A data transmission system for multi-platform interaction of medical data

Abstract

The application relates to the technical field of medical data transmission, and discloses a data transmission system for multi-platform interaction of medical data, which comprises a multi-source information acquisition module, a task allocation module and a data transmission module; the multi-source information acquisition module integrates doctor skill evaluation, patient allocation, electronic medical records, operation videos and literature and the like, provides comprehensive and rich basic data for subsequent work, breaks information islands, realizes information sharing and efficient utilization; the task allocation module accurately allocates tasks according to doctor skill evaluation data, ensures that the doctor's ability matches the task, improves medical work efficiency and quality, and avoids resource waste and unreasonable allocation; and the data transmission module transmits task resource packages containing patient medical records, operation videos and literature to doctors according to skill evaluation and task allocation, so that the doctors can quickly obtain required information, the overall medical service level is improved, and high-quality medical experience is provided for patients.

Description

Technical Field

[0001] This invention relates to the field of medical data transmission technology, and more specifically to a data transmission system for multi-platform interaction of medical data. Background Technology

[0002] With the rapid development of information technology, hospitals are gradually transforming into smart hospitals. This transformation has played an important role in improving the quality of medical services, optimizing resource allocation, and enhancing patient experience.

[0003] In the current technology, many large hospitals already have relatively mature smart healthcare systems. Patients can use their mobile phones to select the department, doctor and specific time slot according to their needs with just a few taps, which greatly reduces the time cost of queuing. After the appointment is successful, the system will push reminder information as soon as possible, covering thoughtful content such as pre-visit precautions and hospital navigation routes, so that patients can make full preparations in advance.

[0004] However, the aforementioned technologies still have significant drawbacks. Large hospitals have a large number of doctors in each department, and patients often lack knowledge about doctors when choosing one, relying instead on limited information such as the doctor's title, the number of patient reviews, and friend recommendations. This can lead to patients not being matched with the most suitable doctor for their condition and can also result in an uneven distribution of high-quality medical resources. For example, some experienced doctors who are not good at promoting themselves may go unnoticed by patients due to a low number of reviews, while some doctors with high titles but relatively little clinical experience may attract a large number of patients simply because of their titles. This unreasonable allocation of resources not only wastes medical resources but also lowers the overall level of medical services, failing to meet the growing demand for high-quality medical services from patients. Summary of the Invention

[0005] The purpose of this invention is to provide a data transmission system for multi-platform interaction of medical data, and to solve the following technical problems: How to allocate medical resources more rationally.

[0006] The objective of this invention can be achieved through the following technical solutions: A data transmission system for multi-platform interaction of medical data, the data transmission system comprising: The multi-source information acquisition module is used to acquire multi-source information data from a multi-source platform; the multi-source information data includes physician skill assessment data, patient allocation information data, patient electronic medical records, medical operation videos, and medical literature. The task allocation module is used to analyze doctors' skill assessment data and allocate tasks to each doctor. The data transmission module is used to distribute task resource packages to each doctor based on the doctor's skill assessment data and tasks.

[0007] As a further aspect of the present invention: the multi-source platform includes: A doctor management platform is used to obtain doctor skills assessment data; The medical information management platform is used to obtain patient allocation information data from each doctor; The intelligent electronic medical record management system is used to obtain patients' electronic medical records; A medical resource management platform for obtaining medical procedure demonstration videos and medical literature.

[0008] As a further aspect of the present invention: the physician skills assessment data includes surgical skills assessment data, consultation ability assessment data, and communication ability assessment data.

[0009] As a further aspect of the present invention: the surgical skill assessment data includes the number of surgeries in the past preset time period, as well as the type of surgery for each surgery, the patient's physical risk coefficient, the duration of the surgery, whether the surgery was successful, and whether there were any surgical complications; The data for assessing consultation capabilities includes the misdiagnosis rate and follow-up rate over a previously preset time period; The communication skills assessment data includes audio data from medical consultations.

[0010] As a further aspect of the present invention, the working process of the task allocation module includes the following steps: S1: Analyze surgical skill assessment data to obtain the surgeon's surgical skill assessment index; S2: Analyze the consultation ability assessment data to obtain the doctor's consultation effectiveness index; S3: Based on the analysis of the doctor's surgical skills assessment index and the doctor's consultation effectiveness index, tasks are assigned.

[0011] As a further aspect of the present invention: In step S1, the process of obtaining the physician's surgical skill assessment index includes the following steps: S11: Categorize the doctor's surgeries within the previously preset time period according to the type of surgery; S12: Analyze the electronic medical records of patients undergoing any type of surgery within a preset time period to obtain the patient's physical risk coefficient. S13: Determine the difficulty coefficient of each doctor's surgery based on the preset curve of the difficulty coefficient of each type of surgery changing with the body risk coefficient; S14: Analyze the difficulty coefficients of each of the doctor's surgeries to obtain the average difficulty coefficient for each type of surgery; S15: Based on the average difficulty coefficient of each surgical type, the preset change curve of the operation time of each surgical type with the difficulty coefficient, the preset change curve of the success rate of each surgical type with the difficulty coefficient, and the preset change curve of the postoperative complication rate of each surgical type with the difficulty coefficient, determine the preset operation time, preset success rate, and preset postoperative complication rate of each surgical type for the doctor. S16: Based on the analysis of the operation time of each surgery, the success rate of each surgical type, the incidence of postoperative complications, the preset operation time of each surgery, the preset success rate of each surgical type, and the preset incidence of postoperative complications, the surgeon's surgical skill assessment index is obtained.

[0012] As a further aspect of the present invention: in step S3, the process of obtaining the doctor's consultation effectiveness index is as follows: S31: Set up a voice acquisition module in the consultation room to collect audio information data of doctors' consultations; S32: The audio information data is processed by the speech recognition module to obtain the voice consultation text information of the consultation. S33: Analyze the content of the voice consultation text using the semantic analysis module to obtain feature information scores; S34: Analyze the score based on the feature information to obtain the doctor's effective consultation index.

[0013] As a further aspect of the present invention: in step S33, the feature information scoring includes a consultation completeness score, a doctor's consultation effectiveness score, and an attitude score.

[0014] As a further aspect of the present invention: in step S3, the task allocation process is as follows: S31: Determine the surgical skill level based on the surgeon's surgical skill assessment index; S32: Determine the doctor's consultation skill level based on the doctor's consultation effectiveness index; S33: Assign tasks based on the doctor's surgical skill level and consultation skill level.

[0015] As a further aspect of the present invention, the medical resources include corresponding patient electronic medical records, relevant medical operation videos, and medical literature.

[0016] The beneficial effects of this invention are: (1) This invention integrates physician skill assessment, patient allocation, electronic medical records, operation videos and literature through a multi-source information acquisition module, providing comprehensive and rich basic data for subsequent work, breaking down information silos, and realizing information sharing and efficient utilization; then, through the task allocation module, tasks are accurately allocated based on physician skill assessment data, ensuring that physicians' abilities match the tasks, improving the efficiency and quality of medical work, and avoiding resource waste and unreasonable allocation; then, through the data transmission module, task resource packages containing patient medical records, operation videos and literature are transmitted to physicians based on skill assessment and task allocation, making it convenient for physicians to quickly obtain the information they need, assisting them to better carry out medical work, helping to improve diagnostic accuracy, formulate more scientific treatment plans, and thus improve the overall level of medical services, providing patients with a better and more efficient medical experience; (2) This invention records the number of surgeries, types, and risk coefficients in detail through surgical skill assessment data, which can accurately measure the surgeon's skill level, arrange suitable doctors for complex surgeries, and ensure the success rate of the surgery; the consultation ability assessment data uses consultation audio, which can comprehensively analyze the consultation communication between doctors and patients and improve the quality of doctor-patient communication; by combining these assessment data, we can understand the doctor's ability in all aspects, provide a reliable basis for task allocation, enable doctors to give full play to their strengths in their areas of expertise, improve the overall efficiency and quality of medical work, provide patients with more accurate and efficient medical services, and enhance patients' trust and satisfaction with the medical process; (3) This invention analyzes and derives corresponding indices from surgical skills and consultation ability, which comprehensively and accurately measures the doctor's overall professional level and avoids the one-sidedness of single-dimensional evaluation. Secondly, the task allocation based on the two indices can ensure that doctors are assigned to work content that is highly matched with their abilities, so that doctors can give full play to their talents in their areas of expertise and improve the accuracy and efficiency of medical work. Furthermore, this task allocation method can effectively avoid the waste of resources caused by the mismatch between tasks and abilities and improve the overall utilization rate of medical resources. Finally, it provides patients with more professional and high-quality medical services, enhances patients' trust and recognition of medical services, and improves patients' medical experience and satisfaction. Attached Figure Description

[0017] The invention will now be further described with reference to the accompanying drawings.

[0018] Figure 1 This is a system module framework diagram for an embodiment of the present invention. Detailed Implementation

[0019] 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 some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 As shown, in one embodiment, a data transmission system for multi-platform interaction of medical data is provided, the data transmission system comprising: The multi-source information acquisition module is used to acquire multi-source information data from a multi-source platform; the multi-source information data includes physician skill assessment data, patient allocation information data, patient electronic medical records, medical operation videos, and medical literature. Specifically, multi-source platforms can include physician management platforms, medical information management platforms, intelligent electronic medical record management systems, and medical resource management platforms. Physician management platforms obtain physician skill assessment data; medical information management platforms obtain patient allocation information data for each physician; intelligent electronic medical record management systems obtain patient electronic medical records; and medical resource management platforms obtain medical operation videos and medical literature. The task allocation module is used to analyze doctors' skill assessment data and allocate tasks to each doctor. The data transmission module is used to allocate data transmission task packages to each doctor based on the doctor's skill assessment data and tasks; the medical resources include corresponding patient electronic medical records, relevant medical operation videos, and medical literature. Through the above technical solution, this embodiment integrates physician skill assessments, patient allocation, electronic medical records, operation videos, and literature through a multi-source information acquisition module, providing comprehensive and rich basic data for subsequent work, breaking down information silos, and achieving information sharing and efficient utilization. Then, the task allocation module accurately allocates tasks based on physician skill assessment data, ensuring that physician capabilities match tasks, improving medical work efficiency and quality, and avoiding resource waste and unreasonable allocation. Finally, the data transmission module transmits task resource packages containing patient medical records, operation videos, and literature to physicians based on skill assessments and task allocation, facilitating rapid access to necessary information, assisting them in better carrying out medical work, helping to improve diagnostic accuracy, develop more scientific treatment plans, and ultimately improve the overall level of medical services, providing patients with a higher quality and more efficient medical experience.

[0021] As one embodiment of the present invention, the physician skills assessment data includes surgical skills assessment data, consultation ability assessment data, and communication ability assessment data; Specifically, the surgical skills assessment data includes the number of surgeries performed within a preset time period, the type of surgery performed each time, the patient's physical risk factor, the duration of the surgery, whether the surgery was successful, and whether there were any surgical complications; the consultation ability assessment data includes consultation audio data. Through the above technical solutions, this embodiment records the number of surgeries, types, and risk factors in detail using surgical skill assessment data. This allows for precise measurement of the surgeon's skill level, enabling the assignment of suitable doctors for complex surgeries and ensuring a high success rate. The consultation ability assessment data uses audio consultations, which can comprehensively analyze the doctor-patient communication and improve the quality of doctor-patient communication. By integrating these assessment data, a comprehensive understanding of the doctor's capabilities can be achieved, providing a reliable basis for task allocation. This allows doctors to leverage their strengths in their areas of expertise, improving the overall efficiency and quality of medical work, providing patients with more precise and efficient medical services, and enhancing patients' trust and satisfaction with the medical process.

[0022] As one embodiment of the present invention, the task allocation module's operation includes the following steps: S1: Analyze surgical skill assessment data to obtain the surgeon's surgical skill assessment index; S2: Analyze the consultation ability assessment data to obtain the doctor's consultation effectiveness index; S3: Based on the analysis of the doctor's surgical skills assessment index and the doctor's consultation effectiveness index, tasks are assigned; Through the above technical solutions, this embodiment analyzes and derives corresponding indices from surgical skills and consultation abilities, comprehensively and accurately measuring the doctor's overall professional level and avoiding the one-sidedness of single-dimensional evaluation. Secondly, by combining these two indices for task allocation, it ensures that doctors are assigned work content that highly matches their abilities, allowing them to utilize their expertise and improving the accuracy and efficiency of medical work. Furthermore, this task allocation method can effectively avoid resource waste caused by mismatch between tasks and abilities, improving the overall utilization rate of medical resources. Finally, it provides patients with more professional and high-quality medical services, enhances patients' trust and recognition of medical services, and improves patients' medical experience and satisfaction.

[0023] In one embodiment of the present invention, the process of obtaining the surgeon's surgical skill assessment index in step S1 includes the following steps: S11: Categorize the doctor's surgeries within the previously preset time period according to the type of surgery; S12: Analyze the electronic medical records of patients undergoing any type of surgery within a preset time period to obtain the patient's physical risk coefficient. Specifically, the assessment is conducted through a patient risk assessment model, which is a trained convolutional neural network model and is existing technology, so it will not be described in detail here.

[0024] S13: Determine the difficulty coefficient of each doctor's surgery based on the preset curve of the difficulty coefficient of each type of surgery changing with the body risk coefficient; Specifically, the preset curves of the difficulty coefficient of each type of surgery changing with the body's risk coefficient are obtained by fitting the historical experience of several designated doctors (doctors who meet the preset standard of medical skills). This is existing technology and will not be described in detail here.

[0025] S14: Analyze the difficulty coefficients of each of the doctor's surgeries to obtain the average difficulty coefficient for each type of surgery; S15: Based on the average difficulty coefficient of each surgical type, the preset change curve of the operation time of each surgical type with the difficulty coefficient, the preset change curve of the success rate of each surgical type with the difficulty coefficient, and the preset change curve of the postoperative complication rate of each surgical type with the difficulty coefficient, determine the preset operation time, preset success rate, and preset postoperative complication rate of each surgical type for the doctor. Specifically, the preset curves for the change of operation time with difficulty coefficient for each type of surgery, the preset curves for the change of success rate with difficulty coefficient for each type of surgery, and the preset curves for the change of postoperative complication rate with difficulty coefficient for each type of surgery are obtained by fitting the historical experience of several designated doctors (doctors who meet the preset standard of doctor skills). These are existing technologies and will not be described in detail here.

[0026] S16: Based on the analysis of the operation time of each surgery, the success rate of each surgical type, the incidence of postoperative complications, and the preset operation time, the preset success rate of each surgical type, and the preset incidence of postoperative complications of each surgery, the surgeon's surgical skill assessment index is obtained. Specifically, through Formula 1: ; Calculate the surgeon's surgical skills assessment index R. i ; Where i is the doctor's ID; N is the number of surgeries performed by the doctor within a preset time period, n∈N; t n t represents the duration of the doctor's nth surgery; n0 Let G be the preset operation time for the doctor's nth surgery; S be the number of surgery types, s∈S; s G represents the success rate of the doctor's s-th surgical type. s0 Preset success rate for this type of surgery by the doctor; G s G represents the postoperative complication rate for the s-th surgical procedure performed by this doctor. s0 The pre-defined postoperative complication rate for this type of surgery performed by the doctor; γ1 is the first weighting coefficient; γ2 is the second weighting coefficient; γ3 is the third weighting coefficient; It should be noted that the preset success rate and preset postoperative complication rate for each type of surgery by this doctor are determined based on the average difficulty coefficient of each type of surgery by this doctor, which is existing technology and will not be detailed here; It should be noted that the first weighting coefficient γ1, the second weighting coefficient γ2, and the third weighting coefficient γ3 are preset values, set based on empirical fitting, and will not be described in detail here. Through the above technical solution, this embodiment first classifies the doctor's surgeries within a preset time period according to surgical type; then, it analyzes the electronic medical record information of patients undergoing any surgical type within the preset time period to obtain the patient's physical risk coefficient, making the assessment of the difficulty of each surgery more consistent with the actual surgical situation; secondly, it determines the difficulty coefficient of each doctor's surgeries based on the preset change curve of the difficulty coefficient of each surgical type with the physical risk coefficient; then, it analyzes the difficulty coefficient of each doctor's surgeries to obtain the average difficulty coefficient of each surgical type; finally, it uses the average difficulty coefficient of each surgical type, the preset change curve of the operation time of each surgical type with the difficulty coefficient, the preset change curve of the success rate of each surgical type with the difficulty coefficient, and other relevant data to determine the difficulty coefficient of each surgical type. The preset curves of postoperative complication rates for different surgical types, along with their corresponding difficulty coefficients, determine the preset duration, success rate, and postoperative complication rate for each surgeon's procedures. Finally, an analysis is conducted based on the preset duration, success rate, and postoperative complication rate of each procedure, taking into account various factors such as average surgical difficulty, duration, success rate, and complications. This ensures that the aforementioned surgeon's surgical skill assessment index is more objective, accurate, and comprehensive. This helps to accurately grasp the surgeon's skill level, providing a reliable basis for subsequent task allocation, ensuring that the surgeon's abilities match the tasks, thereby improving medical efficiency and quality, and providing patients with better medical services.

[0027] In one embodiment of the present invention, the process of obtaining the doctor's consultation effectiveness index in step S3 is as follows: S31: Set up a voice acquisition module in the consultation room to collect audio information data of doctors' consultations; S32: The audio information data is processed by the speech recognition module to obtain the voice consultation text information of the consultation. S33: Analyze the content of the voice consultation text using the semantic analysis module to obtain feature information scores; Specifically, the feature information scoring includes a consultation completeness score, a doctor consultation effectiveness score, and an attitude score. The consultation completeness score is obtained by comparing the doctor's questions in this consultation with the doctor's pre-set consultation questions. The doctor consultation effectiveness score is scored based on the deviation between the doctor's textual description of each question in this consultation and the patient's answers to each question. The doctor's attitude score is scored based on the doctor's answers to the questions raised by the patient. The semantic analysis module is a trained convolutional neural network model, which is existing technology, and the training process will not be described in detail here.

[0028] S34: Analyze the score based on the feature information to obtain the doctor's effective consultation index; Specifically, through Formula 2: ; Calculate the doctor's consultation effectiveness index Y. i ; Where K represents the number of consultations the doctor has conducted within a preset time period, k∈K; Z k Score the completeness of the doctor's consultation during the kth consultation; X k The effectiveness score for the doctor's k-th consultation; T k The attitude score for the doctor during the k-th consultation; α1 is the first weight coefficient; α2 is the second weight coefficient; α3 is the third weight coefficient; It should be noted that the preset success rate and preset postoperative complication rate for each type of surgery by this doctor are determined based on the average difficulty coefficient of each type of surgery by this doctor, which is existing technology and will not be detailed here; It should be noted that the first weight coefficient α1, the second weight coefficient α2, and the third weight coefficient α3 are preset values, set based on empirical fitting, and will not be described in detail here. Through the above technical solution, this embodiment, with the help of voice acquisition, recognition, and semantic analysis modules, can comprehensively and accurately acquire key information from consultations, providing a reliable basis for evaluation. The multi-dimensional scoring, including consultation completeness, effectiveness, and attitude, can meticulously and objectively reflect the doctor's consultation ability, avoiding the one-sidedness of a single evaluation. This doctor consultation effectiveness index provides an important reference for task allocation, ensuring that doctors leverage their strengths in communication scenarios, improving the quality of doctor-patient communication, enhancing patients' trust and cooperation in the medical process, and ultimately improving the overall level of medical services and patient satisfaction.

[0029] In one embodiment of the present invention, the task allocation process in step S3 is as follows: S31: Determine the surgical skill level based on the surgeon's surgical skill assessment index; Specifically, surgical skill levels include advanced, intermediate, and basic. S32: Determine the doctor's consultation skill level based on the doctor's consultation effectiveness index; Specifically, the consultation skill levels include advanced and intermediate; S33: Assign tasks based on the doctor's surgical skill level and consultation skill level; Specifically, when a doctor's surgical skill level is rated as advanced, it indicates that the doctor has accumulated rich experience and mastered excellent techniques in surgeries of current difficulty. Therefore, in subsequent task allocation, surgeries with a difficulty level higher than their current level can be appropriately assigned to this doctor. If the surgical skill level is intermediate, the difficulty level of the surgeries the doctor currently performs should be maintained to ensure that they continue to accumulate experience in familiar and well-controlled surgical areas, gradually improving their technical level while ensuring surgical quality and patient safety. If the surgical skill level is low, the difficulty level of surgeries assigned to this doctor should be appropriately reduced. Regarding consultation skills, if the doctor's level is advanced, they can be assigned older patients with weaker comprehension. Given that the doctor possesses excellent consultation skills, they can patiently and meticulously communicate with these patients, accurately grasping their condition and needs, thereby improving consultation efficiency and accuracy. At the same time, consultation training should continue for this doctor, and they should be encouraged to participate in discussions of difficult cases, sharing consultation experiences and ideas to motivate other doctors to improve together. If the doctor's consultation skills are at an intermediate level, they should be assigned to patients of average age with relatively common conditions. Regular consultation skills training courses and mock consultations should be organized, with feedback and guidance from doctors with advanced consultation skills. This will gradually improve their consultation abilities, enabling them to more accurately and comprehensively understand the patient's condition and provide a reliable basis for subsequent treatment. These patients present a moderate level of communication difficulty, allowing the doctor to utilize their existing consultation skills while further honing and improving their techniques through interaction with the patient.

[0030] Through the above technical solution, this embodiment accurately allocates tasks based on surgical and consultation skill levels, allowing doctors to utilize their expertise and avoiding pressure and errors caused by mismatch between abilities and tasks. This helps doctors accumulate experience, improve skills, and achieve personal career development. Achieving reasonable task allocation can avoid resource waste, improve the overall utilization rate of medical resources, make medical work more accurate and efficient, and thus improve the overall level of medical services, providing strong support for the stable development of the medical industry.

[0031] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A data transmission system for multi-platform interaction of medical data, characterized in that, The data transmission system includes: The multi-source information acquisition module is used to acquire multi-source information data from a multi-source platform; the multi-source information data includes physician skill assessment data, patient allocation information data, patient electronic medical records, medical operation videos, and medical literature. The task allocation module is used to analyze doctors' skill assessment data and allocate tasks to each doctor. The data transmission module is used to distribute task resource packages to each doctor based on the doctor's skill assessment data and tasks.

2. The data transmission system for multi-platform interaction of medical data according to claim 1, characterized in that, The multi-source platform includes: A doctor management platform is used to obtain doctor skills assessment data; The medical information management platform is used to obtain patient allocation information data from each doctor; The intelligent electronic medical record management system is used to obtain patients' electronic medical records; A medical resource management platform for obtaining medical procedure demonstration videos and medical literature.

3. A data transmission system for multi-platform interaction of medical data according to claim 2, characterized in that, The physician skills assessment data includes surgical skills assessment data, consultation skills assessment data, and communication skills assessment data.

4. A data transmission system for multi-platform interaction of medical data according to claim 3, characterized in that, The surgical skills assessment data includes the number of surgeries performed in the past preset time period, as well as the type of surgery, patient's physical risk factor, surgery duration, success rate of surgery, and presence of surgical complications for each surgery. The data for assessing consultation capabilities includes the misdiagnosis rate and follow-up rate over a previously preset time period; The communication skills assessment data includes audio data from medical consultations.

5. A data transmission system for multi-platform interaction of medical data according to claim 4, characterized in that, The task allocation module works by following these steps: S1: Analyze surgical skill assessment data to obtain the surgeon's surgical skill assessment index; S2: Analyze the consultation ability assessment data to obtain the doctor's consultation effectiveness index; S3: Based on the analysis of the doctor's surgical skills assessment index and the doctor's consultation effectiveness index, tasks are assigned.

6. A data transmission system for multi-platform interaction of medical data according to claim 5, characterized in that, In step S1, the process of obtaining the physician's surgical skill assessment index includes the following steps: S11: Categorize the doctor's surgeries within the previously preset time period according to the type of surgery; S12: Analyze the electronic medical records of patients undergoing any type of surgery within a preset time period to obtain the patient's physical risk coefficient. S13: Determine the difficulty coefficient of each doctor's surgery based on the preset curve of the difficulty coefficient of each type of surgery changing with the body risk coefficient; S14: Analyze the difficulty coefficients of each of the doctor's surgeries to obtain the average difficulty coefficient for each type of surgery; S15: Based on the average difficulty coefficient of each surgical type, the preset change curve of the operation time of each surgical type with the difficulty coefficient, the preset change curve of the success rate of each surgical type with the difficulty coefficient, and the preset change curve of the postoperative complication rate of each surgical type with the difficulty coefficient, determine the preset operation time, preset success rate, and preset postoperative complication rate of each surgical type for the doctor. S16: Based on the analysis of the operation time of each surgery, the success rate of each surgical type, the incidence of postoperative complications, the preset operation time of each surgery, the preset success rate of each surgical type, and the preset incidence of postoperative complications, the surgeon's surgical skill assessment index is obtained.

7. A data transmission system for multi-platform interaction of medical data according to claim 6, characterized in that, In step S3, the process of obtaining the doctor's consultation effectiveness index is as follows: S31: Set up a voice acquisition module in the consultation room to collect audio information data of doctors' consultations; S32: The audio information data is processed by the speech recognition module to obtain the voice consultation text information of the consultation. S33: Analyze the content of the voice consultation text using the semantic analysis module to obtain feature information scores; S34: Analyze the score based on the feature information to obtain the doctor's effective consultation index.

8. A data transmission system for multi-platform interaction of medical data according to claim 7, characterized in that, In step S33, the feature information scoring includes a consultation completeness score, a doctor consultation effectiveness score, and an attitude score.

9. A data transmission system for multi-platform interaction of medical data according to claim 8, characterized in that, In step S3, the task allocation process is as follows: S31: Determine the surgical skill level based on the surgeon's surgical skill assessment index; S32: Determine the doctor's consultation skill level based on the doctor's consultation effectiveness index; S33: Assign tasks based on the doctor's surgical skill level and consultation skill level.

10. A data transmission system for multi-platform interaction of medical data according to claim 9, characterized in that, The medical resources include corresponding patient electronic medical records, relevant medical operation videos, and medical literature.

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