Medical quality control index interpretation method and system, medium and program product

Abstract

The invention discloses a medical quality control index interpretation method and system, a medium and a program product, and the method comprises the steps: constructing a hierarchical index dictionary; establishing an index analysis rule base based on the hierarchical index dictionary; a preset index name standardization algorithm is used, the hierarchical index dictionary and the index analysis rule base are utilized to convert the index names into a preset standardization format, and a plurality of standardized index names are obtained; establishing an index semantic mapping matrix, and respectively matching core words, modifiers and qualifiers in the plurality of standardized index names with preset index connotation elements to obtain a hierarchical semantic mapping result; establishing a hierarchical index interpretation template library; and selecting and combining the initial template elements in the hierarchical index interpretation template library according to a hierarchical semantic mapping result to generate an interpretation text. According to the method and the device, possible differences of index definition interpretation between different medical institutions and regions are reduced, and the index interpretation consistency between the medical institutions and regions is improved.

Description

Technical Field

[0001] The present application belongs to the field of medical information interpretation, and in particular, relates to a method, system, medium and program product for interpreting medical quality control indicators. Background Art

[0002] Medical quality control indicators are important tools for measuring and evaluating the quality of medical services. These indicators help medical institutions and management departments monitor, evaluate and improve the quality of medical services. Appropriate indicators applied in hospital management can promote the standardization, specialization, standardization and refinement of medical management and control work. Analyzing the definition of indicators and accurately interpreting them is essential for the standardized calculation of medical quality control indicators, which can promote the improvement of hospital medical quality.

[0003] In relevant technologies, the meaning of indicator names can be analyzed based on relevant national normative documents, hospital field surveys and clinical research, and the indicator names can be accurately interpreted. The specific connotations and clear value descriptions referred to in the indicators can be described in standard and accurate terms.

[0004] However, different medical institutions and regions may have different interpretations of the indicator definitions, resulting in different descriptions of the specific connotations and clear values ​​referred to in the indicators, which will in turn reduce the consistency between medical institutions and regions and is not conducive to improving the medical quality of the medical system. Summary of the invention

[0005] The present application provides a method, system, medium and program product for interpreting medical quality control indicators, which are used to reduce the possible differences in the interpretation of indicator definitions between different medical institutions and regions, thereby improving the consistency of indicator interpretation between various medical institutions and regions.

[0006] In the first aspect, the present application provides a method for interpreting medical quality control indicators, constructing a hierarchical indicator dictionary, which decomposes the indicator nouns of medical quality control indicators into core words, modifiers, and qualifiers; Establish an indicator parsing rule base based on the hierarchical indicator dictionary, which includes hierarchical matching rules, word combination rules and context association rules; Using a preset indicator name standardization algorithm, using a hierarchical indicator dictionary and an indicator parsing rule base, the indicator names in different expressions are converted into a preset standardized format to obtain a number of standardized indicator names; Establish an indicator semantic mapping matrix, and match the core words, modifiers and qualifiers in the names of several standardized indicators with the preset indicator connotation elements based on the indicator semantic mapping matrix to obtain a hierarchical semantic mapping result; A hierarchical indicator interpretation template library is established to set corresponding interpretation frameworks and key elements for word combinations at different levels; initial template elements are selected and combined in the hierarchical indicator interpretation template library according to the hierarchical semantic mapping results to generate interpretation texts, and the initial template elements are the template elements with the highest similarity to the hierarchical semantic mapping results.

[0007] By adopting the above technical solutions and building a hierarchical indicator dictionary, the precise decomposition of indicator names is achieved, which helps to more accurately understand the core content, modification information and limiting conditions of the indicators. The establishment of the indicator parsing rule library ensures the consistent processing of indicators with different expressions, and improves the accuracy and reliability of interpretation. The application of standardized algorithms further unifies the indicator expression, facilitating the comparison and analysis of indicators across institutions and regions. The creation of the indicator semantic mapping matrix establishes a clear correspondence between the indicator name and the connotation elements, which helps to understand the meaning of the indicator more comprehensively and deeply. The application of the hierarchical indicator interpretation template library not only improves the efficiency of interpretation, but also ensures the standardization and integrity of the interpretation results. It reduces the possible differences in the interpretation of indicator definitions between different medical institutions and regions, thereby improving the consistency of indicator interpretation between various medical institutions and regions.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, constructing a hierarchical index dictionary specifically includes: Obtain a set of medical quality control indicator names, which includes indicator names published by authoritative organizations, indicator names used by medical institutions at all levels, indicator names in medical literature, and indicator names used in clinical practice; Define the word classification standard, and decompose the indicator names in the medical quality control indicator name set into core words, modifiers and qualifiers. The core words represent the measurement objects of the indicator names, the modifiers represent the words that describe the core words, and the qualifiers are the words that refine the classification conditions. A hierarchical indicator dictionary was constructed based on the medical quality control indicator name set and word classification standards.

[0009] By adopting the above technical solutions, the comprehensiveness and representativeness of the indicator dictionary are ensured. By collecting the indicator names published by authoritative organizations, the indicator names used by medical institutions at all levels, the indicator names in medical literature, and the indicator names used in clinical practice, the dictionary covers comprehensive terms in the field of medical quality control and adapts to different scenarios and needs. The practice of decomposing indicator names into core words, modifiers, and qualifiers greatly improves the accuracy of indicator understanding. Core words reflect the measurement object, modifiers describe the core words, and qualifiers refine the classification conditions. This hierarchical structure makes the meaning of the indicator clearer and more specific.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, a word classification standard is defined to decompose the indicator names in the medical quality control indicator name set into core words, modifiers and qualifiers, specifically including: Collect the core concepts of common indicators, common modifying words and common limiting words to obtain a word feature library; Calculate the similarity between the indicator names in the medical quality control indicator name set and the indicator core concepts, commonly used modifying words and commonly used limiting words, and obtain the indicator core concept similarity, modifying similarity and limiting similarity; Define a word classification standard based on similarity, and assign an indicator name to the word category corresponding to the word whose similarity is greater than a preset similarity threshold; According to the word feature library and word classification standards, the indicator names in the medical quality control indicator name set are decomposed into core words, modifiers and qualifiers. The core words are the words corresponding to the core concept similarity of the indicator greater than the preset similarity threshold, the modifiers are the words corresponding to the modifying similarity greater than the preset similarity threshold, and the qualifiers are the words corresponding to the restrictive similarity greater than the preset similarity threshold.

[0011] By adopting the above technical solutions, we collect the core concepts, modifying words and limiting words of common indicators to build a word feature library, which improves the professionalism and accuracy of classification and reduces classification errors. The method of calculating similarity is used to classify words, and quantitative analysis is introduced to make the classification process more objective and reliable, improving the accuracy and coverage of classification. The classification process is based on clear rules and calculations, and its results are consistent and repeatable, which is conducive to the comparison and analysis of indicators of different institutions or different periods.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, after decomposing the indicator names in the medical quality control indicator name set into core words, modifiers and qualifiers according to the word feature library and the word classification standard, the method further includes: Access a thesaurus of medical synonyms and antonyms; Identify synonyms and near-synonyms for core words, modifiers, and qualifiers; Convert synonyms and antonyms into pre-set standard terms based on medical synonym and antonym thesauri; Create a mapping table of synonyms and near-synonyms to preset standard terms.

[0013] By adopting the above technical solution, the system can obtain the medical synonyms and antonyms word library, identify and process the term variants unique to the medical field, and improve the accuracy and comprehensiveness of the indicator interpretation. In the medical field, the same concept may have multiple ways of expression, ensuring that indicators with different expressions can be correctly understood and classified. Converting synonyms and antonyms into preset standard terms unifies the expression of indicators, provides a consistent basis for subsequent analysis and comparison, and improves the reliability of indicator interpretation. Establishing a mapping table between synonyms and antonyms and preset standard terms not only retains the information of the original expression, but also establishes a clear connection between different expressions. This mapping relationship is conducive to tracking the original expression of the indicator, and also facilitates the unified management and updating of terms. It can adapt to the dynamic changes of medical terminology. With the development of medicine, new terms and expressions continue to emerge. By updating the word library and mapping table, the system can continue to maintain the timeliness of its processing capabilities.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, after selecting and combining initial template elements in the hierarchical indicator interpretation template library according to the hierarchical semantic mapping result to generate the interpretation text, the method further includes: Establish a scoring dimension and use it to score the interpretation text to obtain a consistency score; When it is determined that the consistency score is greater than a preset threshold, the interpreted text is marked as a qualified text; When it is determined that the consistency score is not greater than the preset threshold, the interpreted text is sent to the review terminal; Receive the revised interpretation text sent by the audit terminal, and parse the revised interpretation text to obtain a revised hierarchical indicator interpretation template library; update the hierarchical indicator interpretation template library according to the revised hierarchical indicator interpretation template library.

[0015] By adopting the above technical solution, establishing scoring dimensions and scoring the interpretation texts, high-quality interpretation texts can be identified and screened, ensuring the reliability and consistency of the interpretation results. For interpretation texts that do not meet the standards, the system will send them to the review terminal for manual review. The interpretation texts after manual review will be more accurate. By receiving and parsing the revised interpretation texts, the system can continuously update and optimize the hierarchical indicator interpretation template library, so that the system can continuously improve its interpretation capabilities over time and adapt to the ever-changing medical quality control needs.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, establishing a scoring dimension specifically includes: Determine the accuracy of the corresponding core words based on the core words and the main measurement concepts; Determine the appropriateness of the corresponding modifiers based on the modifiers and core words; Determine the clarity of the corresponding qualifiers based on the qualifiers and indicator nouns; Determine the completeness of the corresponding interpretation based on all the key elements contained in the interpretation text and indicator nouns; The corresponding weights are assigned to the accuracy of core words, the appropriateness of modifiers, the clarity of qualifiers and the completeness of interpretation to obtain the scoring dimensions.

[0017] By adopting the above technical solutions, the accuracy of core words, the appropriateness of modifiers, the clarity of qualifiers and the completeness of interpretation are evaluated respectively, which realizes a comprehensive and detailed quality evaluation of the interpretation text, can fully capture all key aspects of the interpretation text, and ensure the comprehensiveness and accuracy of the evaluation. The evaluation of the accuracy of core words ensures that the interpretation text accurately grasps the main measurement object of the indicator. The evaluation of the appropriateness of modifiers ensures that the description of core concepts is accurate and appropriate, avoiding possible misunderstandings or ambiguities. The evaluation of the clarity of qualifiers ensures that the scope and conditions of the indicator are clearly defined. The evaluation of the completeness of interpretation ensures that the interpretation text covers all key elements in the indicator noun, avoiding the omission of information. By assigning different weights to each evaluation dimension, the system can flexibly adjust the evaluation criteria according to the characteristics and importance of different indicators, so that the evaluation results are more in line with actual needs. The system can adapt to different types of medical quality control indicators, improving the versatility and adaptability of the system.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, the interpretation text is scored using a scoring dimension to obtain a consistency score, specifically including: The consistency score corresponding to the interpreted text is calculated using a weighted average algorithm based on the scoring dimensions; The calculation function in the weighted average algorithm is: N=a×S+b×Y+c×Z+d×M In the function, N is the consistency score, a is the weight corresponding to the consistency score, S is the core word accuracy, b is the weight corresponding to the modifier appropriateness, Y is the modifier appropriateness, c is the weight corresponding to the qualifier clarity, Z is the qualifier clarity, d is the weight corresponding to the interpretation completeness, M is the interpretation completeness, and the sum of a, b, c and d is 1.

[0019] By adopting the above technical solution and using the weighted average algorithm, we can comprehensively consider the four key dimensions of core word accuracy, modifier appropriateness, qualifier clarity and interpretation completeness to obtain a consistency score that fully reflects the quality of interpretation. This comprehensive scoring method can more accurately reflect the overall quality of the interpreted text and avoid the one-sidedness that may be caused by a single-dimensional evaluation.

[0020] In the second aspect, an embodiment of the present application provides a medical quality control indicator interpretation system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a system, causes the system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer program product, characterized in that when the computer program product runs on a system, the system executes the method described in any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application provides a method for interpreting medical quality control indicators. Through the construction of a hierarchical indicator dictionary, the accurate decomposition of the indicator name is achieved, which helps to more accurately understand the core content, modification information and qualification conditions of the indicator. The establishment of an indicator parsing rule base ensures the consistent processing of indicators with different expressions, and improves the accuracy and reliability of the interpretation. The application of standardized algorithms further unifies the indicator expression, which facilitates the comparison and analysis of indicators across institutions and regions. The creation of an indicator semantic mapping matrix establishes a clear correspondence between the indicator name and the connotation elements, which helps to understand the meaning of the indicator more comprehensively and deeply. The application of a hierarchical indicator interpretation template library not only improves the efficiency of interpretation, but also ensures the standardization and completeness of the interpretation results. It achieves the reduction of possible differences in the interpretation of indicator definitions between different medical institutions and regions, thereby improving the consistency of indicator interpretation between various medical institutions and regions.

[0024] 2. This application provides a method for interpreting medical quality control indicators, obtains a thesaurus of medical synonyms and antonyms, and the system can identify and process terminology variants unique to the medical field, thereby improving the accuracy and comprehensiveness of indicator interpretation. In the medical field, the same concept may have multiple ways of expression, ensuring that indicators with different expressions can be correctly understood and classified. Synonyms and antonyms are converted into preset standard terms, which unifies the expression of indicators, provides a consistent basis for subsequent analysis and comparison, and improves the reliability of indicator interpretation. A mapping table of synonyms and antonyms with preset standard terms is established, which not only retains the information of the original expression, but also establishes a clear connection between different expressions. This mapping relationship is conducive to tracking the original expression of the indicator, and also facilitates the unified management and updating of terms. It can adapt to the dynamic changes of medical terminology. With the development of medicine, new terms and expressions continue to emerge. By updating the thesaurus and mapping table, the system can continue to maintain the timeliness of its processing capabilities.

[0025] 3. This application provides a method for interpreting medical quality control indicators, which evaluates the accuracy of core words, the appropriateness of modifiers, the clarity of qualifiers and the completeness of interpretation, respectively, and realizes a comprehensive and detailed quality assessment of the interpretation text, which can fully capture all key aspects of the interpretation text and ensure the comprehensiveness and accuracy of the assessment. The evaluation of the accuracy of core words ensures that the interpretation text accurately grasps the main measurement objects of the indicators. The evaluation of the appropriateness of modifiers ensures that the description of core concepts is accurate and appropriate, avoiding possible misunderstandings or ambiguities. The evaluation of the clarity of qualifiers ensures that the scope of application and conditions of the indicators are clearly defined. The evaluation of the completeness of interpretation ensures that the interpretation text covers all key elements in the indicator nouns and avoids the omission of information. By assigning different weights to each evaluation dimension, the system can flexibly adjust the evaluation criteria according to the characteristics and importance of different indicators, so that the evaluation results are more in line with actual needs. The system can adapt to different types of medical quality control indicators, improving the versatility and adaptability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a method for interpreting medical quality control indicators in an embodiment of the present application.

[0027] Figure 2 It is another flow chart of a method for interpreting medical quality control indicators in an embodiment of the present application.

[0028] Figure 3 It is a schematic diagram of the physical device structure of a medical quality control indicator interpretation system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0030] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0031] In the current medical system, medical quality control indicators play a vital role. These indicators are not only key tools for measuring the quality of medical services, but also an important basis for promoting medical institutions to continuously improve and enhance their service levels. However, with the continuous development and complexity of the medical system, medical institutions at all levels and in all regions often face huge challenges in interpreting and applying these indicators.

[0032] Take a certain province as an example. The province has hundreds of medical institutions of different sizes and levels, from tertiary hospitals to primary clinics, distributed in every corner of cities and rural areas. In order to improve the quality of medical care in the province, the Provincial Health Commission has formulated a series of medical quality control indicators. However, in the actual implementation process, there are significant differences in the understanding and application of these indicators among various medical institutions.

[0033] For example, for the indicator of "surgical site infection rate", one tertiary hospital understood it as the proportion of surgical site infections among all surgical patients, while another secondary hospital interpreted it as the proportion of infections limited to patients undergoing clean surgery. This difference in interpretation led to the two hospitals using different standards in data collection and reporting, making it difficult for provincial management departments to accurately evaluate and compare the actual performance of each hospital.

[0034] For example, when interpreting the "patient satisfaction" indicator, some hospitals only considered the feedback from inpatients and ignored the evaluation of outpatients. Other hospitals had different understandings on how to handle transferred cases when calculating the "average length of stay", with some hospitals including the transfer time and others only calculating the actual length of stay in the hospital. These differences not only affect the comparability of data between hospitals, but also make it difficult to evaluate the overall quality of medical care in the province.

[0035] What is more complicated is that with the rapid development of medical technology and the emergence of new treatment methods, some new quality control indicators have also emerged. For example, in the process of promoting day surgery, there are great differences among hospitals on how to define and calculate the indicator of "day surgery transfer rate". Some hospitals also include planned transfers to hospital, while others only count unplanned transfers to hospital.

[0036] These differences in interpretation not only affect the comparability of data between medical institutions, but also cause trouble for provincial health management departments when formulating policies and allocating resources. More importantly, this inconsistency may cause some medical institutions to ignore the real improvement of medical quality in the process of pursuing improvement in indicator numbers, and may even take some inappropriate measures to beautify the data.

[0037] In order to solve the above technical problems, this application provides a method for interpreting medical quality control indicators, which is used to reduce the possible differences in the interpretation of indicator definitions between different medical institutions and regions, thereby improving the consistency of indicator interpretation between various medical institutions and regions. Figure 1 , a method for interpreting a medical quality control indicator in an embodiment of the present application is described: See also Figure 1 , which is a flow chart of a method for interpreting medical quality control indicators in an embodiment of the present application.

[0038] S101. Obtain a name set of medical quality control indicators; The system obtains a set of medical quality control indicator names, which includes indicator names published by authoritative organizations, indicator names used by medical institutions at all levels, indicator names in medical literature, and indicator names used in clinical practice.

[0039] In medical quality management, standardization of indicator names is the key to improving the accuracy of quality assessment. The system first needs to collect indicator names from various channels, including official indicators released by authoritative agencies, indicators used by hospitals at all levels in daily work, indicators mentioned in medical literature, and indicators commonly used in clinical practice. The purpose of this step is to establish a comprehensive indicator name library to lay the foundation for subsequent analysis and standardization.

[0040] For example, the system may collect the following metric names: Authoritative organizations publish: incidence of surgical site infection; Hospital use: postoperative wound infection rate; Medical Literature: Surgery-Associated Infection Index; Clinical practice: surgical wound infection rate.

[0041] S102. Define the word classification standard and decompose the indicator names in the medical quality control indicator name set into core words, modifiers and qualifiers; Define the word classification standard, and decompose the indicator names in the medical quality control indicator name set into core words, modifiers and qualifiers. The core words represent the measurement objects of the indicator names, the modifiers represent the words that describe the core words, and the qualifiers are the words that refine the classification conditions. Specifically: collect the core concepts of common indicators, common modifiers and common qualifiers, and obtain the word feature library; Calculate the similarity between the indicator names in the medical quality control indicator name set and the indicator core concepts, commonly used modifying words and commonly used limiting words, and obtain the indicator core concept similarity, modifying similarity and limiting similarity; Define a word classification standard based on similarity, and assign an indicator name to the word category corresponding to the word whose similarity is greater than a preset similarity threshold; According to the word feature library and word classification standards, the indicator names in the medical quality control indicator name set are decomposed into core words, modifiers and qualifiers. The core words are the words corresponding to the core concept similarity of the indicator greater than the preset similarity threshold, the modifiers are the words corresponding to the modifying similarity greater than the preset similarity threshold, and the qualifiers are the words corresponding to the restrictive similarity greater than the preset similarity threshold.

[0042] The core of this step is to establish a word classification system to accurately identify the key components in the indicator name. The system will first build a word feature library, including common indicator core concepts (such as "infection", "satisfaction", "mortality", etc.), modifying words (such as "postoperative", "in-hospital", "30 days", etc.) and limiting words (such as "clean surgery", "diabetic patients", "ICU", etc.).

[0043] Next, the system calculates the similarity between the words in the metric name and the words in the feature library. For example, for the metric "incidence of surgical site infection": The similarity between “infection” and the core concept “infection” might be 0.9; The similarity between “surgery” and the modifier “postoperative” might be 0.8; The similarity between “part” and the qualifier “specific part” might be 0.7; Assuming the similarity threshold set by the system is 0.6, "infection" will be classified as a core word, "surgery" and "site" will be classified as modifiers, and "incidence" may be identified as another core word, indicating the type of measurement.

[0044] Of course, there may be some synonyms and antonyms, so in order to facilitate description and induction, obtain a medical synonym and antonym thesaurus; Identify synonyms and near-synonyms for core words, modifiers, and qualifiers; Convert synonyms and antonyms into pre-set standard terms based on medical synonym and antonym thesauri; Create a mapping table of synonyms and near-synonyms to preset standard terms.

[0045] After obtaining the mapping table, when subsequently classifying words, the mapping table can be used to directly classify synonyms and near-synonyms accurately and quickly.

[0046] S103, constructing a hierarchical indicator dictionary based on the medical quality control indicator name set and word classification standards; The system uses the information obtained in the first two steps to build a structured indicator dictionary, which not only contains the various components of the indicator, but also reflects the hierarchical relationship between these components.

[0047] For example, for the metric "Incidence of Surgical Site Infections," a hierarchical metric dictionary might look like this: Core word layer: Infect; Incidence rate.

[0048] Modifier layer: Operation; Part.

[0049] This hierarchical structure allows the system to understand the meaning of the indicator more precisely and provides a basis for comparison and mapping between different indicators. For example, the system can recognize that "surgical site infection rate" and "postoperative wound infection rate" are the same in core concepts, but differ in the way they are expressed.

[0050] S104, establishing an indicator parsing rule base based on the hierarchical indicator dictionary; In this step, the system creates a set of rules for parsing and understanding the structure and meaning of various indicator names. These rules are based on a hierarchical indicator dictionary and take into account the specificity and complexity of medical quality indicators.

[0051] The indicator parsing rule base may contain the following types of rules: Word order rules: define the typical order in which core words, modifiers, and qualifiers appear in indicator names.

[0052] For example: [qualifier]+[modifier]+[core word]+[measurement type].

[0053] Synonym rules: define the equivalence relationship between synonyms or near-synonyms.

[0054] For example: {"surgery", "postoperative", "surgery-related"} can be considered as equivalent modifiers.

[0055] Abbreviation expansion rules: Define the full form of common abbreviations.

[0056] For example: ICU - Intensive Care Unit.

[0057] Composite indicator parsing rules: Processing complex indicators containing multiple core concepts.

[0058] For example: "Incidence of surgical site infection" = [Core word 1: infection] + [Core word 2: incidence].

[0059] Context-sensitive rules: Adjust the parsing method according to different medical fields or departments.

[0060] For example, in surgery, "incision" usually refers to surgical incisions; in dermatology, it may refer to other types of skin wounds.

[0061] For example: For the metric "incidence of surgical site infections", the system might apply the following rules: Apply word order rules: identify “surgery” as a modifier, “site” as a modifier, “infection” as a core word, and “incidence” as a measurement type.

[0062] Apply the synonym rule: consider "surgical site" and "surgical incision" equivalent.

[0063] Apply context-sensitive rules: In a surgical context, interpret “site” as “surgical incision.”

[0064] S105, using a preset indicator name standardization algorithm, using a hierarchical indicator dictionary and an indicator parsing rule library to convert indicator names in different expressions into a preset standardized format, and obtaining a plurality of standardized indicator names; In this step, the system will use a pre-set standardization algorithm, combined with a hierarchical indicator dictionary and an indicator parsing rule base, to convert the indicator names with various different expressions into a unified standard format. This process aims to eliminate differences in expression so that the same or similar indicators from different sources can be consistently understood and compared.

[0065] A normalization algorithm may include the following steps: Tokenization and part-of-speech tagging: Split the indicator name into individual words and tag the part of speech of each word.

[0066] Core word identification: Use the hierarchical indicator dictionary to identify the core words in the indicator.

[0067] Modifier and Qualifier Classification: Based on the dictionary and rule base, the remaining words are classified as modifiers or qualifiers.

[0068] Synonym replacement: Use the synonym rules in the rule base to replace non-standard expressions with standard expressions.

[0069] Abbreviation expansion: Replace common abbreviations with their full forms.

[0070] Word Rearrangement: Rearrange the order of words according to a predefined standard format.

[0071] Generate standardized name: Combine the processed words to generate the final standardized indicator name.

[0072] For example, for the following indicators with different expressions: "Incidence of surgical site infection" Postoperative wound infection rate “SSI occurrence rate” Surgery-related infection index The normalization process might be as follows: "Incidence of surgical site infection" Word segmentation results: [surgery, site, infection, incidence] Core word recognition: [infection] Modifier category: [surgery, part] Measurement Type Identification: [Incidence] Standardized outcome: incidence of surgical site infection Postoperative wound infection rate Word segmentation results: [postoperative, wound, infection, rate] Core word recognition: [infection] Modifier category: [postoperative, wound] Synonym replacement: [postoperative]->[surgery], [wound]->[site] Measurement type identification: [Rate] -> [Occurrence rate] Standardized outcome: incidence of surgical site infection “SSI occurrence rate” Word segmentation results: [SSI, occurrence, proportion] Abbreviation expansion: SSI -> Surgical site infection Core word recognition: [infection] Modifier category: [surgery, part] Measurement type identification: [Ratio] -> [Occurrence] Standardized outcome: incidence of surgical site infection Surgery-related infection index Word segmentation results: [surgery, related, infection, index] Core word recognition: [infection] Modifier category: [surgery, related] Synonym replacement: [related]->[part] Measurement type identification: [Index] -> [Occurrence] Standardized outcome: incidence of surgical site infection S106, establishing an indicator semantic mapping matrix, and matching the core words, modifiers and qualifiers in the names of several standardized indicators with the preset indicator connotation elements respectively based on the indicator semantic mapping matrix to obtain a hierarchical semantic mapping result; In this step, the system first builds an indicator semantic mapping matrix, and then uses this matrix to match the various components of the standardized indicator name with the pre-defined indicator connotation elements. The purpose of this process is to transform the indicator name into a deeper level of semantic understanding, providing a basis for subsequent interpretation.

[0073] The indicator semantic mapping matrix may contain the following dimensions: Measurement objects: such as patients, surgeries, drugs, etc. Quality dimensions: such as safety, effectiveness, timeliness, patient-centeredness, etc. Measurement methods: such as ratio, proportion, mean, median, etc. Time range: such as within 30 days after surgery, during hospitalization, annually, etc. Scope of application: such as all patients, patients with specific diseases, specific types of surgery, etc. For example, for the standardized indicator "incidence of surgical site infection", the semantic mapping process may be as follows: Core word mapping: "Infection" -> Measurement object: Complications; Quality dimension: Safety Modifier Mapping: "Surgery"->Scope of application: surgical patients "Part"->Measurement object refinement: surgical incision Measurement type mapping: "Incidence" -> Measurement method: Ratio Based on this mapping, the system can generate the following hierarchical semantic mapping results: Measurement object: Complications (surgical wound infection) Quality Dimension: Safety Measurement method: Ratio Scope of application: surgical patients Time range: (needs further clarification, usually within 30 days after surgery) This hierarchical semantic mapping result provides richer contextual information for the indicator, enabling the system to more accurately understand the actual meaning and application scenarios of the indicator. For example, the system now knows that this indicator is used to evaluate the safety of surgery, targets surgical patient groups, and measures the occurrence of a specific type of complication (incision infection).

[0074] Furthermore, this semantic mapping provides a basis for comparing and linking different metrics. For example, the system can easily identify all metrics related to surgical safety, or all metrics that use ratios as a measurement method.

[0075] S107. Establish a hierarchical indicator interpretation template library; Establish a hierarchical indicator interpretation template library to set corresponding interpretation frameworks and key elements for word combinations at different levels.

[0076] In this step, the system creates a multi-level indicator interpretation template library. The purpose of this template library is to provide an appropriate interpretation framework for indicators of different types and complexity, ensuring that the interpretation results are both accurate and easy to understand. The hierarchical design allows the system to select the appropriate interpretation depth based on the complexity of the indicator and the needs of the user.

[0077] The hierarchical indicator interpretation template library may include the following levels: Basic layer: provides simple and direct indicator definitions and calculation methods.

[0078] Extension layer: includes detailed explanation, scope of application and restrictions of the indicator.

[0079] Application layer: Provides application examples and precautions of indicators in actual medical quality management.

[0080] Comparison layer: Contains comparisons and differences with related indicators.

[0081] Improvement layer: Provides suggestions and best practices for improving indicators.

[0082] For example, for the indicator "incidence of surgical site infection", the template library may contain the following template elements: Base layer template: {Indicator name} refers to the proportion of the number of cases of {measurement object} occurring in {applicable scope} to the total number of {applicable scope} within {time range}.

[0083] Extension layer template: {Indicator name} is mainly used to evaluate {quality dimension}. It reflects the occurrence of {measurement object} in {scope of application} and is an important indicator for measuring the quality of {related medical process}. The calculation method of this indicator is: ({calculation formula}). When interpreting this indicator, you need to take into account {list of precautions}.

[0084] Application layer template: In actual applications, {indicator name} can be used for {application scenario list}. Medical institutions can regularly monitor this indicator to promptly identify potential {problem types} and take corresponding {improvement measures types}.

[0085] Contrast layer template: {Indicator name} has some similarities with {Related indicator name}, but the main difference is {Difference description}. In some cases, it may be necessary to consider these indicators together to obtain a more comprehensive assessment.

[0086] Improved layer templates: To improve {indicator name}, medical institutions may consider taking the following measures: {list of improvement suggestions}. When implementing these measures, pay attention to {implementation considerations}.

[0087] By establishing such a hierarchical template library, the system can generate appropriate interpretation texts for indicators of different complexity and users with different needs. For example, for primary medical institutions, the system may mainly use basic layer and application layer templates; while for the quality management department of a large hospital, the system may use templates at all levels to provide comprehensive interpretation.

[0088] S108. Select and combine initial template elements in the hierarchical indicator interpretation template library according to the hierarchical semantic mapping result to generate an interpretation text.

[0089] According to the hierarchical semantic mapping results, the initial template elements are selected and combined in the hierarchical indicator interpretation template library to generate the interpretation text. The initial template elements are the template elements with the highest similarity to the hierarchical semantic mapping results.

[0090] The system will use the hierarchical semantic mapping results obtained in the previous step to select appropriate template elements from the hierarchical indicator interpretation template library and combine them into a coherent interpretation text. This process should not only consider the semantic content of the indicator, but also the needs and background of the user.

[0091] Taking the incidence of surgical site infection as an example, suppose the user is the quality management department of a medium-sized hospital and needs to have a comprehensive understanding of this indicator. The system may perform the following operations: Select template: Based on the user context, the system decides to use the templates of the base layer, extension layer, application layer, and improvement layer.

[0092] Populate Template: The system populates the selected template with the hierarchical semantic mapping results.

[0093] Combined text: The system combines the filled template elements into a coherent interpretation text.

[0094] In the above embodiment, by collecting the indicator names published by authoritative organizations, the indicator names used by medical institutions at all levels, the indicator names in medical literature, and the indicator names used in clinical practice, the dictionary covers comprehensive terms in the field of medical quality control and adapts to different scenarios and needs. Through the construction of a hierarchical indicator dictionary, the accurate decomposition of the indicator names is achieved, which helps to more accurately understand the core content, modification information and limiting conditions of the indicators. The establishment of an indicator parsing rule library ensures the consistent processing of indicators with different expressions, and improves the accuracy and reliability of interpretation. The application of standardized algorithms further unifies the indicator expression, which facilitates the comparison and analysis of indicators across institutions and regions. The creation of an indicator semantic mapping matrix establishes a clear correspondence between the indicator name and the connotation elements, which helps to understand the meaning of the indicator more comprehensively and deeply. The application of a hierarchical indicator interpretation template library not only improves the efficiency of interpretation, but also ensures the standardization and integrity of the interpretation results. It is achieved to reduce the possible differences in the interpretation of indicator definitions between different medical institutions and regions, thereby improving the consistency of the interpretation of indicators between various medical institutions and regions.

[0095] The above embodiment finally obtains the interpretation text, which is automatically generated by the computer. In general, it is necessary to judge whether the interpretation text meets the standard. If it does not meet the standard, it still needs to be modified. Figure 2 , another method for interpreting medical quality control indicators in the embodiment of the present application is described: See also Figure 2 , is another flow chart of a method for interpreting medical quality control indicators in an embodiment of the present application.

[0096] S201, establishing a scoring dimension and using the scoring dimension to score the interpretation text to obtain a consistency score; The system establishes a scoring dimension and uses the scoring dimension to score the interpretation text to obtain a consistency score. Specifically: the accuracy of the corresponding core words is determined based on the core words and the main measurement concepts; Determine the appropriateness of the corresponding modifiers based on the modifiers and core words; Determine the clarity of the corresponding qualifiers based on the qualifiers and indicator nouns; Determine the completeness of the corresponding interpretation based on all the key elements contained in the interpretation text and indicator nouns; The corresponding weights are respectively assigned to the accuracy of the core words, the appropriateness of the modifiers, the clarity of the qualifiers, and the completeness of the interpretation to obtain the scoring dimensions; The consistency score corresponding to the interpreted text is calculated using a weighted average algorithm based on the scoring dimensions; The calculation function in the weighted average algorithm is: N=a×S+b×Y+c×Z+d×M In the function, N is the consistency score, a is the weight corresponding to the consistency score, S is the core word accuracy, b is the weight corresponding to the modifier appropriateness, Y is the modifier appropriateness, c is the weight corresponding to the qualifier clarity, Z is the qualifier clarity, d is the weight corresponding to the interpretation completeness, M is the interpretation completeness, and the sum of a, b, c and d is 1.

[0097] Suppose the system needs to interpret the medical quality control indicator "incidence of pressure ulcers in hospitalized patients".

[0098] The system first establishes scoring dimensions, including core word accuracy, modifier appropriateness, qualifier clarity, and interpretation completeness. For the indicator "incidence of pressure ulcers in hospitalized patients", the system may generate the following interpretation text: “This indicator reflects the proportion of pressure ulcers among inpatients in a hospital and is used to assess the quality of care and the level of patient safety management.” The system scores as follows: Core word accuracy: The system identified the core word as "pressure ulcer incidence", which exactly matches the indicator name and was assigned a full score of 10 points.

[0099] Appropriateness of modifiers: The system identified the modifier as “inpatient”, which is consistent with the indicator description and was assigned a score of 9.

[0100] Clarity of qualifiers: The system did not find specific qualifiers, but the interpretation mentioned the scope of hospitals, and assigned 8 points.

[0101] Completeness of interpretation: The system judged that the interpretation text included the purpose of the indicator (assessing the quality of care and the level of patient safety management) and assigned 9 points.

[0102] The system then calculates the consistency score using the preset weights: Assume that the weights are a=0.3, b=0.2, c=0.2, d=0.3 N=0.3×10+0.2×9+0.2×8+0.3×9=9.1 The system uses natural language processing techniques, such as part-of-speech tagging and dependency syntax analysis, to identify core words, modifiers, and qualifiers. At the same time, pre-trained medical language models (such as BioBERT) are used to evaluate the accuracy and completeness of the interpretation. This process involves machine learning and deep learning technologies, which can perform semantic understanding and relevance analysis on the text.

[0103] S202: if it is determined that the consistency score is greater than a preset threshold, marking the interpreted text as a qualified text; Assume that the preset qualified threshold of the system is 8.5 points. In this example, the calculated consistency score is 9.1 points, which is greater than the preset threshold of 8.5 points. Therefore, the system marks this interpretation text as qualified text.

[0104] This step involves simple conditional judgment and data tagging. The system uses database operations or file operations to update the status of the interpreted text. At the same time, the system may use logging technology to track the scoring and judgment process of each interpreted text for subsequent analysis and improvement.

[0105] S203. When it is determined that the consistency score is not greater than the preset threshold, the interpreted text is sent to the review terminal; although the interpreted text is marked as qualified in this example, to illustrate this step, it is assumed that the system generates another interpreted text with a consistency score of 8.0, which is lower than the preset threshold of 8.5.

[0106] In this case, the system will send the interpretation text to the review terminal. The review terminal can be a dedicated software interface for medical quality control experts. The system uses network communication technology (such as HTTP protocol or WebSocket) to transmit the interpretation text to the review terminal.

[0107] To ensure the security of data transmission, the system may use encryption technology (such as SSL / TLS) to protect sensitive medical information. At the same time, the system will use identity authentication and authorization mechanisms to ensure that only experts can access the interpretation texts that need to be reviewed.

[0108] In addition, the system may use task queue management technology (such as RabbitMQ or Apache Kafka) to handle the large number of interpretation texts that need to be reviewed, ensuring that the workload is evenly distributed to different review experts. The system may also monitor the length of the review queue in real time and automatically expand the review resources when necessary.

[0109] In addition, the information awaiting review in the review queue can also be assigned different levels. The higher the level, the higher the priority of the review, so that more important tasks can be resolved quickly.

[0110] S204, receiving the revision interpretation text sent by the review terminal, and parsing the revision interpretation text to obtain a revision hierarchical indicator interpretation template library; Continuing with the example in the previous step, assume that the review expert modifies the interpretation text as follows: "This indicator measures the proportion of new pressure ulcers among hospitalized patients in a specific period of time. It is an important indicator for evaluating the quality of care, the level of patient safety management, and the effectiveness of prevention measures. A lower incidence rate generally indicates that the hospital is doing a good job in preventing and identifying pressure ulcers early." The system receives this revised interpretation and parses it using natural language processing techniques. Specifically, the system may: Use word frequency analysis and phrase extraction techniques to identify key words and phrases, such as “specific time period,” “new occurrence,” “effectiveness of preventive measures,” etc.

[0111] Use syntactic analysis techniques to understand the text structure and identify different parts such as the definition, purpose and explanation of the indicator.

[0112] Use named entity recognition to identify medical terms and concepts such as "pressure ulcers," "quality of care," and more.

[0113] Use text similarity algorithms to compare the text before and after revision to find out the key points of expert revisions.

[0114] Based on these analyses, the system updates the hierarchical indicator interpretation template library. For example, the system may create or update the following templates: Definition template: "{metric name} measures the proportion of {events} in {measurement object} during {timeframe}." Usage template: "It is an important indicator for evaluating {assessment item 1}, {assessment item 2}, and {assessment item 3}." Explanation template: "A {higher / lower} {metric name} often indicates {meaning}." These templates are stored in a database, which may use a relational database (such as MySQL) or a document database (such as MongoDB). The system may also use version control technology to track the change history of the template.

[0115] S205. Update the hierarchical indicator interpretation template library according to the revised hierarchical indicator interpretation template library.

[0116] In this step, the system compares and integrates the newly generated revised template with the existing template library. This continuous optimization process ensures that the system can continue to learn and improve, generating more accurate and comprehensive interpretations of medical quality control indicators.

[0117] In the above embodiment, by establishing a scoring dimension and scoring the interpretation text, high-quality interpretation texts can be identified and screened, ensuring the reliability and consistency of the interpretation results. For interpretation texts that do not meet the standards, the system will send them to the review terminal for manual review, and the interpretation texts after manual review will be more accurate. By receiving and parsing the revised interpretation texts, the system can continuously update and optimize the hierarchical indicator interpretation template library, so that the system can continuously improve its interpretation capabilities over time and adapt to the ever-changing medical quality control needs.

[0118] The following describes the system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the physical device structure of a medical quality control indicator interpretation system provided in an embodiment of the present application.

[0119] It should be noted that Figure 3 The structure of the system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0120] like Figure 3 As shown, the system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through a bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0121] The following components are connected to the I / O interface 305: an input section 306 including a camera, an infrared sensor, etc.; an output section 307 including a liquid crystal display (LCD) and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0122] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 309, and / or installed from a removable medium 311. When the computer program is executed by a central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0123] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.

[0124] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0125] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the system described in the above embodiment; or may exist independently without being assembled into the system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiment.

[0126] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0127] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0128] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0129] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A method for interpreting medical quality control indicators, characterized in that: include: Constructing a hierarchical indicator dictionary, wherein the hierarchical indicator dictionary decomposes indicator nouns of medical quality control indicators into core words, modifiers and qualifiers; Establishing an indicator parsing rule base based on the hierarchical indicator dictionary, wherein the indicator parsing rule base includes hierarchical matching rules, word combination rules and context association rules; Using a preset indicator name standardization algorithm, the hierarchical indicator dictionary and the indicator parsing rule library are used to convert indicator names in different expressions into a preset standardized format to obtain a number of standardized indicator names; Establishing an indicator semantic mapping matrix, and matching the core words, modifiers and qualifiers in the names of the several standardized indicators with the preset indicator connotation elements respectively based on the indicator semantic mapping matrix to obtain a hierarchical semantic mapping result; Establish a hierarchical indicator interpretation template library to set corresponding interpretation frameworks and key elements for word combinations at different levels; According to the hierarchical semantic mapping result, initial template elements are selected and combined in the hierarchical indicator interpretation template library to generate an interpretation text, wherein the initial template element is a template element with the highest similarity to the hierarchical semantic mapping result.

2. The method according to claim 1, characterized in that The constructing of the hierarchical index dictionary specifically includes: Obtain a medical quality control indicator name set, wherein the medical quality control indicator name set includes indicator names published by authoritative institutions, indicator names used by medical institutions at all levels, indicator names in medical literature, and indicator names used in clinical practice; Define a word classification standard, decompose the indicator names in the medical quality control indicator name set into core words, modifiers and qualifiers, the core words represent the measurement objects of the indicator names, the modifiers represent words that describe the core words, and the qualifiers are words that refine the classification conditions; A hierarchical indicator dictionary is constructed based on the medical quality control indicator name set and the word classification standard.

3. The method according to claim 2, characterized in that The definition word classification standard decomposes the indicator names in the medical quality control indicator name set into core words, modifiers and qualifiers, specifically including: Collect the core concepts of common indicators, common modifying words and common limiting words to obtain a word feature library; Calculate the similarity between the indicator names in the medical quality control indicator name set and the indicator core concepts, the commonly used modifying words and the commonly used limiting words, and obtain the indicator core concept similarity, modifying similarity and limiting similarity; The word classification standard is defined according to the similarity, and the word classification standard is to assign an indicator name to the word category corresponding to the similarity greater than a preset similarity threshold; According to the word feature library and the word classification standard, the indicator names in the medical quality control indicator name set are decomposed into core words, modifiers and qualifiers, wherein the core words are words whose similarity to the core concept of the indicator is greater than the preset similarity threshold, the modifiers are words whose modifying similarity is greater than the preset similarity threshold, and the qualifiers are words whose restrictive similarity is greater than the preset similarity threshold.

4. The method according to claim 3, characterized in that After decomposing the indicator names in the medical quality control indicator name set into core words, modifiers and qualifiers according to the word feature library and the word classification standard, the method further includes: Access a thesaurus of medical synonyms and antonyms; Identify synonyms and near-synonyms of the core word, the modifiers, and the qualifiers; Converting the synonyms and near-synonyms into preset standard terms according to the medical synonym and near-synonym thesaurus; A mapping table of the synonyms and the near synonyms with the preset standard terms is established.

5. The method according to claim 1, characterized in that After selecting and combining initial template elements in the hierarchical indicator interpretation template library according to the hierarchical semantic mapping result to generate an interpretation text, the method further includes: Establishing a scoring dimension and using the scoring dimension to score the interpretation text to obtain a consistency score; When it is determined that the consistency score is greater than a preset threshold, marking the interpreted text as a qualified text; When it is determined that the consistency score is not greater than the preset threshold, sending the interpreted text to a review terminal; Receiving the revision interpretation text sent by the audit terminal, and parsing the revision interpretation text to obtain a revision hierarchical indicator interpretation template library; The hierarchical indicator interpretation template library is updated according to the revised hierarchical indicator interpretation template library.

6. The method according to claim 5, characterized in that The establishment of the scoring dimensions specifically includes: Determine the accuracy of the corresponding core words based on the core words and the main measurement concepts; Determining the appropriateness of the corresponding modifiers based on the modifiers and the core words; Determine the clarity of the corresponding qualifier based on the qualifier and the indicator noun; Determine the corresponding interpretation completeness based on all key elements contained in the interpretation text and the indicator noun; The core word accuracy, the modifier appropriateness, the qualifier clarity and the interpretation completeness are respectively assigned corresponding weights to obtain scoring dimensions.

7. The method according to claim 5 or 6, characterized in that: The use of the scoring dimension to score the interpretation text to obtain a consistency score specifically includes: Calculating the consistency score corresponding to the interpreted text using a weighted average algorithm according to the scoring dimension; The calculation function in the weighted average algorithm is: N=a×S+b×Y+c×Z+d×M In the function, N is the consistency score, a is the weight corresponding to the consistency score, S is the core word accuracy, b is the weight corresponding to the modifier appropriateness, Y is the modifier appropriateness, c is the weight corresponding to the qualifier clarity, Z is the qualifier clarity, d is the weight corresponding to the interpretation completeness, M is the interpretation completeness, and the sum of a, b, c and d is 1.

8. A medical quality control indicator interpretation system, characterized in that: The system comprises: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a system, the system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a system, the system is caused to execute the method according to any one of claims 1 to 7.

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