Intelligent ICU clinical information management platform and method

Abstract

The invention relates to the technical field of information management, in particular to an intelligent ICU clinical information management platform, which comprises a data acquisition module for acquiring physiological parameters, environmental parameters, multi-omics data and clinical data of a patient in real time; the intelligent early warning module analyzes the physiological parameters of the patient in real time, obtains an analysis result, performs anomaly detection based on the analysis result, and sends alarm data if an anomaly exists; the personalized treatment recommendation module is used for generating a personalized treatment scheme for the abnormal detection result; the remote consultation module is used for carrying out online consultation on the personalized treatment scheme to determine a final treatment scheme; the intelligent sickbed management module monitors and records nursing information of the patient based on the final treatment scheme and transmits the nursing information to the central management system; and the medical quality control module is used for carrying out quality detection on the data acquired by the data acquisition module, the analysis result, the personalized treatment scheme, the final treatment scheme and the nursing information. According to the invention, effective management of ICU clinical information can be realized.

Description

Technical Field

[0001] The present application relates to the technical field of information management, and in particular to a smart ICU clinical information management platform and method. Background Art

[0002] With the continuous advancement of medical technology and the rapid development of information technology, the management level of intensive care units (ICUs) has been significantly improved. However, the existing ICU clinical information management system still has some key problems, which limit its potential in improving the quality and efficiency of medical services.

[0003] Data island phenomenon: The information systems of different medical institutions are not interconnected, resulting in the phenomenon of information islands, which seriously affects cross-institutional patient information sharing and collaborative treatment; Insufficient data security and privacy protection: The existing system lacks measures in data security and privacy protection, which can easily lead to the leakage of sensitive information and bring unnecessary risks to patients; Insufficient intelligent decision support: Traditional clinical information management systems have limited capabilities in data analysis and processing, and cannot make full use of big data and artificial intelligence technologies to assist doctors in making more accurate diagnosis and treatment decisions.

[0004] Therefore, how to achieve effective management of ICU clinical information remains to be resolved. Summary of the invention

[0005] In order to achieve effective management of ICU clinical information, the present application provides a smart ICU clinical information management platform and method.

[0006] In the first aspect, the present application provides a smart ICU clinical information management platform, which adopts the following technical solutions: A smart ICU clinical information management platform, including: The data acquisition module is used to collect the patient's physiological parameters, environmental parameters, multi-omics data and clinical data from various medical devices and sensors in real time after determining the corresponding ones; The intelligent early warning module analyzes the patient's corresponding physiological parameters in real time based on a deep learning algorithm, obtains corresponding analysis results, and performs corresponding abnormality detection based on the analysis results. If an abnormality exists, an alarm data corresponding to the abnormality detection result is issued; A personalized treatment recommendation module, after an alarm is issued, retrieves the multi-omics data and clinical data corresponding to the patient, and generates a personalized treatment plan for the abnormal test results based on the multi-omics data and the clinical data, wherein the multi-omics data includes genomics, metabolomics, and proteomics, and the clinical data includes medical records, laboratory test results, and imaging data; A remote consultation module is used to send the personalized treatment plan to the remote collaboration device corresponding to the multidisciplinary expert team. Each expert in the multidisciplinary expert team can conduct an online consultation on the personalized treatment plan through video conferencing or sharing electronic medical records, and determine the corresponding final treatment plan for the personalized treatment plan based on the consultation opinions of all experts; An intelligent bed management module, an intelligent bed integrated with multiple sensors, monitors and records the patient's nursing information based on the final treatment plan, and transmits the nursing information to a central management system, wherein the nursing information includes turning time, medication time, patient's body position data, and turning times; The medical quality control module monitors the quality of the data collected by the data collection module, and performs quality inspection on the analysis results, the personalized treatment plan, the final treatment plan and the nursing information.

[0007] By adopting the above technical solutions, through the integration of data collection, intelligent early warning, personalized treatment recommendation, remote consultation and intelligent bed management modules, comprehensive monitoring and intelligent analysis of patients' physiological parameters, environmental parameters, multi-omics data and clinical data are achieved. The platform can detect abnormalities and issue alarms in real time, generate personalized treatment plans, and determine the final treatment plan through remote consultation by a multidisciplinary team of experts. At the same time, the intelligent bed management module automatically monitors and records patients' nursing information to ensure the real-time and accuracy of data; the medical quality control module provides comprehensive quality management support by ensuring data quality and consistency. Through this series of functions, the platform effectively improves the management efficiency and accuracy of ICU clinical information, optimizes the utilization of medical resources, and improves the continuity and safety of patient treatment.

[0008] Optionally, the intelligent early warning module also includes: classifying the anomaly into multiple levels, and determining the corresponding severity of the anomaly based on the anomaly detection result; calling a pre-set early warning mechanism, and determining the corresponding target alarm level based on the severity of the anomaly; and determining the corresponding response measures based on the target alarm level.

[0009] By adopting the above technical solution, by classifying abnormalities into multiple levels and determining the severity of abnormalities based on the abnormality detection results, the pre-set early warning mechanism is called up to determine the corresponding target alarm level and response measures, thereby achieving accurate early warning and rapid response to abnormalities of different severities, ensuring that medical staff can take appropriate measures in a timely manner to improve patient safety and treatment outcomes.

[0010] Optionally, after treating the patient based on the final treatment plan, it also includes: continuously acquiring the patient's real-time physiological data, wherein the real-time physiological data includes heart rate, blood pressure, blood oxygen saturation, body temperature, and respiratory rate; evaluating the evaluation implementation effect corresponding to the final treatment plan based on the real-time physiological data, wherein the implementation effect includes monitoring whether the patient's physiological parameters have improved and whether new abnormalities have appeared; feeding back the evaluation implementation results to medical staff, and generating corresponding plan adjustment suggestions based on the evaluation implementation results, wherein the plan adjustment suggestions include drug dosage, changing treatment time, adding auxiliary treatment, etc.

[0011] By adopting the above technical solutions, by continuously acquiring the patient's real-time physiological data, evaluating the implementation effect of the final treatment plan, monitoring whether the patient's physiological parameters have improved and whether new abnormalities have appeared, and feeding back the evaluation results to medical staff, and generating corresponding plan adjustment suggestions, we can ensure the continuity and effectiveness of the treatment plan and improve the patient's treatment effect and safety.

[0012] Optionally, it includes: retrieving the corresponding analysis results and the severity of the abnormality, determining the corresponding required medical resources based on the analysis results, wherein the required medical resources include medical staff, medical equipment, and medicines; determining the corresponding resource scheduling degree based on the medical resources and the severity of the abnormality; and performing corresponding optimized resource scheduling and task allocation based on the resource scheduling degree.

[0013] By adopting the above technical solution, the required medical resources (such as medical staff, medical equipment, and drugs) are determined by retrieving the analysis results and the severity of the abnormality, and the degree of resource scheduling is determined according to the severity of the abnormality. Optimized resource scheduling and task allocation are carried out to ensure that resources can be deployed quickly and efficiently in emergency situations, thereby improving the timeliness and effectiveness of patient treatment.

[0014] Optionally, the remote consultation module: when sharing electronic medical records for online consultation on personalized treatment plans, determines the roles of experts participating in the online consultation, and determines corresponding expert responsibilities and authorities based on the expert roles; determines corresponding editing permissions for the personalized treatment plan based on the expert responsibilities and authorities; when the expert is editing, the expert's editing record of the personalized treatment plan is synchronously displayed to other experts.

[0015] By adopting the above technical solution, by determining the roles and responsibilities of experts participating in the online consultation, granting corresponding editing permissions, and displaying the editing records to other experts in real time when the experts edit the personalized treatment plan, it is ensured that the multidisciplinary expert team collaborates efficiently during the consultation process, which can improve the accuracy and efficiency of the consultation.

[0016] Optionally, when a user accesses information, it also includes: determining the corresponding target user and current access information, retrieving the user's historical access records, determining user behavior based on the historical access records and current access information, wherein the historical access records include the corresponding login time, login location, operation frequency, and operation type; determining corresponding context information based on the target user, wherein the context information includes the department where the user is located, the current medical task, and the patient's status; and adjusting the target user's access rights based on the user behavior and the context information.

[0017] By adopting the above technical solution, by analyzing the user's historical access records and current access information, combined with the user's contextual information (such as the department, current medical tasks, patient status), the user's access rights are dynamically adjusted to ensure minimized data exposure and improve data security and system reliability.

[0018] Optionally, after treating the patient based on the final treatment plan, the smart bed management module also includes: collecting the patient's body temperature data and environmental humidity data based on sensors integrated on the smart bed; automatically adjusting the temperature of the mattress based on the body temperature data, and automatically adjusting the humidity of the mattress based on the environmental humidity data; determining a corresponding change trend according to a body temperature data set and an environmental humidity data set corresponding to the body temperature data, and making adjustments in advance based on the change trend, wherein the change trend includes a change trend of the body temperature data and a change trend of the environmental humidity data.

[0019] By adopting the above technical solution, the patient's body temperature and environmental humidity data are collected in real time through the intelligent bed management module, the temperature and humidity of the mattress are automatically adjusted, and adjustments are made in advance according to the changing trends of body temperature and environmental humidity to ensure the patient's comfort and safety during treatment.

[0020] In the second aspect, this application provides a smart ICU clinical information management platform, which adopts the following technical solutions: A smart ICU clinical information management method, the method comprising: Determine the corresponding physiological parameters, environmental parameters, multi-omics data and clinical data of the patient collected in real time from various medical devices and sensors; analyze the corresponding physiological parameters of the patient in real time based on the deep learning algorithm, obtain the corresponding analysis results, and perform corresponding abnormality detection based on the analysis results. If there is an abnormality, issue alarm data corresponding to the abnormality detection result; After the alarm is issued, the multi-omics data and clinical data corresponding to the patient are retrieved, and a personalized treatment plan for the abnormal test results is generated based on the multi-omics data and the clinical data, wherein the multi-omics data includes genomics, metabolomics, and proteomics, and the clinical data includes medical records, laboratory test results, and imaging data; The personalized treatment plan is sent to the remote collaboration device corresponding to the multidisciplinary expert team. Each expert in the multidisciplinary expert team can conduct an online consultation on the personalized treatment plan through video conferencing or sharing electronic medical records, and determine the corresponding final treatment plan for the personalized treatment plan based on the consultation opinions of all experts; The smart bed integrating multiple sensors monitors and records the patient's nursing information based on the final treatment plan, and transmits the nursing information to the central management system, wherein the nursing information includes turning time, medication time, patient's body position data and turning times.

[0021] In the third aspect, the present application provides a smart ICU clinical information management platform, which adopts the following technical solutions: A smart ICU clinical information management platform includes a processor, in which a program of the smart ICU clinical information management method described above is run.

[0022] In a fourth aspect, the present application provides a storage medium, which adopts the following technical solution: A storage medium stores a program of the above-mentioned smart ICU clinical information management method.

[0023] In summary, the present application includes at least one of the following beneficial technical effects: 1. By integrating modules such as data collection, intelligent early warning, personalized treatment recommendation, remote consultation and intelligent bed management, comprehensive monitoring and intelligent analysis of patients' physiological parameters, environmental parameters, multi-omics data and clinical data are realized. The platform can detect abnormalities and issue alarms in real time, generate personalized treatment plans, and determine the final treatment plan through remote consultation by a multidisciplinary team of experts. At the same time, the intelligent bed management module automatically monitors and records patients' nursing information to ensure the real-time and accuracy of data. The medical quality control module provides comprehensive quality management support by ensuring data quality and consistency. Through this series of functions, the platform effectively improves the management efficiency and accuracy of ICU clinical information, optimizes the utilization of medical resources, and improves the continuity and safety of patient treatment.

[0024] 2. In addition, the platform ensures the security and reliability of data through functions such as dynamic adjustment of access rights, multi-level early warning mechanism, real-time evaluation of treatment effects and intelligent resource scheduling. In particular, by analyzing user behavior and contextual information, dynamically adjusting access rights reduces the risk of data leakage; through the multi-level early warning mechanism, accurate early warning and rapid response to abnormalities of different severity are ensured; through real-time evaluation of treatment effects, the continuity and effectiveness of treatment plans are ensured; through intelligent resource scheduling, the utilization of medical resources is optimized, ensuring the timeliness and effectiveness of patient treatment. These functions work together to achieve effective management and optimization of ICU clinical information. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a structural block diagram of a smart ICU clinical information management platform according to an exemplary embodiment.

[0026] Figure 2 It is a flowchart of a smart ICU clinical information management method according to an exemplary embodiment. DETAILED DESCRIPTION

[0027] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings.

[0028] In the description of this specification, the description with reference to the terms "certain embodiments", "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0029] The present application embodiment discloses a smart ICU clinical information management platform, referring to Figure 1 ,include: The data acquisition module 100 is used to collect the patient's physiological parameters, environmental parameters, multi-omics data and clinical data from various medical devices and sensors in real time after determining the corresponding; Among them, a variety of sensors are installed in the ICU, including but not limited to electrocardiogram monitors, blood pressure monitors, blood oximeters, thermometers, respiratory rate monitors, etc.; these sensors are then used to collect the patient's physiological parameters (such as heart rate, blood pressure, blood oxygen saturation, body temperature, respiratory rate, etc.) and environmental parameters (such as room temperature, humidity, etc.) in real time.

[0030] Multi-omics data of patients can also be collected through gene sequencing, metabolomics analysis, proteomics analysis and other means; in addition, clinical data such as patients' medical records, laboratory test results, imaging data, etc. can be extracted from the hospital information system.

[0031] Finally, the various collected data will be transmitted to the central management system via wired or wireless means, thus ensuring comprehensive monitoring of the patient's physiological state and environmental conditions, thereby ensuring the real-time nature of the data and providing timely data support for subsequent intelligent analysis and early warning.

[0032] The intelligent early warning module 110 analyzes the patient's corresponding physiological parameters in real time based on a deep learning algorithm, obtains corresponding analysis results, and performs corresponding anomaly detection based on the analysis results. If an abnormality exists, an alarm data corresponding to the abnormal detection result is issued.

[0033] Among them, the collected physiological parameters are first preprocessed, including data cleaning, denoising, normalization, etc., to ensure the quality and consistency of the data, and the processed physiological parameters are analyzed in real time using deep learning algorithms (such as convolutional neural networks, recurrent neural networks, etc.) to obtain analysis results. It should be pointed out here that for the management platform of this application, the deep learning algorithm can be obtained by calling the existing algorithm, or the platform can be pre-trained based on historical ICU clinical information, and the management platform only needs to call it.

[0034] As for the analysis results obtained based on deep neural networks, the key features of physiological parameters, such as the fluctuation pattern of heart rate and the changing trend of blood pressure, are extracted through the deep learning model. The preprocessed physiological parameters are input into the trained deep learning model for real-time analysis to obtain the analysis results.

[0035] In the process of abnormality detection, the normal range and abnormal threshold of physiological parameters can be set according to historical data and medical standards, and then the analysis results of the deep learning model can be compared with the set threshold to identify physiological parameters that exceed the normal range. The identified abnormalities are divided into multiple levels, such as mild abnormalities, moderate abnormalities, and severe abnormalities, and classified according to the severity of the abnormalities; the analysis results of multiple physiological parameters can also be combined to perform multimodal analysis to improve the accuracy and reliability of abnormality detection.

[0036] It should be noted here that according to medical standards and clinical experience, abnormalities are divided into multiple levels, such as mild abnormality, moderate abnormality, and severe abnormality. Set thresholds: Set specific thresholds for each abnormality level. For example: Mild abnormalities: heart rate is 90-100 beats / minute, blood pressure is 130-140 / 85-90 mmHg.

[0037] Moderate abnormality: heart rate is 101-120 beats / minute, blood pressure is 141-160 / 91-100 mmHg.

[0038] Severe abnormalities: heart rate exceeds 120 beats / minute and blood pressure exceeds 160 / 100 mmHg.

[0039] In addition, it is necessary to combine the analysis results of multiple physiological parameters and conduct multimodal analysis to ensure the accuracy and reliability of abnormal classification. For example, when both heart rate and blood pressure are abnormal at the same time, a higher abnormality level may be required.

[0040] In addition, for the design of the early warning mechanism, it is necessary to design a complete early warning mechanism in advance, including the alarm levels and response measures corresponding to different abnormal levels, and store the early warning mechanism in the system database to ensure that it can be called at any time; when an abnormality is detected, the corresponding early warning mechanism is called from the database according to the severity of the abnormality.

[0041] The alarm levels and response measures corresponding to different abnormal levels include: Minor abnormality: Level 1 alarm, alerting medical staff through system prompts or gentle alarms.

[0042] Moderate abnormality: Level 2 alarm, alerting medical staff through audible alarms and visual prompts.

[0043] Severe abnormality: Level 3 alarm, notifying relevant personnel through sound alarm, visual prompt and SMS / email.

[0044] In order to identify whether the patient's physiological parameters are abnormal and the severity of the abnormality according to the set threshold, the alarm level must be determined first. The corresponding target alarm level can be determined according to the severity of the abnormality. For example: a slight abnormality is determined as a level 1 alarm; a moderate abnormality is determined as a level 2 alarm; a severe abnormality is determined as a level 3 alarm. Then, alarm data containing abnormal detection results and alarm levels is generated, including abnormality type, abnormal parameters, abnormal time, recommended measures, etc.

[0045] Finally, in the process of determining the corresponding response measures for the target alarm level, design corresponding response measures for each alarm level, such as: Level 1 Alarm: Record abnormal conditions, increase monitoring frequency, and observe the patient's condition.

[0046] Level 2 alert: Immediately notify the doctor on duty for preliminary examination and treatment.

[0047] Level 3 alert: Immediately notify the attending physician and head nurse, activate the emergency plan, and carry out emergency treatment.

[0048] Depending on the determined alarm level, appropriate response measures are executed, such as: Record abnormal situations: record abnormal detection results and alarm data in the system log for subsequent analysis and review; increase monitoring frequency: for minor abnormalities, increase monitoring frequency and closely observe changes in the patient's physiological parameters; notify the doctor on duty: for moderate abnormalities, immediately notify the doctor on duty for preliminary examination and treatment; initiate emergency plan: for severe abnormalities, immediately notify the attending physician and head nurse, initiate emergency plan, and conduct emergency treatment; provide treatment suggestions: provide corresponding treatment suggestions based on the type and severity of the abnormality, such as adjusting drug dosage, adding auxiliary treatment, etc.

[0049] In the above detailed execution process, the intelligent early warning module realizes the accurate classification of abnormal conditions of patients, the rapid retrieval of early warning mechanisms and the timely determination of response measures, ensuring the accuracy and reliability of abnormal detection and improving the timeliness and effectiveness of alarms. This series of functions effectively improves the efficiency and accuracy of ICU clinical information management, optimizes the utilization of medical resources, and ensures the safety and comfort of patients during treatment.

[0050] After the alarm is issued, the personalized treatment recommendation module 120 retrieves the multi-omics data and clinical data corresponding to the patient, and generates a personalized treatment plan for the abnormal detection result based on the multi-omics data and clinical data.

[0051] When the intelligent early warning module detects an abnormality and issues an alarm, it triggers the personalized treatment recommendation module, which retrieves the patient's multi-omics data (genomics, metabolomics, proteomics) and clinical data (medical records, laboratory test results, and imaging data). Then, based on the retrieved multi-omics data and clinical data, the patient's physiological state and condition are comprehensively analyzed to generate a personalized treatment plan for the abnormal detection results.

[0052] In addition, when generating a treatment plan, multiple objectives (such as efficacy, side effects, cost, etc.) are taken into consideration, and the optimal treatment recommendations are provided through a multi-objective optimization algorithm. Finally, a personalized treatment plan is generated, including drug type, dosage, treatment time, auxiliary treatment measures, etc.

[0053] The remote consultation module 130 is used to send the personalized treatment plan to the remote collaboration device corresponding to the multidisciplinary expert team. Each expert in the multidisciplinary expert team can conduct an online consultation on the personalized treatment plan through video conferencing or sharing electronic medical records, and comprehensively consider the consultation opinions of all experts to determine the corresponding final treatment plan for the personalized treatment plan.

[0054] Among them, the generated personalized treatment plan is sent to the remote collaboration equipment of the multidisciplinary expert team. It is necessary to determine the roles of the experts participating in the online consultation, such as the attending physician, head nurse, pharmacist, etc.; and based on the expert role, determine the corresponding expert responsibilities and powers, and grant corresponding editing permissions. Expert team members can conduct online consultations on personalized treatment plans through video conferencing or shared electronic medical records, and perform real-time editing and annotation; and when the expert is editing, the editing records will be displayed to other experts in real time to ensure the synchronization and consistency of information. Finally, the consultation opinions of all experts are integrated to determine the final treatment plan.

[0055] Specifically, they include: The first step is to determine the roles of the experts participating in the online consultation when sharing electronic medical records for personalized treatment plans, and determine the corresponding expert responsibilities and powers based on the expert roles.

[0056] Before starting the online consultation, the responsible doctor or system administrator invites members of the multidisciplinary expert team to participate in the consultation and clarifies the role of each expert, such as attending physician, head nurse, pharmacist, radiologist, etc. Each invited expert confirms his or her role in the system and stores the expert's role information in the system database.

[0057] Configure corresponding responsibilities and permissions according to the role of the expert, for example: Attending physician: Can view and edit the medical records and treatment plans of all patients and make treatment recommendations.

[0058] Head nurse: Can review the patient's medical history and treatment plan, record nursing information, and make nursing recommendations.

[0059] Pharmacist: Can review the patient’s medical history and medication regimen and make recommendations for medication adjustments.

[0060] Radiologist: can review the patient’s imaging data and make recommendations for imaging diagnosis.

[0061] Then, the experts' responsibilities and permissions are stored in the system database. When the experts log in to the system, their roles and permissions are verified to ensure that only experts with corresponding permissions can access and edit relevant data.

[0062] The second step is to determine the editing permissions corresponding to the personalized treatment plan based on the expert's responsibilities and permissions.

[0063] Correspondingly, according to the responsibilities and permissions of the experts, the editing permissions for the personalized treatment plan are assigned. When the experts edit the personalized treatment plan, the system automatically limits their editing scope to ensure that the experts can only edit the content within their authority. For example: Attending Physician: Can edit all sections, including medication, physical therapy, nursing measures, etc.

[0064] Head nurse: Can edit the nursing measures section and record nursing information.

[0065] Pharmacist: Can edit the medication treatment section and make suggestions for medication adjustments.

[0066] Radiologist: Can edit the imaging diagnosis section and make imaging diagnosis suggestions.

[0067] The third step is to synchronously display the expert's editing record of the personalized treatment plan to other experts when the expert is editing.

[0068] Among them, when the expert edits the personalized treatment plan, the system will synchronously display the editing records in real time to ensure that all experts participating in the consultation can see the latest editing content, record each expert's editing operations, including editing time, editing content, editor and other information, to ensure the integrity and traceability of the editing records.

[0069] In addition, the system platform also supports version control, records the editing history of each version, allows experts to view and restore previous versions, and provides real-time communication functions such as chat windows, voice calls, etc., to facilitate communication and discussion between expert team members.

[0070] The remote consultation module implements refined management of the roles, responsibilities, permissions, editing permissions, and synchronization of editing records of experts participating in online consultations. The specific functions are as follows: Role management: clarify the roles and responsibilities of each expert to ensure the orderly progress of the consultation process.

[0071] Permission management: Configure and verify the responsibilities and permissions of experts based on their roles to ensure data security and reliability.

[0072] Editing permissions: Assign editing permissions for personalized treatment plans based on the experts’ responsibilities and permissions to ensure that experts can only edit content within their authority.

[0073] Real-time synchronization: When experts make edits, the editing records are displayed synchronously in real time to ensure that all experts can see the latest edits, improving the efficiency and accuracy of the consultation.

[0074] The intelligent bed management module 140 is an intelligent bed integrating multiple sensors, which monitors and records the patient's nursing information based on the final treatment plan and transmits the nursing information to the central management system.

[0075] Among them, a variety of sensors are integrated on the smart bed, such as pressure sensors, temperature sensors, humidity sensors, etc., which collect data such as the patient's body temperature, environmental humidity, and nursing information (such as turning over time, medication time, body position data, number of turning over, etc.) in real time through the sensors.

[0076] Through the intelligent bed management module that integrates multiple sensors, real-time monitoring and recording of patient temperature, environmental humidity and nursing information are achieved, and these data are transmitted to the central management system. The specific functions are as follows: Pressure sensor: monitors the patient's body position and pressure distribution, prevents the occurrence of pressure sores, and ensures the patient's safety and comfort; Temperature sensor: monitors the patient's body temperature and the temperature of the mattress, automatically adjusts the mattress temperature to ensure the patient's comfort; Humidity sensor: monitors the ambient humidity and the humidity of the mattress, automatically adjusts the humidity of the mattress to prevent skin problems such as eczema; Nursing information record: records detailed nursing information, provides comprehensive nursing records, ensures the standardization and effectiveness of nursing operations, and improves nursing quality and patient satisfaction.

[0077] After treating the patient based on the final treatment plan, the smart bed management module also includes: The first step is to collect the patient's body temperature data and environmental humidity data based on the sensors integrated in the smart bed.

[0078] Specifically, a temperature sensor and a humidity sensor are installed on the smart bed to ensure that the position and installation method of the sensor can accurately collect the patient's body temperature and environmental humidity data; the patient's body temperature data is collected in real time through the temperature sensor, and the environmental humidity data is collected in real time through the humidity sensor, and then the collected body temperature data and environmental humidity data are transmitted to the control system of the smart bed via wired or wireless means.

[0079] The second step is to automatically adjust the temperature of the mattress based on body temperature data, and automatically adjust the humidity of the mattress based on ambient humidity data.

[0080] Among them, the patient's body temperature data is monitored in real time, and a preset temperature adjustment algorithm can be applied according to the patient's body temperature data to calculate the appropriate mattress temperature.

[0081] First, by setting the temperature threshold, for example, when the patient's temperature is below 36°C, the mattress temperature is automatically increased; when the patient's temperature is above 37.5°C, the mattress temperature is automatically reduced. Through the intelligent control system, the mattress heating or cooling device is automatically adjusted to make the mattress temperature reach the preset appropriate range, and the adjusted mattress temperature data is fed back to the control system to ensure the accuracy and stability of temperature regulation.

[0082] Similarly, by real-time monitoring of environmental humidity data, the preset humidity adjustment algorithm can be applied according to the environmental humidity data to calculate the appropriate mattress humidity. By setting the humidity threshold, for example, when the environmental humidity is higher than 70%, the mattress humidity is automatically reduced; when the environmental humidity is lower than 30%, the mattress humidity is appropriately increased. Through the intelligent control system, the mattress dehumidification or humidification device is automatically adjusted to make the mattress humidity reach the preset appropriate range, and the adjusted mattress humidity data is fed back to the control system to ensure the accuracy and stability of humidity adjustment.

[0083] The third step is to determine the corresponding change trend according to the body temperature data set and the environmental humidity data set corresponding to the body temperature data, and make adjustments in advance based on the change trend.

[0084] Among them, body temperature data and environmental humidity data over a period of time are collected to form a data set. Through the data analysis algorithm, the change trends of the body temperature data and environmental humidity data are determined. Based on the change trends, future changes in body temperature and environmental humidity are predicted and adjustments are made in advance.

[0085] Temperature change trend: Analyze the change trend of body temperature data over time, for example, whether the body temperature is gradually increasing or decreasing; Humidity change trend: Analyze the change trend of ambient humidity data over time, for example, whether the humidity is gradually increasing or decreasing.

[0086] Finally, according to the predicted trends of body temperature and ambient humidity, the temperature and humidity of the mattress are adjusted in advance. If the body temperature is predicted to rise, the mattress temperature is lowered in advance; if the body temperature is predicted to decrease, the mattress temperature is increased in advance. If the ambient humidity is predicted to increase, the mattress humidity is lowered in advance; if the ambient humidity is predicted to decrease, the mattress humidity is increased in advance.

[0087] Through the above detailed execution process, the intelligent bed management module realizes real-time monitoring and automatic adjustment of patient body temperature and environmental humidity, ensures the accuracy and real-time nature of the data, makes adjustments in advance by analyzing the trend of changes, monitors the adjustment effect in real time, and records the operation results to support subsequent optimization. This series of functions effectively improves the efficiency and accuracy of ICU clinical information management, optimizes the nursing process, and ensures the safety and comfort of patients during treatment.

[0088] The medical quality control module 150 performs quality monitoring on the data collected by the data collection module, and performs quality inspection on the analysis results, the personalized treatment plan, the final treatment plan and the nursing information.

[0089] Among them, when the data acquisition module collects the patient's physiological parameters, environmental parameters, multi-omics data and clinical data from various medical devices and sensors (such as ECG monitors, blood pressure monitors, etc.) in real time, the medical quality control module will simultaneously check the quality of these data. This ensures that the collected data is accurate and meets the standards, providing a reliable basis for subsequent processing, which includes but is not limited to pre-processing such as data cleaning, denoising, and normalization to improve data consistency and reliability.

[0090] After real-time analysis of the patient's physiological parameters based on deep learning algorithms, the obtained analysis results will be further reviewed by the medical quality control module to confirm their accuracy and rationality. This can ensure the authenticity and effectiveness of abnormal detection results, prevent false alarms or missed reports due to data quality issues, and ensure timely and accurate judgment of the patient's condition.

[0091] When the system generates personalized treatment recommendations based on multi-omics data and clinical data, the medical quality control module will conduct a comprehensive evaluation of the plan to ensure its scientificity, rationality and safety. By strictly controlling the content of the treatment plan, inappropriate treatment measures caused by data bias or algorithm limitations can be avoided, thereby improving the practical application value of personalized treatment plans.

[0092] After the multidisciplinary expert team discusses and determines the final treatment plan online, the plan must undergo a final inspection by the medical quality control module to confirm whether it fully complies with medical standards and the patient's specific conditions. This ensures that the final treatment plan not only incorporates the opinions of multiple experts, but is also fully verified before actual operation, thus ensuring the effectiveness and safety of the treatment to the greatest extent.

[0093] The intelligent bed management module automatically monitors and records the patient's nursing information (such as turning time, medication status, etc.), and the medical quality control module is responsible for regularly checking the completeness and accuracy of this information. This maintains the high quality of nursing records, supports medical staff to better understand the patient's condition, and is also conducive to tracing any problems in the nursing process in the future, promoting continuous improvement of nursing service quality.

[0094] It should be pointed out here that the above-mentioned medical quality control module performs quality inspection on the data collected and generated in the previous scheme, and will also perform corresponding quality inspection on the subsequent data in the embodiment scheme of this application.

[0095] In short, the medical quality control module plays a vital role in the entire smart ICU clinical information management system. Through a comprehensive quality inspection mechanism, it ensures that the information and decisions output at each link are based on the highest quality data, thereby significantly improving the overall medical service level and ensuring the safety and health of critically ill patients.

[0096] After the patient has been treated with the final treatment plan, it also includes: First, continuously obtain the patient's real-time physiological data.

[0097] As mentioned above, a variety of physiological parameter monitoring equipment is installed in the ICU ward, such as electrocardiogram monitors, blood pressure monitors, blood oximeters, thermometers, respiratory rate monitors, etc. These devices collect the patient's physiological data in real time, including heart rate, blood pressure, blood oxygen saturation, body temperature, respiratory rate, etc. The collected physiological data is transmitted to the central management system via wired or wireless means, and the collected physiological data is preprocessed, including data cleaning, denoising, normalization, etc., to ensure the quality and consistency of the data.

[0098] Then, the implementation effect of the final treatment plan is evaluated based on real-time physiological data.

[0099] The processed physiological data is stored in the database of the central management system, and the data analysis algorithm is used to perform real-time analysis on the patient's physiological parameters to evaluate the effectiveness of the treatment plan. Based on the analysis results, the effectiveness of the final treatment plan is evaluated and an evaluation report is generated.

[0100] 1. Monitor whether the patient's physiological parameters have improved, for example, whether the heart rate has returned to normal, whether the blood pressure has dropped, whether the blood oxygen saturation has increased, etc.; 2. Monitor whether new abnormalities have occurred, for example, a sudden increase in heart rate, a sharp increase in blood pressure, a decrease in blood oxygen saturation, etc.

[0101] Secondly, the evaluation implementation results will be fed back to medical staff, and corresponding plan adjustment suggestions will be generated based on the evaluation implementation results.

[0102] First, an evaluation report is generated, including changes in the patient's physiological parameters, evaluation of treatment effects, whether new abnormalities have occurred, etc., and then the evaluation report is displayed on the interface of the central management system. Medical staff can view the patient's physiological parameters and treatment effects in real time through the system.

[0103] If the evaluation results show that there are new abnormalities, the system will automatically issue an alarm to remind medical staff to deal with them in time; the evaluation results can be fed back to medical staff through various channels (such as system prompts, text messages, emails, etc.) to ensure timely communication of information.

[0104] Then, if the evaluation results show that certain physiological parameters have not achieved the expected effect, it is recommended to adjust the drug dosage, for example, increase or decrease the dosage of a certain drug; if the evaluation results show that the treatment time is inappropriate, it is recommended to adjust the treatment time, for example, advance or postpone certain treatment operations; if the evaluation results show that additional support is needed, it is recommended to add auxiliary treatment measures, for example, increase physical therapy, psychological counseling, etc. Generate specific program adjustment suggestions, which include the specific content of the adjustment, operation steps, expected effects, etc.

[0105] Through the above detailed execution process, the intelligent bed management module realizes the continuous collection and analysis of patients' real-time physiological data, evaluates the implementation effect of the treatment plan, monitors the improvement of physiological parameters and the emergence of new abnormalities, and promptly feeds back the evaluation results to medical staff, while generating specific suggestions for program adjustment. This series of functions effectively improves the efficiency and accuracy of ICU clinical information management, optimizes the treatment plan, and ensures the safety and comfort of patients during treatment.

[0106] The following steps are also included in the treatment of patients: The first step is to retrieve the corresponding analysis results and the severity of the abnormality, and determine the corresponding required medical resources based on the analysis results.

[0107] Among them, the patient's physiological parameter analysis results and abnormal detection results are retrieved from the central management system, including abnormal type, abnormal parameters, abnormal time, etc.; according to the pre-set thresholds and multimodal analysis results, the severity of the abnormality is evaluated and divided into mild abnormality, moderate abnormality, severe abnormality, etc.

[0108] Based on the severity of the abnormality and the analysis results, determine the required medical resources and generate a list of required medical resources, including medical staff, medical equipment, medicines, etc. It is necessary to check whether there are sufficient medical resources in the hospital resource library to ensure the availability of resources.

[0109] For medical resources, for example: Mild abnormalities: May only require increased monitoring frequency and no additional medical resources.

[0110] Moderate abnormalities: may require preliminary examination and treatment by the doctor on duty, as well as some basic medical equipment and medicines.

[0111] Severe abnormalities: may require urgent treatment by the attending physician and head nurse, as well as advanced medical equipment and emergency drugs.

[0112] The second step is to determine the corresponding resource scheduling level based on medical resources and the severity of the abnormality.

[0113] Among them, a resource scheduling strategy is formulated based on the severity of the abnormality and the required medical resources. For example: Minor abnormalities: The dispatch level is low, and medical staff are mainly alerted through system prompts.

[0114] Moderate abnormality: The dispatch level is moderate and the on-duty doctor needs to be notified immediately for preliminary examination and treatment.

[0115] Severe abnormality: The dispatch level is high, and the attending physician and head nurse need to be notified immediately to activate the emergency plan.

[0116] And according to the severity of the abnormality, the priority of resource scheduling is set to ensure that emergency situations are handled first; the generated resource scheduling plan includes detailed information such as scheduling time, personnel, equipment, and drugs.

[0117] The third step is to optimize resource scheduling and task allocation based on the degree of resource scheduling.

[0118] Among them, according to the resource scheduling plan, medical resources will be allocated to the corresponding medical staff and departments, for example: medical staff: notify the attending physician, head nurse, doctor on duty, etc., and arrange for them to conduct corresponding inspections and treatments; medical equipment: ensure that the required medical equipment is in place in time, such as ECG monitors, ventilators, etc.; medicines: ensure that the required medicines are prepared in time, such as first aid medicines, antibiotics, etc.

[0119] Specific tasks are then assigned to the corresponding medical staff, and a task list is generated, including task content, execution time, and responsible person. During the resource scheduling and task allocation process, resource usage and task execution progress are monitored in real time. At the same time, a feedback mechanism is established so that medical staff can promptly provide feedback on task execution after completing the task. The system records feedback information to support subsequent analysis and optimization.

[0120] When a user accesses information, the following steps are also performed, including: The first step is to determine the corresponding target user and current access information, retrieve the user's historical access records, and determine the user behavior based on the historical access records and current access information.

[0121] When a user logs into the system, identity authentication is performed through a user name and password to ensure the legitimacy of the user's identity. The user's current access information needs to be recorded, including access time, access location, visited pages or functional modules, etc.; based on the login information, the current target user is determined to ensure the personalization and security of subsequent operations.

[0122] Retrieve the target user's historical access records from the system database, including login time, login location, operation frequency, operation type, etc. Organize the retrieved historical access records to ensure the data is structured and analyzable. Analyze the user's access behavior patterns based on historical access records, such as the time periods that users frequently visit, commonly used modules, operation frequency, etc.

[0123] In addition, historical access records and current access information can be combined to identify the user's access behavior patterns, for example, whether the user frequently accesses a module in a specific time period, and whether the user often performs a certain type of operation; based on the behavior pattern, the user's possible next operation can be predicted, for example, the user may need to view the detailed information of a patient, or need to perform a certain treatment operation.

[0124] The second step is to determine the corresponding context information based on the target user.

[0125] First, the contextual information of the target user is collected, including the department the user is in, the current medical task, the patient's status, etc., and then the accuracy and timeliness of the collected contextual information are verified to ensure the authenticity and reliability of the information, and the contextual information is associated with the user's behavior pattern. For example, the department the user is in may affect the module he accesses, and the current medical task may affect his operation type.

[0126] The third step is to adjust the access rights of target users based on user behavior and contextual information.

[0127] Among them, the user's access permission requirements are evaluated according to the user's behavior pattern and contextual information. For example, if the user frequently accesses a module and currently has an urgent medical task, it may be necessary to temporarily increase his or her access permissions. Then, based on the evaluation results, the user's access permissions are dynamically adjusted, for example, increasing or decreasing the access permissions of certain modules, and restricting or opening certain operations.

[0128] Through the above detailed execution process, the management platform realizes comprehensive management of user access information and dynamic permission adjustment. The specific functions are as follows: determine the target user through user identity authentication and access information records, retrieve historical access records to analyze user behavior patterns, combine current access information and contextual information (such as department, medical task, patient status) to predict user operations and evaluate the rationality of behavior, dynamically adjust user access rights, ensure minimum data exposure, and improve data security and system reliability. This series of functions effectively improves the efficiency and security of ICU clinical information management, optimizes user access experience, and ensures the safety and privacy protection of patients during treatment.

[0129] The present application embodiment discloses a smart ICU clinical information management method, referring to Figure 2,include: S200, determining corresponding physiological parameters, environmental parameters, multi-omics data and clinical data of the patient collected in real time from various medical devices and sensors.

[0130] S210, based on the deep learning algorithm, the corresponding physiological parameters of the patient are analyzed in real time, the corresponding analysis results are obtained, and the corresponding abnormality detection is performed based on the analysis results. If an abnormality exists, the alarm data corresponding to the abnormal detection result is issued.

[0131] S220, after the alarm is issued, the multi-omics data and clinical data corresponding to the patient are retrieved, and a personalized treatment plan for the abnormal test results is generated based on the multi-omics data and clinical data, wherein the multi-omics data includes genomics, metabolomics, and proteomics, and the clinical data includes medical records, laboratory test results, and imaging data.

[0132] S230, sending the personalized treatment plan to the corresponding remote collaboration device of the multidisciplinary expert team. Each expert in the multidisciplinary expert team can conduct an online consultation on the personalized treatment plan through video conferencing or sharing electronic medical records, and determine the corresponding final treatment plan for the personalized treatment plan based on the consultation opinions of all experts.

[0133] S240 is a smart bed that integrates multiple sensors. It monitors and records the patient's nursing information based on the final treatment plan and transmits the nursing information to the central management system. The nursing information includes turning time, medication time, patient's body position data and number of turning times.

[0134] The embodiment of the present application also discloses a smart ICU clinical information management platform, including a processor, in which a program of the above-mentioned smart ICU clinical information management method is run.

[0135] The embodiment of the present application also discloses a storage medium storing a program of the above-mentioned smart ICU clinical information management method.

[0136] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A smart ICU clinical information management platform, characterized in that: include: The data acquisition module is used to collect the patient's physiological parameters, environmental parameters, multi-omics data and clinical data from various medical devices and sensors in real time after determining the corresponding ones; The intelligent early warning module analyzes the patient's corresponding physiological parameters in real time based on a deep learning algorithm, obtains corresponding analysis results, and performs corresponding abnormality detection based on the analysis results. If an abnormality exists, an alarm data corresponding to the abnormality detection result is issued; A personalized treatment recommendation module, after an alarm is issued, retrieves the multi-omics data and clinical data corresponding to the patient, and generates a personalized treatment plan for the abnormal test results based on the multi-omics data and the clinical data, wherein the multi-omics data includes genomics, metabolomics, and proteomics, and the clinical data includes medical records, laboratory test results, and imaging data; A remote consultation module is used to send the personalized treatment plan to the remote collaboration device corresponding to the multidisciplinary expert team. Each expert in the multidisciplinary expert team can conduct an online consultation on the personalized treatment plan through video conferencing or sharing electronic medical records, and determine the corresponding final treatment plan for the personalized treatment plan based on the consultation opinions of all experts; An intelligent bed management module, an intelligent bed integrated with multiple sensors, monitors and records the patient's nursing information based on the final treatment plan, and transmits the nursing information to a central management system, wherein the nursing information includes turning time, medication time, patient's body position data, and turning times; The medical quality control module monitors the quality of the data collected by the data collection module, and performs quality inspection on the analysis results, the personalized treatment plan, the final treatment plan and the nursing information.

2. The smart ICU clinical information management platform according to claim 1, characterized in that: The intelligent early warning module also includes: Classifying the anomalies into multiple levels, the anomaly detection results determining the corresponding anomaly severity; Retrieve the pre-set early warning mechanism and determine the corresponding target alarm level based on the severity of the abnormality; A corresponding response measure is determined based on the target alarm level.

3. The smart ICU clinical information management platform according to claim 1, characterized in that: After the patient is treated based on the final treatment plan, it also includes: Continuously obtain the patient's real-time physiological data, including heart rate, blood pressure, blood oxygen saturation, body temperature, and respiratory rate; Evaluating the implementation effect of the final treatment plan based on the real-time physiological data, wherein the implementation effect includes monitoring whether the patient's physiological parameters have improved and whether new abnormalities have appeared; The evaluation implementation results are fed back to medical staff, and corresponding regimen adjustment suggestions are generated based on the evaluation implementation results, where the regimen adjustment suggestions include drug dosage, change of treatment time, addition of auxiliary treatment, etc.

4. The smart ICU clinical information management platform according to claim 2, characterized in that: include: Retrieving the corresponding analysis results and the severity of the abnormality, and determining the corresponding required medical resources based on the analysis results, wherein the required medical resources include medical personnel, medical equipment, and medicines; Determining a corresponding resource scheduling degree based on the medical resources and the severity of the abnormality; Based on the resource scheduling degree, corresponding optimized resource scheduling and task allocation are performed.

5. The smart ICU clinical information management platform according to claim 1 is characterized in that: The remote consultation module: When sharing electronic medical records for online consultation on personalized treatment plans, determine the roles of experts participating in the online consultation, and determine the corresponding responsibilities and authorities of the experts based on the expert roles; Determining the editing authority corresponding to the personalized treatment plan based on the expert's responsibilities and authority; When the expert is editing, the expert's editing record of the personalized treatment plan is synchronously displayed to other experts.

6. The smart ICU clinical information management platform according to claim 1, characterized in that: When users access information, it also includes: Determine the corresponding target user and current access information, retrieve the user's historical access records, and determine the user behavior based on the historical access records and the current access information, wherein the historical access records include the corresponding login time, login location, operation frequency, and operation type; Determining corresponding context information based on the target user, wherein the context information includes the department where the user is located, the current medical task, and the patient's status; The access rights of the target user are adjusted based on the user behavior and the context information.

7. The smart ICU clinical information management platform according to claim 3 is characterized in that: After treating the patient based on the final treatment plan, the smart bed management module also includes: Collect the patient's body temperature data and environmental humidity data based on sensors integrated in the smart bed; Automatically adjusting the temperature of the mattress based on the body temperature data, and automatically adjusting the humidity of the mattress based on the ambient humidity data; The corresponding change trend is determined according to the body temperature data set and the environmental humidity data set corresponding to the body temperature data, and adjustments are made in advance based on the change trend, wherein the change trend includes the change trend of the body temperature data and the change trend of the environmental humidity data.

8. A smart ICU clinical information management method, characterized in that: Methods include: Determine the corresponding real-time acquisition of patients' physiological parameters, environmental parameters, multi-omics data and clinical data from various medical devices and sensors; Analyze the patient's corresponding physiological parameters in real time based on the deep learning algorithm, obtain the corresponding analysis results, and perform corresponding abnormality detection based on the analysis results. If there is an abnormality, issue alarm data corresponding to the abnormality detection results; After the alarm is issued, the multi-omics data and clinical data corresponding to the patient are retrieved, and a personalized treatment plan for the abnormal test results is generated based on the multi-omics data and the clinical data, wherein the multi-omics data includes genomics, metabolomics, and proteomics, and the clinical data includes medical records, laboratory test results, and imaging data; The personalized treatment plan is sent to the remote collaboration device corresponding to the multidisciplinary expert team. Each expert in the multidisciplinary expert team can conduct an online consultation on the personalized treatment plan through video conferencing or sharing electronic medical records, and determine the corresponding final treatment plan for the personalized treatment plan based on the consultation opinions of all experts; The smart bed integrating multiple sensors monitors and records the patient's nursing information based on the final treatment plan, and transmits the nursing information to the central management system, wherein the nursing information includes turning time, medication time, patient's body position data and turning times.

9. A smart ICU clinical information management platform, characterized in that: It includes a processor, in which a program of the smart ICU clinical information management method as described in claim 8 is run.

10. A storage medium, characterized in that: A program for the smart ICU clinical information management method as described in claim 8 is stored.

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