Hospital comprehensive evaluation method, system and equipment based on electronic medical record library and medium
By configuring de-identification and standardized procedures to connect with the hospital's heterogeneous data system, multi-dimensional indicator data is extracted and a comprehensive evaluation value is generated. This solves the problems of low efficiency in heterogeneous data integration, inconsistent data standards, and single evaluation dimensions, and achieves efficient and accurate multi-dimensional dynamic evaluation.
Patent Information
- Application Number
- CN202511552207.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-28
- Publication Date
- 2026-02-10
AI Technical Summary
In existing technologies, hospital information systems suffer from low efficiency in integrating heterogeneous data, inconsistent data standards, a lack of effective desensitization and standardization mechanisms, and a single evaluation dimension that lacks dynamism, making it difficult to achieve multi-dimensional and dynamic comprehensive evaluation.
By configuring desensitization and standardization procedures for heterogeneous data systems, data interfaces can be connected and formatted uniformly. Key data of multi-dimensional indicators can be extracted, and a comprehensive evaluation value can be generated using weighted coefficients to provide a multi-dimensional dynamic comprehensive evaluation.
It improves the efficiency of heterogeneous data integration, ensures data standard consistency and privacy security, enables multi-dimensional and dynamic comprehensive hospital evaluation, provides a real-time and reliable data foundation, and supports hospital management and decision-making.
Smart Images
Figure CN121506345A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of comprehensive evaluation technology, and in particular to a method, system, equipment and medium for comprehensive hospital evaluation based on an electronic medical record database. Background Technology
[0002] The current state of hospital IT infrastructure development has resulted in a coexistence of various heterogeneous data systems, including business systems such as HIS, EMR, LIS, and PACS, as well as management platforms such as OA and HRM. These systems employ different vendors, architectures, and data standards, leading to fragmented and heterogeneous medical data. To achieve data interoperability, existing technologies typically employ ETL tools for data extraction and transformation, or middleware to facilitate inter-system interface connections. Some hospitals have attempted to build medical data warehouses, but most are limited to integrating structured data, with limited capabilities for processing unstructured data. At the data application level, existing evaluation methods largely rely on manually entered statistical reports or simple analysis of data from a single system using BI tools, making it difficult to achieve multi-dimensional and dynamic comprehensive evaluation.
[0003] Existing technologies suffer from the following drawbacks: First, heterogeneous data integration is inefficient. Traditional ETL tools require customized data transformation rules for each system, leading to long data collection cycles and high costs. Second, data standards are inconsistent, and there is a lack of effective de-identification and standardization mechanisms, affecting the accuracy of evaluation results. Third, evaluation dimensions are limited. Existing methods often focus on single indicators such as medical quality or operational efficiency, lacking comprehensive consideration of key dimensions such as medical technology difficulty and resource consumption. Finally, the evaluation process lacks dynamism. Existing evaluations are mostly periodic static analyses, failing to reflect the hospital's operational status in real time. Summary of the Invention
[0004] This application provides a method, system, equipment, and medium for comprehensive hospital evaluation based on an electronic medical record database, in order to solve the problems of low efficiency in integrating heterogeneous data, inconsistent data standards, lack of effective de-identification and standardization mechanisms, and single hospital evaluation dimensions in existing solutions.
[0005] Firstly, this application provides a comprehensive hospital evaluation method based on an electronic medical record database, the method comprising: Determine the types and number of heterogeneous data systems in the current hospital; based on the types of heterogeneous data systems, configure corresponding de-identification procedures and standardization procedures in the heterogeneous data systems; determine the number and types of data acquisition interfaces for the current hospital; and complete the connection between the data acquisition interfaces and the corresponding heterogeneous data systems. The data acquisition interface returns standard medical data after de-identification and standardization processes; based on the patient identifier in the standard medical data, all standard medical data of the same patient identifier in the current hospital are aggregated as a subset of patient data. Extract key data of preset indicators from the patient data subset; the key data of preset indicators shall include at least the following: data related to indicators of medical quality and safety, data related to indicators of treatment efficiency, data related to indicators of medical technical difficulty, and data related to indicators of resource consumption. Using the specific content of all the current hospital's associated data, generate preset indicator values; among which, the preset indicator values include at least: medical quality and safety dimension indicator values, diagnosis and treatment efficiency dimension indicator values, medical technical difficulty dimension indicator values, and resource consumption dimension indicator values; Obtain the weighting coefficients of the preset indicator values, and then use the weighted average to obtain the current comprehensive evaluation value of the hospital.
[0006] In one implementation of this application, before configuring the corresponding de-identification procedure and standardization procedure in the heterogeneous data system based on the heterogeneous data system type, the method further includes: Obtain the desensitization procedure and standardized processing procedure; Configure the correspondence between heterogeneous data system types and desensitization and standardization procedures.
[0007] In one implementation of this application, before the data acquisition interface returns the standard medical data processed by the de-identification and standardization procedures, the method further includes: Input the initial data into the desensitization program, and process the preset sensitive fields in the initial data into hash values; Obtain the data type of each field in the initial data after data anonymization. Based on the preset data type sorting of standard medical data, the fields are split and merged into standard medical data.
[0008] In one implementation of this application, key data of preset indicators are extracted from a subset of patient data, specifically including: Extract data related to indicators of medical quality and safety; among which, data related to indicators of medical quality and safety should include at least whether the patient died, whether the patient was readmitted, and whether complications existed. Extract data related to the diagnostic and treatment efficiency indicators; among which, the data related to the diagnostic and treatment efficiency indicators should include at least: total hospital stay and preoperative hospital stay; Extract data related to indicators of medical technical difficulty; among which, the data related to indicators of medical technical difficulty should include at least: whether it is a multi-case surgery, whether it is a level 3 surgery, and whether it is a level 4 surgery; Extract the associated data of resource consumption dimension indicators; among which, the associated data of resource consumption dimension indicators shall include at least: cost consumption, the proportion of medicinal materials involved in cost consumption, and the proportion of consumables involved in cost consumption.
[0009] In one implementation of this application, preset indicator values are generated using the specific content of all associated data of the current hospital, specifically including: Obtain the specific content of the correlation data of medical quality and safety dimension indicators, diagnosis and treatment efficiency dimension indicators, medical technology difficulty dimension indicators, and resource consumption dimension indicators corresponding to the subset of all patient data; The following data were calculated: case fatality rate, readmission rate, complication rate, average total length of stay, average preoperative length of stay, percentage of multi-case surgeries, percentage of level 3 surgeries, percentage of level 4 surgeries, percentage of pharmaceuticals in total cost, and percentage of consumables in total cost. The weighted values of medical quality and safety indicators are obtained based on mortality rate, readmission rate, and complication rate. The efficacy index value was obtained by weighting the average total length of hospital stay and the average length of hospital stay before surgery. The weighted index value of the medical technical difficulty dimension is obtained by calculating the proportion of multiple cases, the proportion of level 3 surgeries, and the proportion of level 4 surgeries. The resource consumption dimension index value is obtained by weighting the proportion of medicinal materials involved in the total cost consumption and the proportion of consumables involved in the total cost consumption.
[0010] Secondly, this application provides a comprehensive hospital evaluation system based on an electronic medical record database, the system comprising: The matching module is used to determine the type and number of heterogeneous data systems in the current hospital, configure corresponding de-identification and standardization procedures in the heterogeneous data systems based on the type of heterogeneous data systems, determine the number and type of data acquisition interfaces for the current hospital, and complete the docking of data acquisition interfaces with the corresponding heterogeneous data systems. The aggregation module is used to obtain standard medical data returned by the data acquisition interface after being processed by de-identification and standardization procedures; based on the patient identifier in the standard medical data, it aggregates all standard medical data of the same patient identifier in the current hospital as a subset of patient data. The acquisition module is used to extract key data of preset indicators from a subset of patient data. This key data includes at least the following: data related to indicators of medical quality and safety, diagnostic and treatment efficiency, medical technical difficulty, and resource consumption. Using the specific content of all related data for the current hospital, preset indicator values are generated. These preset indicator values include at least the following: values for indicators of medical quality and safety, diagnostic and treatment efficiency, medical technical difficulty, and resource consumption. Weighting coefficients for these preset indicator values are obtained, and a comprehensive evaluation value for the current hospital is obtained through weighting.
[0011] In one implementation of this application, the matching module includes a processing unit. Used to input initial data into the desensitization program, which processes the preset sensitive fields in the initial data into hash values; Obtain the data type of each field in the initial data after data anonymization. Based on the preset data type sorting of standard medical data, the fields are split and merged into standard medical data.
[0012] In one implementation of this application, the acquisition module includes an extraction unit. This data is used to extract correlation data for indicators related to medical quality and safety. Among these, the correlation data for indicators related to medical quality and safety should include at least whether the patient died, whether the patient was readmitted to the hospital, and whether complications occurred. Extract data related to the diagnostic and treatment efficiency indicators; among which, the data related to the diagnostic and treatment efficiency indicators should include at least: total hospital stay and preoperative hospital stay; Extract data related to indicators of medical technical difficulty; among which, the data related to indicators of medical technical difficulty should include at least: whether it is a multi-case surgery, whether it is a level 3 surgery, and whether it is a level 4 surgery; Extract the associated data of resource consumption dimension indicators; among which, the associated data of resource consumption dimension indicators shall include at least: cost consumption, the proportion of medicinal materials involved in cost consumption, and the proportion of consumables involved in cost consumption.
[0013] Thirdly, this application provides a hospital comprehensive evaluation device based on an electronic medical record database, the device comprising: processor; And a memory containing executable code, which, when executed, causes the processor to perform a hospital comprehensive evaluation method based on an electronic medical record database, as described above.
[0014] Fourthly, this application provides a non-volatile computer storage medium storing computer instructions, which, when executed, implement a hospital comprehensive evaluation method based on an electronic medical record database as described above.
[0015] As can be seen from the above technical solutions, this application has the following advantages: Improve the efficiency of heterogeneous data integration: By configuring anonymization and standardized processing procedures for heterogeneous data systems and connecting the data acquisition interface with these systems, automated processing of multi-source heterogeneous data is achieved. Traditional ETL tools require custom development of data transformation rules for each system, resulting in long data collection cycles and high costs. This solution, through standardized interfaces and program configurations, reduces manual intervention and the need for customized development, making the data collection process more efficient and faster. This standardized processing mechanism not only reduces the complexity of data integration but also shortens the time cycle from data collection to processing, providing a real-time and reliable data foundation for subsequent comprehensive evaluation.
[0016] Unify data standards and enhance data anonymization processes: After the data acquisition interface returns the data, a standardized processing procedure is used to uniformly format the medical data, resolving the issue of inconsistent data standards in traditional methods. Simultaneously, the anonymization process ensures the security of sensitive information during transmission and processing, preventing biased evaluation results due to data leaks or inconsistent standards. This dual mechanism of standardization and anonymization allows medical data to maintain consistency while meeting privacy protection requirements during integration, thereby improving the accuracy and reliability of evaluation results.
[0017] Achieve multi-dimensional dynamic comprehensive evaluation: By extracting key data from preset indicators (including dimensions such as medical quality and safety, treatment efficiency, medical technical difficulty, and resource consumption), and using weighted coefficients to generate a comprehensive evaluation value, this method overcomes the limitations of single-dimensional evaluation methods. This multi-dimensional dynamic evaluation mechanism not only comprehensively reflects the hospital's overall performance in terms of medical quality, operational efficiency, technical difficulty, and resource consumption, but also dynamically adjusts evaluation indicators through real-time data acquisition and processing, ensuring that evaluation results are synchronized with actual operational status. This dynamism enables hospital managers and decision-makers to promptly identify problems and take targeted measures, thereby improving the hospital's overall operational level. Attached Figure Description
[0018] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 This is a flowchart of a hospital comprehensive evaluation method based on an electronic medical record database provided in an embodiment of this application.
[0020] Figure 2 This is a schematic diagram of the internal structure of a hospital comprehensive evaluation system based on an electronic medical record database, provided in an embodiment of this application.
[0021] Figure 3 This is a schematic diagram of the internal structure of a hospital comprehensive evaluation device based on an electronic medical record database, provided in an embodiment of this application. Detailed Implementation
[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0023] Those skilled in the art should understand that the embodiments described below are merely preferred embodiments of this disclosure and do not imply that this disclosure can only be implemented through these preferred embodiments. These preferred embodiments are merely used to explain the technical principles of this disclosure and are not intended to limit the scope of protection of this disclosure. Based on the preferred embodiments provided by this disclosure, all other embodiments obtained by those skilled in the art without creative effort should still fall within the scope of protection of this disclosure.
[0024] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0025] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.
[0026] The embodiment provides a comprehensive hospital evaluation method based on an electronic medical record database, such as... Figure 1 As shown in the embodiments of this application, the method mainly includes the following steps: Step 110: Determine the type and number of heterogeneous data systems in the current hospital; based on the type of heterogeneous data system, configure the corresponding de-identification program and standardization processing program in the heterogeneous data system; determine the number and type of data acquisition interfaces for the current hospital; and complete the connection between the data acquisition interfaces and the corresponding heterogeneous data systems.
[0027] In some embodiments, before configuring the corresponding de-identification procedure and standardization procedure in the heterogeneous data system based on the heterogeneous data system type, the method further includes: Obtain the desensitization procedure and standardized processing procedure; Configure the correspondence between heterogeneous data system types and desensitization and standardization procedures.
[0028] Understandably, by clearly defining the types and quantities of heterogeneous data systems in the hospital and configuring corresponding anonymization and standardized processing procedures, automated processing of multi-source heterogeneous data was achieved. Simultaneously, by determining the types and quantities of data acquisition interfaces and completing the interface connection with heterogeneous data systems, the efficiency and accuracy of data collection were ensured. This resolved the evaluation result bias issues caused by inconsistent data standards and the lack of anonymization mechanisms in traditional methods, providing a reliable data foundation for subsequent comprehensive evaluation.
[0029] Step 120: Obtain the data. The data acquisition interface returns the standard medical data after the anonymization and standardization processes. Based on the patient identifier in the standard medical data, all standard medical data of the same patient identifier in the current hospital are aggregated as a subset of patient data.
[0030] Understandably, standard medical data, processed through anonymization and standardization procedures, is acquired via a data acquisition interface and then aggregated based on patient identifiers to form a subset of patient data. This process directly achieves unified integration and standardized processing of multi-source heterogeneous data, ensuring data consistency and integrity. The anonymization process effectively protects patient privacy and avoids the risk of sensitive information leakage; the standardization process eliminates data format differences, providing a reliable data foundation for subsequent analysis. By aggregating patient identifiers, all medical records of the same patient can be quickly linked, supporting patient-centered comprehensive evaluation and avoiding evaluation bias caused by data dispersion in traditional methods.
[0031] Before the data acquisition interface returns standard medical data that has undergone de-identification and standardization procedures, the method also includes: Input the initial data into the desensitization program, and process the preset sensitive fields in the initial data into hash values; Obtain the data type of each field in the initial data after data anonymization. Based on the preset data type sorting of standard medical data, the fields are split and merged into standard medical data.
[0032] Understandably, by inputting the initial data into the anonymization program and processing the preset sensitive fields into hash values, patient privacy is effectively protected, avoiding the risk of leakage of sensitive information during data processing. Simultaneously, by obtaining the data type of each field in the anonymized initial data and sorting them according to the preset data type of standard medical data, the fields are split and merged into standard medical data, ensuring the uniformity and standardization of the data format. This process directly solves the problem of data integration difficulties caused by data format differences in traditional methods, providing a reliable data foundation for subsequent medical data analysis and evaluation. Through standardization, the efficiency and accuracy of data processing can be improved, reducing errors and biases caused by inconsistent data formats, thereby supporting more precise medical decision-making and management.
[0033] Step 130: Extract key data of preset indicators from the patient data subset; generate preset indicator values using the specific content of all related data of the current hospital; obtain the weighting coefficients of the preset indicator values, and obtain the comprehensive evaluation value of the current hospital by weighting.
[0034] The key data for the preset indicators include at least the following: data related to indicators of medical quality and safety, data related to indicators of treatment efficiency, data related to indicators of medical technical difficulty, and data related to indicators of resource consumption; the preset indicator values include at least the following: values of indicators of medical quality and safety, values of indicators of treatment efficiency, values of indicators of medical technical difficulty, and values of indicators of resource consumption.
[0035] Key data for pre-defined indicators are extracted from a subset of patient data, specifically including: Extract data related to indicators of medical quality and safety; among which, data related to indicators of medical quality and safety should include at least whether the patient died, whether the patient was readmitted, and whether complications existed. Extract data related to the diagnostic and treatment efficiency indicators; among which, the data related to the diagnostic and treatment efficiency indicators should include at least: total hospital stay and preoperative hospital stay; Extract data related to indicators of medical technical difficulty; among which, the data related to indicators of medical technical difficulty should include at least: whether it is a multi-case surgery, whether it is a level 3 surgery, and whether it is a level 4 surgery; Extract the associated data of resource consumption dimension indicators; among which, the associated data of resource consumption dimension indicators shall include at least: cost consumption, the proportion of medicinal materials involved in cost consumption, and the proportion of consumables involved in cost consumption.
[0036] In some embodiments, preset indicator values are generated using the specific content of all associated data of the current hospital, specifically including: Obtain the specific content of the correlation data of medical quality and safety dimension indicators, diagnosis and treatment efficiency dimension indicators, medical technology difficulty dimension indicators, and resource consumption dimension indicators corresponding to the subset of all patient data; The following data were calculated: case fatality rate, readmission rate, complication rate, average total length of stay, average preoperative length of stay, percentage of multi-case surgeries, percentage of level 3 surgeries, percentage of level 4 surgeries, percentage of pharmaceuticals in total cost, and percentage of consumables in total cost. The weighted values of medical quality and safety indicators are obtained based on mortality rate, readmission rate, and complication rate. The efficacy index value was obtained by weighting the average total length of hospital stay and the average length of hospital stay before surgery. The weighted index value of the medical technical difficulty dimension is obtained by calculating the proportion of multiple cases, the proportion of level 3 surgeries, and the proportion of level 4 surgeries. The resource consumption dimension index value is obtained by weighting the proportion of medicinal materials involved in the total cost consumption and the proportion of consumables involved in the total cost consumption.
[0037] Understandably, by extracting key data from preset indicators in a subset of patient data—including data related to medical quality and safety (e.g., whether there were deaths, readmissions, or complications), treatment efficiency (e.g., total length of stay, preoperative length of stay), medical technical difficulty (e.g., whether it was a multi-case surgery, a level 3 surgery, or a level 4 surgery), and resource consumption (e.g., cost consumption, drug ratio, and consumable ratio)—comprehensive data collection across multiple dimensions of the hospital is achieved. This process directly addresses the problem of single data dimensions in traditional evaluation methods, providing a rich and accurate data foundation for subsequent comprehensive evaluation. By extracting key data from preset indicators, the actual performance of the hospital in terms of medical quality, treatment efficiency, technical difficulty, and resource consumption can be more comprehensively reflected, avoiding evaluation bias caused by missing data.
[0038] By utilizing all relevant data from the hospital, pre-defined indicator values are generated. These include calculating key indicators such as mortality rate, readmission rate, complication rate, average total length of stay, average preoperative length of stay, percentage of multi-case surgeries, percentage of tertiary surgeries, percentage of quaternary surgeries, percentage of pharmaceutical costs in total cost, and percentage of consumable costs in total cost. Based on the specific content of these indicators, weighted calculations are performed to obtain indicator values for medical quality and safety, treatment efficiency, medical technical difficulty, and resource consumption. This process directly achieves a multi-dimensional quantitative evaluation of the hospital, providing reliable data support for the subsequent calculation of comprehensive evaluation values. The generation of pre-defined indicator values more objectively reflects the hospital's actual performance in each dimension, avoiding the influence of subjective factors on the evaluation results in traditional evaluation methods.
[0039] By obtaining weighted coefficients for preset indicator values, a comprehensive evaluation value for the current hospital is obtained. This process directly achieves a multi-dimensional comprehensive evaluation of the hospital, providing a comprehensive and accurate reference for hospital management decisions. The calculation of the comprehensive evaluation value more intuitively reflects the hospital's overall performance in terms of medical quality, diagnostic and treatment efficiency, technical difficulty, and resource consumption, avoiding the one-sidedness of evaluation results caused by the single data dimension in traditional evaluation methods. Furthermore, the introduction of weighted coefficients allows for flexible adjustments based on the actual importance of different dimensions, ensuring the scientific and reasonable nature of the evaluation results.
[0040] As described above, this embodiment achieves automated processing of multi-source heterogeneous data by configuring de-identification and standardized processing procedures for heterogeneous data system types and connecting the data acquisition interface with the heterogeneous data system. Traditional ETL tools require customized development of data transformation rules for each system, resulting in long data collection cycles and high costs. This solution, through standardized interfaces and program configurations, reduces manual intervention and the need for customized development, making the data collection process more efficient and faster. This standardized processing mechanism not only reduces the complexity of data integration but also shortens the time cycle from data collection to processing, providing a real-time and reliable data foundation for subsequent comprehensive evaluation.
[0041] After the data acquisition interface returns the data, a standardized processing procedure is used to uniformly format the medical data, resolving the issue of inconsistent data standards in traditional methods. Simultaneously, the anonymization process ensures the security of sensitive information during transmission and processing, preventing biased evaluation results due to data leaks or inconsistent standards. This dual mechanism of standardization and anonymization allows medical data to maintain consistency while meeting privacy protection requirements during integration, thereby improving the accuracy and reliability of evaluation results.
[0042] By extracting key data from preset indicators (including dimensions such as medical quality and safety, treatment efficiency, medical technical difficulty, and resource consumption), and using weighted coefficients to generate a comprehensive evaluation value, this method overcomes the limitations of single-dimensional evaluation methods. This multi-dimensional dynamic evaluation mechanism not only comprehensively reflects the hospital's overall performance in terms of medical quality, operational efficiency, technical difficulty, and resource consumption, but also dynamically adjusts evaluation indicators through real-time data acquisition and processing, ensuring that evaluation results are synchronized with actual operational status. This dynamism enables hospital managers and decision-makers to promptly identify problems and take targeted measures, thereby improving the hospital's overall operational level.
[0043] In addition, this application Figure 2 This application provides a hospital comprehensive evaluation system based on an electronic medical record database. For example... Figure 2 As shown in the embodiments of this application, the system mainly includes: The matching module 210 is used to determine the type and number of heterogeneous data systems in the current hospital, configure corresponding de-identification procedures and standardization procedures in the heterogeneous data systems based on the type of heterogeneous data systems, determine the number and type of data acquisition interfaces for the current hospital, and complete the docking of the data acquisition interfaces with the corresponding heterogeneous data systems.
[0044] Matching module 210 includes a processing unit, Used to input initial data into the desensitization program, which processes the preset sensitive fields in the initial data into hash values; Obtain the data type of each field in the initial data after data anonymization. Based on the preset data type sorting of standard medical data, the fields are split and merged into standard medical data.
[0045] The aggregation module 220 is used to obtain the standard medical data returned by the data acquisition interface after the de-identification and standardization processes; based on the patient identifier in the standard medical data, it aggregates all standard medical data of the same patient identifier in the current hospital as a subset of patient data.
[0046] The acquisition module 230 is used to extract key data of preset indicators from the patient data subset. The key data of preset indicators includes at least: data related to indicators of medical quality and safety, data related to indicators of treatment efficiency, data related to indicators of medical technical difficulty, and data related to indicators of resource consumption. Using the specific content of all related data of the current hospital, preset indicator values are generated. The preset indicator values include at least: values of indicators of medical quality and safety, values of indicators of treatment efficiency, values of indicators of medical technical difficulty, and values of indicators of resource consumption. Weighting coefficients of the preset indicator values are obtained, and a comprehensive evaluation value for the current hospital is obtained by weighting these values.
[0047] The acquisition module 230 includes extraction units. This data is used to extract correlation data for indicators related to medical quality and safety. Among these, the correlation data for indicators related to medical quality and safety should include at least whether the patient died, whether the patient was readmitted to the hospital, and whether complications occurred. Extract data related to the diagnostic and treatment efficiency indicators; among which, the data related to the diagnostic and treatment efficiency indicators should include at least: total hospital stay and preoperative hospital stay; Extract data related to indicators of medical technical difficulty; among which, the data related to indicators of medical technical difficulty should include at least: whether it is a multi-case surgery, whether it is a level 3 surgery, and whether it is a level 4 surgery; Extract the associated data of resource consumption dimension indicators; among which, the associated data of resource consumption dimension indicators shall include at least: cost consumption, the proportion of medicinal materials involved in cost consumption, and the proportion of consumables involved in cost consumption.
[0048] The above are method embodiments of this application. Based on the same inventive concept, embodiments of this application also provide a hospital comprehensive evaluation device based on an electronic medical record database. Figure 3 As shown, the device includes: a processor; and a memory storing executable code thereon, which, when executed, causes the processor to perform a hospital comprehensive evaluation method based on an electronic medical record database as described in the above embodiments.
[0049] Specifically, the server determines the type and number of heterogeneous data systems in the current hospital, and configures corresponding de-identification and standardization procedures in these systems based on their types. It also determines the number and type of data acquisition interfaces for the current hospital, completes the connection between these interfaces and the corresponding heterogeneous data systems, acquires the standard medical data returned by the data acquisition interfaces after processing by the de-identification and standardization procedures, and aggregates all standard medical data for the same patient identifier in the current hospital as a subset of patient data based on the patient identifier in the standard medical data. Preset key indicator data is extracted from the patient data subset; this preset key indicator data includes at least the following: data related to medical quality and safety dimensions, diagnostic and treatment efficiency dimensions, medical technical difficulty dimensions, and resource consumption dimensions. Using the specific content of all related data in the current hospital, preset indicator values are generated; these preset indicator values include at least the following: medical quality and safety dimension indicator values, diagnostic and treatment efficiency dimension indicator values, medical technical difficulty dimension indicator values, and resource consumption dimension indicator values. Finally, weighting coefficients for the preset indicator values are obtained, and a weighted average is used to obtain the comprehensive evaluation value for the current hospital.
[0050] In addition, this application embodiment also provides a non-volatile computer storage medium storing executable instructions, which, when executed, implement the hospital comprehensive evaluation method based on an electronic medical record database as described above.
[0051] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A comprehensive hospital evaluation method based on an electronic medical record database, characterized in that, The method includes: Determine the types and number of heterogeneous data systems in the current hospital; based on the types of heterogeneous data systems, configure corresponding de-identification procedures and standardization procedures in the heterogeneous data systems; determine the number and types of data acquisition interfaces for the current hospital; and complete the connection between the data acquisition interfaces and the corresponding heterogeneous data systems. The data acquisition interface returns standard medical data after de-identification and standardization processes; based on the patient identifier in the standard medical data, all standard medical data of the same patient identifier in the current hospital are aggregated as a subset of patient data. Extract key data of preset indicators from the patient data subset; the key data of preset indicators shall include at least the following: data related to indicators of medical quality and safety, data related to indicators of treatment efficiency, data related to indicators of medical technical difficulty, and data related to indicators of resource consumption. Using the specific content of all the current hospital's associated data, generate preset indicator values; among which, the preset indicator values include at least: medical quality and safety dimension indicator values, diagnosis and treatment efficiency dimension indicator values, medical technical difficulty dimension indicator values, and resource consumption dimension indicator values; Obtain the weighting coefficients of the preset indicator values, and then use the weighted average to obtain the current comprehensive evaluation value of the hospital.
2. The hospital comprehensive evaluation method based on electronic medical record database according to claim 1, characterized in that, Before configuring corresponding de-identification and standardization procedures in heterogeneous data systems based on their types, the method further includes: Obtain the desensitization procedure and standardized processing procedure; Configure the correspondence between heterogeneous data system types and desensitization and standardization procedures.
3. The hospital comprehensive evaluation method based on electronic medical record database according to claim 1, characterized in that, Before the data acquisition interface returns standard medical data processed by the anonymization and standardization procedures, the method further includes: Input the initial data into the desensitization program, and process the preset sensitive fields in the initial data into hash values; Obtain the data type of each field in the initial data after data anonymization. Based on the preset data type sorting of standard medical data, the fields are split and merged into standard medical data.
4. The hospital comprehensive evaluation method based on electronic medical record database according to claim 1, characterized in that, Key data for pre-defined indicators are extracted from a subset of patient data, specifically including: Extract data related to indicators of medical quality and safety; among which, data related to indicators of medical quality and safety should include at least whether the patient died, whether the patient was readmitted, and whether complications existed. Extract data related to the diagnostic and treatment efficiency indicators; among which, the data related to the diagnostic and treatment efficiency indicators should include at least: total hospital stay and preoperative hospital stay; Extract data related to indicators of medical technical difficulty; among which, the data related to indicators of medical technical difficulty should include at least: whether it is a multi-case surgery, whether it is a level 3 surgery, and whether it is a level 4 surgery; Extract the associated data of resource consumption dimension indicators; among which, the associated data of resource consumption dimension indicators shall include at least: cost consumption, the proportion of medicinal materials involved in cost consumption, and the proportion of consumables involved in cost consumption.
5. The hospital comprehensive evaluation method based on electronic medical record database according to claim 4, characterized in that, Using the specific content of all currently associated data of the hospital, preset indicator values are generated, including: Obtain the specific content of the correlation data of medical quality and safety dimension indicators, diagnosis and treatment efficiency dimension indicators, medical technology difficulty dimension indicators, and resource consumption dimension indicators corresponding to the subset of all patient data; The following data were calculated: case fatality rate, readmission rate, complication rate, average total length of stay, average preoperative length of stay, percentage of multi-case surgeries, percentage of level 3 surgeries, percentage of level 4 surgeries, percentage of pharmaceuticals in total cost, and percentage of consumables in total cost. The weighted values of medical quality and safety indicators are obtained based on mortality rate, readmission rate, and complication rate. The efficacy index value was obtained by weighting the average total length of hospital stay and the average length of hospital stay before surgery. The weighted index value of the medical technical difficulty dimension is obtained by calculating the proportion of multiple cases, the proportion of level 3 surgeries, and the proportion of level 4 surgeries. The resource consumption dimension index value is obtained by weighting the proportion of medicinal materials involved in the total cost consumption and the proportion of consumables involved in the total cost consumption.
6. A comprehensive hospital evaluation system based on an electronic medical record database, characterized in that, The system includes: The matching module is used to determine the type and number of heterogeneous data systems in the current hospital, configure corresponding de-identification and standardization procedures in the heterogeneous data systems based on the type of heterogeneous data systems, determine the number and type of data acquisition interfaces for the current hospital, and complete the docking of data acquisition interfaces with the corresponding heterogeneous data systems. The aggregation module is used to obtain standard medical data returned by the data acquisition interface after being processed by de-identification and standardization procedures; based on the patient identifier in the standard medical data, it aggregates all standard medical data of the same patient identifier in the current hospital as a subset of patient data. The acquisition module is used to extract key data of preset indicators from a subset of patient data. This key data includes at least the following: data related to indicators of medical quality and safety, diagnostic and treatment efficiency, medical technical difficulty, and resource consumption. Using the specific content of all related data for the current hospital, preset indicator values are generated. These preset indicator values include at least the following: values for indicators of medical quality and safety, diagnostic and treatment efficiency, medical technical difficulty, and resource consumption. Weighting coefficients for these preset indicator values are obtained, and a comprehensive evaluation value for the current hospital is obtained through weighting.
7. The hospital comprehensive evaluation system based on electronic medical record database according to claim 6, characterized in that, The matching module includes a processing unit. Used to input initial data into the desensitization program, which processes the preset sensitive fields in the initial data into hash values; Obtain the data type of each field in the initial data after data anonymization. Based on the preset data type sorting of standard medical data, the fields are split and merged into standard medical data.
8. The hospital comprehensive evaluation system based on electronic medical record database according to claim 6, characterized in that, The acquisition module includes extraction units. This data is used to extract correlation data for indicators related to medical quality and safety. Among these, the correlation data for indicators related to medical quality and safety should include at least whether the patient died, whether the patient was readmitted to the hospital, and whether complications occurred. Extract data related to the diagnostic and treatment efficiency indicators; among which, the data related to the diagnostic and treatment efficiency indicators should include at least: total hospital stay and preoperative hospital stay; Extract data related to indicators of medical technical difficulty; among which, the data related to indicators of medical technical difficulty should include at least: whether it is a multi-case surgery, whether it is a level 3 surgery, and whether it is a level 4 surgery; Extract the associated data of resource consumption dimension indicators; among which, the associated data of resource consumption dimension indicators shall include at least: cost consumption, the proportion of medicinal materials involved in cost consumption, and the proportion of consumables involved in cost consumption.
9. A hospital comprehensive evaluation device based on an electronic medical record database, characterized in that, The device includes: processor; And a memory having executable code stored thereon, which, when executed, causes the processor to perform a hospital comprehensive evaluation method based on an electronic medical record database as described in any one of claims 1-5.
10. A non-volatile computer storage medium, characterized in that, It stores computer instructions, which, when executed, implement a hospital comprehensive evaluation method based on an electronic medical record database as described in any one of claims 1-5.