocessing the medical record data to be analyzed, including structured processing and terminology standardization, this process ensures that medical record data from different sources and in different formats can be processed and analyzed in a unified and standardized manner, avoiding the results of the analysis of the medical record data to be affected by the doctor's writing habits.

[0072] 2. By determining the mapping relationship between target medical information and evidence-based nodes, and evaluating the execution results of clinical pathways based on evidence-based evidence, this method helps reduce the impact of doctors' personal experience and subjective judgment on diagnosis and treatment plans, and improves the scientificity and rationality of diagnosis and treatment decisions.

[0073] 3. For each diagnostic information in the target medical information, if there is a corresponding target clinical pathway, the execution of the target clinical pathway of the diagnostic information can be evaluated based on the mapping relationship between the target medical information and the evidence-based node, and the evidence-based evidence corresponding to the target medical information and at least one evidence-based node contained in the target clinical pathway. This helps to promptly discover and resolve problems that may arise during the execution process, optimize the execution process of the clinical pathway, improve the quality and efficiency of medical services, and provide scientific, objective, and traceable evidence chain support for diagnostic selection.

[0074] 4. Clinical pathway management based on evidence-based medicine can more accurately evaluate the cost-effectiveness of different treatment options, thereby helping hospitals and decision makers to allocate medical resources more rationally. At the same time, by reducing unnecessary medical interventions and waste, it can also reduce the medical burden of patients and improve the overall efficiency of medical services.

[0075] 5. By recording and preserving evidence-based archived information, including the execution results of clinical pathways, the weights and scores of evidence-based nodes, and data source paths, this process enhances the transparency of diagnosis and treatment decisions and makes the medical process traceable and auditable. This not only helps to improve medical quality, but also provides valuable data support for subsequent medical research and quality improvement.

[0076] Embodiment 2:

[0077] For the convenience of explanation, according to the functions included in the diagnostic multidimensional analysis method, the computer device is divided into an evidence-based knowledge module, a data extraction module, an evidence-based matching module, an evidence-based fusion module and an evidence-based storage module. Figure 2 This is a schematic diagram of a functional module provided in an embodiment of the present application. The following is a description of the diagnostic multidimensional analysis method provided in the present application through specific embodiments and in combination with various functional modules:

[0078] Assume that the evidence-based knowledge module currently only supports the clinical pathway of "limb fracture", which is represented in the following table:

[0079]

[0080]

[0081] The above clinical pathway is imported and the final computer executable evidence-based node is formed as follows:

[0082]

[0083]

[0084]

[0085] The data extraction module obtains the medical record data to be analyzed of patient A, such as "Patient A (hospitalization serial number PN0001) complained of accidentally falling, unbearable pain in the calf, and inability to walk upright. The diagnosis on the first page of the medical record is 1. Limb fracture 2. Hemiplegia. The list diagnosis is 1. Limb fracture, and the patient recovered well after surgery."

[0086] The data extraction module preprocesses the medical record data to be analyzed and obtains target medical data from the preprocessed medical record data to be processed.

[0087] Then, the data extraction module determines the mapping relationship between the target medical information and the evidence-based nodes based on the input data containing the target medical data through the natural language processing large model.

[0088] For example, based on the input data containing the target medical data:

[0089] Now input the following medical record: Patient A, medical record homepage: ******, list: ****. Please extract the diagnosis set mapping relationship based on the medical record and return it in the following array form:

[0090]

[0091] Then match the text similarity with ICD-10 to get the final mapping relationship:

[0092]

[0093] The evidence-based matching module can merge and deduplicate the various diagnostic information contained in the target medical information. Then, for each diagnostic information, according to the pre-configured clinical pathway matching priority, determine from the pre-configured clinical pathways whether there is a target clinical pathway corresponding to the diagnostic information. Among them, the matching priority of the public latest clinical pathway is higher than the matching priority of the custom imported clinical pathway. In this example, the target medical information includes two diagnostic information, one is "limb fracture" and the other is "hemiplegia". For the "limb fracture", the public latest clinical pathway is matched first to determine whether there is a matching target clinical pathway in the public latest clinical pathway, and then further analysis is performed based on the data recorded in the target clinical pathway. For the "hemiplegia", no matching clinical pathway was found, and the target clinical pathway was set to empty, and there is no evidence-based basis for the time being. It can be executed separately after the matching clinical pathway is imported later.

[0094] For the case of multiple patients, they are also assembled into the following array format.

[0095]

[0096] After receiving the notification from the evidence-based matching module that "limb fracture" corresponds to the target clinical pathway, the evidence-based fusion module can obtain the data of the target clinical pathway from the evidence-based knowledge module and the target medical data from the data extraction module. Then, the following operations are performed:

[0097] a. Evidence-based node matching

[0098] Match the [diagnostic basis] in the clinical pathway with the [medical history collection and initial diagnosis records].

[0099] Match the [Rehabilitation Assessment and Plan] in the clinical pathway with the [Rehabilitation Record].

[0100] Match the [treatment plan] in the clinical pathway with the [treatment record].

[0101] Match the [standard hospital stay] in the clinical pathway with the [discharge record].

[0102] Match the [examination items during hospitalization] with the [examination results] in the clinical pathway.

[0103] b. Difference Analysis

[0104] For each evidence-based node, determine the node execution result corresponding to the evidence-based node according to the evidence-based evidence and key data corresponding to the evidence-based node, and obtain the difference between the medical record content and the clinical pathway, analyze the reasons and record them. Among them, the difference may be caused by individual differences of patients, complications or other special circumstances.

[0105] After the matching operation, the following results are produced:

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112] The evidence-based fusion module determines the execution result of the target clinical pathway based on the node execution results corresponding to the at least one evidence-based node. According to the evidence-based results corresponding to the target medical information and each diagnostic information, the evidence-based archive information is determined and stored in the evidence-based storage module.

[0113] Embodiment 3:

[0114] Based on the same inventive concept, the present application also provides a diagnostic multidimensional analysis system, Figure 3 A schematic diagram of the structure of a diagnostic multidimensional analysis system provided in an embodiment of the present application, the system comprising:

[0115] The data extraction unit 31 is used to pre-process the acquired medical record data to be analyzed, and extract target medical information from the pre-processed medical record data to be analyzed; wherein the pre-processing includes structured processing and terminology standardization, and the target medical information includes diagnosis information;

[0116] A determination unit 32, configured to determine a mapping relationship between the target medical information and the evidence-based node, and map the diagnosis information in the mapping relationship to an International Classification of Diseases code;

[0117] The processing unit 33 is used for determining, for each diagnosis information in the target medical information, the execution result of the target clinical pathway according to the mapping relationship, the target medical information, and the evidence-based evidence corresponding to at least one evidence-based node contained in the target clinical pathway, if it is determined from the pre-configured clinical pathways that there is a target clinical pathway corresponding to the diagnosis information; wherein the execution result includes one or more of the following: the clinical pathway type adopted, whether the at least one evidence-based node is executed, the execution result corresponding to the at least one evidence-based node, the weight corresponding to the at least one evidence-based node, the extraction data source path corresponding to the at least one evidence-based node, the node score corresponding to the at least one evidence-based node, and the final score of the target clinical pathway; if it is determined from the pre-configured clinical pathways that there is no target clinical pathway corresponding to the diagnosis information, then it is determined that the clinical pathway of the diagnosis information is missing;

[0118] The storage unit 34 is used to determine and save the evidence-based archiving information according to the target medical information and the evidence-based results corresponding to each diagnostic information; wherein the evidence-based result of any diagnostic information is the execution result of the diagnostic information, or the clinical pathway of the diagnostic information is lost.

[0119] The diagnostic multidimensional analysis system in this embodiment is presented in the form of functional modules, where the modules refer to application specific integrated circuits (ASICs), processors and memories that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0120] The further functional description of each of the above units is the same as that of the above corresponding embodiments and will not be repeated here.

[0121] Embodiment 4:

[0122] See also Figure 4 , Figure 4 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present application, such as Figure 4As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other, and can be installed on a common mainboard or installed in other ways as required. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output system (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 4 A processor 10 is taken as an example.

[0123] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0124] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0125] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the use of a computer device based on the presentation of a small program landing page, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0126] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0127] The computer device also includes an input system 30 and an output system 40. The processor 10, the memory 20, the input system 30 and the output system 40 may be connected via a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0128] The input system 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output system 40 may include a display device, an auxiliary lighting system (e.g., LED) and a tactile feedback system (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0129] Embodiment 5:

[0130] On the basis of the above embodiments, an embodiment of the present application further provides a computer-readable storage medium, in which a computer program executable by a processor is stored. When the program runs on the processor, the processor implements the following steps when executing:

[0131] Preprocessing the acquired medical record data to be analyzed, and extracting target medical information from the preprocessed medical record data to be analyzed; wherein the preprocessing includes structured processing and terminology standardization, and the target medical information includes diagnostic information;

[0132] Determine a mapping relationship between the target medical information and the evidence-based node, and map the diagnostic information in the mapping relationship to the International Classification of Diseases code;

[0133] For each diagnostic information in the target medical information, if it is determined from the pre-configured clinical pathways that there is a target clinical pathway corresponding to the diagnostic information, then the execution result of the target clinical pathway is determined according to the mapping relationship, the target medical information, and the evidence-based evidence corresponding to at least one evidence-based node contained in the target clinical pathway; wherein the execution result includes one or more of the following: the clinical pathway type adopted, whether the at least one evidence-based node is executed, the execution result corresponding to the at least one evidence-based node, the weight corresponding to the at least one evidence-based node, the extracted data source path corresponding to the at least one evidence-based node, the node score corresponding to the at least one evidence-based node, and the final score of the target clinical pathway; if it is determined from the pre-configured clinical pathways that there is no target clinical pathway corresponding to the diagnostic information, then it is determined that the clinical pathway of the diagnostic information is missing;

[0134] According to the target medical information and the evidence-based results corresponding to each diagnostic information, the evidence-based archiving information is determined and saved; wherein the evidence-based result of any diagnostic information is the execution result of the diagnostic information, or the clinical pathway of the diagnostic information is lost.

[0135] Since the principle of solving the problem by the above-mentioned computer-readable storage medium is similar to that of the diagnostic multidimensional analysis method, the implementation of the above-mentioned computer-readable storage medium can refer to Examples 1-2 of the method, and the repeated parts will not be repeated.

[0136] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A diagnostic multidimensional analysis method, characterized in that: The method comprises: Preprocessing the acquired medical record data to be analyzed, and extracting target medical information from the preprocessed medical record data to be analyzed; wherein the preprocessing includes structured processing and terminology standardization, and the target medical information includes diagnostic information; Determine a mapping relationship between the target medical information and the evidence-based node, and map the diagnostic information in the mapping relationship to the International Classification of Diseases code; For each diagnostic information in the target medical information, if it is determined from the pre-configured clinical pathways that there is a target clinical pathway corresponding to the diagnostic information, then the execution result of the target clinical pathway is determined according to the mapping relationship, the target medical information, and the evidence-based evidence corresponding to at least one evidence-based node contained in the target clinical pathway; wherein the execution result includes one or more of the following: the clinical pathway type adopted, whether the at least one evidence-based node is executed, the execution result corresponding to the at least one evidence-based node, the weight corresponding to the at least one evidence-based node, the extracted data source path corresponding to the at least one evidence-based node, the node score corresponding to the at least one evidence-based node, and the final score of the target clinical pathway; if it is determined from the pre-configured clinical pathways that there is no target clinical pathway corresponding to the diagnostic information, then it is determined that the clinical pathway of the diagnostic information is missing; According to the target medical information and the evidence-based results corresponding to each diagnostic information, the evidence-based archiving information is determined and saved; wherein the evidence-based result of any diagnostic information is the execution result of the diagnostic information, or the clinical pathway of the diagnostic information is lost.

2. The method according to claim 1, characterized in that The at least one evidence-based node includes diagnostic basis, rehabilitation assessment and plan, treatment plan, standard hospital stay, examination items during hospitalization, and resource node.

3. The method according to claim 2, characterized in that The medical record data to be analyzed include medical history collection and initial diagnosis records, examination results, treatment records, rehabilitation records, checklist records, and discharge records; The target medical information includes the medical history information in the medical history collection and initial diagnosis records, the results of laboratory tests and imaging examinations in the examination results, the drug treatment, surgical records and other treatment measures in the treatment records, the time, content, and operation methods of each stage in the rehabilitation records, the three-headed type summary name and quantity in the list record, and the discharge time and diagnosis in the discharge record; wherein the three-headed types include medicines, diagnosis and treatment, and consumables.

4. The method according to claim 1, characterized in that After obtaining the target medical information, before determining the target clinical pathways corresponding to each diagnostic information in the target medical information from the pre-configured clinical pathways, the method further includes: The various diagnostic information contained in the target medical information are merged and deduplicated.

5. The method according to claim 1, characterized in that The pre-configured clinical pathways include a public latest clinical pathway and a custom imported clinical pathway, and the matching priority of the public latest clinical pathway is higher than the matching priority of the custom imported clinical pathway.

6. The method according to claim 1, characterized in that The final score is determined according to the weights respectively corresponding to the at least one evidence-based node, the node scores respectively corresponding to the at least one evidence-based node, and the number of the at least one evidence-based node.

7. The method according to claim 1, characterized in that The method further comprises: For each diagnostic information in the target medical information, if the execution result corresponding to the diagnostic information is saved and the final score corresponding to the diagnostic information is lower than the pre-configured score threshold, it is determined that the diagnostic information has a low correlation with the target clinical pathway corresponding to the diagnostic information.

8. The method according to claim 1, characterized in that The medical record data to be analyzed include medical record data for which evidence-based archiving information has not been established, and medical record data for which evidence-based archiving information has been established and for which a complaint request has been received.

9. A diagnostic multidimensional analysis system, characterized in that: The system comprises: A data extraction unit, used to pre-process the acquired medical record data to be analyzed, and extract target medical information from the pre-processed medical record data to be analyzed; wherein the pre-processing includes structured processing and terminology standardization, and the target medical information includes diagnostic information; A determination unit, used to determine a mapping relationship between the target medical information and the evidence-based node, and map the diagnosis information in the mapping relationship to an International Classification of Diseases code; A processing unit, for determining, for each diagnostic information in the target medical information, an execution result of the target clinical pathway according to the mapping relationship, the target medical information, and the evidence-based evidence respectively corresponding to at least one evidence-based node contained in the target clinical pathway, if it is determined from the pre-configured clinical pathways that there is a target clinical pathway corresponding to the diagnostic information; wherein the execution result includes one or more of the following: the clinical pathway type adopted, whether the at least one evidence-based node is executed, the execution result respectively corresponding to the at least one evidence-based node, the weight respectively corresponding to the at least one evidence-based node, the extracted data source path respectively corresponding to the at least one evidence-based node, the node score respectively corresponding to the at least one evidence-based node, and the final score of the target clinical pathway; if it is determined from the pre-configured clinical pathways that there is no target clinical pathway corresponding to the diagnostic information, then determining that the clinical pathway of the diagnostic information is missing; A storage unit is used to determine and save evidence-based archiving information based on the target medical information and the evidence-based results corresponding to each diagnostic information; wherein the evidence-based result of any diagnostic information is the execution result of the diagnostic information, or the clinical pathway of the diagnostic information is lost.

10. A computer device, characterized in that: The computer device comprises a processor, and the processor is used to implement the steps of the diagnostic multidimensional analysis method as described in any one of claims 1 to 8 when executing the computer program stored in the memory.

11. A computer-readable storage medium, characterized in that: It stores a computer program executable by a computer device. When the program is run on the computer device, the computer device executes the steps of the diagnostic multidimensional analysis method as described in any one of claims 1 to 8.

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