CN118471535A - A medical service effectiveness evaluation method, system and medium - Google Patents

Abstract

The present invention discloses a method, system and medium for evaluating the effectiveness of medical services. By constructing a medical service platform, user interaction, feedback and service data of the target medical unit are collected, the medical process analysis is performed, and the user medical data path is generated. The patient's medical treatment is simulated to generate a simulated data path, and the service quality is compared and evaluated. The target medical unit data is integrated into a first data set, and the second data set is formed with the data of other medical units. Apriori correlation analysis is performed to extract the associated data set. The associated medical data path is filtered through semantic analysis as the predicted service data, the service quality of each link is evaluated, and the predicted evaluation data is generated. The effectiveness evaluation of information-based medical services and the effectiveness service prediction are realized.

Description

A medical service effectiveness evaluation method, system and medium

Technical Field

The present invention relates to the field of medical services and data analysis, and more specifically, to a medical service effectiveness evaluation method, system and medium.

Background Art

With the continuous advancement of medical technology and the growing demand for medical services, how to accurately evaluate the quality of medical services has become an urgent problem to be solved. Traditional medical service evaluation methods often rely on subjective evaluation and questionnaire surveys, which are difficult to accurately reflect the true effect of medical services. In the existing technology, there is a lack of information service evaluation based on medical platforms and service effectiveness analysis based on scientific and objective means. Most of the evaluations of the platforms are based on manual experience, which makes it difficult to achieve effective problem analysis and functional improvements on the service platforms. Therefore, there is an urgent need for a medical service effectiveness evaluation method.

Summary of the invention

The present invention overcomes the defects of the prior art and proposes a medical service effectiveness evaluation method, system and medium.

A first aspect of the present invention provides a method for evaluating the effectiveness of medical services, comprising:

Based on multiple preset medical units, a medical service platform is constructed, and user interaction data, user feedback data, and medical service data of a target medical unit within a preset time period are collected through the medical service platform;

Based on the user interaction data, user feedback data, and medical service data, a data statistical analysis of the user's medical consultation process is performed, and the medical consultation process includes multiple links to generate a user medical consultation data path;

Count the number and type of patients in the target medical unit within a preset time period, simulate the number and type of patients in the medical service platform, and generate simulated medical data paths;

Performing a service quality evaluation on each link of the user's medical treatment data path and the simulated medical treatment data path, and generating an effectiveness evaluation value for each link, and generating first evaluation data based on all the effectiveness evaluation values;

Integrate the user interaction data, user feedback data, and medical service data of the target medical unit to form a first data set; integrate the corresponding interaction and feedback data in the remaining preset medical units to form a second data set, use the first data set and the second data set as sample data, perform association analysis based on the Apriori algorithm, and extract the associated data set through the generated association rules;

Generate an associated medical visit data path based on the associated data set, perform semantic analysis on the associated medical visit data path and the user medical visit data path, and filter the data based on semantic similarity to obtain a filtered associated medical visit data path;

The screened associated medical data paths are used as the predicted service data of the target medical unit, and the service quality of each link is evaluated to generate predictive evaluation data.

In this solution, a medical service platform is constructed based on multiple preset medical units, and the user interaction data, user feedback data, and medical service data of a target medical unit within a preset time period are collected through the medical service platform, specifically:

Build a medical service platform based on multiple preset medical units;

Establishing a network connection between the user terminal of the preset medical unit and the medical service platform;

The medical service platform collects user interaction data, user feedback data, and medical service data of a target medical unit within a preset time period.

In this solution, the data statistical analysis of the user's medical treatment process based on the user interaction data, user feedback data, and medical service data is performed. The medical treatment process includes multiple links to generate a user medical treatment data path, specifically:

Divide the consultation process based on medical service data to form multiple links and corresponding service content data;

Through user interaction data and user feedback data, data statistics and classification are performed on multiple links, and combined with service content data, data path information for each link is generated;

Based on the queue structure, the data path information of each link is used as queue metadata and the medical data path is generated;

Perform medical treatment path analysis on all users and generate user medical treatment data paths.

In this solution, the number and type of patients in the target medical unit within a preset time period are counted, and a medical consultation simulation is performed based on the number and type of patients in the medical service platform, and a simulated medical consultation data path is generated, specifically:

Count the number and type of patients in the target medical unit within a preset time period;

Generate simulated test user information based on the number of patients and patient types;

In the medical service platform, statistics are collected on the real-time medical resources of the target medical unit within a preset time period;

Medical service simulation is performed through real-time medical resources and simulated test user information, and a simulated medical treatment path is generated for each simulated user. The simulated medical treatment path data is stored in the form of a queue structure to form a simulated medical treatment data path.

In this solution, the service quality of each link of the user's medical data path and the simulated medical data path is evaluated, and the effectiveness evaluation value of each link is generated. The first evaluation data is generated based on all the effectiveness evaluation values, specifically:

Taking the user as the analysis unit, based on the user's medical treatment data path and the simulated medical treatment data path, the user's actual data path and the simulated data path are compared and analyzed. The comparative analysis process compares each link separately, and the data comparison and service deviation analysis are performed to generate the effectiveness evaluation value of each link;

Integrate the effectiveness evaluation values of each link to form a user's service effectiveness evaluation information;

An integrated evaluation and analysis of the medical service is performed based on the service effectiveness evaluation information of all users to obtain first evaluation data.

In this solution, the user interaction data, user feedback data, and medical service data of the target medical unit are integrated to form a first data set; the corresponding interaction and feedback data in the remaining preset medical units are integrated to form a second data set, and the first data set and the second data set are used as sample data to perform association analysis based on the Apriori algorithm, and the associated data set is extracted through the generated association rules, specifically:

Integrate, clean, and process outliers the user interaction data, user feedback data, and medical service data of the target medical unit to form a first data set;

Integrate the corresponding interaction and feedback data in the remaining preset medical units to form a second data set;

The first data set and the second data set are used as sample data, the sample data are converted into candidate item sets, and the association analysis based on the Apriori algorithm is performed, the minimum support and the minimum confidence are set, and frequent item sets and association rules are screened and analyzed from the candidate item sets;

Through frequent item sets and association rules, data with association are screened out from the first data set and the second data set, and marked as associated data sets.

In this solution, the associated medical visit data path is generated based on the associated data set, the associated medical visit data path and the user medical visit data path are semantically analyzed, and data is filtered based on semantic similarity to obtain the filtered associated medical visit data path, which is specifically:

Analyze and count the medical service content based on the associated data set, generate the medical treatment data path corresponding to the user in the data, and obtain the associated medical treatment data path;

Perform text format conversion and semantic feature analysis on the associated medical treatment data path and the user medical treatment data path to form associated feature data and user feature data;

Based on the standard Euclidean distance, user feature data is used as standard data, the similarity between the associated feature data and the standard data is analyzed, a similarity threshold is set, and the associated feature data is filtered. Based on the filtering results, the associated medical data path is mapped to obtain the filtered associated medical data path.

The second aspect of the present invention further provides a medical service effectiveness evaluation system, the system comprising: a memory, a processor, the memory comprising a medical service effectiveness evaluation program, the medical service effectiveness evaluation program when executed by the processor to implement the following steps:

Based on multiple preset medical units, a medical service platform is constructed, and user interaction data, user feedback data, and medical service data of a target medical unit within a preset time period are collected through the medical service platform;

Based on the user interaction data, user feedback data, and medical service data, a data statistical analysis of the user's medical consultation process is performed, and the medical consultation process includes multiple links to generate a user medical consultation data path;

Count the number and type of patients in the target medical unit within a preset time period, simulate the number and type of patients in the medical service platform, and generate simulated medical data paths;

Performing a service quality evaluation on each link of the user's medical treatment data path and the simulated medical treatment data path, and generating an effectiveness evaluation value for each link, and generating first evaluation data based on all the effectiveness evaluation values;

Integrate the user interaction data, user feedback data, and medical service data of the target medical unit to form a first data set; integrate the corresponding interaction and feedback data in the remaining preset medical units to form a second data set, use the first data set and the second data set as sample data, perform association analysis based on the Apriori algorithm, and extract the associated data set through the generated association rules;

Generate an associated medical visit data path based on the associated data set, perform semantic analysis on the associated medical visit data path and the user medical visit data path, and filter the data based on semantic similarity to obtain a filtered associated medical visit data path;

The screened associated medical data paths are used as the predicted service data of the target medical unit, and the service quality of each link is evaluated to generate predictive evaluation data.

The third aspect of the present invention also provides a computer-readable storage medium, which includes a medical service effectiveness evaluation program. When the medical service effectiveness evaluation program is executed by a processor, the steps of the medical service effectiveness evaluation method as described in any one of the above items are implemented.

The present invention discloses a method, system and medium for evaluating the effectiveness of medical services. By constructing a medical service platform, user interaction, feedback and service data of the target medical unit are collected, the medical process analysis is performed, and the user medical data path is generated. The patient's medical treatment is simulated to generate a simulated data path, and the service quality is compared and evaluated. The target medical unit data is integrated into a first data set, and the second data set is formed with the data of other medical units. Apriori correlation analysis is performed to extract the associated data set. The associated medical data path is filtered through semantic analysis as the predicted service data, the service quality of each link is evaluated, and the predicted evaluation data is generated. The effectiveness evaluation of information-based medical services and the effectiveness service prediction are realized.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 shows a flow chart of a method for evaluating the effectiveness of medical services according to the present invention;

FIG. 2 shows a block diagram of a medical service effectiveness evaluation system according to the present invention.

DETAILED DESCRIPTION

In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

FIG1 shows a flow chart of a method for evaluating the effectiveness of medical services according to the present invention.

As shown in FIG1 , the first aspect of the present invention provides a method for evaluating the effectiveness of medical services, comprising:

S102, building a medical service platform based on multiple preset medical units, and collecting user interaction data, user feedback data, and medical service data of a target medical unit within a preset time period through the medical service platform;

S104, performing data statistical analysis on the user's medical consultation process based on the user interaction data, user feedback data, and medical service data, where the medical consultation process includes multiple links, and generating a user medical consultation data path;

S106, counting the number and type of patients in the target medical unit within a preset time period, performing a medical consultation simulation based on the number and type of patients in the medical service platform, and generating a simulated medical consultation data path;

S108, performing service quality evaluation on each link of the user's medical treatment data path and the simulated medical treatment data path, and generating effectiveness evaluation values of each link, and generating first evaluation data based on all effectiveness evaluation values;

S110, integrating the user interaction data, user feedback data, and medical service data of the target medical unit to form a first data set; integrating the corresponding interaction and feedback data in the remaining preset medical units to form a second data set, using the first data set and the second data set as sample data, performing a correlation analysis based on the Apriori algorithm, and extracting a correlation data set through the generated association rules;

S112, generating an associated medical visit data path based on the associated data set, performing semantic analysis on the associated medical visit data path and the user medical visit data path, and filtering data based on semantic similarity to obtain a filtered associated medical visit data path;

S114, using the screened associated medical data paths as the predicted service data of the target medical unit, and evaluating the service quality of each link to generate predicted evaluation data.

It should be noted that the medical service platform can serve multiple preset medical units. When data sharing and demand analysis occur among multiple medical units, the medical service platform can be used to conduct integrated analysis of multi-unit data. For example, analysis of the number of medical services in a region, comprehensive medical satisfaction, patient treatment trends, etc. Since medical data is involved, data desensitization operations are generally required when conducting multi-unit data analysis.

The user medical data path includes the path data of all users within a preset time period. The medical data path includes data entry information, data entry time, user interaction time and waiting time, service content information and other data of each link. The various links include appointment, registration, payment, examination, medical treatment and other links. Each link will generate corresponding user interaction data and interaction time and other information, and the information data of each link is integrated into a user's medical data path, through which the effectiveness of the service can be effectively evaluated and analyzed.

According to an embodiment of the present invention, the medical service platform is constructed based on multiple preset medical units, and the user interaction data, user feedback data, and medical service data of a target medical unit within a preset time period are collected through the medical service platform, specifically:

Build a medical service platform based on multiple preset medical units;

Establishing a network connection between the user terminal of the preset medical unit and the medical service platform;

The medical service platform collects user interaction data, user feedback data, and medical service data of a target medical unit within a preset time period.

It should be noted that the user interaction data specifically refers to the interaction data of the user terminal in each link up to the entire medical process, such as the interactive data such as the user's login data input, user basic data entry, appointment time, registration time, waiting time, registration information, etc. in the appointment and registration process. The user feedback data is generally the user's feedback information on each process, such as service rating, service quality analysis and other information. It can also be based on the platform's feedback evaluation of the user's medical process based on the interactive data of the user's medical process to generate service quality information. The medical service data includes the transmission data generated by the medical service process. The transmission data is the analysis data generated by the platform for the medical service provided to the user, such as case data, diagnosis data, medical resource data, etc., which are generally imported into the medical database. Through user interaction data, user feedback data, medical service data, etc., it is possible to analyze the corresponding medical path of patient users within a certain period of time, and further achieve the purpose of service effectiveness evaluation.

According to an embodiment of the present invention, the data statistical analysis of the user's medical treatment process based on the user interaction data, user feedback data, and medical service data, the medical treatment process includes multiple links, and the user medical treatment data path is generated, specifically:

Divide the consultation process based on medical service data to form multiple links and corresponding service content data;

Through user interaction data and user feedback data, data statistics and classification are performed on multiple links, and combined with service content data, data path information for each link is generated;

Based on the queue structure, the data path information of each link is used as queue metadata and the medical data path is generated;

Perform medical treatment path analysis on all users and generate user medical treatment data paths.

It should be noted that the data path of each link includes the data transmission path, the service content data under the link, the service interaction and feedback information, the upper and lower link relationship corresponding to the link, etc. By dividing multiple data paths and combining them to form the entire medical data path, the quality comparison and effectiveness evaluation of the service can be carried out based on the data direction, transmission path and service content of each link, so as to evaluate the effectiveness of the service in a more scientific, refined and information-based way, and reduce human subjective factors. The medical data path is data stored based on the queue structure, and each metadata corresponds to the path data of each link. In the medical process, each link in the medical path of each user has a certain order and data logic based on the time dimension. When storing, the present invention chooses to store in the form of a queue, which conforms to the form of upper and lower association of data in the process link, and through the queue form, when the simulation path comparison is performed later, efficient process link comparison can be performed and the service effectiveness can be accurately evaluated based on the comparison results. In traditional technology, service evaluation often relies on user feedback analysis and simple human medical supervision analysis, and does not use process and information means to conduct comprehensive evaluation, and the objectivity is not strong.

According to an embodiment of the present invention, the number and type of patients in the target medical unit within a preset time period are counted, and a medical consultation simulation is performed based on the number and type of patients in the medical service platform, and a simulated medical consultation data path is generated, specifically:

Count the number and type of patients in the target medical unit within a preset time period;

Generate simulated test user information based on the number of patients and patient types;

In the medical service platform, statistics are collected on the real-time medical resources of the target medical unit within a preset time period;

Medical service simulation is performed through real-time medical resources and simulated test user information, and a simulated medical treatment path is generated for each simulated user. The simulated medical treatment path data is stored in the form of a queue structure to form a simulated medical treatment data path.

It should be noted that during the test simulation process, the platform performs test simulation based on a standardized process, that is, the simulation process is to perform a medical test simulation in an ideal state under the existing medical resources, and generate a data path in an ideal (or standard) state. This data path will have a certain deviation from the actual situation and can be used as a reference standard. The real-time medical resources generally include the number of appointments, the number of medical unit staff, medical equipment resources, etc.

According to an embodiment of the present invention, the user medical data path and the simulated medical data path are evaluated for service quality at each link, and an effectiveness evaluation value of each link is generated, and the first evaluation data is generated based on all the effectiveness evaluation values, specifically:

Taking the user as the analysis unit, based on the user's medical treatment data path and the simulated medical treatment data path, the user's actual data path and the simulated data path are compared and analyzed. The comparative analysis process compares each link separately, and the data comparison and service deviation analysis are performed to generate the effectiveness evaluation value of each link;

Integrate the effectiveness evaluation values of each link to form a user's service effectiveness evaluation information;

An integrated evaluation and analysis of the medical service is performed based on the service effectiveness evaluation information of all users to obtain first evaluation data.

It should be noted that the said all users refer to (patient) users of the target medical unit within a preset time period.

According to an embodiment of the present invention, the user interaction data, user feedback data, and medical service data of the target medical unit are integrated to form a first data set; the corresponding interaction and feedback data in the remaining preset medical units are integrated to form a second data set, and the first data set and the second data set are used as sample data to perform association analysis based on the Apriori algorithm, and the associated data set is extracted through the generated association rules, specifically:

Integrate, clean, and process outliers the user interaction data, user feedback data, and medical service data of the target medical unit to form a first data set;

Integrate the corresponding interaction and feedback data in the remaining preset medical units to form a second data set;

The first data set and the second data set are used as sample data, the sample data are converted into candidate item sets, and the association analysis based on the Apriori algorithm is performed, the minimum support and the minimum confidence are set, and frequent item sets and association rules are screened and analyzed from the candidate item sets;

Through frequent item sets and association rules, data with association are screened out from the first data set and the second data set, and marked as associated data sets.

It should be noted that the data processing process of the second data set is consistent with that of the first data set, but the data sources are different. The corresponding interaction and feedback data are user interaction data, user feedback data, and medical service data.

It is worth mentioning that in smaller medical units or in some newly added medical units, due to the small amount of data generated, it is often difficult to mine the service evaluation value of user interaction feedback data, and the effectiveness evaluation has certain limitations, and the results may be inaccurate. Therefore, through the present invention, the medical data collected by the target medical unit and the other units are first integrated to form a data set. Through the integration of the data set, the data range can be expanded. Further, based on the Apriori algorithm, data items with correlation can be screened out from the data set. This process realizes the full value mining of medical data and extracts the associated data. For example, in other medical units, there are some data that are consistent or similar to the target medical unit in the collected user interaction and feedback data. At this time, part of its data can be mined and extracted through the association algorithm to form an associated data set. Next, secondary screening is performed based on semantic analysis, and the screened data path information is used as the predicted service data of the target medical unit. The predicted service data has a high correlation and service feature correlation with the target medical unit. As its predicted data, it can effectively perform data mining and service effectiveness evaluation for the target medical unit based on the medical big data of the other units.

By dividing the first and second data sets, the present invention can facilitate the location analysis of associated data, and can grasp whether the associated item set belongs to the first data set or the second data set, and can perform corresponding data division and proportion analysis, which is helpful to evaluate the data mining potential of the target medical unit and the preset medical unit. The associated data set includes a partial item set in the first and second data sets.

According to an embodiment of the present invention, the method of generating an associated medical visit data path based on an associated data set, performing semantic analysis on the associated medical visit data path and the user medical visit data path, and filtering data based on semantic similarity to obtain the filtered associated medical visit data path is specifically as follows:

Analyze and count the medical service content based on the associated data set, generate the medical treatment data path corresponding to the user in the data, and obtain the associated medical treatment data path;

Perform text format conversion and semantic feature analysis on the associated medical treatment data path and the user medical treatment data path to form associated feature data and user feature data;

Based on the standard Euclidean distance, user feature data is used as standard data, the similarity between the associated feature data and the standard data is analyzed, a similarity threshold is set, and the associated feature data is filtered. Based on the filtering results, the associated medical data path is mapped to obtain the filtered associated medical data path.

It should be noted that the purpose of screening the associated medical data paths is to remove service medical data that differs greatly from the user medical data paths of the target medical unit from the associated medical data paths through the dimension of semantic analysis.

In the process of semantic analysis, semantic analysis is specifically performed on each link of the medical data path to form corresponding semantic feature data, thereby realizing a detailed analysis process. The purpose of the screening process is to extract data paths in the associated data that are similar to the medical services of the target medical unit. The semantic analysis can be performed using a CNN-based learning model. The screening result is feature data whose similarity is greater than a similarity threshold, and the corresponding data is mapped and screened from the corresponding associated medical data path based on the screened feature data.

According to an embodiment of the present invention, the present invention further includes:

In the medical service platform, based on the time period of an emergency medical event, the user's medical treatment data path of each preset medical unit is collected;

Integrate all user medical data paths to form medical service data;

Based on the Apriori algorithm, the internal data correlation analysis of medical service data is performed, and the associated data set is extracted through the generated association rules and marked as the warning associated data set;

Storing the warning-related data set in the database of the medical service platform;

In each preset time period, the user medical data path of the target medical unit is compared with the warning-related data set based on the medical service path, and medical warning information is generated based on the comparison results.

It should be noted that the medical emergencies include medical events for infectious diseases, seasonal high-incidence medical events, etc. In medical emergencies, medical units in a certain area often have certain similarities in medical services, such as the same type of cases, a sharp increase in the number of a certain disease, similarities in appointments for medical services, etc. Based on each emergency, the warning-related data set can be added and updated to dynamically adjust the warning data.

The present invention collects the user medical treatment data paths of medical units within the time period of the emergency event, analyzes the correlation data in the data path dimension, extracts the data with high correlation for storage, and in the subsequent real-time analysis, based on the early warning data, compares the user medical treatment data paths obtained by the real-time analysis with the early warning data for similarity. In the comparison process, service similarity comparison is performed based on multiple links of each data path. By comparing with the early warning-related data, the similarity between the current medical service and the medical service of the emergency situation can be grasped, so as to grasp whether there is a trend of emergency medical time by information technology, and realize the effectiveness evaluation of medical service early warning.

FIG. 2 shows a block diagram of a medical service effectiveness evaluation system according to the present invention.

The second aspect of the present invention further provides a medical service effectiveness evaluation system 2, which includes: a memory 21 and a processor 22, wherein the memory includes a medical service effectiveness evaluation program, and when the medical service effectiveness evaluation program is executed by the processor, the following steps are implemented:

Based on multiple preset medical units, a medical service platform is constructed, and user interaction data, user feedback data, and medical service data of a target medical unit within a preset time period are collected through the medical service platform;

Based on the user interaction data, user feedback data, and medical service data, a data statistical analysis of the user's medical consultation process is performed, and the medical consultation process includes multiple links to generate a user medical consultation data path;

Count the number and type of patients in the target medical unit within a preset time period, simulate the number and type of patients in the medical service platform, and generate simulated medical data paths;

Performing a service quality evaluation on each link of the user's medical treatment data path and the simulated medical treatment data path, and generating an effectiveness evaluation value for each link, and generating first evaluation data based on all the effectiveness evaluation values;

Integrate the user interaction data, user feedback data, and medical service data of the target medical unit to form a first data set; integrate the corresponding interaction and feedback data in the remaining preset medical units to form a second data set, use the first data set and the second data set as sample data, perform association analysis based on the Apriori algorithm, and extract the associated data set through the generated association rules;

Generate an associated medical visit data path based on the associated data set, perform semantic analysis on the associated medical visit data path and the user medical visit data path, and filter the data based on semantic similarity to obtain a filtered associated medical visit data path;

The screened associated medical data paths are used as the predicted service data of the target medical unit, and the service quality of each link is evaluated to generate predictive evaluation data.

It should be noted that the medical service platform can serve multiple preset medical units. When data sharing and demand analysis occur among multiple medical units, the medical service platform can be used to conduct integrated analysis of multi-unit data. For example, analysis of the number of medical services in a region, comprehensive medical satisfaction, patient treatment trends, etc. Since medical data is involved, data desensitization operations are generally required when conducting multi-unit data analysis.

The user medical data path includes the path data of all users within a preset time period. The medical data path includes data entry information, data entry time, user interaction time and waiting time, service content information and other data of each link. The various links include appointment, registration, payment, examination, medical treatment and other links. Each link will generate corresponding user interaction data and interaction time and other information, and the information data of each link is integrated into a user's medical data path, through which the effectiveness of the service can be effectively evaluated and analyzed.

According to an embodiment of the present invention, the medical service platform is constructed based on multiple preset medical units, and the user interaction data, user feedback data, and medical service data of a target medical unit within a preset time period are collected through the medical service platform, specifically:

Build a medical service platform based on multiple preset medical units;

Establishing a network connection between the user terminal of the preset medical unit and the medical service platform;

The medical service platform collects user interaction data, user feedback data, and medical service data of a target medical unit within a preset time period.

It should be noted that the user interaction data specifically refers to the interaction data of the user terminal in each link up to the entire medical process, such as the interactive data such as the user's login data input, user basic data entry, appointment time, registration time, waiting time, registration information, etc. in the appointment and registration process. The user feedback data is generally the user's feedback information on each process, such as service rating, service quality analysis and other information. It can also be based on the platform's feedback evaluation of the user's medical process based on the interactive data of the user's medical process to generate service quality information. The medical service data includes the transmission data generated by the medical service process. The transmission data is the analysis data generated by the platform for the medical service provided to the user, such as case data, diagnosis data, medical resource data, etc., which are generally imported into the medical database. Through user interaction data, user feedback data, medical service data, etc., it is possible to analyze the corresponding medical path of patient users within a certain period of time, and further achieve the purpose of service effectiveness evaluation.

According to an embodiment of the present invention, the data statistical analysis of the user's medical treatment process based on the user interaction data, user feedback data, and medical service data, the medical treatment process includes multiple links, and the user medical treatment data path is generated, specifically:

Divide the consultation process based on medical service data to form multiple links and corresponding service content data;

Through user interaction data and user feedback data, data statistics and classification are performed on multiple links, and combined with service content data, data path information for each link is generated;

Based on the queue structure, the data path information of each link is used as queue metadata and the medical data path is generated;

Perform medical treatment path analysis on all users and generate user medical treatment data paths.

It should be noted that the data path of each link includes the data transmission path, the service content data under the link, the service interaction and feedback information, the upper and lower link relationship corresponding to the link, etc. By dividing multiple data paths and combining them to form the entire medical data path, the quality comparison and effectiveness evaluation of the service can be carried out based on the data direction, transmission path and service content of each link, so as to evaluate the effectiveness of the service in a more scientific, refined and information-based way, and reduce human subjective factors. The medical data path is data stored based on the queue structure, and each metadata corresponds to the path data of each link. In the medical process, each link in the medical path of each user has a certain order and data logic based on the time dimension. When storing, the present invention chooses to store in the form of a queue, which conforms to the form of upper and lower association of data in the process link, and through the queue form, when the simulation path comparison is performed later, efficient process link comparison can be performed and the service effectiveness can be accurately evaluated based on the comparison results. In traditional technology, service evaluation often relies on user feedback analysis and simple human medical supervision analysis, and does not use process and information means to conduct comprehensive evaluation, and the objectivity is not strong.

According to an embodiment of the present invention, the number and type of patients in the target medical unit within a preset time period are counted, and a medical consultation simulation is performed based on the number and type of patients in the medical service platform, and a simulated medical consultation data path is generated, specifically:

Count the number and type of patients in the target medical unit within a preset time period;

Generate simulated test user information based on the number of patients and patient types;

In the medical service platform, statistics are collected on the real-time medical resources of the target medical unit within a preset time period;

Medical service simulation is performed through real-time medical resources and simulated test user information, and a simulated medical treatment path is generated for each simulated user. The simulated medical treatment path data is stored in the form of a queue structure to form a simulated medical treatment data path.

It should be noted that during the test simulation process, the platform performs test simulation based on a standardized process, that is, the simulation process is to perform a medical test simulation in an ideal state under the existing medical resources, and generate a data path in an ideal (or standard) state. This data path will have a certain deviation from the actual situation and can be used as a reference standard. The real-time medical resources generally include the number of appointments, the number of medical unit staff, medical equipment resources, etc.

According to an embodiment of the present invention, the user medical data path and the simulated medical data path are evaluated for service quality at each link, and an effectiveness evaluation value of each link is generated, and the first evaluation data is generated based on all the effectiveness evaluation values, specifically:

Taking the user as the analysis unit, based on the user's medical treatment data path and the simulated medical treatment data path, the user's actual data path and the simulated data path are compared and analyzed. The comparative analysis process compares each link separately, and the data comparison and service deviation analysis are performed to generate the effectiveness evaluation value of each link;

Integrate the effectiveness evaluation values of each link to form a user's service effectiveness evaluation information;

An integrated evaluation and analysis of the medical service is performed based on the service effectiveness evaluation information of all users to obtain first evaluation data.

It should be noted that the said all users refer to (patient) users of the target medical unit within a preset time period.

According to an embodiment of the present invention, the user interaction data, user feedback data, and medical service data of the target medical unit are integrated to form a first data set; the corresponding interaction and feedback data in the remaining preset medical units are integrated to form a second data set, and the first data set and the second data set are used as sample data to perform association analysis based on the Apriori algorithm, and the associated data set is extracted through the generated association rules, specifically:

Integrate, clean, and process outliers the user interaction data, user feedback data, and medical service data of the target medical unit to form a first data set;

Integrate the corresponding interaction and feedback data in the remaining preset medical units to form a second data set;

The first data set and the second data set are used as sample data, the sample data are converted into candidate item sets, and the association analysis based on the Apriori algorithm is performed, the minimum support and the minimum confidence are set, and frequent item sets and association rules are screened and analyzed from the candidate item sets;

Through frequent item sets and association rules, data with association are screened out from the first data set and the second data set, and marked as associated data sets.

It should be noted that the data processing process of the second data set is consistent with that of the first data set, but the data sources are different. The corresponding interaction and feedback data are user interaction data, user feedback data, and medical service data.

It is worth mentioning that in smaller medical units or in some newly added medical units, due to the small amount of data generated, it is often difficult to mine the service evaluation value of user interaction feedback data, and the effectiveness evaluation has certain limitations, and the results may be inaccurate. Therefore, through the present invention, the medical data collected by the target medical unit and the other units are first integrated to form a data set. Through the integration of the data set, the data range can be expanded. Further, based on the Apriori algorithm, data items with correlation can be screened out from the data set. This process realizes the full value mining of medical data and extracts the associated data. For example, in other medical units, there are some data that are consistent or similar to the target medical unit in the collected user interaction and feedback data. At this time, part of its data can be mined and extracted through the association algorithm to form an associated data set. Next, secondary screening is performed based on semantic analysis, and the screened data path information is used as the predicted service data of the target medical unit. The predicted service data has a high correlation and service feature correlation with the target medical unit. As its predicted data, it can effectively perform data mining and service effectiveness evaluation for the target medical unit based on the medical big data of the other units.

By dividing the first and second data sets, the present invention can facilitate the location analysis of associated data, and can grasp whether the associated item set belongs to the first data set or the second data set, and can perform corresponding data division and proportion analysis, which is helpful to evaluate the data mining potential of the target medical unit and the preset medical unit. The associated data set includes a partial item set in the first and second data sets.

According to an embodiment of the present invention, the method of generating an associated medical visit data path based on an associated data set, performing semantic analysis on the associated medical visit data path and the user medical visit data path, and filtering data based on semantic similarity to obtain the filtered associated medical visit data path is specifically as follows:

Analyze and count the medical service content based on the associated data set, generate the medical treatment data path corresponding to the user in the data, and obtain the associated medical treatment data path;

Perform text format conversion and semantic feature analysis on the associated medical treatment data path and the user medical treatment data path to form associated feature data and user feature data;

Based on the standard Euclidean distance, user feature data is used as standard data, the similarity between the associated feature data and the standard data is analyzed, a similarity threshold is set, and the associated feature data is filtered. Based on the filtering results, the associated medical data path is mapped to obtain the filtered associated medical data path.

It should be noted that the purpose of screening the associated medical data paths is to remove service medical data that differs greatly from the user medical data paths of the target medical unit from the associated medical data paths through the dimension of semantic analysis.

In the process of semantic analysis, semantic analysis is specifically performed on each link of the medical data path to form corresponding semantic feature data, thereby realizing a detailed analysis process. The purpose of the screening process is to extract data paths in the associated data that are similar to the medical services of the target medical unit. The semantic analysis can be performed using a CNN-based learning model. The screening result is feature data whose similarity is greater than a similarity threshold, and the corresponding data is mapped and screened from the corresponding associated medical data path based on the screened feature data.

According to an embodiment of the present invention, the present invention further includes:

In the medical service platform, based on the time period of an emergency medical event, the user's medical treatment data path of each preset medical unit is collected;

Integrate all user medical data paths to form medical service data;

Based on the Apriori algorithm, the internal data correlation analysis of medical service data is performed, and the associated data set is extracted through the generated association rules and marked as the warning associated data set;

Storing the warning-related data set in the database of the medical service platform;

In each preset time period, the user medical data path of the target medical unit is compared with the warning-related data set based on the medical service path, and medical warning information is generated based on the comparison results.

It should be noted that the medical emergencies include medical events for infectious diseases, seasonal high-incidence medical events, etc. In medical emergencies, medical units in a certain area often have certain similarities in medical services, such as the same type of cases, a sharp increase in the number of a certain disease, similarities in appointments for medical services, etc. Based on each emergency, the warning-related data set can be added and updated to dynamically adjust the warning data.

The present invention collects the user medical treatment data paths of medical units within the time period of the emergency event, analyzes the correlation data in the data path dimension, extracts the data with high correlation for storage, and in the subsequent real-time analysis, based on the early warning data, compares the user medical treatment data paths obtained by the real-time analysis with the early warning data for similarity. In the comparison process, service similarity comparison is performed based on multiple links of each data path. By comparing with the early warning-related data, the similarity between the current medical service and the medical service of the emergency situation can be grasped, so as to grasp whether there is a trend of emergency medical time by information technology, and realize the effectiveness evaluation of medical service early warning.

The third aspect of the present invention also provides a computer-readable storage medium, which includes a medical service effectiveness evaluation program. When the medical service effectiveness evaluation program is executed by a processor, the steps of the medical service effectiveness evaluation method as described in any one of the above items are implemented.

The present invention discloses a method, system and medium for evaluating the effectiveness of medical services. By constructing a medical service platform, user interaction, feedback and service data of the target medical unit are collected, the medical process analysis is performed, and the user medical data path is generated. The patient's medical treatment is simulated to generate a simulated data path, and the service quality is compared and evaluated. The target medical unit data is integrated into a first data set, and the second data set is formed with the data of other medical units. Apriori correlation analysis is performed to extract the associated data set. The associated medical data path is filtered through semantic analysis as the predicted service data, the service quality of each link is evaluated, and the predicted evaluation data is generated. The effectiveness evaluation of information-based medical services and the effectiveness service prediction are realized.

In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

A person skilled in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, etc. Various media that can store program codes.

Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims (9)

1. A method for evaluating the effectiveness of medical services, comprising: Based on multiple preset medical units, a medical service platform is constructed, and user interaction data, user feedback data, and medical service data of a target medical unit within a preset time period are collected through the medical service platform; Based on the user interaction data, user feedback data, and medical service data, a data statistical analysis of the user's medical consultation process is performed, and the medical consultation process includes multiple links to generate a user medical consultation data path; Count the number and type of patients in the target medical unit within a preset time period, simulate the number and type of patients in the medical service platform, and generate simulated medical data paths; Performing a service quality evaluation on each link of the user's medical treatment data path and the simulated medical treatment data path, and generating an effectiveness evaluation value for each link, and generating first evaluation data based on all the effectiveness evaluation values; Integrate the user interaction data, user feedback data, and medical service data of the target medical unit to form a first data set; integrate the corresponding interaction and feedback data in the remaining preset medical units to form a second data set, use the first data set and the second data set as sample data, perform association analysis based on the Apriori algorithm, and extract the associated data set through the generated association rules; Generate an associated medical visit data path based on the associated data set, perform semantic analysis on the associated medical visit data path and the user medical visit data path, and filter the data based on semantic similarity to obtain a filtered associated medical visit data path; The screened associated medical data paths are used as the predicted service data of the target medical unit, and the service quality of each link is evaluated to generate predictive evaluation data. 2. A medical service effectiveness evaluation method according to claim 1, characterized in that a medical service platform is constructed based on multiple preset medical units, and user interaction data, user feedback data, and medical service data of a target medical unit within a preset time period are collected through the medical service platform, specifically: Build a medical service platform based on multiple preset medical units; Establishing a network connection between the user terminal of the preset medical unit and the medical service platform; The medical service platform collects user interaction data, user feedback data, and medical service data of a target medical unit within a preset time period. 3. A medical service effectiveness evaluation method according to claim 2, characterized in that the data statistical analysis of the user's medical treatment process is performed based on the user interaction data, user feedback data, and medical service data. The medical treatment process includes multiple links, and the user medical treatment data path is generated, specifically: Divide the consultation process based on medical service data to form multiple links and corresponding service content data; Through user interaction data and user feedback data, data statistics and classification are performed on multiple links, and combined with service content data, data path information for each link is generated; Based on the queue structure, the data path information of each link is used as queue metadata and the medical data path is generated; Perform medical treatment path analysis on all users and generate user medical treatment data paths. 4. A medical service effectiveness evaluation method according to claim 3, characterized in that the number and type of patients in the target medical unit within a preset time period are counted, and in the medical service platform, a medical consultation simulation is performed based on the number and type of patients, and a simulated medical consultation data path is generated, specifically: Count the number and type of patients in the target medical unit within a preset time period; Generate simulated test user information based on the number of patients and patient types; In the medical service platform, statistics are collected on the real-time medical resources of the target medical unit within a preset time period; Medical service simulation is performed through real-time medical resources and simulated test user information, and a simulated medical treatment path is generated for each simulated user. The simulated medical treatment path data is stored in the form of a queue structure to form a simulated medical treatment data path. 5. A medical service effectiveness evaluation method according to claim 4, characterized in that the user's medical data path and the simulated medical data path are evaluated for service quality at each link, and an effectiveness evaluation value of each link is generated, and the first evaluation data is generated based on all the effectiveness evaluation values, specifically: Taking the user as the analysis unit, based on the user's medical treatment data path and the simulated medical treatment data path, the user's actual data path and the simulated data path are compared and analyzed. The comparative analysis process compares each link separately, and the data comparison and service deviation analysis are performed to generate the effectiveness evaluation value of each link; Integrate the effectiveness evaluation values of each link to form a user's service effectiveness evaluation information; An integrated evaluation and analysis of the medical service is performed based on the service effectiveness evaluation information of all users to obtain first evaluation data. 6. A medical service effectiveness evaluation method according to claim 5, characterized in that the user interaction data, user feedback data, and medical service data of the target medical unit are integrated to form a first data set; the corresponding interaction and feedback data in the remaining preset medical units are integrated to form a second data set, and the first data set and the second data set are used as sample data to perform association analysis based on the Apriori algorithm, and the associated data set is extracted through the generated association rules, specifically: Integrate, clean, and process outliers the user interaction data, user feedback data, and medical service data of the target medical unit to form a first data set; Integrate the corresponding interaction and feedback data in the remaining preset medical units to form a second data set; The first data set and the second data set are used as sample data, the sample data are converted into candidate item sets, and the association analysis based on the Apriori algorithm is performed, the minimum support and the minimum confidence are set, and frequent item sets and association rules are screened and analyzed from the candidate item sets; Through frequent item sets and association rules, data with association are screened out from the first data set and the second data set, and marked as associated data sets. 7. A medical service effectiveness evaluation method according to claim 6, characterized in that the associated medical data path is generated based on the associated data set, the associated medical data path and the user medical data path are semantically analyzed, and data is screened based on semantic similarity to obtain the screened associated medical data path, specifically: Analyze and count the medical service content based on the associated data set, generate the medical treatment data path corresponding to the user in the data, and obtain the associated medical treatment data path; Perform text format conversion and semantic feature analysis on the associated medical treatment data path and the user medical treatment data path to form associated feature data and user feature data; Based on the standard Euclidean distance, user feature data is used as standard data, the similarity between the associated feature data and the standard data is analyzed, a similarity threshold is set, and the associated feature data is filtered. Based on the filtering results, the associated medical data path is mapped to obtain the filtered associated medical data path. 8. A medical service effectiveness evaluation system, characterized in that the system comprises: a memory and a processor, wherein the memory comprises a medical service effectiveness evaluation program, and when the medical service effectiveness evaluation program is executed by the processor, the following steps are implemented: Based on multiple preset medical units, a medical service platform is constructed, and user interaction data, user feedback data, and medical service data of a target medical unit within a preset time period are collected through the medical service platform; Based on the user interaction data, user feedback data, and medical service data, a data statistical analysis of the user's medical consultation process is performed, and the medical consultation process includes multiple links to generate a user medical consultation data path; Count the number and type of patients in the target medical unit within a preset time period, simulate the number and type of patients in the medical service platform, and generate simulated medical data paths; Performing a service quality evaluation on each link of the user's medical treatment data path and the simulated medical treatment data path, and generating an effectiveness evaluation value for each link, and generating first evaluation data based on all the effectiveness evaluation values; Integrate the user interaction data, user feedback data, and medical service data of the target medical unit to form a first data set; integrate the corresponding interaction and feedback data in the remaining preset medical units to form a second data set, use the first data set and the second data set as sample data, perform association analysis based on the Apriori algorithm, and extract the associated data set through the generated association rules; Generate an associated medical visit data path based on the associated data set, perform semantic analysis on the associated medical visit data path and the user medical visit data path, and filter the data based on semantic similarity to obtain a filtered associated medical visit data path; The screened associated medical data paths are used as the predicted service data of the target medical unit, and the service quality of each link is evaluated to generate predictive evaluation data. 9. A computer-readable storage medium, characterized in that the computer-readable storage medium includes a medical service effectiveness evaluation program, and when the medical service effectiveness evaluation program is executed by a processor, the steps of the medical service effectiveness evaluation method as described in any one of claims 1 to 7 are implemented.