Single disease quality monitoring method, system, equipment and storage medium

Abstract

The present application discloses a single disease quality monitoring method, system, device and storage medium, the method comprising: obtaining a specified configuration model; determining a specified source data set and a specified target data set according to the specified configuration model; performing data cleaning and / or data standardization on the specified source data set according to the specified configuration indicators corresponding to the specified configuration model to obtain standard data; determining a mapping relationship between the specified source data set and the specified target data set; integrating the standard data with the specified target data set according to the mapping relationship to obtain a target integrated data set. By applying the method provided in the embodiments of the present application, the collection and reporting of single disease quality monitoring data can be automatically completed.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a single disease quality monitoring method, system, device and storage medium. Background Art

[0002] The single disease quality monitoring platform is used to continuously monitor the quality control indicators of single diseases and publish quality control results. The quality management and control of single diseases takes the disease as the management unit, and conducts medical quality management by building quality control indicators and evaluation systems based on the entire process of disease diagnosis and treatment. It requires health administrative departments at all levels and all types of medical institutions at all levels to report relevant data and information to standardize clinical diagnosis and treatment behaviors and continuously improve medical quality and medical safety.

[0003] The monitoring and reporting of medical quality data and information, along with the accompanying data reporting, has increased the burden on clinical staff. The individual diseases and monitoring indicators involved in these reports vary. Currently, the collection and reporting of quality control data for individual diseases relies on traditional methods, which are largely semi-manual and semi-informatized, resulting in a significant data reporting burden. Summary of the Invention

[0004] In order to solve the above-mentioned problems existing in the background technology, the embodiments of the present application creatively provide a single disease quality monitoring method, system, device and storage medium.

[0005] According to a first aspect of an embodiment of the present application, a single disease quality monitoring method is provided, the method comprising: obtaining a specified configuration model; determining a specified source data set and a specified target data set based on the specified configuration model; determining a mapping relationship between the specified source data set and the specified target data set; performing data cleaning and / or data standardization on the specified source data set according to specified configuration indicators corresponding to the specified configuration model to obtain standard data; integrating the standard data with the specified target data set according to the mapping relationship to obtain a target integrated data set.

[0006] According to one embodiment of the present application, data cleaning processing is performed on the specified source data set according to the specified configuration indicators corresponding to the specified configuration model to obtain standard data, including: determining heterogeneous multi-source data in the specified source data set; and performing data cleaning on the heterogeneous multi-source data to obtain standard data.

[0007] According to one embodiment of the present application, data standardization processing is performed on the specified source data set according to the specified configuration indicators corresponding to the specified configuration model to obtain standard data, including: determining multi-definition word data in the specified source data set; and standardizing the multi-definition word data to obtain standard data.

[0008] According to one embodiment of the present application, integrating the standard data with the specified target data set according to the mapping relationship to obtain a target integrated data set includes: extracting data from the standard data according to the mapping relationship to obtain extracted data; and integrating the extracted data according to the specified target data set to obtain a target integrated data set.

[0009] According to an embodiment of the present application, determining the designated source data set and the designated target data set according to the designated configuration model includes: determining an exclusion index and a designated target data set corresponding to the designated configuration model.

[0010] According to one embodiment of the present application, the method further includes: auditing the target integrated dataset to obtain an audit result corresponding to the target integrated dataset; if the audit result is that the audit is passed, reporting the target integrated dataset to a designated server; if the audit result is that the audit is failed, modifying the target integrated dataset to re-determine the target integrated dataset.

[0011] According to one embodiment of the present application, determining the specified source data set and the specified target data set based on the specified configuration model includes: determining the inclusion and exclusion index and the specified target data set corresponding to the specified configuration model; and performing inclusion and exclusion grouping processing on the original source data set according to the inclusion and exclusion index to obtain the specified source data set.

[0012] According to one embodiment of the present application, the auditing of the target integrated dataset to obtain an audit result corresponding to the target integrated dataset includes: determining a patient identifier corresponding to the specified source dataset; associating the patient identifier based on a unique patient identifier to obtain a patient primary index identifier; associating the specified source dataset based on the patient primary index identifier to obtain associated data; and auditing the target integrated dataset based on the associated data to obtain an audit result corresponding to the target integrated dataset.

[0013] According to one embodiment of the present application, auditing the target integrated dataset to obtain an audit result corresponding to the target integrated dataset includes: determining an audit rule based on the specified configuration model; and auditing the target integrated dataset according to the audit rule to obtain an audit result corresponding to the target integrated dataset.

[0014] According to the second aspect of the embodiment of the present application, a single disease quality monitoring system is provided, which includes: an acquisition module for obtaining a specified configuration model; a determination module for determining a specified source data set and a specified target data set based on the specified configuration model; the determination module is also used to determine the mapping relationship between the specified source data set and the specified target data set; a processing module for performing data cleaning and / or data standardization on the specified source data set according to the specified configuration indicators corresponding to the specified configuration model to obtain standard data; and an integration module for integrating the standard data with the specified target data set according to the mapping relationship to obtain a target integrated data set.

[0015] According to one embodiment of the present application, the processing module includes: a determination submodule for determining heterogeneous multi-source data in the specified source data set; and a cleaning submodule for performing data cleaning on the heterogeneous multi-source data to obtain standard data.

[0016] According to one embodiment of the present application, the determining submodule is further configured to determine the multi-definition word data in the specified source data set; the processing module further includes: a standardization submodule configured to perform standardization processing on the multi-definition word data to obtain standard data.

[0017] According to one embodiment of the present application, the integration module includes: an extraction submodule for extracting data from the standard data according to the mapping relationship to obtain extracted data; and an integration submodule for integrating the extracted data according to the specified target data set to obtain a target integrated data set.

[0018] According to one embodiment of the present application, the system further includes: an audit module, configured to audit the target integrated dataset to obtain an audit result corresponding to the target integrated dataset; a reporting module, configured to report the target integrated dataset to a designated server if the audit result is that the audit is passed; and a modification module, configured to modify the target integrated dataset to redefine the target integrated dataset if the audit result is that the audit is failed.

[0019] According to one embodiment of the present application, the determination module includes: determining an inclusion and exclusion index corresponding to the specified configuration model and a specified target data set; and performing inclusion and exclusion grouping processing on the original source data set according to the inclusion and exclusion index to obtain a specified source data set.

[0020] According to one embodiment of the present application, the audit module includes: determining a patient identifier corresponding to the specified source data set; associating the patient identifier based on a unique patient identifier to obtain a patient primary index identifier; associating the specified source data set based on the patient primary index identifier to obtain associated data; and auditing the target integrated data set based on the associated data to obtain an audit result corresponding to the target integrated data set.

[0021] According to an embodiment of the present application, the audit module includes: determining audit rules according to the specified configuration model; and auditing the target integrated data set according to the audit rules to obtain an audit result corresponding to the target integrated data set.

[0022] According to the third aspect of the embodiments of the present application, a device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any method described in the above-mentioned feasible embodiments.

[0023] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method as described in any one of the above-mentioned possible implementation methods is implemented.

[0024] The embodiments of the present application provide a single disease quality monitoring method, system, device and storage medium, which pre-set a specified configuration model corresponding to each single disease to form a more general framework, perform data cleaning and / or data standardization on the specified source data set according to the specified configuration indicators to obtain standard data, and define different data integration logics according to the mapping relationship between the specified source data set and the specified target data set through the configuration method of the mapping relationship, and use the mapping relationship to integrate the standard data with the specified target data set to obtain a target integrated data set, thereby realizing automatic structured extraction of the specified source data set without manual collection, thereby improving the processing efficiency and accuracy of the single disease quality monitoring data.

[0025] It should be understood that the teachings of this application do not necessarily achieve all of the beneficial effects described above, but that specific technical solutions can achieve specific technical effects, and other embodiments of this application can also achieve beneficial effects not mentioned above. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and other objects, features and advantages of the exemplary embodiments of the present application will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present application are shown in an illustrative and non-limiting manner, in which:

[0027] In the drawings, the same or corresponding reference numerals denote the same or corresponding parts.

[0028] Figure 1 The following is a schematic diagram of the implementation process of a single disease quality monitoring method in the embodiment of the present application. Figure 1 ;

[0029] Figure 2 The following is a schematic diagram of the implementation process of a single disease quality monitoring method in the embodiment of the present application. Figure 2 ;

[0030] Figure 3 A flowchart of an implementation scenario of a single disease quality monitoring method according to an embodiment of the present application is shown;

[0031] Figure 4 A schematic diagram of the implementation modules of a single disease quality monitoring system according to an embodiment of the present application is shown;

[0032] Figure 5 A schematic block diagram of an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0033] The principles and spirit of the present application will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided solely to enable those skilled in the art to better understand and implement the present application, and are not intended to limit the scope of the present application in any way. Rather, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0034] The technical solution of the present application is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] Figure 1 The following is a schematic diagram of the implementation process of a single disease quality monitoring method in the embodiment of the present application. Figure 1 .

[0036] See also Figure 1 According to the first aspect of the embodiment of the present application, a single disease quality monitoring method is provided, the method comprising: operation 101, obtaining a specified configuration model; operation 102, determining a specified source data set and a specified target data set according to the specified configuration model; operation 103, performing data cleaning and / or data standardization on the specified source data set according to the specified configuration indicators corresponding to the specified configuration model to obtain standard data; operation 104, determining a mapping relationship between the specified source data set and the specified target data set; operation 105, integrating the standard data with the specified target data set according to the mapping relationship to obtain a target integrated data set.

[0037] The single disease quality monitoring method provided in the embodiment of the present application pre-sets a designated configuration model corresponding to each single disease to form a more general framework, performs data cleaning and / or data standardization processing on the designated source data set according to the designated configuration indicators to obtain standard data, and defines different data integration logics according to the mapping relationship between the designated source data set and the designated target data set through the configuration method of the mapping relationship, and integrates the standard data with the designated target data set using the mapping relationship to obtain the target integrated data set, thereby realizing automatic structured extraction of the designated source data set without manual collection, thereby improving the processing efficiency and accuracy of the single disease quality monitoring data.

[0038] In operation 101 of this method, a configuration model refers to a single disease model specifically customized according to the requirements of the national single disease quality management and control platform. For example, the current national single disease quality management and control platform requires 51 single diseases. Therefore, the number of single disease models in this method is 51. It is understood that if the requirements of the national single disease quality management and control platform change, the number and integration logic of the corresponding single disease models can be adaptively adjusted. The designated configuration model uses a single disease model computing engine (Model-based Compute Engine) to meet the national data quality requirements for different diseases, forming a universal data framework. This data framework is configured to define the integration logic of different data, thereby achieving the purpose of building a single disease-based configuration model. The designated configuration model refers to at least one model in the configuration model. The method for specifying the designated configuration model can be determined based on actual circumstances. For example, in one scenario, the device needs to process the designated source dataset corresponding to each configuration model every day. In this case, the device can process the designated source dataset corresponding to each configuration model, and the corresponding configuration model is the designated configuration model. For example, when data processing is required for the configuration model corresponding to the disease type "cerebral infarction", the configuration model corresponding to the disease type "cerebral infarction" is the designated configuration model.

[0039] In operation 102 of the present method, since the data frameworks of different configuration models are customized according to specific requirements, that is, the data frameworks of different configuration models may be the same or different, it is necessary to determine the designated target data set according to the designated configuration model. The designated target data set is used to characterize the data framework corresponding to the designated configuration model. For example, when the designated configuration model is used to refer to the configuration model corresponding to the disease type "cerebral infarction", the designated target data set is used to refer to the data framework that needs to be reported corresponding to the disease type "cerebral infarction". The designated target data set may refer to the type of data that needs to be reported, but may not include the specific content corresponding to the data type. For example, the designated target data set may include data types such as "time type" and "examination item", but the specific time information and examination item results are not included in the designated target data set.

[0040] The specified source dataset is used to represent the content information corresponding to the specified configuration model. The specified source dataset can come from the patient's electronic medical record or non-electronic medical record, and can specifically include but is not limited to at least one of the following information: outpatient and emergency medical record information, medical record homepage information, admission record information, discharge record information, first medical course record information, daily medical course record information, medical record information, vital signs information, examination information, test information, medical order information, surgical record information, hand numbness information, etc. For example, when the specified configuration model is used to refer to the configuration model corresponding to the disease "cerebral infarction", the specified source dataset can be case data related to "cerebral infarction". The specified source dataset can contain heterogeneous multi-source data and multi-definition word data.

[0041] In operation 103 of the present method, since the data in the specified source data set contains heterogeneous multi-source data, it is necessary to perform data cleaning processing on the heterogeneous multi-source data according to the specified configuration indicators corresponding to the specified configuration model to resolve the situation where the data representation requirements such as format, standard, value range, etc. of the heterogeneous multi-source data are inconsistent with the data representation requirements such as format, standard, value range, etc. of the specified configuration indicators. The heterogeneous multi-source data that does not meet the data representation requirements of the specified configuration indicators will be processed to be converted into standard data that meets the data representation requirements of the specified configuration indicators.

[0042] Specifically, operation 103 includes: First, determine the heterogeneous multi-source data in the specified source dataset; then, perform data cleaning on the heterogeneous multi-source data to obtain standard data. This method needs to first determine the data with the data type of heterogeneous multi-source data in the specified source dataset, and then perform data cleaning operations on it. Among them, the determination method of the data type can be determined by keyword recognition and other methods. For example, in a certain specified source dataset, the representation of the date is "January 1, 2000". The representation requirement of the date in the specified configuration index data is stipulated as "xxxx / yy / zz". By performing data cleaning on "January 1, 2000", it is converted to "2000 / 01 / 01" so that the date meets the specified configuration index. Specifically, this method can achieve data cleaning through format conversion and calculation logic backfill.

[0043] In another case, the data in the specified dataset also includes polysemous data. Specifically, operation 103 includes: First, determine the polysemous data in the specified source dataset; then, perform standardization processing on the polysemous data to obtain standard data. This method needs to first determine the data with the data type of polysemous data in the specified source dataset, and then perform data standardization processing operations on it. For example, in the case of multi-vendor informatization of medical systems used in different hospitals, and different vendors have inconsistent descriptions of medical processes, understandings, and descriptions of terms. By performing standardization processing on the polysemous data, the polysemous data is converted into a unified expression form. For example, "type 2 diabetes" is represented as "diabetes type 2", "diabetes 2", "diabetes type ii" and other expression forms in different medical systems. According to the specified configuration index corresponding to the national single-disease quality and control platform, it is represented by a unified term to obtain standard data.

[0044] It can be understood that when the data in the specified dataset belongs to both polysemous data and heterogeneous multi-source data, in this case, this method can perform both standardization processing and cleaning processing on this data. For example, in one case, the data is a polysemous word in traditional Chinese, such as "腦梗", then it is necessary to first unify it into the simplified Chinese format "脑梗" through data cleaning, and then perform standardization processing on this word according to the specified configuration index corresponding to the specified configuration model, so that the polysemous data is converted into a unified expression form.

[0045] In operation 104 of this method, according to the data integration logic defined by the specified configuration model, this method can correspond the data in the specified source dataset with the data in the specified target dataset to determine the mapping relationship between the specified source dataset and the specified target dataset. For example, correspond the "date" in the specified source dataset to the "date" in the specified target dataset. It should be added that if a certain data in the specified target dataset cannot be corresponded in the specified source dataset, it can be set to null.

[0046] In operation 105 of the present method, after determining the mapping relationship, the standard data can be integrated with the specified target data set according to the configuration method and data integration logic set by the specified configuration model to obtain the target integrated data set, so that the conversion from the specified source data set to the target integrated data set can meet the items and formats required by the national reporting platform, and realize automated structured extraction of the specified source data set without manual collection, thereby improving the processing efficiency and accuracy of single disease quality monitoring data.

[0047] According to one embodiment of the present application, operation 104 integrates the standard data with the specified target data set according to the mapping relationship to obtain the target integrated data set, including: first, extracting data from the standard data according to the mapping relationship to obtain extracted data; then, integrating the extracted data according to the specified target data set to obtain the target integrated data set.

[0048] Specifically, this method extracts and processes data from standard data according to the mapping relationship, realizes structured processing of standard data, obtains extracted data, and then fills the extracted data into corresponding positions one by one according to the data integration logic in the specified target data set to obtain the target integrated data set. For example, the specified source data set contains medical advice information, and the medical advice information contains information on the condition, medication information, precautions information and other contents, which are represented in the form of a paragraph of integrated text. After data cleaning and / or data standardization, standard data with composite specified configuration indicators is obtained. There are corresponding content filling positions for the condition information, corresponding content filling positions for the medication information, and corresponding content filling positions for the precautions information in the target data set, thereby realizing processing, conversion and structured processing of the standard data, realizing data integration of the specified target data set and the extracted data, and obtaining the target integrated data set of single disease quality monitoring data that meets the reporting requirements of the national single disease quality management and control platform.

[0049] Figure 2 The following is a schematic diagram of the implementation process of a single disease quality monitoring method in the embodiment of the present application. Figure 2 .

[0050] See also Figure 2 According to one embodiment of the present application, after obtaining the target integrated data set, the method further includes: operation 201, reviewing the target integrated data set to obtain a review result corresponding to the target integrated data set; operation 202, if the review result is that the review is passed, reporting the target integrated data set to the designated server; operation 203, if the review result is that the review is not passed, modifying the target integrated data set to redefine the target integrated data set.

[0051] In this method, after obtaining the target integrated dataset, it is also necessary to review the target integrated dataset to determine whether it truly meets the target integrated dataset of single disease quality monitoring data reported by the National Single Disease Quality Management and Control Platform.

[0052] In operation 201, the target integrated dataset may be reviewed manually and / or automatically by the device. When the target integrated dataset is reviewed manually, the device may display the data content corresponding to the target integrated dataset so that the displayed content can be manually reviewed. Furthermore, to facilitate the review, unreviewed target integrated datasets may be assigned a corresponding status identifier, allowing the reviewer to quickly and accurately identify the unreviewed target integrated datasets. Specifically, the status identifier may be an identifier such as "newly added" or "pending review." When the target integrated dataset is reviewed automatically by the device, the method may set multiple corresponding review rules in a specified configuration model, such as determining whether any required items in the target integrated dataset are missing. If any required items are missing, the review result is considered "review failed." If the target integrated dataset does not meet any review rule, the review result may be determined to be "review failed." Correspondingly, if the target integrated dataset meets all review rules, the review result may be determined to be "review passed."

[0053] Based on the review result of operation 201, the method proceeds to operation 202. Specifically, if the review result is approved, the method proceeds to operation 202 to submit the target integrated dataset to a designated server. Depending on the needs, the designated server can be the national single disease quality management and control platform or another third-party platform that requires the target integrated dataset. After submission, the method can change the status indicator corresponding to the target integrated dataset to "Submitted" to avoid duplicate reviews.

[0054] If the audit result is "failed," the method executes operation 203 to modify the target integrated dataset to re-determine the target integrated dataset. Specifically, according to one embodiment of the present application, operation 203, auditing the target integrated dataset to obtain an audit result corresponding to the target integrated dataset, includes: first, determining audit rules based on a specified configuration model; then, auditing the target integrated dataset based on the audit rules to obtain an audit result corresponding to the target integrated dataset. The method can first match the target integrated dataset with all audit rules. If the audit result is "failed," all audit rules that do not meet the requirements are also fed back to facilitate data modification of the target integrated dataset. Accordingly, data modification can be performed manually, or the device can re-execute the corresponding data processing operations based on the unsatisfied audit rules to achieve the purpose of re-determining the target integrated dataset. It should be noted that if the audit fails, the method can change the status indicator corresponding to the target integrated dataset to "under repair" to prompt repair. It is understood that after the repair is completed, the method can change the status indicator corresponding to the target integrated dataset to "pending review" to prompt the audit. According to one embodiment of the present application, operation 102 determines a specified source data set and a specified target data set according to a specified configuration model, including: first, determining an inclusion / reduction index and a specified target data set corresponding to the specified configuration model; then, performing inclusion / reduction grouping processing on the original source data set according to the inclusion / reduction index to obtain the specified source data set.

[0055] The original source data set refers to all original medical record data obtained by a medical institution or medical data processing agency within a specified time period. The original medical record data can correspond to different single disease situations. The original case data includes, but is not limited to, at least one of the following information: outpatient and emergency medical record information, medical record homepage information, admission record information, discharge record information, first medical course record information, daily medical course record information, medical record information, vital signs information, examination information, laboratory information, medical order information, surgical record information, hand numbness information, etc. The designated configuration model corresponds to the processing of single disease quality monitoring data. Based on this, this method can determine the inclusion and exclusion indicators corresponding to the designated configuration model according to the requirements of the national single disease quality management and control platform, and then exclude the original source data sets that meet the inclusion and exclusion indicators from the group processing to obtain the designated source data set.

[0056] According to one embodiment of the present application, operation 201 is to review the target integrated dataset to obtain a review result corresponding to the target integrated dataset, including: first, determining the patient identifier corresponding to the specified source dataset; then, associating the patient identifier based on the unique patient identifier to obtain a patient primary index identifier; associating the specified source dataset based on the patient primary index identifier to obtain associated data; and then, reviewing the target integrated dataset based on the associated data to obtain a review result corresponding to the target integrated dataset.

[0057] Furthermore, when reviewing the target integrated dataset, this method can use the patient's primary index identifier to jointly query the original source dataset corresponding to the patient, and review the target integrated dataset by describing and understanding the data production logic of the original source dataset. This can quickly review the correctness of the target integrated dataset, greatly reduce the difficulty of reporting the target integrated dataset, and improve the reporting accuracy of the target integrated dataset.

[0058] Specifically, this method can associate the patient identifier related to the specified source data set based on the unique patient identifier corresponding to the patient. Specifically, the unique patient identifier can be a unique identity number related to the patient, such as an ID number. Correspondingly, the patient identifier can be multiple number devices related to the patient, such as a manual number, an outpatient number, an inpatient number, and a physical examination number. The unique patient identifier and the patient identifier are associated through a patient identifier association policy mechanism to achieve the purpose of establishing a unified master index service, that is, to determine the corresponding patient master index identifier based on the patient's medical treatment. In this way, the original source data set of the patient, that is, the patient's original medical record data, can be determined through the patient master index identifier, and the original medical record data can be jointly checked to achieve the purpose of quickly and accurately reviewing the target integrated data set.

[0059] To further understand the above implementation, a specific implementation scenario is provided below for illustration.

[0060] Figure 3 A flowchart of an implementation scenario of a single disease quality monitoring method according to an embodiment of the present application is shown.

[0061] See also Figure 3 In this specific implementation scenario, this method was applied to a hospital-based single disease quality monitoring system. This system, in accordance with the requirements of the National Single Disease Quality Management and Control Platform, developed 51 single disease models, including models for malignant tumors, cardiovascular diseases, neurological diseases, respiratory diseases, and childhood leukemia, among other common and frequently occurring diseases.

[0062] A single disease model computing engine (Model based Compute Engine) is used to meet the national data quality requirements for different diseases, forming a more general framework. By configuring the disease model, different data integration logics are defined to build a data model based on a single disease. The configuration of the disease model includes at least one of the following configurations: the inclusion and exclusion configuration of each disease, the field configuration contained in each disease, the value range of each field, the field combination verification, the field data retrieval path, and the data retrieval logic. In accordance with the requirements of the national single disease quality management and control platform, this method processes the patient medical record data according to the corresponding disease model at a specified time to obtain the corresponding reporting data and report it.

[0063] The inclusion and sorting configuration for each disease type is used to group electronic medical record data from the full data set that meets the inclusion and sorting requirements for that disease type. For example, the disease model corresponding to cerebral infarction requires grouping the patient's medical record homepage information, initial medical record information, test information, and medical order information. The field configuration contained in each disease type is used to determine the corresponding fields from the included information and fill them into the data frame corresponding to the disease model. For example, age and name are filled in the patient's basic information in the data frame, and the diagnosis process, testing process, precautions, and other information are filled in the patient's case information. The value range of each field is used as the basis for data cleaning, so that the information filled into the data frame conforms to a unified unit and / or format. For example, the date filled into the data frame is uniformly represented as "AAAA-BB-CC", and the age filled into the data frame is uniformly expressed as "X years and Y months". Field combination validation, field retrieval path, and retrieval logic are used as the basis for mapping between the specified source dataset and the data to be populated in the data frame. For example, after data cleaning, the age of "20.5 years old" in the specified source dataset is populated in the age position in the data frame in the format of "20 years and 6 months".

[0064] When used, the device integrates outpatient and emergency medical records, medical record homepages, admission records, discharge records, initial medical history records, daily medical history records, medical records, vital signs, examinations, tests, medical orders, surgical records, and hand numbness records to obtain comprehensive data. This comprehensive data is then grouped according to the inclusion and exclusion configuration for each disease type, obtaining the designated source dataset corresponding to that disease type. A data mapping relationship is then established between the designated source dataset and the target dataset determined by the disease type model, based on the field's data retrieval path and data retrieval logic.

[0065] Afterwards, the designated source dataset is cleaned based on the field configuration, value range, and field combination validation for each disease category. Format conversion and computational logic backfilling address the issue of inconsistent formats, standards, and value ranges in the heterogeneous, multi-source data set. This ensures consistency in the format, standards, and value ranges of the designated source dataset, converting the data in the designated source dataset to standard data. Furthermore, due to the current state of hospital informatization using multiple vendors, the descriptions of medical processes and the understanding and description of terminology within the designated source dataset are inconsistent. Therefore, the multi-definition word data in the designated source dataset is standardized according to the national single disease quality and control platform standards, converting the multi-definition word data into standard data.

[0066] Then, the specified source dataset and target dataset are collected and aggregated through the data mapping relationship between the specified source dataset and the target dataset determined according to the disease model, the field data retrieval path, and the data retrieval logic to obtain reporting data that meets the reporting conditions for reporting to the national single disease quality and control platform.

[0067] Afterwards, the reported data that meets the reporting conditions will be displayed on the reporting system interface corresponding to the National Single Disease Quality and Control Platform, and the data status corresponding to the reported data will be marked as "new". The reporting system will review the reported data with the data status marked as "new" through manual or automatic system review. The system review can use the preset reporting quality control rules as the basis to realize the review of the reported data. The data status corresponding to the reported data will be marked as "under review". During the review process, the collected reported data is converted into the items and formats required by the national reporting platform on the reporting system interface, and the original medical record data, that is, the full amount of data corresponding to the patient's primary index identifier, is linked to the collected patient's primary index identifier. By describing and understanding the data production logic, the correctness of the reported data can be quickly reviewed, greatly reducing the difficulty of data reporting while improving the accuracy of the reported data. Among them, the patient's primary index identifier is based on the patient's clinical diagnosis and treatment activities, with the patient's visit as the main line, to establish a unified primary index service, and merge and associate the patient's manual number, outpatient number, hospitalization number, and physical examination number to obtain the patient's primary index identifier.

[0068] If the review result indicates that the reported data has passed, the National Single Disease Quality and Control Platform's API is called for batch upload. The data status of successfully uploaded reports is updated to "Submitted." If the review result indicates that the reported data has failed, the data status is updated to "Under Repair," and the reported data is re-reviewed, modified, and resubmitted until it is successfully submitted. In other cases, the reviewed reported data can be provided to a third-party service platform for subsequent processing.

[0069] For example, when it is necessary to report cases related to cardiovascular disease, the system calls the corresponding cardiovascular disease model and, based on the inclusion and exclusion configuration of cardiovascular diseases in the disease model, selects electronic medical record data that meets the inclusion and exclusion configuration of cardiovascular diseases from the full data as the designated source data set.

[0070] The cardiovascular disease model generates an information box to be filled in based on the corresponding data framework on the corresponding display page of the system. The mapping relationship between the medical data in the specified source data set and the information box to be filled in is determined by configuring the fields contained in each disease type, the field data acquisition path, and the data acquisition logic. Then, the medical data is cleaned and standardized through the value range of each field, field combination verification, field configuration, etc., so that the corresponding content is generated and displayed in the information box to be filled in.

[0071] The content in the information box is then manually or automatically reviewed to determine the corresponding review result. During data review, a unified master index service is established to simultaneously display the designated source dataset corresponding to the patient. This means that the system's display page allows viewing both the generated target integrated dataset and the original electronic medical record data corresponding to the integrated data within each target integrated dataset. The accuracy of the entered information can be determined manually or automatically by comparing the integrated data with the original electronic medical record data. Furthermore, the completeness of the target integrated dataset can be determined by identifying the missing data within the target integrated dataset. The target integrated dataset is evaluated based on accuracy and completeness to determine the corresponding review result. Specifically, if the accuracy or completeness does not meet a set threshold, the data status indicator of the target integrated dataset can be updated to "Under Repair," allowing the target data to be regenerated from the original data manually or automatically. If the accuracy or completeness meets the set threshold, the target integrated dataset can be uploaded using the API of the National Single Disease Quality and Control Platform. The data status indicator of the successfully uploaded reported data is updated to "Submitted," completing the reporting of the quality monitoring data for the single disease.

[0072] Figure 4 A schematic diagram of the implementation modules of a single disease quality monitoring system according to an embodiment of the present application is shown.

[0073] See also Figure 4According to the second aspect of the embodiment of the present application, a single disease quality monitoring system is provided, and the system includes: an acquisition module 401, used to obtain a specified configuration model; a determination module 402, used to determine a specified source data set and a specified target data set according to the specified configuration model; the determination module 402 is also used to determine the mapping relationship between the specified source data set and the specified target data set; a processing module 403, used to perform data cleaning and / or data standardization on the specified source data set according to the specified configuration indicators corresponding to the specified configuration model to obtain standard data; an integration module 404, used to integrate the standard data with the specified target data set according to the mapping relationship to obtain a target integrated data set.

[0074] According to one embodiment of the present application, the processing module 403 includes: a determination submodule 4031 for determining heterogeneous multi-source data in a specified source data set; and a cleaning submodule 4032 for performing data cleaning on the heterogeneous multi-source data to obtain standard data.

[0075] According to one embodiment of the present application, the determination submodule 4031 is further configured to determine the multi-definition word data in the specified source data set; the processing module 403 further includes: a standardization submodule 4033 configured to perform standardization processing on the multi-definition word data to obtain standard data.

[0076] According to one embodiment of the present application, the integration module 404 includes: an extraction submodule 4041, which is used to extract data from the standard data according to the mapping relationship to obtain extracted data; and an integration submodule 4042, which is used to integrate the extracted data according to the specified target data set to obtain a target integrated data set.

[0077] According to one embodiment of the present application, the system further includes: an audit module 405, which is used to audit the target integrated dataset to obtain an audit result corresponding to the target integrated dataset; a reporting module 406, which is used to report the target integrated dataset to a designated server if the audit result is that the audit is passed; and a modification module 407, which is used to modify the target integrated dataset if the audit result is that the audit is failed to re-determine the target integrated dataset.

[0078] According to an embodiment of the present application, the determination module 402 includes: determining an inclusion index and a specified target data set corresponding to a specified configuration model; and performing inclusion and exclusion grouping processing on the original source data set according to the inclusion and exclusion index to obtain the specified source data set.

[0079] According to one embodiment of the present application, the audit module 405 includes: determining the patient identifier corresponding to the specified source data set; associating the patient identifier based on the unique patient identifier to obtain the patient primary index identifier; associating the specified source data set based on the patient primary index identifier to obtain associated data; and auditing the target integrated data set based on the associated data to obtain an audit result corresponding to the target integrated data set.

[0080] According to an embodiment of the present application, the audit module 405 includes: determining an audit rule according to a specified configuration model; and auditing the target integrated data set according to the audit rule to obtain an audit result corresponding to the target integrated data set.

[0081] It should be noted that the above description of the embodiment of the quality monitoring system for a single disease is different from the above description of the embodiment of the quality monitoring system for a single disease. Figures 1 to 3 The description of the method embodiment shown is similar, with the same Figures 1 to 3 The method embodiments shown in the figure have similar beneficial effects, so they will not be described in detail. For technical details not disclosed in the embodiment of the single disease quality monitoring system of this application, please refer to the aforementioned Figures 1 to 3 The description of the method embodiment shown in the figure is understood, and in order to save space, it is not repeated here.

[0082] According to the third aspect of the embodiments of the present application, a device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any of the methods in the above-mentioned feasible embodiments.

[0083] According to a fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method of any one of the above-mentioned possible implementation modes is implemented.

[0084] According to an embodiment of the present application, the present application also provides an electronic device and a readable storage medium.

[0085] Figure 5 A schematic block diagram of an example electronic device that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0086] like Figure 5 As shown, the device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 505 into a random access memory (RAM) 503. Various programs and data required for the operation of the device 500 can also be stored in the RAM 503. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0087] Various components in device 500 are connected to I / O interface 505, including: input unit 506, such as a keyboard, mouse, etc.; output unit 507, such as various types of displays, speakers, etc.; storage unit 505, such as a magnetic disk, optical disk, etc.; and communication unit 509, such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows device 500 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0088] The computing unit 501 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as the single disease quality monitoring method. For example, in some embodiments, the single disease quality monitoring method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the single disease quality monitoring method described above can be performed. Alternatively, in other embodiments, the computing unit 501 can be configured to perform the single disease quality monitoring method by any other suitable means (e.g., by means of firmware).

[0089] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0090] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0091] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or 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 foregoing.

[0092] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0093] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0094] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0095] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0096] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the present disclosure, "plurality" means two or more, unless otherwise specifically defined.

[0097] The above description is merely a specific embodiment of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this disclosure should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A single disease quality monitoring method, characterized in that: The method comprises: Get the specified configuration model; Determine a specified source data set and a specified target data set according to the specified configuration model; performing data cleaning and / or data standardization processing on the specified source data set according to the specified configuration indicators corresponding to the specified configuration model to obtain standard data, wherein the specified source data set includes heterogeneous multi-source data and multi-definition word data; Determine a mapping relationship between the specified source data set and the specified target data set; Integrating the standard data with the designated target data set according to the mapping relationship to obtain a target integrated data set; Reviewing the target integrated data set to obtain a review result corresponding to the target integrated data set; The determining the specified source data set and the specified target data set according to the specified configuration model includes: Determining an exclusion index and a designated target data set corresponding to the designated configuration model; The original source data set is sorted into groups according to the sorting index to obtain a specified source data set.

2. The method according to claim 1, characterized in that Performing data cleaning on the specified source data set according to the specified configuration indicators corresponding to the specified configuration model to obtain standard data includes: Determining the heterogeneous multi-source data in the specified source data set; Data cleaning is performed on the heterogeneous multi-source data to obtain standard data.

3. The method according to claim 1, characterized in that Performing data standardization processing on the specified source data set according to the specified configuration indicators corresponding to the specified configuration model to obtain standard data includes: Determining the multi-definition word data in the specified source data set; The multi-definition word data is standardized to obtain standard data.

4. The method according to claim 1, wherein The step of integrating the standard data with the designated target data set according to the mapping relationship to obtain a target integrated data set includes: Extracting data from the standard data according to the mapping relationship to obtain extracted data; The extracted data are integrated according to the designated target data set to obtain a target integrated data set.

5. The method according to claim 1, wherein The reviewing the target integrated data set to obtain a review result corresponding to the target integrated data set includes: If the audit result is passed, the target integrated data set is reported to the designated server; If the audit result is that the audit fails, data modification is performed on the target integrated data set to re-determine the target integrated data set.

6. The method according to claim 5, characterized in that The reviewing the target integrated data set to obtain a review result corresponding to the target integrated data set includes: Determining a patient identifier corresponding to the specified source data set; Associating the patient identifier with the patient identifier based on the unique patient identifier to obtain a patient primary index identifier; Associating the specified source data set according to the patient primary index identifier to obtain associated data; The target integrated data set is audited based on the associated data to obtain an audit result corresponding to the target integrated data set.

7. The method according to claim 5, characterized in that The reviewing the target integrated data set to obtain a review result corresponding to the target integrated data set includes: determining audit rules according to the specified configuration model; The target integrated data set is audited according to the audit rules to obtain an audit result corresponding to the target integrated data set.

8. A single disease quality monitoring system, characterized by: The system comprises: Obtaining module, used to obtain the specified configuration model; a determination module, configured to determine a specified source data set and a specified target data set according to the specified configuration model; a processing module, configured to perform data cleaning and / or data standardization on the specified source data set according to the specified configuration indicators corresponding to the specified configuration model to obtain standard data, wherein the specified source data set includes heterogeneous multi-source data and multi-definition word data; The determining module is further configured to determine a mapping relationship between the specified source data set and the specified target data set; an integration module, configured to integrate the standard data with the designated target data set according to the mapping relationship to obtain a target integrated data set; an audit module, configured to audit the target integrated data set to obtain an audit result corresponding to the target integrated data set; The determination module is further configured to determine an inclusion / rejection index and a designated target data set corresponding to the designated configuration model; and perform inclusion / rejection grouping processing on the original source data set according to the inclusion / rejection index to obtain a designated source data set.

9. A device, characterized in that The device comprises: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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