Training management method for improving nurse nursing information competency

Abstract

The invention relates to the technical field of medical training, and discloses a training management method for improving nurse nursing information competency, comprising the following steps: S1, through investigation and demand analysis, S2, through cloud computing and big data technologies, S3, introducing a high-fidelity simulation device into a physical simulation module, S4, establishing a dynamically updated case library, and S5, establishing a dynamic update case library; s5, constructing a physical simulation module, S6, organizing teacher special training and a remote tutoring mechanism with an apprentier, S7, introducing an artificial intelligence-based personalized learning recommendation system into a training platform, and S8, combining the artificial intelligence recommendation system with real-time data provided by the physical simulation module. Through special training, nurses not only can grasp theoretical knowledge in a simulated environment, but also can improve the ability to deal with complex clinical conditions through situation drilling and actual operation, and the nurses can deeply understand nursing theories and improve theoretical support and practical knowledge of the nurses in the training process.

Description

Technical Field

[0001] The present invention relates to the technical field of medical training, and specifically to a training management method for improving the nursing information competence of nurses. Background Art

[0002] With the continuous development of medical services and nursing models, the requirements for nurses in modern clinical nursing work are increasing day by day, especially in aspects such as nursing information acquisition, information understanding and analysis, information application, and information technology application. Existing training methods usually focus on single theoretical knowledge imparting or operation skill training, lacking systematic and comprehensive special training for each link of nursing information processing.

[0003] Currently, most nursing trainings still adopt traditional classroom lectures or simple simulation operations, which are difficult to fully reproduce the information processing requirements in complex clinical environments. As a result, when facing emergencies, emergency decision-making, and multi-source information integration, nurses are unable to efficiently transform theoretical knowledge into practical abilities. At the same time, the existing training lacks comprehensive analysis of multi-dimensional data, and it is difficult for nurses to understand the internal connection between nursing theory and actual operation as a whole, thus affecting the quality and efficiency of clinical nursing services. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the present invention provides a training management method for improving the nursing information competence of nurses, which solves the problems that traditional training methods focus on theoretical knowledge or single skill operation, lack comprehensive training for nursing information acquisition, understanding and analysis, and information technology application, resulting in nurses being difficult to give full play to their theoretical support role in complex clinical environments, and that existing simulation environments are mostly static or single scenarios, unable to fully reflect the diversity and dynamic changes of real clinical situations, restricting the ability of nurses to handle complex clinical situations.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A training management method for improving the nursing information competence of nurses, including the following steps:

[0006] S1. Through research and needs analysis, analyze the actual shortcomings and training needs of nurses in nursing, and conduct exercises and learning through VR simulation teaching;

[0007] S2. Through cloud computing and big data technologies, integrate resources and share data, build a physical simulation module, and establish a remote tutoring and teacher collaboration system;

[0008] S3. Introduce high-fidelity simulation devices into the physical simulation module, build a multi-level scenario simulation environment in combination with VR / AR immersion technology, and then conduct training through scenario dynamic switching and personalized training;

[0009] S4. Establish a dynamically updated case library, organize clinical experts and senior nurses to jointly collect nursing cases, and classify and provide personalized recommendations for the cases through artificial intelligence algorithms;

[0010] S5. By constructing a physical simulation module, use sensor data and online learning records to quantitatively assess nurses in simulated and virtual environments, and timely adjust the training difficulty and content through data closed-loop feedback;

[0011] S6. Through organizing special training for teachers and a mechanism of teacher-led remote tutoring, through online expert comments and cross-institutional experience sharing, promote and apply the platform in training units at all levels;

[0012] S7. Introduce an artificial intelligence-based personalized learning recommendation system into the training platform, and according to the performance of trainees, adjust the learning content and path in real time to customize personalized training plans for each nurse;

[0013] S8. Through the artificial intelligence recommendation system combined with the real-time data provided by the physical simulation module, use machine learning algorithms to continuously optimize the learning path, and dynamically adjust the recommended content according to the real-time feedback of trainees and system data;

[0014] S9. Finally, through the combination of the artificial intelligence recommendation system and the physical simulation module, form a closed-loop feedback mechanism to improve nurses' nursing information competence.

[0015] Preferably, in S1, the investigation includes observing nurses' work scenarios, reviewing nurses' operation records, analyzing skill requirements, and conducting patient satisfaction surveys. The VR simulation teaching includes VR clinical scenario simulation, VR emergency situation simulation, and VR equipment usage simulation.

[0016] Preferably, in S2, the physical simulation module, virtual reality module, dynamic case library, and intelligent evaluation module. The data sharing includes nurse training data sharing, nursing research data sharing, and clinical practice data sharing. The cloud computing technology includes infrastructure as a service and virtualization technology.

[0017] Preferably, in S3, the high-fidelity simulation equipment includes a simulated human model, medical equipment simulator, and simulated ward facilities. The multi-level scenario simulation environment includes ward scenarios in different departments, emergency situations, daily nursing situations, and infection control situations.

[0018] Preferably, in S4, the case library includes case information, nursing records, treatment plans, and imaging materials. The nursing cases include surgical nursing cases, pediatric nursing cases, and disease nursing cases. The classification includes disease type classification, nursing difficulty classification, and nursing scenario classification. The personalized recommendation includes recommendations based on nurses' work experience and nurses' professional directions.

[0019] Preferably, in S5, the quantitative assessment includes real-time monitoring of decision-making and emergency response. The training difficulty includes knowledge depth, situational pressure, and decision-making difficulty. The training content includes basic nursing knowledge, nursing operation skills, and emergency handling procedures. The learning record includes course learning progress, operation error records, and self-assessment records.

[0020] Preferably, in S6, the special teacher training includes teaching method training, communication skill training, and platform usage training. The teacher-led remote tutoring mechanism includes formulating a tutoring plan, regular remote communication, and practical guidance. The expert review includes assessment of operation skills, nursing decision analysis, and emergency handling ability assessment. The cross-institutional experience sharing includes sharing of successful cases, exchange of problem-solving experiences, and insights on new technology applications.

[0021] Preferably, in S7, the performance includes historical data, learning behavior, and error types. The personalized learning recommendation includes customized course recommendation, targeted practice recommendation, and learning resource recommendation. The performance includes learning achievements, operation proficiency, and error types and frequencies. The learning content includes nursing theory knowledge, nursing operation skills, and emergency handling knowledge. The personalized training plan includes practical operation training planning, schedule arrangement, and learning course and resource arrangement.

[0022] Preferably, in S8, the machine learning algorithm includes neural network algorithm, random forest algorithm, or decision tree algorithm. The learning path includes the sequence of course arrangement, recommendation of learning resources, and steps for skill improvement. The real-time feedback includes feedback on learning progress, learning effect assessment, and learning status feedback. The system data includes operation data, feedback data, and learning history records. The recommended content includes practical cases, learning materials, and expert guidance resources.

[0023] Preferably, in S9, the information competence includes nursing information acquisition ability, information understanding and analysis ability, information application ability, and information technology application ability. The closed-loop feedback mechanism includes data analysis, practical improvement, and adjustment of recommendations.

[0024] The present invention provides a training management method for improving nurses' nursing information competence. It has the following beneficial effects:

[0025] 1. Through special training on nursing information acquisition, information understanding and analysis, information application, and information technology, nurses can not only master theoretical knowledge in a simulated environment but also improve their ability to handle complex clinical situations through scenario drills and practical operations. Combining with multi-dimensional data, nurses can deeply understand the relationship between nursing theory and actual operations during the training process, thereby further improving nurses' theoretical support and practical knowledge.

[0026] 2. By leveraging big data, machine learning algorithms, and artificial intelligence recommendation systems, the present invention conducts a detailed analysis of nurses' historical data, learning behaviors, and error records, enabling customized courses, targeted exercises, and resource recommendations, and thus formulating personalized training plans according to different nurses' professional directions and experience.

[0027] 3. By utilizing expert reviews, sharing of successful cases, and exchanges of problem-solving experiences, the present invention enables direct mutual learning among different institutions, achieving the interconnection and sharing of clinical practice data, nursing research data, and training data, and promoting the improvement of the overall nursing level and the dynamic allocation of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a method flow chart of a training management method for improving nurses' nursing information competence according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] Next, the technical solutions of the present invention will be clearly and completely described in conjunction with the drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0030] Please refer to the attached Figure 1 , an embodiment of the present invention provides a training management method for improving nurses' nursing information competence, including the following steps:

[0031] S1. Through research and needs analysis, analyze the actual shortfalls and training needs of nurses in nursing, and conduct exercises and learning through VR simulation teaching;

[0032] S2. Through cloud computing and big data technologies, integrate resources and share data, construct a physical simulation module, and establish a remote tutoring and teacher collaboration system;

[0033] S3. Introduce high-fidelity simulation devices into the physical simulation module, construct a multi-level scenario simulation environment in combination with VR / AR immersion technology, and then conduct training through scenario dynamic switching and personalized training;

[0034] S4. Establish a dynamically updated case library, organize clinical experts and senior nurses to jointly collect nursing cases, and classify and provide personalized recommendations for the cases through artificial intelligence algorithms;

[0035] S5. By constructing a physical simulation module, use sensor data and online learning records to quantitatively assess nurses in the simulation and virtual environments, and timely adjust the training difficulty and content through data closed-loop feedback;

[0036] S6. Promote and apply the platform in training units at all levels by organizing special training for teachers and remote tutoring mechanism for teachers and apprentices, and through online expert comments and cross-institutional experience sharing;

[0037] S7. Introduce an AI-based personalized learning recommendation system into the training platform to adjust the learning content and path in real time according to the performance of the trainees to tailor a personalized training plan for each nurse;

[0038] S8. Through the artificial intelligence recommendation system combined with the real-time data provided by the physical simulation module, the learning path is continuously optimized using machine learning algorithms, and the recommended content is dynamically adjusted based on the real-time feedback of students and system data;

[0039] S9. Finally, through the combination of artificial intelligence recommendation system and physical simulation module, a closed-loop feedback mechanism is formed to improve nurses' nursing information competence.

[0040] In S1, the survey includes observation of nurse work scenes, review of nurse operation records, skill demand analysis and patient satisfaction survey; VR simulation teaching includes VR clinical scene simulation, VR emergency situation simulation and VR equipment use simulation.

[0041] Specifically, first, observe the nurses' work scenes, identify the weak links in nursing information management and technology application by observing the nurses' work processes, operating standards and daily tasks on site, review the nurses' operation records, evaluate the accuracy and emergency response capabilities of nurses in nursing tasks by analyzing historical operation data, and analyze the skills demand analysis based on observation and data results, combined with the actual needs of nursing work, analyze the gaps in nurses' abilities in information collection, transmission and decision-making, and determine the training focus. Finally, conduct a patient satisfaction survey, based on patients' feedback on nursing services, to evaluate nurses' problems in information management and nursing quality;

[0042] In the VR simulation teaching part, we first use VR clinical scene simulation to build a real ward environment through virtual reality technology, simulate common nursing operations and equipment use, and improve nurses' operational capabilities and emergency response capabilities. VR emergency situation simulation simulates sudden events such as cardiac arrest and allergic reactions to help nurses improve their judgment and decision-making capabilities in emergency situations. VR equipment use simulation allows nurses to master the operation and troubleshooting of medical equipment through virtual training. By combining research and VR simulation teaching, it can effectively improve nurses' nursing information competence and operational skills, and enhance their ability to respond in complex clinical environments.

[0043] S2 includes physical simulation module, virtual reality module, dynamic case library and intelligent evaluation module; data sharing includes nurse training data sharing, nursing research data sharing and clinical practice data sharing; cloud computing technology includes infrastructure as a service and virtualization technology.

[0044] Specifically, by adopting cloud computing and big data technologies and supported by Infrastructure as a Service (IaaS) and virtualization technologies, the unified integration and data sharing of hospital internal and cross-institutional resources are realized, thus constructing a comprehensive training management platform including a physical simulation module, a virtual reality module, a dynamic case library, and an intelligent evaluation module. The physical simulation module reproduces a real nursing environment by integrating high-precision simulation devices and physical training facilities;

[0045] The virtual reality module uses VR / AR technologies to construct immersive and interactive virtual training scenarios, enabling nurses to familiarize themselves with various operation procedures and emergency responses under simulated conditions; the dynamic case library updates and manages data from various clinical practices and research in real time, including case information, nursing records, and treatment plans, to support targeted training; the intelligent evaluation module quantitatively assesses nurses' operation skills, decision-making speed, and emergency responses by collecting sensor data and online learning records, and realizes real-time feedback and adjustment. The platform realizes the sharing of nurses' training data, nursing research data, and clinical practice data to promote the fusion analysis of multi-level data and the dynamic allocation of resources, thereby effectively enhancing the training effect and nursing information competency.

[0046] In S3, the high-fidelity simulation devices include simulated human models, medical device simulators, and simulated ward facilities, and the multi-level scenario simulation environment includes ward scenarios of different departments, emergency scenarios, daily nursing scenarios, and infection control scenarios.

[0047] Specifically, by adopting the method of combining high-fidelity simulation devices with a multi-level scenario simulation environment, realistic and flexible nursing operation training is achieved. The high-fidelity simulation devices include simulated human models, medical device simulators, and simulated ward facilities. Among them, the simulated human model is used to accurately reproduce the human anatomical structure, vital signs, and pathological changes. The medical device simulator can simulate the working states and operation characteristics of various medical instruments, while the simulated ward facilities construct an operation space consistent with the actual clinical environment; at the same time, the multi-level scenario simulation environment covers ward scenarios of different departments, emergency scenarios, daily nursing scenarios, and infection control scenarios. By presetting various scenario parameters, nurses can switch to targeted training scenarios in the simulated environment to achieve training in multiple aspects such as routine nursing procedures, emergency handling, and infection prevention and control, so as to continuously improve operation skills and emergency response capabilities in dynamically changing simulated scenarios, ensuring that the training effect is real, effective, and meets the requirements of clinical practice.

[0048] In S4, the case library includes case information, nursing records, treatment plans, and imaging data. Nursing cases include surgical nursing cases, pediatric nursing cases, and disease nursing cases. The classification includes disease type classification, nursing difficulty classification, and nursing scenario classification. Personalized recommendations include recommendations based on nurses' work experience and professional directions.

[0049] Specifically, by first constructing a dynamically updated case library that covers various data such as case information, nursing records, treatment plans, and imaging data, and collecting diverse nursing cases including surgical nursing cases, pediatric nursing cases, and disease nursing cases, and by establishing criteria such as disease type classification, nursing difficulty classification, and nursing scenario classification to systematically organize and meticulously file various cases, the structured management and efficient retrieval of data are achieved. At the same time, the platform uses intelligent algorithms combined with nurses' work experience and professional directions to provide personalized recommendations, pushing targeted case resources and training content to nurses to facilitate their better application and improvement of nursing information processing capabilities in practice, thus achieving a match between the training content and clinical actual needs, and effectively enhancing nurses' comprehensive nursing skills and information competence.

[0050] In S5, quantitative assessment includes real-time monitoring of decision-making and emergency response. Training difficulty includes knowledge depth, situational pressure, and decision-making difficulty. Training content includes basic nursing knowledge, nursing operation skills, and emergency handling procedures. Learning records include course learning progress, operation error records, and self-assessment records.

[0051] Specifically, by adopting a quantitative assessment mechanism to conduct real-time monitoring of nurses' decision-making ability and emergency response ability during training, and by integrating the sensor data of the physical simulation module with the online learning records, the intelligent assessment module can conduct a refined assessment of nurses' operation performance in simulated scenarios. The training difficulty is dynamically adjusted according to the depth of basic nursing knowledge, the pressure level in the simulated scenario, and the complexity of the decision-making process. The training content covers basic nursing theory, practical operation skills, and emergency handling procedures to ensure the comprehensiveness and hierarchy of the training system. At the same time, the learning record system completely records the course progress, operation errors, and self-assessment results, providing a scientific basis for subsequent data closed-loop feedback and continuous optimization of the training plan, thus effectively enhancing nurses' comprehensive nursing information processing ability and practical response level.

[0052] In S6, special teacher training includes teaching method training, communication skill training, and platform usage training. The mechanism of master-apprentice remote guidance includes formulating a guidance plan, regular remote communication, and practical guidance. Expert comments include operation skill assessment, nursing decision analysis, and emergency handling ability assessment. Cross-institutional experience sharing includes sharing of successful cases, exchange of problem-solving experiences, and insights on new technology applications.

[0053] Specifically, by adopting a multi-level teacher training and remote tutoring mechanism, the training platform can be effectively promoted and applied in training institutions at all levels. The special teacher training includes systematic training for nursing teaching staff on teaching methods, communication skills, and platform usage to improve their teaching level in the information-based environment. At the same time, a master-apprentice remote tutoring mechanism is established. By formulating a detailed tutoring plan, arranging regular remote communication, and providing practical guidance, senior experts can track and guide the training process of trainees in real time. In addition, the expert review session comprehensively evaluates the nurses' operation skills, nursing decision-making, and emergency handling abilities, providing objective and professional feedback. Finally, through cross-institutional experience sharing, by sharing successful cases, exchanging problem-solving experiences, and transmitting the experience of new technology applications, resource complementarity and experience integration between different medical institutions are achieved, thereby overall improving the quality of nursing training and the nurses' nursing information competency.

[0054] In S7, the performance includes historical data, learning behavior, and error types. The personalized learning recommendations include customized course recommendations, targeted practice recommendations, and learning resource recommendations. The performance includes academic performance, operation proficiency, and error types and frequencies. The learning content includes nursing theory knowledge, nursing operation skills, and emergency handling knowledge. The personalized training plan includes practical operation training plans, progress arrangements, and learning course and resource arrangements.

[0055] Specifically, by introducing an artificial intelligence-based personalized learning recommendation system into the training platform, this system comprehensively utilizes the performance of nurses such as historical data, learning behavior, and error types to systematically analyze the nurses' academic performance, operation proficiency, and error frequencies. Then, through customized course recommendations, targeted practice recommendations, and learning resource recommendations, personalized learning content is provided to nurses. Among them, the recommended learning content covers nursing theory knowledge, nursing operation skills, and emergency handling knowledge, and a personalized training plan is generated based on the analysis results. This plan details the practical operation training plan, progress arrangement, and the configuration of relevant courses and resources, thus achieving precise, dynamic, and continuous ability improvement.

[0056] In S8, the machine learning algorithms include neural network algorithms, random forest algorithms, or decision tree algorithms. The learning path includes the sequence of course arrangements, the recommendation of learning resources, and the steps of skill improvement. The real-time feedback includes the feedback of learning progress, the evaluation of learning effects, and the feedback of learning status. The system data includes operation data, feedback data, and learning history records. The recommended content includes practical cases, learning materials, and expert guidance resources.

[0057] Specifically, by adopting machine learning algorithms including neural networks, random forests or decision trees, in-depth data mining and model training are carried out on the operation data, feedback data and learning history records collected within the system, so as to extract the key behavioral characteristics and skill improvement rules demonstrated by nurses during the training process, and based on this, a scientific and reasonable learning path is generated, which covers the course arrangement sequence, learning resource recommendation and skill improvement steps; at the same time, the platform dynamically monitors and evaluates the learning progress, learning effect and learning status of nurses through a real-time feedback mechanism, ensuring that the feedback data can be transmitted immediately and used to adjust subsequent training strategies, so as to achieve precise control and personalized improvement of the training process; in addition, combining real-time feedback and system data analysis, the platform further recommends practice cases, learning materials and expert guidance resources to nurses, forming a closed-loop feedback system, effectively promoting the continuous improvement of nurses' comprehensive ability and nursing information competence.

[0058] In S9, information competence includes nursing information acquisition ability, information understanding and analysis ability, information application ability and information technology application ability, and the closed-loop feedback mechanism includes data analysis, practice improvement and adjustment recommendation.

[0059] Specifically, by subdividing nurses' nursing information competence into nursing information acquisition ability, information understanding and analysis ability, information application ability and information technology application ability, and by integrating an artificial intelligence recommendation system and a physical simulation module, a data-driven closed-loop feedback mechanism is established. This mechanism comprehensively analyzes the operation data, feedback data and learning history records generated during the training process, timely identifies the deficiencies of nurses in information collection, processing and application, and accordingly proposes targeted practice improvement measures and adjustment recommendations, so as to dynamically optimize the training content and plan; this closed-loop feedback method based on data analysis, practice improvement and adjustment recommendation ensures that the training system can continuously adapt to the needs of clinical practice, continuously improve nurses' comprehensive nursing information processing ability, and ultimately achieve the effect of significantly improving the overall nursing quality.

[0060] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A training management method for improving the nursing information competence of nurses, characterized in that, It includes the following steps: S1. Through research and needs analysis, analyze the actual short - comings and training needs of nurses in nursing, and conduct exercises and learning through VR simulation teaching; S2. Through cloud computing and big data technologies, integrate resources and share data, build a physical simulation module and establish a remote tutoring and teacher collaboration system; S3. Introduce high - fidelity simulation equipment into the physical simulation module, combine VR / AR immersion technology to build a multi - level scenario simulation environment, and then conduct training through scenario dynamic switching and personalized training; S4. Establish a dynamically updated case library, organize clinical experts and senior nurses to jointly collect nursing cases, and classify and provide personalized recommendations for cases through artificial intelligence algorithms; S5. Through building a physical simulation module, use sensor data and online learning records to quantitatively assess nurses in simulation and virtual environments, and timely adjust the training difficulty and content through data closed - loop feedback; S6. Through organizing special teacher training and a teacher - apprentice remote tutoring mechanism, through online expert comments and cross - institutional experience sharing, promote and apply the platform in training units at all levels; S7. Introduce an artificial - intelligence - based personalized learning recommendation system into the training platform, and according to the performance of trainees, adjust the learning content and path in real time to customize a personalized training plan for each nurse; S8. Through the artificial intelligence recommendation system combined with the real - time data provided by the physical simulation module, use machine learning algorithms to continuously optimize the learning path, and dynamically adjust the recommended content according to the real - time feedback of trainees and system data; S9. Finally, through the combination of the artificial intelligence recommendation system and the physical simulation module, form a closed - loop feedback mechanism to improve nurses' nursing information competence.

2. The training management method for improving the nursing information competence of nurses according to claim 1, wherein: In S1, the research includes observing nurses' work scenarios, reviewing nurses' operation records, analyzing skill requirements, and conducting patient satisfaction surveys. The VR simulation teaching includes VR clinical scenario simulation, VR emergency situation simulation, and VR equipment usage simulation.

3. The training management method for improving the nursing information competence of nurses according to claim 1 is characterized in that: In S2, the physical simulation module, virtual reality module, dynamic case library, and intelligent evaluation module. The data sharing includes nurse training data sharing, nursing research data sharing, and clinical practice data sharing. The cloud computing technology includes infrastructure as a service and virtualization technology.

4. The training management method for improving the nursing information competence of nurses according to claim 1, characterized in that: In S3, the high - fidelity simulation equipment includes a simulated human model, medical device simulators, and simulated ward facilities. The multi - level scenario simulation environment includes ward scenarios in different departments, emergency scenarios, daily nursing scenarios, and infection control scenarios.

5. A training management method for improving the nursing information competence of nurses according to claim 1, characterized in that: In S4, the case library includes case information, nursing records, treatment plans, and imaging materials. The nursing cases include surgical nursing cases, pediatric nursing cases, and disease nursing cases. The classification includes disease type classification, nursing difficulty classification, and nursing scenario classification. The personalized recommendation includes recommendations based on nurses' work experience and nurses' professional directions.

6. The training management method for improving the nursing information competence of nurses according to claim 1, characterized in that: In S5, the quantitative assessment includes real-time monitoring of decision-making and emergency response. The training difficulty includes knowledge depth, situational pressure, and decision-making difficulty. The training content includes basic nursing knowledge, nursing operation skills, and emergency response procedures. The learning record includes course learning progress, operation error records, and self-assessment records.

7. A training management method for improving the nursing information competence of nurses according to claim 1, characterized in that: In S6, the special teacher training includes teaching method training, communication skill training, and platform usage training. The teacher-apprentice remote tutoring mechanism includes formulating a tutoring plan, regular remote communication, and practical guidance. The expert review includes operation skill assessment, nursing decision analysis, and emergency response ability assessment. The cross-institutional experience sharing includes successful case sharing, problem-solving experience exchange, and new technology application insights.

8. A training management method for improving the nursing information competence of nurses according to claim 1, characterized in that: In S7, the performance includes historical data, learning behavior, and error types. The personalized learning recommendation includes customized course recommendation, targeted practice recommendation, and learning resource recommendation. The performance includes learning achievements, operation proficiency, and error types and frequencies. The learning content includes nursing theory knowledge, nursing operation skills, and emergency response knowledge. The personalized training plan includes practical operation training planning, schedule arrangement, and learning course and resource arrangement.

9. A training management method for improving the nursing information competence of nurses according to claim 1, characterized in that: In S8, the machine learning algorithm includes neural network algorithm, random forest algorithm, or decision tree algorithm. The learning path includes the sequence of course arrangement, recommendation of learning resources, and skill improvement steps. The real-time feedback includes feedback on learning progress, learning effect assessment, and learning status feedback. The system data includes operation data, feedback data, and learning history records. The recommended content includes practical cases, learning materials, and expert guidance resources.

10. A training management method for improving the nursing information competence of nurses according to claim 1, characterized in that: In S9, the information competence includes nursing information acquisition ability, information understanding and analysis ability, information application ability, and information technology application ability. The closed-loop feedback mechanism includes data analysis, practice improvement, and adjusted recommendation.

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