Intelligent evaluation and feedback system for standardized training of resident physicians

Abstract

The invention relates to the technical field of medical education, and discloses an intelligent evaluation and feedback system for standardized training of an inpatient physician, and the system comprises a data collection module which is used for collecting evaluation data from the clinical operation record, the theoretical test score, the patient feedback and the teacher score of the inpatient physician; the evaluation module is used for calculating a comprehensive evaluation result of the resident physician according to the multi-target nonlinear optimization model, and the comprehensive evaluation result is obtained by weighting calculation of a plurality of evaluation dimensions; and the optimization control module is used for adjusting the training resource allocation and feedback strategy of the resident physician according to the result of the evaluation module, and the optimization control module adjusts the training process through an optimal control method. The multi-target optimization calculation is accelerated through the quantum calculation optimization module, and the optimization effect of improving the resource allocation efficiency and training feedback in the resident physician training process is achieved, so that the optimal resource allocation can be given in a shorter time, the resident physician training period is further shortened, and the real-time performance and flexibility of training are improved.

Description

Technical Field

[0001] The present invention relates to the field of medical education technology, and in particular to an intelligent evaluation and feedback system for standardized training of resident doctors. Background Art

[0002] Most of the existing standardized training and evaluation systems for resident doctors are based on traditional manual records, fixed evaluation criteria, and simple algorithms to evaluate the ability of residents. Generally, the electronic medical record system collects data such as clinical operation records, theoretical test scores, and patient feedback from residents. The evaluation system will output results based on these data, usually relying on fixed scoring criteria or templates for evaluation. Feedback is provided based on these evaluation results. Although it can provide certain guidance for residents, it often lacks the ability to be personalized and dynamically adjusted, and cannot flexibly adapt to the training needs and individual differences of residents.

[0003] However, the existing resident training system has significant deficiencies in dealing with personalized, dynamic evaluation and feedback. First, the evaluation standards in the existing technology are often unified and fixed, and lack the ability to adjust according to the specific abilities of the residents and the progress of the training. Secondly, the static nature of the feedback mechanism makes it impossible to adjust the feedback content in a timely manner according to the real-time performance of the residents. The feedback is usually delayed and not targeted enough, making it difficult to quickly respond to the progress or shortcomings of the residents. In addition, the traditional training resource allocation method relies on manual judgment, lacks an optimization mechanism, and cannot achieve efficient resource allocation. Finally, the existing computing methods are inefficient when processing complex and multi-dimensional data, and it is difficult to perform real-time optimization and adjustment in a big data environment. Therefore, the existing technology fails to fully meet the needs of dynamic, personalized, and efficient optimization in resident training. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an intelligent evaluation and feedback system for standardized training of resident physicians, which solves the problems of delayed evaluation and feedback, insufficient personalization, unreasonable allocation of training resources and low computing efficiency in the existing standardized training system for resident physicians.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent evaluation and feedback system for standardized training of resident doctors, comprising: A data collection module is used to collect evaluation data from residents' clinical operation records, theoretical examination scores, patient feedback, and mentor ratings; An evaluation module, used to calculate a comprehensive evaluation result of the resident according to a multi-objective nonlinear optimization model, wherein the comprehensive evaluation result is obtained by calculating a plurality of weighted evaluation dimensions; An optimization control module, used to adjust the training resource allocation and feedback strategy of the resident doctors according to the results of the evaluation module, and the optimization control module adjusts the training process through an optimal control method; A feedback generation module, which is used to generate a personalized training feedback plan for the resident based on the comprehensive evaluation results, and dynamically adjust the training content and strategy based on the results of each evaluation and the performance of the resident; A data storage and update module, which is used to store the evaluation data of the residents and update the evaluation results in real time according to the new training data. The data storage and update module also ensures the synchronization of data between modules; The quantum computing optimization module is used to accelerate and optimize computing tasks during evaluation and training to improve computing efficiency when processing large-scale data. The quantum computing optimization module uses a quantum annealing algorithm to solve multi-objective optimization problems.

[0006] Preferably, the evaluation module calculates the ability of residents based on a multi-objective nonlinear optimization model, and the evaluation model obtains a comprehensive evaluation value by weighting the results of each evaluation dimension, and each evaluation dimension includes clinical skills, theoretical knowledge, communication skills and teamwork ability.

[0007] Preferably, the comprehensive evaluation result calculated by the evaluation module satisfies the following conditions: The scoring range of all evaluation dimensions is limited to a predetermined interval, which is 0 to 100; The sum of the weights of each evaluation dimension is equal to 1, and the weights are dynamically allocated according to the clinical training stage and professional subject of the resident physician; Each time a new assessment is conducted, the assessment module dynamically adjusts the weights and competency scores of each assessment dimension according to the resident's current performance, and the adjustment process is based on the resident's historical data and real-time performance.

[0008] Preferably, the optimization control module dynamically adjusts the training process of the resident doctors according to the evaluation results of the resident doctors through the optimal control method, and the dynamic adjustment is based on the changes in the evaluation results, and the adjustment content includes training frequency, resource allocation and feedback intensity.

[0009] Preferably, the optimization control module establishes a dynamic model that describes the changes in the resident physician's ability status, and the dynamic model is modeled by a state equation, and the state equation is in the form of:

[0010] in, represents the competence state vector of the resident physician, is a control variable, which represents external intervention in the training process (such as feedback intensity, resource allocation, etc.), is a function that describes the variation in resident competence.

[0011] Preferably, the feedback generation module generates a personalized training feedback plan based on the comprehensive evaluation results, wherein the feedback plan includes specific training suggestions for each evaluation dimension, and dynamically adjusts the training content and frequency according to the performance and ability development of the resident through the feedback mechanism.

[0012] Preferably, the feedback generation module utilizes a Bayesian inference algorithm to update the resident's competence status and adjust the content of the training feedback accordingly when new data is acquired, wherein the adjustment includes dynamically updating the weights and intensities of various dimensions in the training program.

[0013] Preferably, the quantum computing optimization module uses a quantum annealing algorithm to perform optimization calculations on multi-objective optimization problems. The quantum annealing algorithm simulates the random motion of quantum particles to perform a global search to improve computing efficiency and accelerate the optimization process. The computing tasks in the optimization process include fast calculation of evaluation results and optimization adjustment of training tasks.

[0014] Preferably, the quantum computing optimization module is used to accelerate the calculation process in multi-dimensional evaluation and training tasks, and quickly optimize the evaluation results through quantum algorithms, reduce the time complexity of traditional calculation methods, and maintain calculation accuracy during the optimization process.

[0015] Preferably, the data acquisition module, evaluation module, optimization control module, feedback generation module and data storage and update module are connected through a data transmission interface, and the data transmission interface ensures data interaction and synchronization between the modules, wherein the data transmission interface adopts a standardized protocol for data exchange to ensure the security and consistency of the data.

[0016] The present invention provides an intelligent evaluation and feedback system for standardized training of resident doctors. It has the following beneficial effects: 1. The present invention accelerates multi-objective optimization calculations through a quantum computing optimization module, thereby achieving the optimization effect of improving resource allocation efficiency and training feedback in the resident physician training process. Compared with the prior art solutions that rely on traditional computing methods to handle complex multi-dimensional optimization tasks, the present invention can provide optimal resource allocation in a shorter time, thereby greatly shortening the resident physician training cycle and improving the real-time and flexibility of the training.

[0017] 2. The present invention successfully realizes the personalization and dynamic adjustment of comprehensive evaluation by applying a multi-objective nonlinear optimization model to the competence assessment of residents. Different from the fixed evaluation standards in the prior art, the present invention can dynamically adjust the evaluation dimensions and weights according to the actual performance and training progress of the residents, thereby providing more accurate training guidance that better meets individual needs.

[0018] 3. The present invention combines a feedback generation module with a real-time dynamic adjustment mechanism to ensure that residents can obtain personalized training feedback in a timely manner based on the evaluation results, thereby achieving more efficient and accurate training goal setting. Compared with the static method of the traditional feedback mechanism, the present invention can adapt to the progress changes of residents in real time, provide feedback for continuous improvement, and avoid the inefficiency caused by lagging or fixed training plans.

[0019] 4. The present invention improves the efficiency of training resource allocation by integrating quantum computing and optimal control theory. Compared with the traditional system that manually adjusts the training plan, the present invention can automatically optimize the training resource allocation of residents based on evaluation data and real-time feedback, thereby solving the inconsistency and inefficiency caused by manual adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the system module of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Please see attached Figure 1 The embodiment of the present invention provides an intelligent evaluation and feedback system for standardized training of resident doctors, including: A data collection module is used to collect evaluation data from residents' clinical operation records, theoretical examination scores, patient feedback, and mentor ratings; An evaluation module is used to calculate the comprehensive evaluation results of residents according to a multi-objective nonlinear optimization model. The comprehensive evaluation results are obtained by weighting multiple evaluation dimensions; An optimization control module is used to adjust the training resource allocation and feedback strategy of residents according to the results of the evaluation module. The optimization control module adjusts the training process through the optimal control method; A feedback generation module is used to generate personalized training feedback plans for residents based on the comprehensive evaluation results, and dynamically adjust the training content and strategies based on the results of each evaluation and the performance of the residents; The data storage and update module is used to store the evaluation data of the residents and update the evaluation results in real time according to the new training data. The data storage and update module also ensures the synchronization of data between modules; The quantum computing optimization module is used to accelerate and optimize the computing tasks in the evaluation and training process to improve the computing efficiency when processing large-scale data. The quantum computing optimization module uses the quantum annealing algorithm to solve multi-objective optimization problems; The evaluation module calculates the ability of residents based on a multi-objective nonlinear optimization model. The evaluation model obtains a comprehensive evaluation value by weighting the results of each evaluation dimension, and each evaluation dimension includes clinical skills, theoretical knowledge, communication skills, and teamwork skills. The comprehensive evaluation results calculated by the evaluation module meet the following conditions: The scoring range of all evaluation dimensions is limited to a predetermined interval, which is 0 to 100; The sum of the weights of each evaluation dimension is equal to 1, and the weights are dynamically allocated according to the resident's clinical training stage and specialty subject; Each time a new assessment is conducted, the assessment module dynamically adjusts the weights and competency scores of each assessment dimension based on the resident’s current performance. The adjustment process is based on the resident’s historical data and real-time performance. The optimization control module dynamically adjusts the training process of residents based on the evaluation results of the residents through the optimal control method. The dynamic adjustment is based on the changes in the evaluation results, and the adjustment content includes training frequency, resource allocation and feedback intensity. The optimization control module establishes a dynamic model to describe the changes in the state of resident physicians’ abilities. The dynamic model is modeled by a state equation, and the state equation is in the form of:

[0023] in, represents the competence state vector of the resident physician, is a control variable, which represents external intervention in the training process (such as feedback intensity, resource allocation, etc.), is a function that describes changes in resident competence; The feedback generation module generates personalized training feedback plans based on the comprehensive evaluation results. The feedback plan includes specific training suggestions for each evaluation dimension and dynamically adjusts the training content and frequency according to the performance and ability development of residents through the feedback mechanism. The feedback generation module uses the Bayesian inference algorithm to update the resident's competence status and adjust the content of the training feedback accordingly when new data is obtained. The adjustment includes dynamically updating the weights and strengths of each dimension in the training program. The quantum computing optimization module uses the quantum annealing algorithm to optimize the multi-objective optimization problem. The quantum annealing algorithm simulates the random motion of quantum particles to perform global search to improve computing efficiency and accelerate the optimization process. The computing tasks in the optimization process include fast calculation of evaluation results and optimization adjustment of training tasks. The quantum computing optimization module is used to accelerate the calculation process in multi-dimensional evaluation and training tasks, and quickly optimize the evaluation results through quantum algorithms, reducing the time complexity of traditional calculation methods and maintaining calculation accuracy during the optimization process; The data acquisition module, evaluation module, optimization control module, feedback generation module and data storage and update module are connected through a data transmission interface, which ensures data interaction and synchronization between modules. The data transmission interface uses standardized protocols for data exchange to ensure data security and consistency.

[0024] Specifically, in this embodiment, the data acquisition module is connected to the hospital's existing electronic medical record system, online examination platform and patient feedback system to collect relevant data of residents from multiple dimensions. Specifically, the clinical operation records of residents are updated in real time through the hospital's operation record system. Theoretical examination results are automatically collected through the online examination system. Patient feedback data is obtained through electronic questionnaires or interviews and stored in a structured form. The instructor's score is filled in by the training instructor through a special scoring system, and the data is directly transmitted to the data acquisition module for storage and processing.

[0025] In general, the data acquisition module uses a unified data interface protocol (such as HL7, FHIR, etc.) to standardize the data from different systems and data sources to ensure data compatibility between different data sources. These data are first transmitted through the interface standard and then enter the storage system after preliminary verification.

[0026] In some embodiments, the data acquisition module can communicate directly with external devices such as clinical equipment and surgical recorders to collect real-time data from actual clinical operations. For example, when a resident doctor performs an operation, the operation record can be connected to the system through the surgical equipment, recorded in real time and uploaded to the data acquisition module. The module can record information such as operation time, operation type, operation results, etc. through the docked sensor equipment, and transmit it to the back-end system for further analysis.

[0027] As an option, the data collection module can also collect patient satisfaction with resident physician services on a regular basis through questionnaires based on patient feedback. After standardized processing, the patient feedback data is automatically uploaded to the data collection module. The data collection module uses natural language processing technology to convert unstructured text data (such as patient evaluation of resident physician communication) into structured data for subsequent analysis.

[0028] Specifically, the data acquisition module will classify and store data according to the type, time of acquisition and source of data. These data will be transmitted to the evaluation module, optimization control module and feedback generation module through the data transmission interface to provide necessary information for subsequent analysis and decision-making. After each data update, the system will ensure data synchronization to ensure that the evaluation module can obtain the latest evaluation data.

[0029] In this embodiment, the data acquisition module not only needs to collect and store data, but also needs to ensure the accuracy and integrity of the data during the data transmission process. During the data transmission process, the collected data may be interfered by external factors, so a certain verification algorithm is required to verify the data. To ensure that the data is not lost or tampered with during the transmission process, the data acquisition module verifies the data using the following formula:

[0030] in: Indicates the validity of the data after verification; Indicates The collected data information, such as clinical operation records, theoretical scores, patient feedback, instructor scores, etc. It is the weight of the corresponding data type, which is used to characterize the importance of each type of data to the final evaluation result. ; is the total number of data types collected.

[0031] In this embodiment, the weight The system dynamically adjusts the weight according to the requirements of the resident's department and training stage. For example, in the primary stage, theoretical test scores and instructor scores may have a higher weight, while in the advanced stage, clinical operation records and patient feedback may have a higher weight. The application of data validity verification formula ensures the rationality and consistency of different types of data during transmission.

[0032] After each evaluation, the data collection module will collect a new round of evaluation data from various data sources, store and update it. In some embodiments, the data collection module will set a timed update mechanism to regularly obtain the latest data from data sources such as clinical operation records, theoretical test scores and patient feedback. After the data is updated, the system will synchronously update the data in the evaluation module to ensure that the evaluation process always uses the latest resident data.

[0033] For example, every time a resident completes a surgery or performs an operation, the data collection module automatically updates its operation record. Each updated operation record is immediately transmitted to the evaluation module and included in the resident's comprehensive evaluation results. For theoretical exam scores, the system regularly collects the latest exam scores from the online exam platform and updates them to the resident's historical score database.

[0034] In addition, the update mechanism of patient feedback is equally important. Every time a patient completes the feedback questionnaire, the data collection module will automatically upload and synchronize the feedback data to the system. The data collection module can ensure the timeliness of patient feedback data and help the evaluation module update the resident's communication ability score in real time.

[0035] In this embodiment, the evaluation module is responsible for comprehensively evaluating the resident doctors based on the multi-dimensional training data of the resident doctors collected from the data acquisition module using a multi-objective nonlinear optimization model, and generating a comprehensive ability score based on the evaluation results. In order to ensure that the resident doctors are evaluated in a timely and effective manner during the training process, the evaluation module must be able to process complex and dynamically changing training data, and provide accurate feedback by adjusting the dimension weights and evaluation algorithms.

[0036] The evaluation module of the present invention not only focuses on the individual competence dimensions of the resident, but also obtains a comprehensive evaluation result by comprehensively analyzing the performance of these dimensions. This process is multi-objective and is carried out in a dynamically changing environment. The weight and importance of each dimension will be adjusted as the resident's performance, training stage, and department requirements change. This design enables the system to adjust the evaluation plan according to the actual training situation of the resident, ensuring that the evaluation results reflect the actual ability of the resident, rather than just static data.

[0037] In this embodiment, the core of the evaluation module is a multi-objective nonlinear optimization model. Each resident will be evaluated on multiple competency dimensions during the training process, which usually include clinical skills, theoretical knowledge, communication skills, teamwork, etc. During the evaluation process, the scores of these dimensions will be aggregated into a comprehensive evaluation result by weighted average.

[0038] In order to ensure the accuracy and dynamic adaptability of the evaluation, each evaluation dimension of the evaluation module has an independent evaluation function, called .in, The number of the evaluation dimension. Represents the resident's performance feature vector. The calculation of the evaluation function depends on the resident's specific performance in this dimension. For example, the evaluation of clinical skills may depend on the success rate of surgery, the accuracy of operation, etc., while the evaluation of communication ability may depend on patient feedback, the fluency of doctor-patient communication, etc.

[0039] In general, the assessment module calculates the score for each assessment dimension in the following way:

[0040] in: Indicates that the resident physician The evaluation scores on each dimension; For the The weight of each assessment dimension reflects the relative importance of the dimension in the comprehensive ability assessment of residents; It is an evaluation function related to this evaluation dimension, which is calculated based on the performance data of the residents on this dimension.

[0041] Indicates the importance of a certain ability dimension to the comprehensive evaluation of residents at different training stages or departmental requirements. Specifically, clinical skills may account for a larger proportion in some stages, while theoretical knowledge may dominate in other stages; The value of the evaluation function is usually the specific performance data of the resident doctor converted into a numerical value. For clinical skills, it can be quantified by successful surgery rate, treatment effect, operation time, etc. For communication skills, the patient feedback score and the communication between the resident doctor and the patient are key parameters.

[0042] The comprehensive evaluation result of the resident is the weighted sum of the evaluation scores of all dimensions, and the calculation formula is as follows:

[0043] in: represents the resident's comprehensive assessment score; and The definition is the same as above; Represents the total number of assessment dimensions, typically multiple core competency dimensions of residency training.

[0044] Generally speaking, residency training is a dynamic process, and residents' performance will change over time. Therefore, the assessment module must be able to dynamically adjust the weights of the assessment dimensions. For example, in the initial stage of residency training, theoretical knowledge may account for a larger proportion; but as the residents' skills gradually improve, the weight of clinical skills may gradually increase.

[0045] In some embodiments, the evaluation module not only relies on the current performance of the resident, but also learns from historical data to dynamically adjust the weights of each dimension. Through machine learning methods, the system can automatically adjust the evaluation function and weights based on the performance trend of the resident.

[0046] For example, when the performance on a certain dimension is poor, the system may automatically increase the weight of that dimension and increase the training for that dimension. Conversely, when the performance on a certain dimension is significantly improved, the system will appropriately reduce the weight of that dimension to avoid over-emphasizing the existing capabilities.

[0047] The evaluation module's algorithm can analyze the most reasonable evaluation results based on multi-dimensional data. The evaluation module may use a deep learning algorithm based on back-propagation to learn the performance of residents in training and automatically adjust the weights based on the training data.

[0048] Specifically, the evaluation module can be executed through the following steps: The resident's score on each assessment dimension was obtained through the initial assessment; Pass the scores to the optimization control module to adjust the training strategy and resource allocation; After each new assessment, the machine learning model relearns and adjusts to accommodate the residents’ changing performance.

[0049] In this embodiment, the optimization control module uses the optimal control theory to optimize the training process of the resident doctors. The core idea of ​​this module is to ensure that the comprehensive ability of the resident doctors can be optimized within the specified time by adjusting the ability status of the resident doctors and external intervention factors (such as feedback intensity and resource allocation). To this end, the optimization control module needs to calculate the appropriate control strategy according to the evaluation results and ability status of the resident doctors in each training cycle.

[0050] In this embodiment, the goal of the optimization control module is to optimize the ability of residents by controlling the variables u(t)u(t)u(t). The optimization problem can be expressed as the following optimization problem:

[0051] in: Indicates the time of the resident The competence status vector at the moment, which contains the scores of the residents in various assessment dimensions (such as clinical skills, theoretical knowledge, communication skills, etc.); are control variables, representing decision factors that affect the residency training process, such as feedback intensity and training frequency; is the objective function, which indicates the resident physician’s The training cost of the resident physician is usually related to the resident physician's competence status and the training resources required. It was the last minute of the resident The objective function is usually a comprehensive objective function of the resident's ability status, which reflects the final evaluation result.

[0052] In the optimization control module, the control variables Represents the parameters that residents can adjust during training, such as training intensity, feedback frequency, resource allocation, etc. These control variables directly affect the changes in the ability status of residents, and thus affect the comprehensive evaluation results of residents. In order to ensure that the training process of residents is reasonable and feasible, control variables Certain constraints need to be met.

[0053] Resource constraints: Each training has limited resources, and control variables need to ensure that these resources can be reasonably allocated within each training cycle. For example, in clinical skills training, limited mentor and case resources need to be appropriately allocated according to the ability status of residents.

[0054] Limits to capacity improvement: There are physiological and cognitive upper limits to the capacity improvement of residents, and the selection of control variables should ensure that the rate of capacity improvement does not exceed these limits. In particular, when the capacity of certain dimensions has reached a high level, the system should limit the training intensity of that dimension to avoid unnecessary overtraining.

[0055] Time constraints: Residency training is usually time-limited, and the choice of control variables should ensure that the established training goals are achieved within the given time.

[0056] In general, the optimization control module not only relies on the initial control decision, but also can be dynamically adjusted through the real-time feedback mechanism. After each new evaluation by the evaluation module, the optimization control module will adjust the control variables according to the latest evaluation results of the resident. Make adjustments to dynamically change training resources and feedback strategies.

[0057] Under this feedback control, when the performance of residents in a certain dimension of ability is lower than expected, the optimization control module will automatically increase the training frequency or feedback intensity of that dimension. If the ability of a certain dimension has been improved, the system will reduce the investment of training resources in that dimension to avoid overtraining.

[0058] For example, if the communication ability of the resident is poorly evaluated, the optimization control module will automatically increase the frequency of training in this dimension, such as increasing the number of simulated patient communication sessions and increasing the intensity of feedback, to help the resident improve his communication ability. Such dynamic adjustments ensure that the resident can get more support in his weak links and maintain the level of ability he has achieved in other areas.

[0059] Specifically, the optimization control module uses optimization algorithms such as gradient descent, genetic algorithm, and particle swarm optimization to search for the optimal control strategy. Through these optimization algorithms, the optimization control module can find the optimal solution in the multi-objective optimization problem and ensure that the training resources of the resident doctors are allocated in the most reasonable way.

[0060] Taking the gradient descent method as an example, the optimization control module can adjust the control variables by calculating the gradient of the objective function , and update it according to the direction of the gradient until the optimal solution is found.

[0061] In this embodiment, the main function of the feedback generation module is to generate a personalized training feedback plan based on the comprehensive evaluation results obtained by the evaluation module and the performance of the resident in each evaluation dimension. The feedback generation module not only evaluates the current ability of the resident, but also adjusts the feedback content based on real-time performance to ensure that each resident can get the most appropriate training plan based on their own weaknesses and development needs.

[0062] The aforementioned data collection module and evaluation module provide basic data support for the feedback generation module. The evaluation module converts the performance of residents in clinical skills, theoretical knowledge, communication skills, and teamwork into comprehensive evaluation results. Based on these evaluation results, the feedback generation module provides dynamic feedback suggestions to residents through algorithms. After each new evaluation result is generated, the feedback generation module will adjust the content and intensity of the feedback based on these results to ensure that the training plan of the resident is optimized and meets their current training needs.

[0063] The feedback generation module not only provides quantitative training suggestions, such as increasing the frequency of a certain skill training or strengthening the feedback of a certain dimension, but also provides more refined customized suggestions based on the comprehensive ability status of the resident. For example, when the resident's assessment score for a certain skill is low, the system will increase the feedback intensity of that dimension and provide more learning resources and operation practice suggestions. At the same time, for those abilities that have reached a higher standard, the system will reduce the feedback frequency accordingly to avoid overtraining.

[0064] In this embodiment, the feedback generation module generates a detailed personalized training feedback plan based on the comprehensive evaluation results of the resident physician. The feedback generation for each evaluation dimension includes two parts: Specific training suggestions: For example, increase or decrease the training content of a certain dimension, and recommend corresponding courses, simulation operations or other training activities.

[0065] Feedback intensity adjustment: Adjust the intensity of feedback, including the frequency and detail of feedback, based on the resident's performance on this dimension.

[0066] The core of the feedback generation module is to dynamically determine the type and intensity of feedback content based on the evaluation results, and automatically generate training plans through algorithms.

[0067] The feedback generation module calculates the training suggestions and feedback strength using the following formula:

[0068] in: Indicates Dimensional feedback content, including training suggestions and feedback intensity; is the weight of the evaluation dimension, indicating the importance of the dimension in feedback generation, satisfying ; Indicates training suggestions for evaluation dimensions Specific training action suggestions, such as increasing the number of training sessions, improving training methods, etc. It represents the intensity of feedback, measuring the frequency and depth of feedback, and the intensity of feedback will be adjusted according to the performance of the resident.

[0069] Specifically, training recommendations are dynamically generated based on the resident's scores on each assessment dimension. If the resident scores low in this dimension, the feedback generation module will recommend increasing the frequency and intensity of the corresponding training. For example, for the clinical skills dimension, the system may recommend more simulation training; for the theoretical knowledge dimension, it may recommend courses or knowledge tests for intensive learning.

[0070] In some embodiments, training recommendations are not based solely on quantitative scores, but also take into account the actual training of the resident. For example, if a resident scores low in surgical skills, the system may recommend specific case discussions or surgical practice courses based on the resident's training history, rather than simply improving by increasing the frequency of training.

[0071] Feedback intensity is calculated based on the resident's performance in that dimension, and is usually expressed as adjustments to the frequency, detail, and method of feedback. If a resident scores lower than expected on the communication dimension, the system will increase the frequency of feedback on that dimension and make the feedback more detailed. For example, the system can regularly send specific feedback suggestions on communication skills to the resident, or even arrange face-to-face feedback meetings.

[0072] The specific calculation of feedback intensity is as follows:

[0073] in: is the intensity of feedback, which measures the frequency and detail of feedback in this dimension; is the adjustment coefficient of feedback intensity, indicating the feedback priority of this dimension; Is a resident physician in Assessment scores on the dimensions; is the highest possible score for this assessment dimension, usually 100.

[0074] According to this formula, the lower the resident's score on a certain dimension, the greater the feedback intensity, and the system will increase training resources and feedback frequency for that dimension.

[0075] Generally speaking, the evaluation process of residents is dynamic, and the performance of residents will change over time. The feedback generation module needs to be able to adjust the content of the feedback in real time to ensure that the feedback plan is always optimized according to the current training progress of the residents.

[0076] In some embodiments, the feedback generation module continuously optimizes the training feedback by combining dynamic evaluation results. When the resident's score in a certain dimension significantly improves, the system will automatically reduce the feedback intensity of that dimension and shift more resources and feedback focus to other dimensions. For example, if the resident's clinical operation ability has reached a high standard, the feedback generation module will reduce the training intensity of that dimension and shift the focus of feedback to other dimensions such as teamwork or communication skills.

[0077] In this embodiment, the task of the data storage and update module is not only to ensure the long-term preservation of data, but more importantly, to update all relevant data in the system according to new data changes, ensuring that subsequent evaluations and feedback are based on the latest information. After each evaluation, training or feedback, the module will update the database and synchronize all changes in real time to ensure that the data such as the resident's ability and training progress are up to date.

[0078] Working in collaboration with the aforementioned assessment module and feedback generation module, the data storage and update module can ensure that each new assessment result is promptly delivered to other modules for processing through real-time updates, ultimately providing residents with personalized training feedback.

[0079] In this embodiment, the data storage and update module synchronizes the evaluation data, training records and feedback information of the residents in real time by cooperating with the data acquisition module, the evaluation module and the feedback generation module. The module uses a structured database to store all relevant data to ensure efficient query and update of data.

[0080] Generally, the data storage and update module will regularly collect data from various modules for storage and update it according to the latest data. Each time the evaluation results of the resident are updated, the data storage module will adjust the relevant records in the database according to these new data. The evaluation results and training data will be stored in a unified database system to ensure the integrity and traceability of the data. This module also needs to encrypt and manage the data to ensure that the privacy information of the resident is not leaked.

[0081] As an option, the data storage and update module uses a cloud computing-based database storage solution. The cloud database supports distributed storage and high concurrent access, ensuring high availability and reliability of the data storage system. In this implementation, information such as resident evaluation data, training feedback, and historical records are stored in the cloud database. Each time an update is made, the new data is transmitted to the cloud database in real time via the network, ensuring that all modules can access the latest training data in real time.

[0082] Specifically, the data storage and update module can use relational databases (such as MySQL, PostgreSQL) or non-relational databases (such as MongoDB) to store data. These database systems have efficient data writing and query capabilities and can support large-scale data storage and processing. The database will be indexed according to key fields such as the resident's ID number and evaluation time to ensure the efficiency of data query.

[0083] Whenever new data is updated, the Data Storage and Update Module synchronizes the data in real time. For example, after a resident completes an assessment, the Assessment Module generates new comprehensive assessment results, which the Data Storage and Update Module receives and updates the resident's history. The updated data is automatically transferred to the Feedback Generation Module to generate new training feedback plans. This real-time update mechanism ensures that the data in the system is always up to date and that personalized feedback and training recommendations can be generated based on the latest assessment results.

[0084] The data synchronization process needs to ensure the accuracy and consistency of the data. In some embodiments, the data storage and update module uses a distributed system architecture to ensure high availability and fault tolerance of the data. For example, the data storage module can use a distributed database (such as Hadoop, Cassandra) to store the training data and feedback results of the residents to ensure that the system can provide the latest data in any case and can quickly recover from data loss.

[0085] In this embodiment, the data storage and update module controls data update based on the following formula:

[0086] in: Indicates updated data, including the resident's latest evaluation results, training records, etc.; represents previously stored data, i.e., historical data of residents; Indicates the amount of new data, usually new evaluation results, training progress, feedback information, etc.

[0087] This formula indicates that the data storage and update module will increment the new data ( ) and previously stored data ( ) to merge and obtain updated data. This process needs to ensure the accuracy of incremental data and avoid duplicate storage of data.

[0088] In addition, the data storage and update module will periodically update and store the data according to the evaluation cycle of the resident physician. In order to ensure the integrity and consistency of the data, the module will verify the data during the storage process to ensure that the data will not be lost or damaged after each update.

[0089] The data storage and update module also needs to consider data security. Generally, in order to protect the private data and training records of residents from being accessed by unauthorized personnel, the data storage module uses encryption technology to protect the data. In some embodiments, the data is encrypted using symmetric encryption (such as AES) or asymmetric encryption technology (such as RSA) during transmission and storage.

[0090] In addition, the module uses a permission management mechanism to ensure that only authorized users can access specific data. For example, only designated evaluators and feedback generators can access the residents' detailed training records and evaluation results, thus protecting the residents' privacy.

[0091] In this embodiment, the core of the quantum computing optimization module is the quantum annealing algorithm, which uses the tunneling effect and quantum superposition principle in quantum mechanics to simulate the state change of particles, quickly search the solution space of the optimization problem, and finally find the global optimal solution.

[0092] Specifically, the quantum computing optimization module first receives the scores of residents on each evaluation dimension from the evaluation module, and constructs an optimization objective function based on these data. This objective function needs to consider multiple evaluation dimensions and the allocation of training resources at the same time. The quantum computing optimization module solves this optimization problem through the quantum annealing algorithm and transmits the results to the optimization control module to adjust the training resources and feedback strategy.

[0093] In the quantum computing optimization module, the optimization problem can be expressed by the following mathematical model:

[0094] in: Indicates that the resident physician Optimization objective function in dimensions; is the weight of this evaluation dimension, indicating the relative importance of this dimension in the comprehensive evaluation of residents. Satisfy the conditions ; It is the characteristic vector of the resident's performance in each dimension, reflecting the score of each ability; It is a control variable, which represents optimization parameters such as training resources, feedback intensity, and training frequency.

[0095] The goal of this formula is to find the optimal solution through the quantum annealing algorithm , that is, how to allocate training resources for residents to optimize the comprehensive evaluation score. The optimization results will help the system decide in which dimensions to increase training investment and in which dimensions to reduce resource allocation, thereby achieving personalized training for residents.

[0096] The quantum annealing algorithm simulates the movement of particles on the potential energy surface and explores the transfer state between different solutions. Specifically, the quantum annealing algorithm represents each variable in the optimization problem through quantum bits and performs a global search in the multi-dimensional solution space. During the search process, the algorithm continuously adjusts the state of the quantum bits, tries multiple solutions using the principle of quantum superposition, and crosses the local optimal solution through the quantum tunneling effect to find the global optimal solution.

[0097] The optimization process of this algorithm relies on the "annealing" process in quantum mechanics, which slowly reduces the energy of the system so that the system eventually stabilizes in an optimal state after multiple iterations. Through this process, the quantum computing optimization module can quickly converge to the optimal solution without being affected by the local optimal solution.

[0098] In the quantum computing optimization module, the objective function It is used to measure the quality of resident training programs. The objective function usually involves multiple factors such as the scores of residents in various evaluation dimensions, the allocation of training resources and their efficiency. The quantum computing optimization module continuously optimizes the objective function through the quantum annealing algorithm and finally finds the optimal training resource allocation plan.

[0099] Specifically, the objective function It can be expressed as:

[0100] In this optimization model, the performance of residents With training resources Therefore, the objective function is optimized by the quantum computing optimization module. , can obtain the best training strategy, thereby improving the training efficiency and capacity improvement of residents.

[0101] Generally speaking, the quantum computing optimization module does not complete the optimization task in one go, but makes feedback adjustments after each system evaluation. Whenever the evaluation results of the resident are updated, the quantum computing optimization module will recalculate the optimal training resource allocation plan based on the new data. Through this dynamic feedback mechanism, the quantum computing optimization module can continuously improve the resident training plan and continuously optimize the efficiency of resource utilization.

[0102] In some embodiments, the quantum computing optimization module works in conjunction with the optimization control module to adjust the training resources of the resident in real time. The quantum computing optimization module transmits the output optimization results to the optimization control module, which adjusts the training intensity, feedback frequency and other parameters of the resident accordingly to ensure the optimization of the training process.

[0103] As an option, the quantum computing optimization module can not only process traditional evaluation data, but also integrate data sources from new technologies. For example, by combining with wearable devices, sensors and other technologies, the quantum computing optimization module can process real-time data from residents in actual operations, such as physiological data, operation time, operation success rate, etc. These real-time data will further enrich the evaluation model of residents, making the optimization process more comprehensive and accurate.

[0104] In another implementation, the quantum computing optimization module can also be combined with methods such as reinforcement learning to further enhance the adaptability and intelligence of the optimization algorithm. Through reinforcement learning, the system can continuously learn and optimize resource allocation strategies through feedback mechanisms to ensure that the training process of resident doctors remains efficient.

[0105] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Intelligent evaluation and feedback system for standardized training of resident doctors, characterized by: include: A data collection module is used to collect evaluation data from residents' clinical operation records, theoretical examination scores, patient feedback, and mentor ratings; An evaluation module, used to calculate a comprehensive evaluation result of the resident according to a multi-objective nonlinear optimization model, wherein the comprehensive evaluation result is obtained by calculating a plurality of weighted evaluation dimensions; An optimization control module, used to adjust the training resource allocation and feedback strategy of the resident doctors according to the results of the evaluation module, and the optimization control module adjusts the training process through an optimal control method; A feedback generation module, which is used to generate a personalized training feedback plan for the resident based on the comprehensive evaluation results, and dynamically adjust the training content and strategy based on the results of each evaluation and the performance of the resident; A data storage and update module, which is used to store the evaluation data of the residents and update the evaluation results in real time according to the new training data. The data storage and update module also ensures the synchronization of data between modules; The quantum computing optimization module is used to accelerate and optimize computing tasks during evaluation and training to improve computing efficiency when processing large-scale data. The quantum computing optimization module uses a quantum annealing algorithm to solve multi-objective optimization problems.

2. The intelligent evaluation and feedback system for standardized resident training according to claim 1 is characterized in that: The evaluation module calculates the ability of residents based on a multi-objective nonlinear optimization model. The evaluation model obtains a comprehensive evaluation value by weighting the results of each evaluation dimension, and each evaluation dimension includes clinical skills, theoretical knowledge, communication skills and teamwork ability.

3. The intelligent evaluation and feedback system for standardized resident training according to claim 1 is characterized in that: The comprehensive evaluation result calculated by the evaluation module meets the following conditions: The scoring range of all evaluation dimensions is limited to a predetermined interval, which is 0 to 100; The sum of the weights of each evaluation dimension is equal to 1, and the weights are dynamically allocated according to the clinical training stage and professional subject of the resident; Each time a new assessment is conducted, the assessment module dynamically adjusts the weights and competency scores of each assessment dimension according to the resident's current performance, and the adjustment process is based on the resident's historical data and real-time performance.

4. The intelligent evaluation and feedback system for standardized resident training according to claim 1, characterized in that: The optimization control module dynamically adjusts the training process of the resident doctors through the optimal control method according to the evaluation results of the resident doctors. The dynamic adjustment is based on the changes in the evaluation results, and the adjustment content includes training frequency, resource allocation and feedback intensity.

5. The intelligent evaluation and feedback system for standardized resident training according to claim 1 is characterized in that: The optimization control module establishes a dynamic model that describes the changes in the state of the resident physician's ability. The dynamic model is modeled by a state equation, and the state equation is in the form of: in, represents the competence state vector of the resident physician, is a control variable, which represents external intervention in the training process (such as feedback intensity, resource allocation, etc.), is a function that describes the variation in resident competence.

6. The intelligent evaluation and feedback system for standardized resident training according to claim 1, characterized in that: The feedback generation module generates a personalized training feedback plan based on the comprehensive evaluation results. The feedback plan includes specific training suggestions for each evaluation dimension, and dynamically adjusts the training content and frequency according to the performance and ability development of the residents through the feedback mechanism.

7. The intelligent evaluation and feedback system for standardized resident training according to claim 1, characterized in that: The feedback generation module uses a Bayesian inference algorithm to update the resident's competence status and adjust the content of the training feedback accordingly when new data is acquired. The adjustment includes dynamically updating the weights and intensities of each dimension in the training program.

8. The intelligent evaluation and feedback system for standardized resident training according to claim 1, characterized in that: The quantum computing optimization module uses a quantum annealing algorithm to perform optimization calculations on multi-objective optimization problems. The quantum annealing algorithm simulates the random motion of quantum particles to perform a global search to improve computing efficiency and accelerate the optimization process. The computing tasks in the optimization process include fast calculation of evaluation results and optimization adjustment of training tasks.

9. The intelligent evaluation and feedback system for standardized resident training according to claim 1, characterized in that: The quantum computing optimization module is used to accelerate the calculation process in multi-dimensional evaluation and training tasks, and quickly optimize the evaluation results through quantum algorithms, reduce the time complexity of traditional calculation methods, and maintain calculation accuracy during the optimization process.

10. The intelligent evaluation and feedback system for standardized resident training according to claim 1, characterized in that: The data acquisition module, evaluation module, optimization control module, feedback generation module and data storage and update module are connected via a data transmission interface, which ensures data interaction and synchronization between the modules. The data transmission interface uses a standardized protocol for data exchange to ensure data security and consistency.

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