Anesthesia assessment big data intelligent supervision system

Abstract

The invention discloses an anesthesia assessment big data intelligent supervision system, which comprises a user interface module and a supervision system module, and is characterized in that the supervision system module comprises a multi-source data acquisition and integration module, a real-time data analysis and assessment module, an intelligent early warning and decision support module and a visual report and long-term tracking module; by integrating advanced information technology and medical knowledge, intelligentization and precision of anesthesia management are realized, all modules of the system cooperate with one another, data acquisition, processing, analysis and application are completed together, comprehensive, accurate and personalized anesthesia risk assessment and scheme suggestions are provided for users, and the user experience is improved. According to the method, doctors can be helped to master the condition of a patient more accurately, a more reasonable treatment scheme is formulated, risks and complications in the anesthesia process are reduced, the treatment effect and satisfaction degree of the patient are improved, configuration of medical resources can be optimized, the efficiency and quality of medical services are improved, and powerful support is provided for sustainable development of medical institutions.

Description

Technical Field

[0001] The present invention relates to the technical field of medical anesthesia management, and in particular to an anesthesia assessment big data intelligent supervision system. Background Art

[0002] Anesthesia, a medical term, has the core meaning of temporarily losing sensation, especially pain, in the patient's entire body or a local area through the use of specific drugs or other scientific methods, thereby ensuring that the patient does not feel pain or discomfort during surgical treatment. Whether it is general anesthesia or local anesthesia, its purpose is to provide patients with a painless environment during surgical treatment, reduce patients' pain and fear, and also help surgeons perform surgical operations more attentively and accurately, thereby improving the success rate and safety of the operation.

[0003] Challenges of current anesthesia management In current medical practice, anesthesia management faces many complex and important issues. Manual records and reliance on the subjective judgment of doctors make it difficult to fully and meticulously capture the various subtle changes in the anesthesia process. Especially when dealing with complex cases or responding to emergencies, this reliance on subjective judgment may lead to delayed or even misjudgment, increase the risk of patients, and affect the treatment effect. This requires not only extremely high professional skills, but also a high degree of adaptability and responsibility of anesthesiologists. However, even so, risks may not be completely avoided. In addition, the current anesthesia management lacks systematicity and standardization. The recording and analysis of anesthesia data still lacks systematicity and standardization, which has become another key challenge in anesthesia management. On the one hand, the lack of a unified data recording standard may lead to huge differences in the data format and content recorded by different medical centers, which not only increases the difficulty of data integration and analysis, but also affects the quality of long-term follow-up studies and the accurate interpretation of data. On the other hand, the lack of an effective data sharing mechanism has prevented the full utilization of precious medical resources, hindering the continuous improvement of medical quality and the accumulation of scientific evidence in clinical practice. Summary of the invention

[0004] The purpose of the present invention is to provide an anesthesia assessment big data intelligent supervision system to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solutions: an anesthesia assessment big data intelligent supervision system, comprising a user interface module and a supervision system module, wherein the supervision system module comprises a multi-source data acquisition and integration module, a real-time data analysis and evaluation module, an intelligent early warning and decision support module, and a visual report and long-term tracking module;

[0006] The multi-source data acquisition and integration module is used to collect data from multiple channels of anesthesia machines, monitors and electronic medical record systems, including key physiological parameters such as heart rate, blood pressure, blood oxygen saturation, respiratory rate, body temperature and anesthetic drug concentration, to ensure the comprehensiveness and accuracy of the data;

[0007] The real-time data analysis and evaluation module is used to analyze data changes during anesthesia in real time, automatically identify abnormal trends, and provide immediate feedback;

[0008] The intelligent early warning and decision support module is used to automatically trigger the early warning mechanism when abnormal physiological indicators or anesthesia depth deviates from the preset range, remind medical staff in various ways, and provide personalized treatment suggestions based on historical data and clinical guidelines to assist doctors in making decisions quickly;

[0009] The visualization report and long-term tracking module is used to generate a detailed anesthesia assessment report, visually display key information during the anesthesia process in the form of charts and trend lines, establish a patient anesthesia database, and support long-term tracking research.

[0010] Preferably, the user interface module includes a data input module, a model selection module and a result display module, and the patient's relevant information is input through the data input module, the appropriate analysis model is selected through the model selection module, and the risk assessment results and anesthesia plan recommendations are output and displayed through the result display module.

[0011] Preferably, the monitoring system module also includes a data security and privacy protection module, which is used to encrypt and store anesthesia data to ensure the security of all anesthesia data and the privacy protection of patients.

[0012] Preferably, the multi-source data acquisition and integration module includes a data acquisition module and a data integration processing module, the data integration processing module includes a data integration module and a data format conversion and standardization processing module, the data integration module is used to perform quality checks on the collected raw data, eliminate erroneous, missing or abnormal data points, and perform feature extraction and selection on the data to extract feature variables that are valuable for anesthesia risk assessment, and the data format conversion and standardization processing module is used to perform appropriate conversion and preprocessing on the data to meet the requirements of subsequent analysis models.

[0013] Preferably, the real-time data analysis and evaluation module includes a model training module, an autonomous record learning module and a risk assessment module. The model training module uses historical data to train related prediction models, and selects appropriate model structures and parameters according to the characteristics and labels of the data. The autonomous learning module is used to save the trained model to the system's model library for use by subsequent risk assessment modules. The risk assessment module performs risk assessment on patients through the trained model, receives characteristic variables provided by the data processing module, and inputs them into the trained model to obtain the patient's anesthesia risk score or probability, and generate a corresponding risk report.

[0014] Preferably, the intelligent early warning and decision support module includes an automatic early warning module and a suggestion auxiliary output module.

[0015] Preferably, the automatic warning module includes a voice warning module and a light warning module, and the suggestion auxiliary output module includes a text opinion presentation module, a graphic opinion presentation module and a voice opinion broadcasting module.

[0016] Preferably, the visual report and long-term tracking module includes a report generation module, a report presentation module and a report storage module.

[0017] Compared with the prior art, the beneficial effects of the present invention are: by integrating advanced information technology and medical knowledge, intelligent and precise anesthesia management is realized; the modules of the system cooperate with each other to jointly complete the data collection, processing, analysis and application, and provide users with comprehensive, accurate and personalized anesthesia risk assessment and program suggestions, which can help doctors more accurately understand the patient's condition, formulate more reasonable treatment plans, reduce the risks and complications during anesthesia, improve the treatment effect and satisfaction of patients, and optimize the allocation of medical resources, improve the efficiency and quality of medical services, and provide strong support for the sustainable development of medical institutions;

[0018] Moreover, establishing a database covering a wide range of anesthesia cases is of great significance for promoting scientific research in the field of anesthesiology. Rich data resources provide valuable experimental materials for scientific researchers, which is helpful to discover new anesthesia mechanisms and optimize anesthesia techniques. In-depth analysis of the data generated during the anesthesia process can reveal potential medical quality problems and management loopholes. The analysis results can provide medical institutions with specific suggestions for improving the anesthesia management process, help hospitals establish a more scientific and standardized anesthesia management system, continuously improve the quality and safety of medical services, and provide patients with better medical services. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the block diagram of the supervision system of the present invention.

[0020] Figure 2 It is a schematic diagram of the internal block diagram of the multi-source data acquisition and integration module of the present invention.

[0021] Figure 3 Schematic diagram of the internal block diagram of the real-time data analysis and evaluation module of the present invention.

[0022] Figure 4 It is a schematic diagram of the internal block diagram of the intelligent early warning and decision support module of the present invention.

[0023] Figure 5 It is a schematic diagram of the internal block diagram of the visual reporting and long-term tracking module of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] See also Figure 1-Figure 5The present invention provides a technical solution: an intelligent supervision system for anesthesia evaluation big data, an intelligent supervision system for anesthesia evaluation big data, including a user interface module and a supervision system module, the user interface module includes a data input module, a model selection module and a result display module, the patient's relevant information is input through the data input module, the appropriate analysis model is selected through the model selection module, and the risk assessment results and anesthesia plan suggestions are output and displayed through the result display module. The supervision system module includes a multi-source data acquisition and integration module, a real-time data analysis and evaluation module, an intelligent early warning and decision support module and a visual report and long-term tracking module. The multi-source data acquisition and integration module is used to collect data from multiple channels of anesthesia machines, monitors and electronic medical record systems. The data collected by the multi-source data collection and integration module include key physiological parameters such as heart rate, blood pressure, blood oxygen saturation, respiratory rate, body temperature and anesthetic drug concentration to ensure the comprehensiveness and accuracy of the data. The multi-source data collection and integration module includes a data collection module and a data integration processing module. The data integration processing module includes a data integration module and a data format conversion and standardization processing module. The data integration module is used to check the quality of the collected raw data, eliminate erroneous, missing or abnormal data points, and perform feature extraction and selection on the data to extract feature variables that are valuable for anesthesia risk assessment. In addition to exchanging information with the above-mentioned equipment through the multi-source data collection and integration module, the supervision system module can also communicate with HIS, CIS, EMR, LIS, The information exchange between RIS, PACS and other systems constitutes one of the links of the hospital information system, making the hospital's information transmission and management system more perfect. The data format conversion and standardization processing module is used to properly convert and preprocess the data to meet the requirements of the subsequent analysis model. The real-time data analysis and evaluation module is used to analyze the data changes during anesthesia in real time, automatically identify abnormal trends, and provide instant feedback. The real-time data analysis and evaluation module includes a model training module, an autonomous record learning module, and a risk assessment module. The model training module uses historical data to train related prediction models and selects appropriate model structures and parameters based on the characteristics and labels of the data. The autonomous learning module is used to save the trained model to the system. The model library of the system is used by the subsequent risk assessment module. The risk assessment module conducts risk assessment on the patient through the trained model, receives the characteristic variables provided by the data processing module, and inputs them into the trained model to obtain the patient's anesthesia risk score or probability, and generates a corresponding risk report. The intelligent early warning and decision support module is used to automatically trigger the early warning mechanism when abnormal physiological indicators are detected or the anesthesia depth deviates from the preset range, remind medical staff in various ways, and provide personalized treatment suggestions based on historical data and clinical guidelines to assist doctors in making decisions quickly. The intelligent early warning and decision support module includes an automatic early warning module and a suggestion auxiliary output module. The automatic early warning module includes a voice warning module and a light warning module.It is suggested that the auxiliary output module includes a text opinion presentation module, a graphic opinion presentation module and a voice opinion broadcast module. The visual report and long-term tracking module is used to generate a detailed anesthesia assessment report, visually display the key information of the anesthesia process in the form of charts and trend lines, establish a patient anesthesia database, and support long-term tracking research. The visual report and long-term tracking module includes a report generation module, a report presentation module and a report storage module. The supervision system module also includes a data security and privacy protection module. The data security and privacy protection module is used to encrypt and store anesthesia data to ensure the security of all anesthesia data and patient privacy protection.

[0026] The working principle of the above technical solution is as follows: Through the anesthesia assessment big data intelligent supervision system, it can accurately capture the changes in the patient's vital signs, promptly identify the risk factors that may cause anesthesia complications, and immediately issue warnings, thereby effectively reducing adverse events caused by anesthesia, and maximizing the protection of the patient's life safety during the operation. It can also provide doctors with objective and comprehensive data support based on big data analysis. The data covers the patient's historical medical history, current physiological state, and historical data of similar operations, etc., which provides a scientific basis for doctors to make key decisions such as formulating anesthesia plans and adjusting drug dosages, helping doctors to make more accurate and personalized treatment choices and improve treatment effects. It can also greatly reduce the burden of data entry, collation, and analysis on medical staff in their daily work. The system can It can quickly process massive amounts of medical data, generate intuitive reports and charts, help doctors quickly understand the patient's condition, speed up decision-making, and thus improve the efficiency and quality of overall medical services. In addition, establishing a database covering a wide range of anesthesia cases is of great significance for promoting scientific research in the field of anesthesia. Rich data resources provide researchers with valuable experimental materials, which helps to discover new anesthesia mechanisms and optimize anesthesia techniques. In-depth analysis of the data generated during the anesthesia process can reveal potential medical quality problems and management loopholes. The analysis results can provide medical institutions with specific suggestions for improving the anesthesia management process, help hospitals establish a more scientific and standardized anesthesia management system, continuously improve the quality and safety of medical services, and provide patients with better medical services.

[0027] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An anesthesia assessment big data intelligent supervision system, including a user interface module and a supervision system module, characterized in that: The supervision system modules include multi-source data collection and integration module, real-time data analysis and evaluation module, intelligent early warning and decision support module and visual report and long-term tracking module; The multi-source data acquisition and integration module is used to collect data from multiple channels of anesthesia machines, monitors and electronic medical record systems, including key physiological parameters such as heart rate, blood pressure, blood oxygen saturation, respiratory rate, body temperature and anesthetic drug concentration, to ensure the comprehensiveness and accuracy of the data; The real-time data analysis and evaluation module is used to analyze data changes during anesthesia in real time, automatically identify abnormal trends, and provide immediate feedback; The intelligent early warning and decision support module is used to automatically trigger the early warning mechanism when abnormal physiological indicators or anesthesia depth deviates from the preset range, remind medical staff in various ways, and provide personalized treatment suggestions based on historical data and clinical guidelines to assist doctors in making decisions quickly; The visualization report and long-term tracking module is used to generate a detailed anesthesia assessment report, visually display key information during the anesthesia process in the form of charts and trend lines, establish a patient anesthesia database, and support long-term tracking research.

2. The anesthesia assessment big data intelligent supervision system according to claim 1 is characterized by: The user interface module includes a data input module, a model selection module and a result display module. The patient's relevant information is input through the data input module, the appropriate analysis model is selected through the model selection module, and the risk assessment results and anesthesia plan recommendations are output and displayed through the result display module.

3. The anesthesia assessment big data intelligent supervision system according to claim 2 is characterized by: The monitoring system module also includes a data security and privacy protection module, which is used to encrypt and store anesthesia data to ensure the security of all anesthesia data and the privacy protection of patients.

4. The anesthesia assessment big data intelligent monitoring system according to claim 3 is characterized by: The multi-source data acquisition and integration module includes a data acquisition module and a data integration processing module. The data integration processing module includes a data integration module and a data format conversion and standardization processing module. The data integration module is used to perform quality checks on the collected raw data, eliminate erroneous, missing or abnormal data points, and perform feature extraction and selection on the data to extract feature variables that are valuable for anesthesia risk assessment. The data format conversion and standardization processing module is used to perform appropriate conversion and preprocessing on the data to meet the requirements of subsequent analysis models.

5. The anesthesia assessment big data intelligent monitoring system according to claim 4 is characterized by: The real-time data analysis and evaluation module includes a model training module, an autonomous record learning module and a risk assessment module. The model training module uses historical data to train related prediction models and selects appropriate model structures and parameters based on the characteristics and labels of the data. The autonomous learning module is used to save the trained model to the system's model library for use by subsequent risk assessment modules. The risk assessment module performs risk assessment on patients through the trained model, receives characteristic variables provided by the data processing module, and inputs them into the trained model to obtain the patient's anesthesia risk score or probability and generate a corresponding risk report.

6. The anesthesia assessment big data intelligent monitoring system according to claim 5 is characterized by: The intelligent early warning and decision support module includes an automatic early warning module and a suggestion auxiliary output module.

7. The anesthesia assessment big data intelligent monitoring system according to claim 6 is characterized by: The automatic warning module includes a voice warning module and a light warning module, and the suggestion auxiliary output module includes a text opinion presentation module, a graphic opinion presentation module and a voice opinion broadcasting module.

8. The anesthesia assessment big data intelligent monitoring system according to claim 7 is characterized by: The visual report and long-term tracking module includes a report generation module, a report presentation module and a report storage module.

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