A quality whole life cycle monitoring method and system

Abstract

The application provides a rule cultivation quality full life cycle monitoring method and system, which comprises the following steps: in a training period, collecting operation logs of rule cultivation personnel in multiple clinical information systems, and reconstructing the operation logs into behavior trajectory sequences reflecting clinical core capabilities; evaluating the quality deviation degree of the rule cultivation personnel in the clinical case processing process based on the behavior trajectory sequences; obtaining the skill mastering vector of the rule cultivation personnel in each clinical core capability based on the quality deviation degrees of multiple clinical operations in a historical period and a preset capability dimension mapping rule; generating the capability evolution characteristics of the rule cultivation personnel in the training period according to the change law of the skill mastering vector over time; and generating a multi-dimensional evaluation result including skill maturity, risk deviation degree, diagnosis and treatment logic consistency and clinical efficiency factors based on the capability evolution characteristics and the quality deviation degree. The application improves the timeliness and accuracy of rule cultivation quality evaluation.

Description

Technical Field

[0001] This invention relates to the field of intelligent education assessment technology, specifically to a method and system for monitoring the quality of standardized residency training throughout its entire lifecycle. Background Technology

[0002] Standardized residency training (SUR) is a crucial step in cultivating qualified clinicians. Currently, the quality evaluation of SUR mainly relies on periodic manual reporting, exit examinations, and subjective assessments by mentors, which presents the following technical problems: 1. Fragmented Data: The clinical records of resident trainees are scattered across multiple heterogeneous systems such as Hospital Information System (HIS), Electronic Medical Record System (EMR), and Picture Archiving and Communication System (PACS), lacking a unified data collection and integration mechanism. This results in the incomplete capture of trainees' clinical operational trajectories. 2. Delayed and Discontinuous Evaluation: Existing assessments are mostly periodic (e.g., department exit assessments, annual assessments), failing to reflect the dynamic evolution of trainees' abilities throughout the training cycle. Mentors struggle to identify weaknesses or growth bottlenecks in trainees' core clinical competencies in real time. 3. Lack of Quantifiable Evaluation Indicators: Traditional evaluations primarily focus on outcomes (e.g., completeness of medical record writing, exam scores), neglecting real-time quality monitoring of clinical procedures. Issues such as omissions of key diagnostic and treatment steps and excessive decision-making delays are difficult to quantify objectively.

[0003] Therefore, there is an urgent need to provide a method and system for monitoring the quality of standardized residency training throughout its entire lifecycle, which can integrate all data and conduct real-time, quantitative, and full lifecycle monitoring of trainees to improve the accuracy of standardized residency training quality evaluation. Summary of the Invention

[0004] In view of this, it is necessary to provide a method and system for monitoring the quality of standardized residency training throughout its entire life cycle, in order to solve the technical problems of data fragmentation, poor real-time evaluation, and lack of objective quantitative indicators in the existing technology, which make it impossible to evaluate the quality of standardized residency training in real time and accurately.

[0005] To address the aforementioned technical problems, in a first aspect, the present invention provides a method for monitoring the quality of standardized residency training throughout its entire lifecycle, comprising: During the training period, the operation logs of the resident trainees in multiple clinical information systems were collected, and the operation logs were reconstructed into a sequence of behavioral trajectories that reflect core clinical competencies. The quality deviation of the clinical case handling process of the resident trainees was assessed based on the behavioral trajectory sequence. Based on the quality deviation of multiple clinical operations within a historical period and the preset capability dimension mapping rules, the skill mastery vector of the resident trainee in each core clinical capability is obtained. Based on the changing pattern of the skill mastery vector over time, the ability evolution characteristics of the trainees during the training cycle are generated. The ability evolution characteristics include at least the ability growth curve and the accumulated ability stock. Based on the aforementioned capability evolution characteristics and the aforementioned quality deviation, a multi-dimensional evaluation result is generated, including skill maturity, risk deviation, consistency of diagnosis and treatment logic, and clinical efficiency factors.

[0006] In one possible implementation, the quality deviation is:

[0007] In the formula, For quality deviation; The clinical behavioral coordinates of the i-th diagnosis and treatment step in the behavioral trajectory sequence; The coordinates of the i-th treatment step in the standard treatment pathway; The Euclidean distance between clinical behavior coordinates and standard treatment pathway coordinates; Let be the weight coefficient of the i-th diagnosis and treatment step; This is a penalty for decision-making delays; This is the delay penalty coefficient; The time difference between the time taken for resident physicians to complete a diagnostic and treatment procedure and the standard time. This refers to the total number of diagnostic and treatment steps included in a clinical case.

[0008] In one possible implementation, the weight coefficient of the process is positively correlated with the importance of the diagnosis and treatment process in the clinical pathway.

[0009] In one possible implementation, the process of obtaining the skill mastery vector of the resident in each core clinical competency based on the quality deviation of multiple clinical operations within a historical period and a preset competency dimension mapping rule includes: The quality deviation is associated with a core clinical competency dimension based on a preset competency dimension mapping rule. Multiple quality deviations under the same core clinical competency dimension are weighted and fused to obtain the skill mastery level; The skill mastery vector is obtained by combining the skill mastery levels of all core clinical competency dimensions.

[0010] In one possible implementation, the accumulated capacity is:

[0011] In the formula, To accumulate existing capacity; For the training cycle; Let be the skill mastery vector at time t; The feature transformation matrix; For bias terms; It is a non-linear mapping function.

[0012] In one possible implementation, the skill maturity is used to characterize the depth of knowledge mastery of resident trainees, and the skill maturity is the ratio of the accumulated capacity stock to the baseline capacity stock.

[0013] In one possible implementation, the risk deviation is the ratio of the maximum quality deviation of all clinical operations during the training period to a preset safety threshold.

[0014] In one possible implementation, the consistency of the diagnostic logic is as follows:

[0015] In the formula, To ensure consistency in diagnostic and treatment logic; The variance of the behavioral trajectory sequence; The variance of the standard treatment pathway; It is a constant.

[0016] In one possible implementation, the clinical efficiency factor is the ratio of the number of diagnostic and treatment steps completed by the resident physician per unit time to the preset benchmark diagnostic and treatment efficiency.

[0017] Secondly, the present invention also provides a system for monitoring the quality of standardized residency training throughout its entire lifecycle, comprising: The operation log reconstruction unit is used to collect operation logs of resident trainees in multiple clinical information systems during the training cycle, and reconstruct the operation logs into a sequence of behavioral trajectories that reflect core clinical competencies. A quality deviation assessment unit is used to assess the quality deviation of the clinical case handling process of the resident trainees based on the behavioral trajectory sequence. The skill mastery vector determination unit is used to obtain the skill mastery vector of the resident in each clinical core competency based on the quality deviation of multiple clinical operations within a historical period and a preset competency dimension mapping rule. The capability evolution characteristic determination unit is used to generate the capability evolution characteristics of the trainees during the training cycle based on the change law of the skill mastery vector over time. The capability evolution characteristics include at least the capability growth curve and the accumulated capability stock. A multi-dimensional evaluation unit is used to generate multi-dimensional evaluation results, including skill maturity, risk deviation, consistency of diagnosis and treatment logic, and clinical efficiency factors, based on the capability evolution characteristics and the quality deviation.

[0018] The beneficial effects of this invention are as follows: The method for monitoring the quality of standardized residency training throughout its entire lifecycle provided by this invention achieves information integration from multiple heterogeneous clinical information systems by collecting the operation logs of resident trainees in multiple clinical information systems during the training cycle. Simultaneously, the operation logs are reconstructed into behavioral trajectory sequences reflecting core clinical competencies, transforming the originally scattered and unstructured operation records into a standardized format that can be quantified and analyzed. This solves the problem of missing trajectories caused by data fragmentation and provides a complete and computable data foundation for subsequent quality assessment.

[0019] Furthermore, this invention determines the quality deviation based on behavioral trajectory sequences, thereby determining the skill mastery vector, and generates capability evolution characteristics including capability growth curves and accumulated capability stock based on the changing pattern of the skill mastery vector over time. The capability evolution characteristics can intuitively display the dynamic changes of various core clinical capabilities of resident trainees throughout the entire training cycle, ensuring the non-jumping trend of capability evolution, avoiding information gaps caused by phased assessments, and improving the real-time performance and accuracy of full life-cycle monitoring of resident training quality.

[0020] Furthermore, the quality deviation quantifies the clinical procedure into a calculable numerical value, breaking through the limitations of traditional evaluations that only focus on results, and achieving objective quantification of the quality of the procedure. The skill mastery vector decomposes the deviation history into various core clinical competency dimensions, forming a fine-grained competency profile. The multi-dimensional evaluation results provide quantitative indicators from four dimensions: skill maturity, risk deviation, consistency of diagnosis and treatment logic, and clinical efficiency factor, replacing single-score evaluations and making the residency training quality assessment more comprehensive and objective, thus achieving a quantitative assessment of residency training quality. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic flowchart of an embodiment of the standardized training quality full life cycle monitoring method provided by the present invention; Figure 2 For the present invention Figure 1 A schematic flowchart of an embodiment of step S103; Figure 3 This is a schematic diagram of an embodiment of the standardized training quality full life cycle monitoring system provided by the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0024] It should be understood that the illustrative drawings are not drawn to scale. The flowcharts used in this invention illustrate operations implemented according to some embodiments of the invention. It should be understood that the operations in the flowcharts may be implemented out of order, and steps without logical contextual relationships may be reversed or performed simultaneously. Furthermore, those skilled in the art, guided by the content of this invention, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor systems and / or microcontroller systems.

[0025] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0026] This invention provides a method and system for monitoring the quality of standardized residency training throughout its entire lifecycle, which will be described below.

[0027] Figure 1 This is a schematic flowchart of an embodiment of the standardized residency training quality lifecycle monitoring method provided by the present invention, as shown below. Figure 1 As shown, the methods for monitoring the quality of standardized residency training throughout its entire lifecycle include: S101. During the training period, collect the operation logs of the resident trainees in multiple clinical information systems, and reconstruct the operation logs into a behavioral trajectory sequence that reflects the core clinical competencies.

[0028] Specifically, clinical information systems include multiple systems such as hospital information systems (HIS), electronic medical records (EMR), and picture archiving systems (PACS).

[0029] The specific implementation process for collecting operation logs is as follows: obtain the business log stream through the interfaces of the above systems to obtain the operation logs. Each record in the operation log includes a timestamp, operation type, and department location.

[0030] The reconstruction of operation logs into a sequence of behavioral trajectories can be achieved through a predefined mapping function. Specifically, feature embedding is performed first, which transforms the unstructured operation logs into high-dimensional space vectors. Then, topological alignment is performed, which uses the mapping function to map the high-dimensional space vectors into a sequence of behavioral trajectories.

[0031] S102. Assess the quality deviation of the clinical case handling process of resident trainees based on behavioral trajectory sequences.

[0032] Among them, the quality deviation is used to characterize the degree of comprehensive deviation of a single clinical operation by a resident physician from the standard clinical pathway in terms of both the execution path and decision-making timeliness in the diagnosis and treatment process.

[0033] S103. Based on the quality deviation of multiple clinical operations within a historical period and the preset capability dimension mapping rules, obtain the skill mastery vector of the resident trainee in each core clinical capability.

[0034] Clinical procedures refer to the specific actions performed by resident trainees in clinical work, such as "ordering blood routine tests," "recording the chief complaint in the electronic medical record system," "reviewing imaging reports," and "prescribing antibiotics." Each procedure is marked with a timestamp, procedure type, and recipient identifier. Treatment stages are the phased classification of clinical procedures according to the treatment process, such as "patient reception and history taking," "request and interpretation of auxiliary examinations," "preliminary diagnosis," "treatment plan development," and "medical record writing." A treatment stage typically includes several related clinical procedures. Core clinical competencies are the comprehensive ability dimensions that resident trainees should master, transferable across cases, such as "diagnostic accuracy," "appropriateness of examinations and tests," "treatment decision-making," and "standardization of medical documentation." Each core clinical competency is jointly reflected by related procedures in multiple treatment stages.

[0035] S104. Based on the changing pattern of the skill mastery vector over time, generate the capability evolution characteristics of the trainees during the training cycle. The capability evolution characteristics include at least the capability growth curve and the accumulated capability stock.

[0036] Among them, the ability growth curve is a continuous curve with time as the horizontal axis and the skill mastery vector as the vertical axis, which is formed by connecting the skill mastery vectors at different times in chronological order; the accumulated ability stock is the integral of the ability growth curve over time during the training period, representing the total ability of the trainee during the entire training period.

[0037] S105. Generate multi-dimensional evaluation results based on capability evolution characteristics and quality deviation, including skill maturity, risk deviation, consistency of diagnosis and treatment logic, and clinical efficiency factors.

[0038] Specifically, the multi-dimensional evaluation results are presented in matrix form, as shown below:

[0039] In the formula, is the multi-dimensional evaluation matrix; For skill maturity; Risk deviation; To ensure consistency in diagnostic and treatment logic; This is a clinical efficiency factor.

[0040] Among them, skill maturity characterizes the depth of knowledge mastery of residents in a specific professional field, determined by the ratio of accumulated ability stock to baseline ability stock, and is used to quantify whether their skill level has reached the expected stage; risk deviation measures the potential risk tendency of residents to cause medical accidents, calculated by the ratio of the maximum deviation of clinical pathways in historical periods to a preset safety threshold, and is used to trigger risk warnings and teaching interventions; consistency of diagnosis and treatment logic reflects the stability of residents' behavioral trajectories among different cases, obtained by comparing the variance of the actual trajectory sequence with the variance of the standard path sequence, and is used to assess the systematicness and resilience of their clinical thinking; clinical efficiency factor quantifies the ratio of the number of diagnosis and treatment steps completed by residents per unit time to the preset baseline diagnosis and treatment efficiency, and is used to evaluate their operational speed level. These four indicators, from the four dimensions of knowledge depth, risk exposure, logical stability, and operational efficiency, together constitute a multi-dimensional evaluation system for the quality of residency training.

[0041] Specifically, the formula for calculating the consistency of diagnostic and treatment logic is as follows:

[0042] In the formula, To ensure consistency in diagnostic and treatment logic; The variance of the behavioral trajectory sequence; The variance of the standard treatment pathway; It is a constant.

[0043] It should be noted that after obtaining the multi-dimensional evaluation results, early warnings and interventions can be implemented based on these results. Specifically, when the risk deviation exceeds a preset safety threshold, the high-risk medical order operation permissions of the resident are immediately locked, and an early warning report containing the deviation trajectory points is sent to the mentor. When the skill maturity is below the stage target value for two consecutive time windows, the resident's typical cases are automatically pushed to the teaching supervisor for review. When the consistency of diagnosis and treatment logic is below the lower limit or the clinical efficiency factor deviates significantly from the benchmark, the abnormal status of the corresponding ability dimension is highlighted on the monitoring interface, and the mentor is advised to arrange targeted specialized training. Through the above mechanisms, a closed-loop management system of monitoring-evaluation-early warning-intervention-feedback is formed, realizing dynamic control and continuous improvement of the quality of residency training.

[0044] It should be understood that the residency training quality lifecycle monitoring method in this embodiment of the invention can be implemented in any device based on the residency training quality lifecycle monitoring method, such as a hospital teaching management server, a residency training behavior data acquisition terminal, or a quality monitoring platform integrated into a hospital information system. Specifically, the residency training quality lifecycle monitoring method is stored in the above-mentioned device as a pre-compiled program or software module. When the device is started or running, the program is invoked, and the residency training quality lifecycle monitoring method is implemented, thereby realizing real-time collection of the clinical activity trajectory of residents, quality deviation assessment, skill mastery vector update, ability evolution characteristic generation, and multi-dimensional evaluation result output.

[0045] Compared with existing technologies, the method for monitoring the entire lifecycle of standardized residency training quality provided in this invention achieves information integration from multiple heterogeneous clinical information systems by collecting the operation logs of resident trainees in multiple clinical information systems during the training cycle. Simultaneously, the operation logs are reconstructed into behavioral trajectory sequences reflecting core clinical competencies, transforming the originally scattered and unstructured operation records into a standardized format that can be quantified and analyzed. This solves the problem of missing trajectories caused by data fragmentation and provides a complete and computable data foundation for subsequent quality assessment.

[0046] Furthermore, this embodiment of the invention determines the quality deviation based on the behavioral trajectory sequence, thereby determining the skill mastery vector, and generates capability evolution characteristics including capability growth curves and capability accumulation based on the change law of the skill mastery vector over time. The capability evolution characteristics can intuitively display the dynamic changes of various clinical core capabilities of residents throughout the training cycle, ensuring the non-jumping trend of capability evolution, avoiding information gaps caused by phased assessments, and improving the real-time performance and accuracy of monitoring the entire life cycle of residency training quality.

[0047] Furthermore, the quality deviation quantifies the clinical procedure into a calculable numerical value, breaking through the limitations of traditional evaluations that only focus on results, and achieving objective quantification of the quality of the procedure. The skill mastery vector decomposes the deviation history into various core clinical competency dimensions, forming a fine-grained competency profile. The multi-dimensional evaluation results provide quantitative indicators from four dimensions: skill maturity, risk deviation, consistency of diagnosis and treatment logic, and clinical efficiency factor, replacing single-score evaluations and making the residency training quality assessment more comprehensive and objective, thus achieving a quantitative assessment of residency training quality.

[0048] In a specific embodiment of the present invention, the mass deviation is:

[0049] In the formula, For quality deviation; The clinical behavioral coordinates of the i-th diagnosis and treatment step in the behavioral trajectory sequence; The coordinates of the i-th treatment step in the standard treatment pathway; The Euclidean distance between clinical behavior coordinates and standard treatment pathway coordinates; Let be the weight coefficient of the i-th diagnosis and treatment step; This is a penalty for decision-making delays; This is the delay penalty coefficient; The time difference between the time taken for resident physicians to complete a diagnostic and treatment procedure and the standard time. This refers to the total number of diagnostic and treatment steps included in a clinical case.

[0050] It should be noted that the coordinates in the behavioral trajectory sequence are not physical spatial coordinates, but rather a mathematical representation that maps multi-dimensional information such as the resident's operation type, temporal characteristics, and resource usage to a high-dimensional behavioral space. A greater deviation in quality indicates a greater difference between the resident's behavioral pattern and the standard pattern for qualified physicians, and a lower level of mastery of corresponding core clinical competencies.

[0051] This invention significantly improves the comprehensiveness and objectivity of clinical procedure quality assessment by setting a quality deviation degree composed of coordinate distance and a decision delay penalty term. Specifically, the coordinate Euclidean distance quantifies the deviation between the actual behavior of the resident physician and the standard path in each diagnostic and treatment step, accurately identifying procedural defects such as omissions of key steps and errors in the order of operations; the decision delay penalty term penalizes the response time of key operations such as issuing medical orders and requesting examinations, effectively reflecting the resident physician's clinical responsiveness and ability to handle acute and critical illnesses. The quality deviation degree formed by the fusion of these two factors achieves dual quantification of whether the clinical procedure is standardized and timely, providing fine-grained input for subsequent skill mastery vector updates and the generation of capability evolution trends.

[0052] To further improve the accuracy of quality deviation, in a specific embodiment of the present invention, the link weight coefficient is positively correlated with the importance of the diagnosis and treatment link in the clinical pathway.

[0053] Given that different stages of clinical practice have significantly different impacts on patient safety, treatment outcomes, and medical quality—for example, decisions made during emergency care directly affect a patient's life, while routine medical record writing has a relatively lower impact on immediate risk—assigning higher weight to critical stages allows quality deviations to more sensitively reflect residents' deficiencies in core procedures, preventing deviations in minor stages from masking critical errors. In other words, by dynamically adjusting the weighting coefficients of each stage of the clinical process, residents can be guided to prioritize and strengthen the operational standardization of highly important stages, thereby promoting the precise improvement of core clinical competencies at the teaching level.

[0054] In some embodiments of the present invention, such as Figure 2 As shown, step S103 includes: S201. Based on the preset capability dimension mapping rules, the quality deviation is associated with a core clinical capability dimension; S202. Weighted fusion of multiple quality deviations under the same core clinical competency dimension to obtain the skill mastery level; S203. Combine the skill mastery levels of all clinical core competency dimensions to obtain a skill mastery vector.

[0055] In a specific example, taking an acute appendicitis case, the entire diagnosis and treatment process is first broken down into six key steps: patient reception and history taking, ordering a complete blood count (CBC), ordering an abdominal CT scan, interpreting the test results, making a preliminary diagnosis, and developing a treatment plan. Each step is pre-associated with a core clinical competency dimension through mapping rules. For example, the first three steps are associated with the ability to make appropriate examinations and tests, the middle two steps with the ability to make accurate diagnoses, and the last step with the ability to make treatment decisions. Based on the difference between the actual performance of the trainees in each step and the standard pathway, the deviation value of each step is calculated. The smaller the deviation value, the better the performance in that step. For the dimension of the ability to make appropriate examinations and tests, which includes the three steps of patient reception and history taking, ordering a CBC, and ordering an abdominal CT scan, the deviation values ​​are 0.1, 0.05, and 0.2, respectively. The weighted average of these deviation values ​​is 0.1167. If the maximum allowable deviation is 0.5, then the skill mastery level of this dimension is 1 - 0.1167 / 0.5 = 0.7667. Similarly, the diagnostic accuracy ability dimension consists of two parts: interpreting examination results and making a preliminary diagnosis, with deviation values ​​of 0.15 and 0.1 respectively. The combined result yields a skill mastery level of 0.75. The treatment decision-making ability dimension only includes the step of formulating a treatment plan, with a deviation value of 0.08, corresponding to a skill mastery level of 0.84. Finally, the skill mastery levels of the three dimensions are combined into a vector in a preset order, namely [0.7667, 0.75, 0.84]. This vector represents the instantaneous mastery status of the resident trainee regarding the rationality of examinations and tests, diagnostic accuracy, and treatment decision-making abilities after this clinical operation.

[0056] In some embodiments of the present invention, the accumulated capacity is:

[0057] In the formula, To accumulate existing capacity; For the training cycle; Let be the skill mastery vector at time t; The feature transformation matrix; For bias terms; It is a non-linear mapping function.

[0058] Because resident trainees rotate through multiple departments throughout the training cycle, each department has different requirements and focuses regarding core clinical competencies. Feature Transformation Matrix Its function is to weight and fuse the skill mastery vectors acquired by resident trainees in various departments according to the contribution of each department to the overall competency evaluation, thereby obtaining a comprehensive skill evolution input. Specifically, Each row corresponds to a department or a capability integration rule, and each column corresponds to a different capability dimension in the skill mastery vector. This is achieved through matrix multiplication. This system can map the stage-by-stage skill performance of trainees from different departments to a unified competency growth space, enabling subsequent nonlinear mapping and integral calculations to reflect the comprehensive competency accumulation of trainees across departments throughout the training cycle. This avoids distortion of competency assessments due to differences in scoring scales between different departments, making full-lifecycle monitoring more scientific and fair.

[0059] Specifically, the nonlinear mapping function uses either Softplus or Sigmoid to identify the leaps in competence among trainees from the initial to the advanced stages.

[0060] This invention, through integration over time, obtains the accumulated capacity of resident trainees throughout the entire training cycle. Compared to the traditional percentage-based assessment, it more accurately reflects the slope of the trainees' learning curve, thereby identifying outstanding doctors with high potential.

[0061] Specifically, in the specific implementation scenarios of standardized training for surgical residents: 1. Trajectory Mining: The system recorded the trajectory of a resident physician when treating a patient with acute appendicitis. From initial consultation and ordering of auxiliary examinations to medical record writing, the system extracted 12 key trajectory points.

[0062] 2. Mapping analysis: After processing with the mapping function, it was identified that this resident deviated from the standard timeline by 45 minutes during the emergency blood routine test.

[0063] 3. Scoring Calculation: Substituting the values ​​into the formula for calculating the quality deviation, the quality deviation is calculated to be 2.45. This is combined with the weighting of each stage. Gain skill maturity.

[0064] 4. Lifecycle Update: Combining the historical data of the residents over the past 6 months, we obtained the capability evolution characteristics. The capability growth curve shows that the growth curve of the residents in the emergency treatment dimension tends to be flat, suggesting that teaching mentors need to strengthen relevant skills training.

[0065] 5. Output Results: Matrix displays risk factors If the value rises to 0.65, a level 2 warning is triggered, and the student's typical case is automatically pushed to the tutor's mobile device for review.

[0066] In summary, the full lifecycle monitoring method for standardized residency training quality proposed in this invention solves the data fragmentation problem at its source by collecting and reconstructing the operation logs of trainees in multiple clinical information systems into behavioral trajectory sequences, achieving complete capture of clinical activity trajectories. Based on this, it calculates the quality deviation of each clinical operation using Euclidean distance and decision delay penalty terms, expanding the traditional evaluation that only focuses on results to a dual quantification of the operation process and response speed, filling the gap in objective indicators of process quality. Furthermore, based on preset capability dimension mapping rules, historical deviations are transformed into fine-grained skill mastery vectors, generating a full-cycle capability growth curve and accumulated inventory. This allows mentors to identify trainees' weaknesses and growth bottlenecks in specific core capabilities in real time, completely overcoming the evaluation lag and discontinuity defects caused by periodic assessments. Finally, it outputs multi-dimensional evaluation results composed of skill maturity, risk deviation, consistency of diagnostic and treatment logic, and clinical efficiency factors, significantly improving the intelligence level of standardized residency training management, the accuracy of teaching intervention, and clinical safety assurance capabilities.

[0067] On the other hand, embodiments of the present invention also provide a system for monitoring the quality of standardized residency training throughout its entire lifecycle, such as... Figure 3 As shown, the standardized residency training quality lifecycle monitoring system 300 includes: Operation log reconstruction unit 301 is used to collect operation logs of resident trainees in multiple clinical information systems during the training cycle and reconstruct the operation logs into a sequence of behavioral trajectories that reflect core clinical competencies. Quality Deviation Assessment Unit 302 is used to assess the quality deviation of the clinical case handling process of resident trainees based on behavioral trajectory sequences. Skill mastery vector determination unit 303 is used to obtain the skill mastery vector of the resident in each clinical core competency based on the quality deviation of multiple clinical operations in historical periods and the preset ability dimension mapping rules. The capability evolution characteristic determination unit 304 is used to generate capability evolution characteristics of standardized training personnel during the training cycle based on the change law of skill mastery vector over time. The capability evolution characteristics include at least the capability growth curve and the accumulated capability stock. The multi-dimensional evaluation unit 305 is used to generate multi-dimensional evaluation results based on capability evolution characteristics and quality deviation, including skill maturity, risk deviation, consistency of diagnosis and treatment logic, and clinical efficiency factors.

[0068] The standardized training quality full life cycle monitoring system 300 provided in the above embodiments can realize the technical solutions described in the above standardized training quality full life cycle monitoring method embodiments. The specific implementation principles of each module or unit can be found in the corresponding content in the above standardized training quality full life cycle monitoring method embodiments, and will not be repeated here.

[0069] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0070] The present invention provides a detailed description of a method and system for monitoring the quality of standardized residency training throughout its entire lifecycle. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, those skilled in the art will recognize that there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for monitoring the quality of standardized residency training throughout its entire lifecycle, characterized in that, include: During the training period, the operation logs of the resident trainees in multiple clinical information systems were collected, and the operation logs were reconstructed into a sequence of behavioral trajectories that reflect core clinical competencies. The quality deviation of the clinical case handling process of the resident trainees was assessed based on the behavioral trajectory sequence. Based on the quality deviation of multiple clinical operations within a historical period and the preset capability dimension mapping rules, the skill mastery vector of the resident trainee in each core clinical capability is obtained. Based on the changing pattern of the skill mastery vector over time, the ability evolution characteristics of the trainees during the training cycle are generated. The ability evolution characteristics include at least the ability growth curve and the accumulated ability stock. Based on the aforementioned capability evolution characteristics and the aforementioned quality deviation, a multi-dimensional evaluation result is generated, including skill maturity, risk deviation, consistency of diagnosis and treatment logic, and clinical efficiency factors.

2. The method for monitoring the quality of standardized residency training throughout its entire lifecycle according to claim 1, characterized in that, The mass deviation is: In the formula, For quality deviation; The clinical behavioral coordinates of the i-th diagnosis and treatment step in the behavioral trajectory sequence; The coordinates of the i-th treatment step in the standard treatment pathway; The Euclidean distance between clinical behavior coordinates and standard treatment pathway coordinates; Let be the weight coefficient of the i-th diagnosis and treatment step; This is a penalty for decision-making delays; This is the delay penalty coefficient; The time difference between the time taken for resident physicians to complete a diagnostic and treatment procedure and the standard time. This refers to the total number of diagnostic and treatment steps included in a clinical case.

3. The method for monitoring the quality of standardized residency training throughout its entire lifecycle according to claim 2, characterized in that, The weight coefficient of each step is positively correlated with the importance of the diagnosis and treatment steps in the clinical pathway.

4. The method for monitoring the quality of standardized residency training throughout its entire lifecycle according to claim 1, characterized in that, The skill mastery vector of the resident physician in each core clinical competency is obtained based on the quality deviation of multiple clinical operations within a historical period and a preset competency dimension mapping rule, including: The quality deviation is associated with a core clinical competency dimension based on a preset competency dimension mapping rule. Multiple quality deviations under the same core clinical competency dimension are weighted and fused to obtain the skill mastery level; The skill mastery vector is obtained by combining the skill mastery levels of all core clinical competency dimensions.

5. The method for monitoring the quality of standardized residency training throughout its entire lifecycle according to claim 2, characterized in that, The accumulated capacity is: In the formula, To accumulate existing capacity; For the training cycle; Let be the skill mastery vector at time t; The feature transformation matrix; For bias terms; It is a non-linear mapping function.

6. The method for monitoring the quality of standardized residency training throughout its entire lifecycle according to claim 1, characterized in that, The skill maturity is used to characterize the depth of knowledge mastery of resident trainees, and the skill maturity is the ratio of the accumulated capacity to the baseline capacity.

7. The method for monitoring the quality of standardized residency training throughout its entire lifecycle according to claim 1, characterized in that, The risk deviation is the ratio of the maximum quality deviation of all clinical operations during the training period to a preset safety threshold.

8. The method for monitoring the quality of standardized residency training throughout its entire lifecycle according to claim 2, characterized in that, The consistency of the diagnostic and treatment logic is as follows: In the formula, To ensure consistency in diagnostic and treatment logic; The variance of the behavioral trajectory sequence; The variance of the standard treatment pathway; It is a constant.

9. The method for monitoring the quality of standardized residency training throughout its entire lifecycle according to claim 1, characterized in that, The clinical efficiency factor is the ratio of the number of diagnostic and treatment steps completed by the trainees per unit time to the preset benchmark diagnostic and treatment efficiency.

10. A system for monitoring the quality of standardized residency training throughout its entire lifecycle, characterized in that, include: The operation log reconstruction unit is used to collect operation logs of resident trainees in multiple clinical information systems during the training cycle, and reconstruct the operation logs into a sequence of behavioral trajectories that reflect core clinical competencies. A quality deviation assessment unit is used to assess the quality deviation of the clinical case handling process of the resident trainees based on the behavioral trajectory sequence. The skill mastery vector determination unit is used to obtain the skill mastery vector of the resident in each clinical core competency based on the quality deviation of multiple clinical operations within a historical period and a preset competency dimension mapping rule. The capability evolution characteristic determination unit is used to generate the capability evolution characteristics of the trainees during the training cycle based on the change law of the skill mastery vector over time. The capability evolution characteristics include at least the capability growth curve and the accumulated capability stock. A multi-dimensional evaluation unit is used to generate multi-dimensional evaluation results, including skill maturity, risk deviation, consistency of diagnosis and treatment logic, and clinical efficiency factors, based on the capability evolution characteristics and the quality deviation.

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