Virtual reality medical education system and method, computer equipment and readable storage medium
The virtual reality medical education system, which integrates modules such as a virtual case database and a medical order knowledge base, solves the problem of module fragmentation and achieves seamless data flow throughout the entire process, enabling accurate evaluation of training effectiveness.
Patent Information
- Application Number
- CN202511055461.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-30
- Publication Date
- 2025-11-11
AI Technical Summary
Existing virtual training systems suffer from fragmented modules and a lack of a unified evaluation model, resulting in a broken training loop and an inability to accurately assess training effectiveness.
A virtual reality medical education system is constructed, integrating a virtual case database, a medical order knowledge base, a full-process simulation and verification module, a medical order processing module, a virtual workstation, a multimodal interactive skills training module, and a quantitative assessment module, to achieve cross-module data fusion and full-process data chain integration.
It enables real-time data exchange between skills training and clinical decision-making, repairs the gaps in the training loop, accurately assesses training effectiveness, and generates multi-dimensional evaluation reports.
Smart Images
Figure CN120931445A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of clinical medical education technology, specifically to a virtual reality medical education system, method, computer equipment, and readable storage medium. Background Technology
[0002] Against the backdrop of rapid iteration and explosive growth of knowledge in the healthcare industry, young medical professionals and those with insufficient clinical experience often find themselves in a predicament of confused treatment approaches and difficulty in choosing treatment plans when faced with complex cases, due to a lack of systematic knowledge support and scientific decision-making guidance. Traditional channels for acquiring knowledge, such as scattered literature reviews and oral traditions based on experience, are not only inefficient but also suffer from problems such as information lag and inconsistent authority, seriously restricting the quality of medical services and patient safety.
[0003] Existing virtual training systems consist of multiple independent modules with no data interconnection between them, no shared databases, and independent operation of hardware systems. The lack of a unified evaluation model leads to a broken training loop.
[0004] Meanwhile, while existing virtual training systems can train users in some clinical medical knowledge, traditional training systems can usually only capture basic operational data to evaluate the user's training effect.
[0005] Therefore, how to build a virtual training system that supports cross-module data fusion and can accurately evaluate training results is a technical problem that urgently needs to be solved in this field. Summary of the Invention
[0006] To address the shortcomings of existing technologies, this invention proposes a virtual reality medical education system, method, computer equipment, and readable storage medium to solve the technical problems of fragmented assessment dimensions and disjointed system modules in existing technologies.
[0007] The technical solution adopted in this invention is a virtual reality medical education system, method, computer equipment, and readable storage medium.
[0008] In a first possible implementation, a virtual reality medical education system is provided, comprising: A virtual medical record database is used to store standardized medical record templates; The medical order knowledge base is used to store medical order rule information; The full-process simulation and verification module is connected to the virtual case database and is used to verify the integrity of the case information entered by the user based on the standardized medical record template. The medical order processing module connects the virtual case database and the medical order knowledge base, and is used to generate medical order conflict detection results based on the standardized medical record template and medical order rules; The virtual workstation connects the full-process simulation and verification module and the medical order processing module, and is used to simulate the entire clinical operation process based on the integrity verification results and the medical order conflict detection results. A multimodal interactive skills training module, connected to the virtual workstation, is used to provide a virtual training environment for the entire clinical procedure. The quantitative assessment module, connected to the virtual workstation, is used to analyze user behavior data during the entire clinical procedure and generate a multi-dimensional assessment report.
[0009] Furthermore, the virtual medical record database includes: The data migration unit is used to desensitize and structure real case data; A real-time verification unit is used to parse the medical record text in the real case data; A storage unit is used to store the medical record text as a standardized medical record template.
[0010] Furthermore, the virtual workstation integrates an electronic medical record simulation unit, a medical order processing unit, a critical value alarm unit, and a multidisciplinary consultation unit; The electronic medical record simulation unit is used to receive the verification results of the full-process simulation verification module and to simulate medical record writing and management. The medical order processing unit is used to receive the conflict detection results from the medical order processing module and simulate the creation and processing of medical orders. The critical value alarm unit is used to monitor simulated case data according to a preset critical value threshold, and trigger an emergency response process when the threshold is reached. The multidisciplinary consultation unit is used to simulate the entire clinical procedure.
[0011] Furthermore, the multimodal interactive skills training module includes: The complication sandbox unit is used to generate complex clinical complication scenarios based on user-defined combinations of anatomical variation parameters, pathophysiological abnormality parameters, and environmental interference parameters. The clinical operation scenario construction unit is used to generate a training environment that includes aseptic operation areas, complication trigger points, and equipment operation interfaces. The multimodal feedback unit integrates spatial attitude sensing data, force feedback signals, and visual recognition results to map the user's operation trajectory in real time.
[0012] Preferably, the clinical operation scenario construction unit is used to generate a training environment that includes a sterile operation area, complication trigger points, and equipment operation interface, including: In emotion-driven doctor-patient interaction scenarios, a defensive questioning mechanism is triggered based on the patient's psychological parameter model; Equipment failure simulation scenarios, including ECG lead misalignment and emergency waveform recognition training; A differentiated wound generation system that dynamically sets wound depth parameters and foreign body distribution based on the type of trauma; Anatomical navigation puncture scenario, used to provide real-time puncture navigation, force feedback simulation and complication combination functions based on three-dimensional anatomical model.
[0013] Furthermore, the complication sandbox unit is used to generate complex clinical complication scenarios based on user-defined combinations of anatomical variation parameters, pathophysiological abnormality parameters, and environmental disturbance parameters, including: Receives user-selected anatomical variation parameters, physiological function abnormality parameters, and medical environment interference parameters; Training scenarios for complex complications are constructed in real time based on parameter combinations, including simulation of pathological signs and dynamic response of physiological indicators; The generated complication scenarios are seamlessly embedded into the clinical operation training process, enabling the reconfigurability of the pathological state in a single training scenario.
[0014] Furthermore, the quantitative evaluation module includes: The operation standard monitoring unit is used to identify the degree of standardization of clinical operations through a spatial posture sensor; Clinical decision analysis unit, used to record response time for complication management; An operation quality assessment unit is used to quantify operational accuracy deviations based on AI image measurement technology. The error chain modeling unit is used to construct a causal relationship model that includes the pathophysiological reactions and end events caused by the standardization of the action, the response time of the complication handling, and the deviation of the operational accuracy. The loss quantification unit is used to generate a loss report based on the causal relationship model.
[0015] In conjunction with the first feasible method, a second feasible method provides a virtual reality medical education method for a virtual reality medical education system, characterized by comprising: The hospital's real medical records data were anonymized, transferred, and structured. Natural language processing was used to parse the medical record text and build standardized medical record templates. The integrity of the user-inputted medical record information is verified based on the standardized medical record template, and an integrity verification result is generated. A medical order rule information database is constructed based on the standardized medical record template. Detect medical order conflicts based on the standardized medical record template and the medical order rule information database; Generate a virtual training environment for the entire clinical procedure; Based on the integrity verification results and the medical order conflict detection results, the entire clinical operation process is simulated in the virtual training environment; Analyze user behavior data throughout the entire clinical procedure to generate a multi-dimensional assessment report.
[0016] In conjunction with the second possible implementation, a third possible implementation includes a computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the virtual reality medical education method.
[0017] In conjunction with the second possible implementation, in the fourth possible implementation, a readable storage medium storing a computer program is characterized in that the computer program, when executed by a processor, implements the virtual reality medical education method.
[0018] As can be seen from the above technical solution, the beneficial technical effects of the present invention are as follows: 1. By integrating four major units—electronic medical records, medical order processing, critical value alarms, and multidisciplinary consultations—onto a unified platform, a full-process data bus is built to achieve real-time data exchange between skills training and clinical decision-making, thus repairing the break in the training closed loop.
[0019] 2. By constructing a causal relationship model containing event causal chains to generate loss reports, the virtual training system can accurately evaluate the training effect. Attached Figure Description
[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.
[0021] Figure 1 This is a system structure diagram of Embodiment 1 of the present invention; Figure 2 This is a flowchart of the method in Embodiment 2 of the present invention. Detailed Implementation
[0022] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are merely illustrative of the technical solution of the present invention and are therefore intended to limit the scope of protection of the present invention.
[0023] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application should have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.
[0024] Example 1 This embodiment provides a virtual reality medical education system. The working principle of Embodiment 1 is explained in detail below: The system structure diagram of this embodiment is as follows: Figure 1 As shown, it includes: The virtual medical record database is used to desensitize, migrate, and structure real medical record data from hospitals. Natural language processing technology is used to parse medical record texts and construct and store standardized medical record templates containing basic patient information, chief complaint, present medical history, examination and test results.
[0025] The medical order knowledge base connects the virtual case database and the standardized medical record template database, and is used to establish association rules between medical orders and disease diagnoses, patient allergy history, and test results using knowledge graph technology. The standardized medical record template library is constructed by the designated hospital's "Clinical Medication Guidelines" and "Diagnosis and Treatment Operation Specifications," combined with medication orders, examination orders, and treatment orders.
[0026] The full-process simulation verification module is connected to the virtual case database and is used to verify the completeness, logic and standardization of the case information entered by the user. Based on the standardized medical record template database, it monitors the writing errors and omissions in the case entered by the user and outputs the verification results.
[0027] The medical order processing module connects the virtual case database and the medical order knowledge base, and is used to generate medical order conflict detection results based on case information and medical order rules; The virtual workstation connects the full-process simulation and verification module and the medical order processing module, and is used to simulate the entire process of clinical operation in the inpatient treatment environment based on the integrity verification results and medical order conflict detection results using digital twin technology. A multimodal interactive skills training module, connected to the virtual workstation, is used to provide a virtual training environment for the entire clinical operation process and integrates force feedback, visual feedback and complication simulation functions; The quantitative assessment module, connected to the virtual workstation, is used to analyze user operational behavior data during the entire clinical procedure. It uses machine learning algorithms to analyze user operational behavior data and generate a multi-dimensional assessment report covering clinical decision-making, operational procedures, and emergency response.
[0028] By integrating the case database, medical order system, skills training and assessment modules through virtual workstations, and opening up the entire data chain, the training loop breakage caused by the fragmentation of traditional system modules can be eliminated.
[0029] In this embodiment, the virtual case database further includes: a data migration unit for desensitizing and structuring real case data; a real-time verification unit for parsing the medical record text in the real case data; and a storage unit for storing the medical record text as a standardized medical record template.
[0030] The data migration unit desensitizes and structures real cases, while the real-time verification unit parses medical record texts and constructs highly realistic standardized templates to solve the problem of traditional virtual cases being detached from clinical reality.
[0031] In this embodiment, the virtual workstation further integrates an electronic medical record simulation unit, a medical order processing unit, a critical value alarm unit, and a multidisciplinary consultation unit to simulate the entire clinical diagnosis and treatment process.
[0032] The electronic medical record simulation unit is used to receive the verification results of the full-process simulation verification module and simulate medical record writing and management; the medical order processing unit is used to receive the conflict detection results of the medical order processing module and simulate the creation and processing of medical orders; the critical value alarm unit is used to monitor the simulated case data according to the preset critical value threshold and trigger the emergency response process and alarm when the threshold is reached. The multidisciplinary consultation unit is used to simulate the entire clinical procedure.
[0033] In the virtual workstation module: the electronic medical record simulation unit uses the HL7 FHIR standard interface to achieve desensitization and migration of real case data; the electronic medical record simulation unit has a built-in red and yellow card warning mechanism to verify the integrity of medical records in real time. The medical order processing unit uses a knowledge graph to associate diagnoses, allergy history, and test results to detect drug dosage exceeding limits and interaction conflicts.
[0034] In this embodiment, the multimodal interactive skills training module further includes: The Complication Sandbox unit is used to generate complex clinical complication scenarios based on user-defined combinations of anatomical variation parameters, pathophysiological abnormality parameters, and environmental interference parameters. The Complication Sandbox unit supports the free combination of anatomical / pathological / environmental parameters to reconstruct comprehensive cases in a single scenario, breaking through the limitations of traditional training frequency.
[0035] The clinical operation scenario construction unit is used to generate a training environment that includes aseptic operation areas, complication trigger points, and equipment operation interfaces. The multimodal feedback unit integrates spatial attitude sensing data, force feedback signals, and visual recognition results to map the user's operation trajectory in real time.
[0036] The real-time blocking unit is used to forcibly interrupt the current operation process and activate emergency response guidance when it detects that the user's operation deviation exceeds the safety threshold.
[0037] Furthermore, in this embodiment, the clinical operation scenario construction unit is used to generate a training environment that includes a sterile operation area, complication trigger points, and a device operation interface, including: In emotion-driven doctor-patient interaction scenarios, a defensive questioning mechanism is triggered based on the patient's psychological parameter model; Equipment failure simulation scenarios, including ECG lead misalignment and emergency waveform recognition training; The differentiated wound generation system dynamically sets wound depth parameters and foreign body distribution according to the type of trauma, and intelligently evaluates the results of debridement operations. Anatomical navigation puncture scenario, used to provide real-time puncture navigation, force feedback simulation and complication combination functions based on three-dimensional anatomical model.
[0038] The aforementioned emotion-driven doctor-patient interaction scenario is used to simulate the patient's emotional state and to perform compliance checks on the user's communication scripts, including: A patient emotion dynamics model was constructed based on the SCL-90 scale. When the anxiety value is >70%, a defensive questioning mechanism is triggered, and the language is checked against the "Regulations on the Prevention of Medical Disputes" in real time for compliance. The equipment fault simulation scenarios include: an electrocardiograph operating unit, a virtual training scenario for operating an electrocardiograph, including electrocardiograph lead positioning, fault identification, and emergency treatment; The differentiated wound generation system includes: dynamically generating differentiated wound parameters based on the type of trauma. The range of these wound parameters includes: depth 0.3-1.2 cm, number of foreign bodies 3-15; after generating the differentiated wound parameters, AI image segmentation technology is used to assess the completeness of debridement. The anatomical navigation puncture scenario integrates three-dimensional positioning of blood vessels and nerves with an adjustable force feedback mechanism to provide real-time puncture navigation, force feedback simulation, and complication combination functions based on a three-dimensional anatomical model.
[0039] The puncture technique training unit includes: reconstructing a patient-specific 3D anatomical model using Mimics software, integrating real-time vascular and nerve navigation, magnetohydrodynamic glove force feedback, and a freely combinable complication sandbox; the puncture training unit supports multi-role collaborative operation modes, including virtual nurses assisting in measuring abdominal pressure during paracentesis.
[0040] Furthermore, in this embodiment, the complication sandbox unit is used to generate complex clinical complication scenarios based on user-defined combinations of anatomical variation parameters, pathophysiological abnormality parameters, and environmental interference parameters, including: Receives user-selected anatomical variation parameters, physiological function abnormality parameters, and medical environment interference parameters; Training scenarios for complex complications are constructed in real time based on parameter combinations, including simulation of pathological signs and dynamic response of physiological indicators; The generated complication scenarios are seamlessly embedded into the clinical operation training process, enabling the reconfigurability of the pathological state in a single training scenario.
[0041] The complication sandbox unit transforms parameter combinations into dynamic responses to pathological signs, seamlessly embedding them into the operational process and supporting repeated training of different complications in the same scenario.
[0042] Furthermore, in this embodiment, the quantitative evaluation module includes: The operational procedure monitoring unit is used to identify the degree of standardization of clinical operations through spatial posture sensors, including: Real-time capture of the area and duration of contamination in sterile areas; The pollution risk index is calculated according to the following formula, and then used to assess the standardization of actions: in, This represents the pollution risk index, where n represents... Indicates the area of contamination of a word. Indicates the standard area of the sterile area. Indicates the duration of pollution. Represents the attenuation constant. The value represents the tissue sensitivity coefficient of the contaminated site, and k represents the instrument disinfection level coefficient. In this embodiment, the value is 0.8 for sterile instruments and 1.2 for non-sterile instruments.
[0043] Clinical decision analysis unit, used to record response time for complication management; An operation quality assessment unit is used to quantify operational accuracy deviations based on AI image measurement technology. The error chain modeling unit is used to construct a causal relationship model that includes the pathophysiological reactions and end events caused by the pollution risk index, complication treatment response time, and operational accuracy deviation. The loss quantification unit is used to generate a loss report based on the causal relationship model.
[0044] The operation standard monitoring unit is embedded in the VR gloves worn by the user through a high-precision inertial measurement unit to capture the user's posture data in real time; furthermore, the operation standard monitoring unit matches the posture data with a standard template through a dynamic time warping algorithm and outputs the action standardization degree.
[0045] The clinical decision analysis unit records the delay time from the triggering of a complication to user intervention, and marks the complication handling response time if it exceeds a set threshold (set to 120 seconds in this embodiment). The error chain modeling unit is used to construct a causal relationship model that includes pathophysiological reactions and end events caused by deviations in action standardization, complication handling response time, and operational precision, including: The error chain modeling unit collects operational data in real time from each monitoring unit within the quantitative evaluation module, including the standardization of actions captured by spatial sensors, response time for handling complications, and deviation of operational accuracy measured by the visual system; after feature extraction, the above data is mapped to a preset medical error classification system. The error chain modeling unit calls the built-in pathophysiological knowledge base to establish an initial association. The pathophysiological knowledge base is constructed using medical ontology and contains at least 2,000 clinical pathway rules. In this embodiment, the clinical pathway rules include a causal chain of "vascular injury - hemorrhagic shock". When a positional deviation of more than 3 mm is detected during the puncture operation, the system automatically associates the vascular penetration risk node and calculates the probability of injury based on the patient's individual parameters.
[0046] The error chain modeling unit extrapolates clinical outcomes through a three-layer dynamic simulation: the first layer, a biomechanical model, calculates the degree of tissue damage in real time, including predicting the area of vascular tearing based on puncture force and angle; the second layer, a physiological model, simulates the chain reaction triggered by the injury, including simulating the bleeding rate based on computational fluid dynamics and predicting the blood pressure drop curve in combination with the patient's compensatory ability; and the third layer, a clinical decision model, evaluates the effectiveness of intervention measures, including calculating the impact of compression hemostasis response time on blood loss.
[0047] The above deduction process uses a temporal Bayesian network to achieve probability propagation. The network nodes contain key state variables from operational errors to terminal events, and the conditional probability tables between nodes are calibrated by clinical experts.
[0048] The loss quantification unit is used to generate a loss report based on the causal relationship model. The loss report includes: biomechanical parameters: force / angle deviation values of the operating instruments; pathological change data: damage area / loss of function of simulated tissues; resource consumption data: virtual medical expenses and time costs; and prognostic indicators: complication rate and survival rate changes.
[0049] In this embodiment, the quantitative evaluation module collects operation data in real time through each monitoring unit, constructs a causal relationship model that includes pathophysiological reactions and end events caused by the standardization of the action, response time to complication handling, and deviation in operation accuracy, and generates a loss report based on the causal relationship model to evaluate the user's training effect, thus solving the deficiency of traditional evaluation that can only capture basic operation data.
[0050] Example 2 In conjunction with Embodiment 1, Embodiment 2 includes a virtual reality medical education method for use with the virtual reality medical education system described in Embodiment 1. The flowchart of the method is shown below. Figure 2 As shown, it includes: The hospital's real medical records data were anonymized, transferred, and structured. Natural language processing was used to parse the medical record text and build standardized medical record templates. The integrity of the user-inputted medical record information is verified based on the standardized medical record template, and an integrity verification result is generated. A medical order rule information database is constructed based on the standardized medical record template. Detect medical order conflicts based on the standardized medical record template and the medical order rule information database; Generate a virtual training environment for the entire clinical procedure; Based on the integrity verification results and the medical order conflict detection results, the entire clinical operation process is simulated in the virtual training environment; Analyze user behavior data throughout the entire clinical procedure to generate a multi-dimensional assessment report.
[0051] Example 3 In conjunction with Embodiment 2, Embodiment 3 includes a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a virtual reality medical education method.
[0052] Example 4 In conjunction with Embodiment 2, Embodiment 4 includes a readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements a virtual reality medical education method.
[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.
Claims
1. A virtual reality medical education system, characterized in that, include: A virtual medical record database is used to store standardized medical record templates; The medical order knowledge base is used to store medical order rule information; The full-process simulation and verification module is connected to the virtual case database and is used to verify the integrity of the case information entered by the user based on the standardized medical record template. The medical order processing module connects the virtual case database and the medical order knowledge base, and is used to generate medical order conflict detection results based on the standardized medical record template and medical order rules; The virtual workstation connects the full-process simulation and verification module and the medical order processing module, and is used to simulate the entire clinical operation process based on the integrity verification results and the medical order conflict detection results. A multimodal interactive skills training module, connected to the virtual workstation, is used to provide a virtual training environment for the entire clinical procedure. The quantitative assessment module, connected to the virtual workstation, is used to analyze user behavior data during the entire clinical procedure and generate a multi-dimensional assessment report.
2. The virtual reality medical education system according to claim 1, characterized in that, The virtual medical record database includes: The data migration unit is used to desensitize and structure real case data; A real-time verification unit is used to parse the medical record text in the real case data; A storage unit is used to store the medical record text as a standardized medical record template.
3. The virtual reality medical education system according to claim 1, characterized in that, The virtual workstation integrates an electronic medical record simulation unit, a medical order processing unit, a critical value alarm unit, and a multidisciplinary consultation unit. The electronic medical record simulation unit is used to receive the verification results of the full-process simulation verification module and to simulate medical record writing and management. The medical order processing unit is used to receive the conflict detection results from the medical order processing module and simulate the creation and processing of medical orders. The critical value alarm unit is used to monitor simulated case data according to a preset critical value threshold, and trigger an emergency response process when the threshold is reached. The multidisciplinary consultation unit is used to simulate the entire clinical procedure.
4. A virtual reality medical education system according to claim 1, characterized in that, The multimodal interactive skills training module includes: The complication sandbox unit is used to generate complex clinical complication scenarios based on user-defined combinations of anatomical variation parameters, pathophysiological abnormality parameters, and environmental interference parameters. The clinical operation scenario construction unit is used to generate a training environment that includes aseptic operation areas, complication trigger points, and equipment operation interfaces. The multimodal feedback unit integrates spatial attitude sensing data, force feedback signals, and visual recognition results to map the user's operation trajectory in real time.
5. A virtual reality medical education system according to claim 4, characterized in that, The clinical operation scenario construction unit is used to generate a training environment that includes a sterile operation area, complication trigger points, and equipment operation interface, including: In emotion-driven doctor-patient interaction scenarios, a defensive questioning mechanism is triggered based on the patient's psychological parameter model; Equipment failure simulation scenarios, including ECG lead misalignment and emergency waveform recognition training; A differentiated wound generation system that dynamically sets wound depth parameters and foreign body distribution based on the type of trauma; Anatomical navigation puncture scenario, used to provide real-time puncture navigation, force feedback simulation and complication combination functions based on three-dimensional anatomical model.
6. A virtual reality medical education system according to claim 4, characterized in that, The complication sandbox unit is used to generate complex clinical complication scenarios based on user-defined combinations of anatomical variation parameters, pathophysiological abnormality parameters, and environmental disturbance parameters, including: Receives user-selected anatomical variation parameters, physiological function abnormality parameters, and medical environment interference parameters; Training scenarios for complex complications are constructed in real time based on parameter combinations, including simulation of pathological signs and dynamic response of physiological indicators; The generated complication scenarios are seamlessly embedded into the clinical operation training process, enabling the reconfigurability of the pathological state in a single training scenario.
7. A virtual reality medical education system according to claim 1, characterized in that, The quantitative evaluation module includes: The operation standard monitoring unit is used to identify the degree of standardization of clinical operations through a spatial posture sensor; Clinical decision analysis unit, used to record response time for complication management; An operation quality assessment unit is used to quantify operational accuracy deviations based on AI image measurement technology. The error chain modeling unit is used to construct a causal relationship model that includes the pathophysiological reactions and end events caused by the standardization of the action, the response time of the complication handling, and the deviation of the operational accuracy. The loss quantification unit is used to generate a loss report based on the causal relationship model.
8. A virtual reality medical education method, used in a virtual reality medical education system as described in any one of claims 1-7, characterized in that, include: The hospital's real medical records data were anonymized, transferred, and structured. Natural language processing was used to parse the medical record text and build standardized medical record templates. The integrity of the user-inputted medical record information is verified based on the standardized medical record template, and an integrity verification result is generated. A medical order rule information database is constructed based on the standardized medical record template. Detect medical order conflicts based on the standardized medical record template and the medical order rule information database; Generate a virtual training environment for the entire clinical procedure; Based on the integrity verification results and the medical order conflict detection results, the entire clinical operation process is simulated in the virtual training environment; Analyze user behavior data throughout the entire clinical procedure to generate a multi-dimensional assessment report.
9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method of claim 8.
10. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in claim 8.