A UTV subject intelligent visit management system and method

Abstract

The present invention discloses a UTV subject intelligent visit management system and method, belonging to the field of medical information technology, wherein the method comprises: a subject characteristic information collection module: used for collecting subject characteristic information of multiple subjects; a subject representativeness analysis module: used for subject representativeness analysis; a compliance analysis module: used for compliance analysis of multiple subjects; an initial visit plan formulation module: used for initial visit plan formulation; a compliance change sequence acquisition module: used for conducting subject visits on the multiple subjects and continuously collecting compliance information of multiple subjects; an optimized visit plan acquisition module: used for processing and obtaining visit optimization steps for optimizing and adjusting the visit plans of multiple subjects, and continuing visit management. The present application solves the technical problems in the prior art that the management efficiency of UTV subjects is low and the data is inaccurate, resulting in insufficient reliability of experimental results.

Description

Technical Field

[0001] The present invention relates to the field of medical information technology, and in particular to a UTV subject intelligent visit management system and method. Background Art

[0002] Utility Theory (UTV) is widely used in fields such as economics, decision science, and psychology to study people's decision-making behaviors and preferences in different situations. With the development of medical and biological sciences, clinical trials and health management increasingly rely on accurate and efficient data collection and analysis. The management and visit process of subjects plays a vital role in these fields.

[0003] Traditional methods of subject visit management usually rely on manual records and offline communication, which is not only inefficient but also prone to inaccurate data and omissions. As a result, traditional visit management methods face many challenges, such as a large number of subjects, diverse data types, and difficulty in ensuring compliance. Summary of the invention

[0004] The present application provides a UTV subject intelligent visit management system and method, aiming to solve the technical problems in the prior art of low efficiency in UTV subject management and inaccurate data, which lead to insufficient reliability of experimental results.

[0005] In view of the above problems, the present application provides a UTV subject intelligent visit management system and method.

[0006] The first aspect disclosed in the present application provides a UTV subject intelligent visit management system, which includes a subject characteristic information collection module: used to collect subject characteristic information of multiple subjects undergoing UTV testing, wherein the subject characteristic information includes physical sign characteristic information and visit characteristic information; a subject representativeness analysis module: used to perform subject representativeness analysis based on multiple physical sign characteristic information to obtain multiple representative information; a compliance analysis module: used to perform compliance analysis of multiple subjects undergoing testing based on multiple subject characteristic information to obtain multiple basic compliance information; an initial visit plan formulation module: used to formulate an initial visit plan based on the multiple representative information and multiple The module comprises a basic compliance information, an initial visit plan, and a plurality of initial visit plans; a compliance change sequence acquisition module: used to conduct subject visits on the plurality of subjects according to the plurality of initial visit plans, and continuously collect the compliance information of the plurality of subjects, to obtain a plurality of compliance change sequences, and to calculate and obtain a plurality of compliance change information; an optimized visit plan acquisition module: used to process and obtain the visit optimization step for optimizing and adjusting the visit plans of the plurality of subjects according to the plurality of representative information and the plurality of compliance change information, perform visit optimization on the plurality of initial visit plans, obtain a plurality of optimized visit plans, and continue visit management.

[0007] Another aspect disclosed in the present application provides a UTV subject intelligent visit management method, the method comprising collecting subject characteristic information of multiple subjects undergoing UTV testing, wherein the subject characteristic information comprises physical sign characteristic information and visit characteristic information; performing subject representativeness analysis based on the multiple physical sign characteristic information to obtain multiple representative information; performing compliance analysis of multiple subjects undergoing testing based on the multiple subject characteristic information to obtain multiple basic compliance information; formulating initial visit plans based on the multiple representative information and the multiple basic compliance information to obtain multiple initial visit plans; performing subject visits on the multiple subjects according to the multiple initial visit plans, and continuously collecting compliance information of the multiple subjects to obtain multiple compliance change sequences, and calculating multiple compliance change information; processing and obtaining a visit optimization step for optimizing and adjusting the visit plans of the multiple subjects based on the multiple representative information and the multiple compliance change information, performing visit optimization on the multiple initial visit plans, obtaining multiple optimized visit plans, and continuing visit management.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] Since it integrates the functions of subject characteristic information collection, subject representativeness analysis, compliance analysis, initial visit plan formulation, compliance change sequence acquisition, and optimized visit plan acquisition, it collects the subject's physical signs and visit characteristic information, analyzes the subject's representativeness and compliance, formulates the initial visit plan, and optimizes and adjusts the visit plan according to the compliance changes in the actual visit, so as to realize the intelligent visit management of the subject and improve the visit effect and efficiency. It solves the technical problems in the prior art of low efficiency of UTV subject management and inaccurate data, which leads to insufficient reliability of experimental results.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A structural schematic diagram of a UTV subject intelligent visit management system is provided for an embodiment of the present application.

[0012] Figure 2 A flow chart of a UTV subject intelligent visit management method is provided for an embodiment of the present application.

[0013] Explanation of the reference numerals: module for collecting subject characteristic information 11, module for analyzing subject representativeness 12, module for analyzing compliance 13, module for formulating initial visit plan 14, module for acquiring compliance change sequence 15, module for acquiring optimized visit plan 16. DETAILED DESCRIPTION

[0014] The overall idea of ​​the technical solution provided by this application is as follows:

[0015] The embodiment of the present application provides a UTV subject intelligent visit management system and method. Through a series of modular functions, comprehensive and intelligent visit management of subjects is achieved. The system first ensures the comprehensiveness and accuracy of the data by collecting the subjects' physical signs and visit characteristic information. Then, using the subject representative analysis module and the compliance analysis module, the subjects are deeply analyzed to obtain representative and basic compliance information. Based on this information, the system can formulate an initial visit plan, and dynamically adjust and optimize the visit plan according to the changes in the subjects' compliance during the actual visit. Through modular design, the entire system realizes closed-loop management from data collection, analysis to program formulation and optimization, aiming to improve visit efficiency and ensure the safety of subjects and the reliability of data.

[0016] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0017] Embodiment 1

[0018] like Figure 1 As shown, the embodiment of the present application provides a UTV subject intelligent visit management system, the system comprising:

[0019] The subject characteristic information collecting module 11 is used to collect the subject characteristic information of multiple subjects undergoing the UTV test, wherein the subject characteristic information includes physical sign characteristic information and visit characteristic information.

[0020] Specifically, the subject's physical examination report and health data are obtained from the subject's authorized medical institution to obtain physical characteristics information. The physical characteristics information includes the subject's physiological health data, such as height, weight, heart rate, blood pressure, etc.; past medical history, that is, recording the subject's past medical history information, including diseases and treatments; family medical history, collecting information on whether there are hereditary or chronic diseases in the subject's family members; allergy history, recording the subject's allergies to any drugs, foods or other substances.

[0021] Design detailed questionnaires for the UTV test to collect feedback and answers from the subjects. These questionnaires can cover the subjects' living habits, psychological state, reactions to specific situations, etc. Use online survey tools (such as Google Forms, SurveyMonkey, etc.) to facilitate subjects to fill out the questionnaires at any time. The access characteristic information is obtained by conducting questionnaires related to the UTV test to obtain feedback and answers from the subjects. These questionnaires may include the subjects' living habits, psychological state, reactions to specific situations, etc.

[0022] By collecting and integrating the subjects' health and medical history information and questionnaire interview data in detail, we can fully understand the subjects' background and characteristics, providing rich data support for subsequent representative analysis and compliance analysis.

[0023] This module ensures a comprehensive understanding of each subject by collecting multi-dimensional subject information, thus laying a solid foundation for subsequent analysis and optimization of the UTV subject intelligent visit management system.

[0024] The subject representativeness analysis module 12 is used to perform subject representativeness analysis based on multiple physical sign characteristic information to obtain multiple representative information.

[0025] Specifically, based on the specific test content, determine which physical characteristics are important and representative in the test results. For example, for a specific test, the test results of middle-aged subjects may be more representative. Combined with the physical characteristic information, preliminarily analyze the representativeness of each subject. For example, analyze which subjects' characteristics are more consistent with the characteristics of the test target group. For subjects with similar physical characteristics, the more there are, the higher their representativeness. Use the cluster quantity information to correct the preliminary representativeness to ensure that the representativeness information is more accurate.

[0026] Ensure that the subject sample can represent the target group in the UTV test, thereby improving the reliability and applicability of the test results and providing an objective and representative analysis basis for compliance analysis and visit plan formulation.

[0027] This module can effectively analyze the representativeness of the subjects, ensure that the selected subject samples in the UTV test are sufficiently representative, and improve the accuracy and reliability of the experimental data.

[0028] The compliance analysis module 13 is used to perform compliance analysis on multiple subjects according to multiple subject characteristic information to obtain multiple basic compliance information.

[0029] Specifically, compliance refers to the degree to which the subject performs the test as required, which directly affects the accuracy of the test results. That is, high compliance means that the subject performs the test strictly as required, and the results are more reliable; low compliance may lead to inaccurate test results.

[0030] Collect the compliance data of subjects with the same representativeness and basic compliance in historical time. Establish a compliance analyzer, take the subject characteristic information as input and the historical compliance data as reference for analysis. Analyze the subject's compliance based on the physical sign characteristic information and calculate the physical sign compliance information. Analyze the subject's compliance based on the visit characteristic information and calculate the visit compliance information. Combine the physical sign compliance information and the visit compliance information to comprehensively calculate and obtain the basic compliance information.

[0031] By analyzing the compliance of the subjects, we can ensure that the test is carried out as required and improve the accuracy of the test results. This module can effectively analyze the compliance of the subjects, provide accurate basic compliance information, and preliminarily determine the current visit plan based on historical data, thereby improving the effectiveness and reliability of the UTV test.

[0032] The initial visit plan formulation module 14 is used to formulate an initial visit plan according to the multiple representative information and the multiple basic compliance information, and obtain multiple initial visit plans.

[0033] Specifically, the representative information and basic compliance information of the current subject are matched with the historical data to preliminarily determine the visit plan suitable for the current subject. Among multiple matching historical visit plans, the most commonly used and effective visit plan is selected by majority voting.

[0034] Find historical subjects with similar characteristics to the current subject. Count the frequency and effectiveness of the visit plans adopted by these historical subjects. Select the plan with the highest frequency and the best effect as the initial visit plan.

[0035] According to the preliminary visit plan, refine the visit plan, including visit time, visit frequency and specific visit content. Verify the preliminary plan to ensure its rationality and feasibility. Generate multiple initial visit plans, and record relevant data and analysis results as the basis for subsequent visit management. Among them, the initial visit plan refers to the preliminary visit plan formulated for the subject during the management of UTV subjects, based on the representative information and compliance information of the subject, combined with historical data.

[0036] This module can effectively formulate the initial visit plan, ensure the scientificity and rationality of the visit plan, and improve the management level of UTV testing and the reliability of data.

[0037] The compliance change sequence acquisition module 15 is used to conduct subject visits on the multiple subjects according to the multiple initial visit plans, and continuously collect the compliance information of the multiple subjects, obtain multiple compliance change sequences, and calculate and obtain multiple compliance change information.

[0038] Specifically, multiple subjects are visited regularly according to the initial visit plan. The visit methods include: face-to-face visits, telephone visits, video visits, etc. During the visit, the subject's compliance information is collected in real time. The subject's specific performance and feedback during the visit are recorded, such as compliance with the visit time and acceptance of the visit method.

[0039] Among them, the reasons for changes in compliance include: Visit time: The subject's compliance may change because the visit time does not meet his or her subjective wishes (such as during rest time). Visit frequency: Too high a visit frequency may make the subject feel overburdened, thus affecting compliance. Visit method: Certain visit methods (such as video visits) may make the subject feel uncomfortable or offended, affecting compliance.

[0040] Continuously record the changes in the subjects' compliance during each visit to form a compliance change sequence. For example, record the changes in the accuracy of the dosage during each visit. Perform statistical analysis on the collected compliance change sequence to calculate the specific compliance change information. Analyze the trend of the subjects' compliance changes to determine which factors have a significant impact on compliance.

[0041] By continuously collecting and analyzing the changes in the subjects' compliance, factors affecting compliance can be discovered and resolved in a timely manner. This provides a scientific basis for optimizing and adjusting the visit plan, ensuring that the visit plan better meets the needs of the subjects and improving the reliability of the test results.

[0042] Through the above steps, the module can effectively monitor and analyze the changes in subjects' compliance, identify and resolve key factors affecting compliance, and provide reliable data support for the optimization and adjustment of visit plans, thereby improving the effectiveness of UTV testing and subjects' compliance.

[0043] The optimized visit plan acquisition module 16 is used to process and obtain the visit optimization step for optimizing and adjusting the visit plans of multiple subjects according to the multiple representative information and the multiple compliance change information, optimize the visits of the multiple initial visit plans, obtain multiple optimized visit plans, and continue the visit management.

[0044] Specifically, the visit optimization step size refers to the amplitude of each adjustment of the visit time, frequency and method during the optimization of the visit plan. The greater the representative information, the greater the compliance change information, the smaller the step size, and the more refined the adjustment. The smaller the step size, the more detailed the optimization adjustment, which can better improve the accuracy of the visit plan.

[0045] The number of optimization convergences refers to the maximum number of iterations required for the optimization process. The greater the representative information, the greater the number of optimization convergences, ensuring that the optimization process can converge more fully and improve the accuracy of the optimization results. The greater the number of optimization convergences, the more times the solution will be adjusted during the optimization process to ensure a more accurate optimization result.

[0046] According to the initial visit plan, the first round of optimization is carried out in combination with representative information and compliance change information. Ensure that the visit time is in line with the subjective wishes of the subjects and avoid visiting at inconvenient time periods for the subjects. According to the feedback of the subjects and changes in compliance, adjust the visit frequency appropriately to avoid excessive frequency leading to a decrease in subject compliance. According to the preferences of the subjects, choose the most appropriate visit method (such as face-to-face visit, telephone visit, video visit).

[0047] Perform multiple rounds of optimization adjustments based on the set visit optimization step and optimization convergence times. In each round of optimization, further adjust the visit plan based on the latest compliance change information. Gradually reduce the visit optimization step to ensure that the optimization process is gradually refined and ultimately reach the optimal visit plan. Within the optimization convergence times, generate multiple optimized visit plans through continuous adjustments. Verify the optimized visit plan to ensure its feasibility and effectiveness.

[0048] Apply the optimized visit plan to the visit management of the subjects, continuously monitor and collect the subjects' compliance information. According to the new compliance data, further adjust and optimize the visit plan to form a closed-loop optimization management.

[0049] Through refined optimization and adjustment, the scientific nature and pertinence of the interview plan can be improved, ensuring the compliance of the subjects and the reliability of the test results. The accuracy of the test results of the more representative subjects can be improved, thereby improving the effectiveness and reliability of the entire UTV test.

[0050] Through the above steps, the module can effectively process and optimize the visit plan, ensure that the visit time, frequency and method are more in line with the needs of the subjects, and improve compliance. Finally, through multiple rounds of fine-tuning, an optimized visit plan is obtained to improve the management level of UTV testing and the reliability of data.

[0051] Furthermore, the subject characteristic information collection module is also used to: collect physical sign data and past record data of multiple subjects undergoing UTV tests to obtain multiple physical sign characteristic information; collect questionnaire interview data of multiple subjects to obtain multiple interview characteristic information; integrate the multiple physical sign characteristic information and multiple interview characteristic information to obtain multiple subject characteristic information.

[0052] Specifically,

[0053] Furthermore, the subject representativeness analysis module is also used to: obtain the test content of the UTV test item and obtain test characteristic information; perform subject representativeness analysis on the multiple physical sign characteristic information according to the test characteristic information to obtain multiple basic representative information, wherein a subject representativeness analyzer is trained by collecting sample test characteristic information, sample physical sign characteristic information and sample basic representative information to obtain multiple basic representative information through analysis; cluster the multiple physical sign characteristic information to obtain multiple physical sign clustering results and multiple cluster numbers; according to the size of the multiple cluster numbers, correct and calculate the basic representative information corresponding to the physical sign characteristic information in the multiple physical sign clustering results to obtain multiple representative information, wherein the size of the cluster number is positively correlated with the size of the corrected representative information.

[0054] Specifically, we first obtain the specific test content of the UTV test project to understand the project's goals, scope, and requirements. Based on the test content, we extract relevant test feature information, which is the basis for representative analysis, including the various test indicators and test methods required in the project.

[0055] According to the extracted test content, relevant test feature information is identified. Test feature information includes but is not limited to test item parameters, test environment, test object requirements, etc. The identified test feature information is classified and sorted to ensure that each test feature information corresponds to specific test content.

[0056] For example, the UTV test project involves cardiovascular health tests, including blood pressure, heart rate and blood lipid level measurements. Blood pressure measurement, test parameters: systolic pressure, diastolic pressure; test environment: sitting state, standard measurement equipment; test subject requirements: subjects must not drink coffee or tea within 30 minutes before the test. Heart rate measurement, test parameters: resting heart rate, heart rate after exercise; test environment: quiet environment, standard heart rate monitoring equipment; test subject requirements: subjects need to remain quiet and avoid emotional fluctuations.

[0057] Collect a large amount of sample data, covering the test characteristic information of different subjects, and collect the physical sign characteristic information of the sample subjects. Record the basic representative information of the sample subjects, which is used to evaluate and train the representative analyzer, such as the representative level, the accuracy of the test results, etc. Input the collected sample test characteristic information, sample physical sign characteristic information and sample basic representative information into the analyzer. Use these data to train the subject representative analyzer so that it can accurately identify and analyze the representativeness of different physical sign characteristic information under different test contents. According to the trained subject representative analyzer, analyze the physical sign characteristic information of the current subject. Through analysis, obtain multiple basic representative information. This information includes the representative level or representative score of the subject under specific test content.

[0058] Among them, the subject representativeness analyzer is used to analyze the representativeness of the subject under a specific test content based on the subject's physical sign characteristic information and test characteristic information. Specifically, remove noise and outliers in the sample data to ensure data quality. Standardize data of different dimensions to ensure the comparability of each characteristic data. Select a suitable machine learning algorithm, such as decision tree, random forest, support vector machine, neural network, etc., to train the subject representativeness analyzer. Use the collected sample data (test characteristic information, physical sign characteristic information and basic representative information) as a training data set. Evaluate the performance of the model and select the best model parameters through the cross-validation method. Use an independent validation data set to evaluate the generalization ability of the model to ensure the model's prediction accuracy for new data. Adjust the model parameters according to the validation results to improve model performance.

[0059] For example: sample test feature information includes the environment of blood pressure testing, conditions for heart rate measurement, etc. Sample physical sign feature information includes age, gender, health status, etc. Sample basic representative information, such as representative level (high, medium, low) or specific representative score (for example, a score from 1 to 10). Representative analyzer training, first, use machine learning algorithms (such as decision trees, support vector machines, etc.) to train the collected sample data to ensure that the analyzer can accurately predict the representative information of the subject. Then input the physical sign feature information of the current subject, use the analyzer to analyze it, and obtain the basic representative information of the subject under the current test content.

[0060] Then, cluster analysis is performed on the multiple physical sign feature information to form multiple physical sign clustering results. The number of clusters of each clustering result is determined, that is, the number of physical sign features included in each cluster.

[0061] According to the size of each cluster, the corresponding basic representative information is corrected and calculated. The larger the number of clusters, the larger the correction value of the representative information. The number of clusters is positively correlated with the size of the corrected representative information, that is, the larger the number of clusters, the higher the corrected representative information. Through the correction calculation, multiple final representative information is obtained, which will be used for the subsequent visit plan formulation and optimization.

[0062] By analyzing the physical characteristics and test characteristics of the subjects, accurate representative information can be obtained to ensure the representativeness and effectiveness of the test results. This provides a scientific basis for the formulation and optimization of visit plans and improves the accuracy and effectiveness of subject management. Through the above steps, the subject representativeness analysis module can effectively obtain and analyze the physical characteristics and test characteristics of the subjects and generate accurate representative information. Combined with cluster analysis and correction calculation, the accuracy and reliability of representative information can be ensured, providing solid data support for the formulation and optimization of subsequent visit plans.

[0063] Furthermore, the compliance analysis module is also used to: collect a sample subject feature information set and a sample compliance information set based on historical UTV subject record data, wherein the sample subject feature information set includes a sample vital sign feature information set and a sample access feature information set; respectively use the sample vital sign feature information set and the sample access feature information set as input, use the sample compliance information set to train a vital sign compliance analysis branch and an access compliance analysis branch, and obtain a compliance analyzer; use the compliance analyzer to perform compliance analysis on multiple vital sign feature information and multiple access feature information, obtain multiple vital sign compliance information and multiple access compliance information, and calculate to obtain multiple basic compliance information.

[0064] Specifically, historical UTV test record data are collected, including the subjects' compliance performance and related visit plans. Sample test characteristics information set: including sample physical characteristics information set (health status, age, gender, past medical history, etc.) and sample visit characteristics information set (questionnaire survey results, visit frequency and time, etc.). Sample compliance information set: records the compliance performance of sample subjects under different visit plans, such as taking medicine on time, regular visits, etc.

[0065] Remove noise and outliers from sample data to ensure data quality. Standardize data of different dimensions to ensure comparability of each feature data. Select appropriate machine learning algorithms, such as logistic regression, decision tree, random forest, neural network, etc., to train the compliance analyzer, which is used to analyze and predict the compliance information of subjects. The compliance analyzer can accurately predict the compliance of new subjects by learning the physical sign feature information and access feature information of subjects in historical data, as well as their compliance performance.

[0066] Use the sample physical sign feature information set and the sample compliance information set to train the physical sign compliance analysis branch to identify the impact of physical sign features on compliance. Use the sample visit feature information set and the sample compliance information set to train the visit compliance analysis branch to identify the impact of visit features on compliance. Use an independent validation dataset to evaluate the generalization ability of the model to ensure the model's prediction accuracy for new data. Adjust the model parameters based on the validation results to improve the model performance. Input the current subject's physical sign feature information and visit feature information into the trained compliance analyzer. The compliance analyzer outputs the current subject's physical sign compliance information and visit compliance information, and calculates the basic compliance information.

[0067] For example: collect data on the subject's health status, age, gender, past medical history, etc. Collect data such as questionnaire survey results, visit frequency, and visit time. Record the subject's compliance performance under different visit plans, such as taking medication on time, regular visits, etc. Select the random forest algorithm and train the physical sign feature information set and the visit feature information set separately. Select the best parameters through cross-validation and evaluate the model performance. Input the physical sign feature information and visit feature information of the new subject into the trained random forest model. The model outputs the subject's physical sign compliance information and visit compliance information, and calculates the basic compliance information.

[0068] By training and learning a large amount of sample data, the compliance analyzer can accurately predict the compliance information of the subjects. Based on the data-driven approach, the scientificity and reliability of subject management and visit plan formulation are improved. Through the above steps, the compliance analysis module can effectively use sample data to train the compliance analyzer, and perform compliance analysis on the subject's physical sign characteristic information and visit characteristic information to obtain multiple basic compliance information. This information provides a solid data foundation for the subsequent visit plan formulation and optimization, ensuring the scientificity of the entire process and the accuracy of the data.

[0069] Furthermore, the initial visit plan formulation module is also used to: perform historical subject indexing based on the multiple basic compliance information and multiple representative information to obtain multiple matching subject sets; and perform majority voting based on the visit plan sets adopted by the multiple matching subject sets to obtain multiple initial visit plans.

[0070] Specifically, the subjects in the historical data are indexed and matched using the existing basic compliance information and representative information. Basic compliance information includes the subject's compliance level or score. Representative information reflects the representativeness of the subject's characteristics in a specific test, such as age, health status, past compliance performance, etc. Specifically, the basic compliance information and representative information are used for algorithm matching to find subjects in the historical data with similar characteristics to the current subject. Matching criteria, such as similar age group, similar health status, similar compliance performance, etc.

[0071] Summarize the visit plans used in the set of matched subjects. The visit plan set includes visit time, visit frequency, visit method, etc. Through the majority voting method, select the most widely used and best-effective visit plan in the matched subjects as the initial visit plan. Specifically, list all the visit plans in the set of matched subjects. Vote on these visit plans and select the plan with the most votes as the initial visit plan. Make sure that the selected initial visit plan has been verified in the matched subjects and has good results.

[0072] For example, there is a new subject, aged 35, of normal weight, and without major medical history. Based on his basic compliance information and representative information, the system finds 20 subjects with similar characteristics in the historical data. Among these 20 historical subjects, the visit plans they adopted are analyzed. Assume that 15 people adopt plan A and 5 people adopt plan B. Through majority voting, plan A is selected as the initial visit plan for the current subject.

[0073] Based on historical data and majority voting mechanism, we select verified effective plans to improve the visit effect. By matching historical subjects with similar characteristics, we can formulate personalized visit plans for new subjects. We use a large amount of historical data and statistical analysis methods to ensure the scientificity and reliability of the initial visit plan. We reduce the trial and error cost of formulating the initial visit plan and improve the visit efficiency. We arrange the visit time and frequency reasonably and optimize the allocation of visit resources. Through personalized visit plans, we can improve the compliance and test effect of subjects.

[0074] Furthermore, the optimized visit plan acquisition module is also used to: determine whether the multiple compliance change information is greater than the compliance change threshold, if not, not process the acquisition of visit optimization step and optimization; if so, set multiple basic step correction coefficients for correcting the preset adjustment step of the visit plan according to the change amplitude of the multiple compliance change information, wherein the preset adjustment step includes the time adjustment step and frequency adjustment step for adjusting the visit time and visit frequency; adjust the multiple basic step correction coefficients according to the size of multiple representative information to obtain multiple step correction coefficients; use the multiple step correction coefficients to correct and calculate the preset adjustment step to obtain multiple visit optimization steps.

[0075] Specifically, the compliance change threshold is a standard value used to determine whether the visit plan needs to be optimized and adjusted. Specifically, by analyzing the compliance change data of all subjects in the past year, the mean and standard deviation of the compliance change are determined. The compliance change threshold is set to the mean plus one standard deviation, that is, threshold = mean + standard deviation. When formulating a visit plan, a reasonable compliance change threshold is set in advance. For example, for some important test items, the threshold can be set to 0.5, that is, if the compliance change exceeds 50%, the visit plan needs to be optimized and adjusted.

[0076] The module first determines whether the multiple compliance change information is greater than the preset compliance change threshold. If the change information does not exceed the threshold, the visit optimization step length acquisition and visit plan optimization adjustment will not be performed.

[0077] If the change information exceeds the threshold, multiple basic step correction coefficients are set to correct the preset adjustment step of the visit plan adjustment according to the change amplitude of the compliance change information. The preset adjustment step includes the time adjustment step and the frequency adjustment step for adjusting the visit time and the visit frequency. According to the size of multiple representative information, multiple basic step correction coefficients are adjusted to obtain multiple step correction coefficients. The larger the representative information, the smaller the corrected step correction coefficient, thereby improving the optimization accuracy of the visit plan for subjects with greater representativeness. Using multiple step correction coefficients, the preset adjustment step is corrected and calculated to obtain multiple visit optimization step sizes.

[0078] Among them, the changes in the subject's compliance (such as attending visits on time, complying with visit requirements, etc.) during the follow-up process. If the change is large, it means that the current visit plan needs to be adjusted. The inverse of the ratio of the change amplitude to the average change amplitude is used as the basic step correction coefficient. The larger the change amplitude, the smaller the basic step correction coefficient, indicating that more subtle adjustments are needed. Represents the importance of the subject in a specific test. The greater the representative information, the more reference value the subject's test results have. The smaller the corrected step length, the more refined the optimization adjustment. For example, the inverse of the ratio of each representative information to the average representative information is multiplied by the basic step correction coefficient for adjustment. The greater the representative information, the smaller the corrected step length.

[0079] For example: The compliance change information of subject A is 0.8, and the threshold is 0.5. 0.8>0.5, optimization adjustment is performed. The change range is 0.8, and the average change range is 0.6. Basic step correction coefficient = 1 / (0.8 / 0.6) = 0.75. The representative information of subject A is 0.9, and the average representative information is 0.7. Step correction coefficient = basic step correction coefficient * (1 / (0.9 / 0.7)) = 0.75 * 0.78 = 0.585. The preset adjustment step is 2 days. Optimization step = 2 * 0.585 = 1.17 days.

[0080] Based on this step, the optimized visit plan acquisition module can dynamically adjust the visit time, frequency and method, thereby improving subject compliance and the accuracy of test results.

[0081] Furthermore, the optimized visit plan acquisition module is also used to: obtain the visit time space, visit frequency space and visit channel space, and combine them to obtain the visit plan space; according to the size of the multiple representative information, the preset optimization convergence times are corrected and calculated to obtain multiple optimization convergence times, wherein the size of the representative information is positively correlated with the size of the corrected optimization convergence times; within the visit plan space, according to the multiple visit optimization step lengths, the initial visit plan is adjusted respectively to obtain multiple first visit plans; according to the multiple first visit plans, combined with multiple subject feature information, compliance analysis is performed to obtain multiple first compliance information, wherein a sample visit plan set, a sample subject feature information set and a sample compliance information set are collected, a compliance analyzer is trained, and multiple first compliance information is analyzed to obtain; continue to adjust and optimize the multiple first visit plans according to the multiple visit optimization step lengths until multiple optimization convergence times are reached respectively, and output multiple visit plans with the largest compliance information to obtain multiple optimized visit plans.

[0082] Specifically, first, we obtain the combination of visit time space, visit frequency space and visit channel space to form a multi-dimensional visit plan space. These spaces include different visit time points, frequencies and methods, such as visit arrangements in different time periods and the choice of using different channels (such as telephone, video, face-to-face) for visits.

[0083] According to the size of multiple representative information, the system will modify and calculate the preset optimization convergence times. The larger the representative information, the more the modified optimization convergence times will be, which means that the system needs more optimization adjustments to ensure that the test plan for the subject is more accurate and suitable.

[0084] In the visit plan space, the system adjusts the initial visit plan according to the multiple visit optimization steps. These steps are calculated based on the compliance change information and representative information, and are used to adjust parameters such as visit time, frequency, and channel to optimize the initial visit plan.

[0085] For each adjusted first visit plan, the system combines multiple subject feature information to perform compliance analysis. This includes collecting a sample visit plan set, a subject feature information set, and a compliance information set, and obtaining multiple first compliance information by training a compliance analyzer. This information is used to evaluate the compliance level of each visit plan.

[0086] The system continues to further adjust and optimize the first visit plan according to multiple visit optimization steps. This process is repeated until the system reaches multiple corrected optimization convergence times. Finally, the system outputs multiple visit plans with the largest compliance information, which are regarded as optimized visit plans to ensure that the subject's compliance and test effect are in the best state.

[0087] This step is carried out through multiple iterations and adjustments, combined with representative information and compliance analysis, to ensure that the test plan for each subject can maximize the experimental requirements and the individual needs of the subject, thereby improving the accuracy and effectiveness of the overall test.

[0088] In summary, the UTV subject intelligent visit management system provided by the embodiment of the present application has the following technical effects:

[0089] 1. By collecting the physical sign characteristic information and visit characteristic information of multiple subjects, multiple representative information is obtained by subject representative analysis, and then basic compliance information is obtained by combining compliance analysis. The system formulates an initial visit plan based on this information, and continuously optimizes the visit plan to adapt to the changes in the subject's compliance. By calculating the compliance change information and representative information, the system adjusts the visit optimization step, and then optimizes the initial visit plan, and finally outputs the optimized visit plan with maximized compliance to improve the test effect and management efficiency of the subjects.

[0090] 2. Collect the physical sign data and past record data of multiple subjects to obtain physical sign characteristic information, and collect questionnaire access data to obtain access characteristic information, and integrate this information to form subject characteristic information. The system performs representative analysis based on the physical sign characteristic information, obtains basic representative information through training and analysis, and corrects the accuracy of the representative information based on the clustering results. These steps ensure that the system can effectively analyze the characteristics and representativeness of the subjects, and provide an accurate basis for the formulation and optimization of subsequent visit plans.

[0091] 3. Perform representative analysis based on physical sign feature information, obtain multiple representative information, and train a representative analyzer for data analysis based on the test feature information and physical sign feature information. The system uses a clustering method to analyze physical sign feature information, correct and optimize representative information, and ensure that the test plan of the subject can accurately reflect his or her individual characteristics and needs, thereby improving the accuracy and effectiveness of the test.

[0092] Embodiment 2

[0093] Based on the same inventive concept as the UTV subject intelligent visit management system in the aforementioned embodiment, Figure 2 As shown, the embodiment of the present application provides a UTV subject intelligent visit management method, the method comprising:

[0094] Collecting subject characteristic information of multiple subjects undergoing UTV testing, wherein the subject characteristic information includes physical sign characteristic information and visit characteristic information;

[0095] Based on multiple physical sign characteristic information, conduct representative analysis of the subjects to obtain multiple representative information;

[0096] According to multiple test characteristic information, a test compliance analysis of multiple subjects is performed to obtain multiple basic compliance information;

[0097] Formulate an initial visit plan based on the multiple representative information and the multiple basic compliance information to obtain multiple initial visit plans;

[0098] According to the multiple initial visit plans, the multiple subjects are visited, and the compliance information of the multiple subjects is continuously collected to obtain multiple compliance change sequences, and multiple compliance change information is calculated;

[0099] According to the multiple representative information and the multiple compliance change information, the visit optimization step for optimizing and adjusting the visit plans of multiple subjects is obtained, the multiple initial visit plans are optimized, multiple optimized visit plans are obtained, and the visit management is continued.

[0100] Furthermore, the embodiment of the present application also includes:

[0101] Collecting physical sign data and past record data of multiple subjects undergoing UTV testing to obtain multiple physical sign characteristic information;

[0102] Collecting questionnaire access data of multiple subjects to obtain multiple access characteristic information;

[0103] The plurality of physical sign characteristic information and the plurality of visit characteristic information are integrated to obtain a plurality of subject characteristic information.

[0104] Furthermore, the embodiment of the present application also includes:

[0105] Obtain the test content of the UTV test item and obtain the test feature information;

[0106] According to the test characteristic information, subject representativeness analysis is performed on the multiple physical sign characteristic information to obtain multiple basic representative information, wherein a subject representativeness analyzer is trained by collecting sample test characteristic information, sample physical sign characteristic information and sample basic representative information, and multiple basic representative information is obtained by analysis;

[0107] Clustering the plurality of physical sign feature information to obtain a plurality of physical sign clustering results and a plurality of clustering quantities;

[0108] According to the size of multiple cluster numbers, the basic representative information corresponding to the physical sign characteristic information in the multiple physical sign clustering results is corrected and calculated to obtain multiple representative information, wherein the size of the cluster number is positively correlated with the size of the corrected representative information.

[0109] Furthermore, the embodiment of the present application also includes:

[0110] According to the historical UTV test record data, a sample test feature information set is collected, and a sample compliance information set is collected, wherein the sample test feature information set includes a sample vital sign feature information set and a sample visit feature information set;

[0111] The sample vital sign feature information set and the sample access feature information set are respectively used as input, and the sample compliance information set is used to train a vital sign compliance analysis branch and an access compliance analysis branch to obtain a compliance analyzer;

[0112] The compliance analyzer is used to perform compliance analysis on multiple vital sign characteristic information and multiple access characteristic information, to obtain multiple vital sign compliance information and multiple access compliance information, and to calculate and obtain multiple basic compliance information.

[0113] Furthermore, the embodiment of the present application also includes:

[0114] Performing historical subject indexing according to the multiple basic compliance information and the multiple representative information to obtain multiple matching subject sets;

[0115] A majority voting process is performed based on the visit plan sets adopted by the multiple matching subject sets to obtain multiple initial visit plans.

[0116] Furthermore, the embodiment of the present application also includes:

[0117] Determine whether the plurality of compliance change information is greater than a compliance change threshold, and if not, do not process obtaining the visit optimization step length and optimization;

[0118] If so, then according to the variation ranges of the plurality of compliance variation information, a plurality of basic step correction coefficients are set to correct the preset adjustment step of the visit plan adjustment, wherein the preset adjustment step includes a time adjustment step and a frequency adjustment step for adjusting the visit time and the visit frequency;

[0119] According to the sizes of the plurality of representative information, the plurality of basic step correction coefficients are adjusted to obtain a plurality of step correction coefficients;

[0120] The plurality of step length correction coefficients are used to perform correction calculations on the preset adjustment step lengths to obtain a plurality of visit optimization step lengths.

[0121] Furthermore, the embodiment of the present application also includes:

[0122] Obtain the visit time space, visit frequency space and visit channel space, and combine them to obtain the visit plan space;

[0123] According to the sizes of the plurality of representative information, a preset optimization convergence number is corrected and calculated to obtain a plurality of optimization convergence numbers, wherein the size of the representative information is positively correlated with the size of the corrected optimization convergence number;

[0124] In the visit plan space, the initial visit plans are adjusted respectively according to the multiple visit optimization step sizes to obtain multiple first visit plans;

[0125] According to the multiple first visit plans, combined with multiple subject feature information, compliance analysis is performed to obtain multiple first compliance information, wherein a sample visit plan set, a sample subject feature information set and a sample compliance information set are collected, a compliance analyzer is trained, and multiple first compliance information is obtained by analysis;

[0126] Continue to adjust and optimize the multiple first visit plans according to the multiple visit optimization step sizes, until multiple optimization convergence times are reached respectively, and output multiple visit plans with the largest compliance information to obtain multiple optimized visit plans.

[0127] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0128] Furthermore, the first or second mentioned above may not only represent an order relationship, but may also represent a specific concept, and / or refer to multiple elements that can be selected individually or in full. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A UTV subject intelligent visit management system, characterized in that: The system comprises: A module for collecting subject characteristic information: used to collect subject characteristic information of multiple subjects undergoing UTV testing, wherein the subject characteristic information includes physical sign characteristic information and visit characteristic information; Subject representativeness analysis module: used to perform subject representativeness analysis based on multiple physical sign characteristic information to obtain multiple representative information; Compliance analysis module: used to perform compliance analysis of multiple subjects according to multiple subject characteristic information to obtain multiple basic compliance information; An initial visit plan formulation module is used to formulate an initial visit plan according to the plurality of representative information and the plurality of basic compliance information, and obtain a plurality of initial visit plans; A compliance change sequence acquisition module: used to conduct subject visits on the multiple subjects according to the multiple initial visit plans, and continuously collect compliance information of the multiple subjects, obtain multiple compliance change sequences, and calculate and obtain multiple compliance change information; An optimized visit plan acquisition module is used to process and acquire the visit optimization step length for optimizing and adjusting the visit plans of multiple subjects according to the multiple representative information and the multiple compliance change information, optimize the visit plans of the multiple initial visit plans, obtain multiple optimized visit plans, and continue the visit management; Wherein, the representativeness analysis module is also used for: Obtain the test content of the UTV test item and obtain the test feature information; According to the test characteristic information, subject representativeness analysis is performed on the multiple physical sign characteristic information to obtain multiple basic representative information, wherein a subject representativeness analyzer is trained by collecting sample test characteristic information, sample physical sign characteristic information and sample basic representative information, and multiple basic representative information is obtained by analysis; Clustering the plurality of physical sign feature information to obtain a plurality of physical sign clustering results and a plurality of clustering quantities; According to the size of the multiple cluster numbers, the basic representative information corresponding to the physical sign characteristic information in the multiple physical sign clustering results is corrected and calculated to obtain multiple representative information, wherein the size of the cluster number is positively correlated with the size of the corrected representative information; Wherein, the compliance analysis module is also used for: According to the historical UTV test record data, a sample test feature information set is collected, and a sample compliance information set is collected, wherein the sample test feature information set includes a sample vital sign feature information set and a sample visit feature information set; The sample vital sign feature information set and the sample access feature information set are respectively used as input, and the sample compliance information set is used to train a vital sign compliance analysis branch and an access compliance analysis branch to obtain a compliance analyzer; Using the compliance analyzer, a plurality of vital sign characteristic information and a plurality of visit characteristic information are subjected to compliance analysis to obtain a plurality of vital sign compliance information and a plurality of visit compliance information, and a plurality of basic compliance information are obtained by calculation; Wherein, the initial visit plan formulation module is also used for: Performing historical subject indexing according to the multiple basic compliance information and the multiple representative information to obtain multiple matching subject sets; A majority voting process is performed based on the visit plan sets adopted by the multiple matching subject sets to obtain multiple initial visit plans.

2. The UTV subject intelligent visit management system according to claim 1, characterized in that: The module for collecting subject characteristic information is also used for: Collecting physical sign data and past record data of multiple subjects undergoing UTV testing to obtain multiple physical sign characteristic information; Collecting questionnaire access data of multiple subjects to obtain multiple access characteristic information; The plurality of physical sign characteristic information and the plurality of visit characteristic information are integrated to obtain a plurality of subject characteristic information.

3. The UTV subject intelligent visit management system according to claim 1, characterized in that: The optimized visit plan acquisition module is also used for: Determine whether the plurality of compliance change information is greater than a compliance change threshold, and if not, do not process obtaining the visit optimization step length and optimization; If so, then according to the variation ranges of the plurality of compliance variation information, a plurality of basic step correction coefficients are set to correct the preset adjustment step of the visit plan adjustment, wherein the preset adjustment step includes a time adjustment step and a frequency adjustment step for adjusting the visit time and the visit frequency; According to the sizes of the plurality of representative information, the plurality of basic step correction coefficients are adjusted to obtain a plurality of step correction coefficients; The plurality of step length correction coefficients are used to perform correction calculations on the preset adjustment step lengths to obtain a plurality of visit optimization step lengths.

4. The UTV subject intelligent visit management system according to claim 3, characterized in that: The optimized visit plan acquisition module is also used for: Obtain the visit time space, visit frequency space and visit channel space, and combine them to obtain the visit plan space; According to the sizes of the plurality of representative information, a preset optimization convergence number is corrected and calculated to obtain a plurality of optimization convergence numbers, wherein the size of the representative information is positively correlated with the size of the corrected optimization convergence number; In the visit plan space, the initial visit plans are adjusted respectively according to the multiple visit optimization step sizes to obtain multiple first visit plans; According to the multiple first visit plans, combined with multiple subject feature information, compliance analysis is performed to obtain multiple first compliance information, wherein a sample visit plan set, a sample subject feature information set and a sample compliance information set are collected, a compliance analyzer is trained, and multiple first compliance information is obtained by analysis; Continue to adjust and optimize the multiple first visit plans according to the multiple visit optimization step sizes, until multiple optimization convergence times are reached respectively, and output multiple visit plans with the largest compliance information to obtain multiple optimized visit plans.

5. A UTV subject intelligent visit management method, characterized in that: The method is applied to a UTV subject intelligent visit management system according to any one of claims 1 to 4, and the method comprises: Collecting subject characteristic information of multiple subjects undergoing UTV testing, wherein the subject characteristic information includes physical sign characteristic information and visit characteristic information; Based on multiple physical sign characteristic information, conduct representative analysis of the subjects to obtain multiple representative information; According to multiple test characteristic information, a test compliance analysis of multiple subjects is performed to obtain multiple basic compliance information; Formulate an initial visit plan based on the multiple representative information and the multiple basic compliance information to obtain multiple initial visit plans; According to the multiple initial visit plans, the multiple subjects are visited, and the compliance information of the multiple subjects is continuously collected to obtain multiple compliance change sequences, and multiple compliance change information is calculated; According to the multiple representative information and the multiple compliance change information, the visit optimization step for optimizing and adjusting the visit plans of multiple subjects is obtained, the multiple initial visit plans are optimized, multiple optimized visit plans are obtained, and the visit management is continued.

Images