Remote monitoring system and method for critically ill patients

Abstract

The invention relates to the technical field of remote monitoring, in particular to a critically ill patient remote monitoring system and method, and the system comprises a life signal collection module, a physiological state analysis module, a remote monitoring response module and a remote intervention operation module. According to the method, the data analysis and real-time feedback process is optimized, the efficiency and accuracy of medical response are improved, the key physiological indexes deviating from the normal range can be quickly and accurately recognized and analyzed by comprehensively analyzing the instant vital sign data and the historical medical records of the patient, the prediction accuracy is improved, and the prediction efficiency is improved. Medical personnel are allowed to receive clear health risk warnings at critical moments, direct communication with a medical network is enhanced through a rapid grading and alarm system, a faster medical intervention response is achieved, meanwhile, a treatment plan can be remotely and dynamically adjusted according to the current physiological state of a patient, the treatment process is more accurate, and the treatment efficiency is improved. And the survival rate and the treatment quality of patients are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of remote monitoring, and in particular, to a remote monitoring system and method for critically ill patients. Background Art

[0002] Remote monitoring technology is a medical information technology used to monitor a patient's physiological parameters and health status in real-time or near real-time, regardless of the physical distance between the patient and the healthcare provider, relying on the integrated application of sensors, communication devices, and data processing software, enabling medical experts to monitor a patient's heart rate, blood pressure, blood oxygen saturation, and other key indicators from a distance. Remote monitoring technology not only provides continuous health monitoring but also supports an emergency response system that analyzes the collected data to predict and respond promptly to potential health crises, which is of great significance for improving chronic disease management, enhancing the efficiency of hospital resource utilization, and extending medical services to remote areas.

[0003] Among them, the remote monitoring system for critically ill patients is a system designed specifically for monitoring patients in a critical state, whose main purpose is to ensure that the vital signs and health status of patients can be tracked in real-time by medical staff even when the medical staff is not beside the patient, by using highly sensitive monitoring devices to collect key physiological data and transmitting the data to a medical center through a secure network for continuous evaluation and emergency intervention when necessary, which is crucial for improving the survival rate and treatment effect of critically ill patients, especially in situations where resources are limited or access to medical facilities is difficult.

[0004] The prior art is insufficient in terms of in-depth data utilization and emergency response speed. Although data can be remotely monitored and collected, there are often delays in quickly identifying important physiological changes and providing real-time feedback, resulting in the inability to make effective medical responses immediately. Such deficiencies can lead to unnecessary delays in dealing with sudden medical situations. In addition, the lack of functions for automated processing of medical responses, such as adjusting monitoring parameters or deficiencies in the warning system, limits its application efficiency at critical moments. These deficiencies affect the patient's receipt of timely and appropriate treatment during a crisis, thus affecting the treatment outcome and the patient's chances of survival. Summary of the Invention

[0005] The object of the present invention is to solve the drawbacks existing in the prior art, and to propose a remote monitoring system and method for critically ill patients.

[0006] To achieve the above object, the present invention adopts the following technical solution: A remote monitoring system for critically ill patients includes:

[0007] The vital sign acquisition module remotely collects the vital sign data of critically ill patients according to sensors, and sends it to the central server through wireless transmission technology. It performs data cleaning and sorting on the received data to obtain the patient's vital sign data packet;

[0008] The physiological state analysis module receives the patient's vital sign data packet, compares the indicators one by one, identifies the abnormal data points that exceed the preset safety range, and conducts a comprehensive trend analysis based on the identification results combined with the patient's medical record history to generate a record of abnormal analysis of physiological indicators;

[0009] The remote monitoring response module performs hierarchical processing based on the record of abnormal analysis of physiological indicators according to the urgency level, issues an alarm according to the hierarchical processing result, synchronizes with the hospital remote monitoring center in combination with the patient's situation, and establishes a direct communication link with medical staff through the medical network to obtain remote response information;

[0010] According to the remote response information, the remote intervention operation module enables the remote medical team to adjust the parameters of the treatment equipment through the remote operation platform, synchronously update the patient's treatment plan to match the current physiological state, and output a record of dynamic feedback of remote monitoring.

[0011] As a further solution of the present invention, the steps for obtaining the patient's vital sign data packet are as follows:

[0012] Continuously monitor the critical vital signs of critically ill patients through sensors, and regularly package the data to generate a basic data packet;

[0013] Use an encrypted wireless network to transmit the basic data packet to the central server, confirm the integrity and security during the data transmission process, and generate a transmitted data packet;

[0014] The central server performs cleaning and data sorting on the transmitted data packet, removes the noise and outliers in the data, and reconstructs the data time series according to the data timestamp to generate the patient's vital sign data packet.

[0015] As a further solution of the present invention, the steps for identifying the abnormal data points are as follows:

[0016] Receive the patient's vital sign data packet, extract the vital sign indicators, attach a timestamp to each indicator, organize and store them in a formatted manner to generate formatted vital sign data;

[0017] Set a safety threshold, compare the formatted vital sign data item by item, identify all the indicator data that exceed the preset range, and obtain the indicator data that exceeds the preset safety range;

[0018] Analyze the indicator data that exceeds the preset safety range, mark all the abnormal data points, reveal potential health risks, and generate a record of abnormal data points.

[0019] As a further solution of the present invention, the physiological index abnormality analysis and recording step is as follows:

[0020] Integrate the abnormal data point records and the patient's medical record history, automatically match the patient's historical health data with the latest abnormal indicators, perform time series analysis on the data, evaluate the change trend of the patient's health status, and generate a preliminary trend analysis result;

[0021] Perform in-depth analysis on the preliminary trend analysis result, combine the current medical research and historical similar case data, and use the formula:

[0022]

[0023] Calculate the overall health risk assessment score R(t), quantitatively evaluate the potential health risks, and obtain the comprehensive trend analysis result. Among them, P i (t) represents the predicted value of the i-th vital sign index at time t, and μ i is the historical average value of index i, and σ i is the historical standard deviation of index i, and n represents the number of indexes;

[0024] According to the comprehensive trend analysis result, analyze the potential health impacts of each abnormal index and recommended medical measures, and generate a physiological index abnormality analysis record.

[0025] As a further solution of the present invention, the execution steps of the grading process are as follows:

[0026] Receive the physiological index abnormality analysis record, mark the abnormality degree and type of each data point, and generate an abnormal data analysis record;

[0027] According to the preset emergency level evaluation criteria, grade the urgency of each item of data in the abnormal data analysis record, and use the formula

[0028]

[0029] Calculate the emergency level G of each abnormal data i , and generate a grading process result. Among them, E i represents the abnormal degree value of the abnormal data point, M represents the median of the abnormal degrees of all data points, and V represents the variance of the abnormal degrees of the data points;

[0030] Summarize the grading process results, analyze the abnormal situations with high emergency levels, perform priority ranking, determine the pertinence and timeliness of medical responses, and generate an emergency treatment priority list.

[0031] As a further solution of the present invention, the steps for obtaining the remote response information are as follows:

[0032] Receive the emergency treatment priority list, extract the patient's emergency level and critical physiological data, automatically issue an alarm according to the emergency level, reinforce the reminder through sound and visual signals, and generate alarm notification information;

[0033] Synchronize the alarm notification information to the hospital remote monitoring center, send the patient's alarm level and real-time physiological data, adjust the monitoring frequency according to the patient's alarm level, and generate a real-time monitoring data stream;

[0034] Establish a direct communication link between medical staff and the monitoring center, receive the real-time monitoring data stream, conduct remote diagnosis and decision-making, initiate an emergency medical response, and record and output remote response information.

[0035] As a further solution of the present invention, the obtaining steps of the remote monitoring dynamic feedback record are as follows:

[0036] Receive the remote response information, directly retrieve the patient's physiological data, through data analysis, compare the current data with the set parameters of the treatment device, and generate a comparison result;

[0037] According to the comparison result, the remote medical team adjusts the device parameters through the remote operation platform, confirms that the adjusted parameters match the patient's current physiological state, and generates a device parameter adjustment plan;

[0038] Apply the device parameter adjustment plan, update the patient's treatment plan, fuse the update result and the new device parameters through data synchronization, and output the remote monitoring dynamic feedback record.

[0039] A method for remote monitoring of critically ill patients includes the following steps:

[0040] S1: Continuously monitor the critical vital signs of critically ill patients through sensors, regularly package the data, transmit it to the central server, perform cleaning and data collation, and generate a patient vital sign data packet;

[0041] S2: Receive the patient vital sign data packet, extract the vital sign indicators, perform formatted storage, identify all indicator data that exceeds the preset range, mark all abnormal data points, reveal potential health risks, and generate an abnormal data point record;

[0042] S3: Integrate the abnormal data point record with the patient's medical record history, perform time series analysis on the data, combine current medical research and historical similar case data, quantitatively evaluate the potential health risks, analyze the potential health impacts of each abnormal indicator, and generate a physiological indicator abnormality analysis record;

[0043] S4: Receive the physiological index abnormal analysis record, mark the degree and type of abnormality for each data point, classify the urgency of each piece of data in the abnormal data analysis record, perform priority ranking, and generate an emergency treatment priority list;

[0044] S5: Receive the emergency treatment priority list, extract the patient's emergency level and critical physiological data, automatically send an alarm, synchronize it to the hospital remote monitoring center and adjust the monitoring frequency, conduct remote diagnosis and decision-making, initiate an emergency medical response, record and output the remote response information;

[0045] S6: Receive the remote response information, compare the current data with the set parameters of the treatment device, adjust the device parameters, confirm that the adjusted parameters match the patient's current physiological state, update the patient's treatment plan, and output the remote monitoring dynamic feedback record.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0047] In the present invention, the data analysis and real-time feedback process are optimized to improve the efficiency and accuracy of medical response. By comprehensively analyzing the patient's immediate vital sign data and historical medical records, it is possible to quickly and accurately identify and analyze the key physiological indicators that deviate from the normal range, improve the prediction accuracy, allow medical staff to receive clear health risk warnings at critical moments, strengthen the direct communication with the medical network through a rapid grading and alarm system, achieve a faster medical intervention response, and at the same time be able to remotely and dynamically adjust the treatment plan according to the patient's current physiological state, making the treatment process more precise and significantly improving the patient's survival rate and treatment quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is the system flow chart of the present invention;

[0049] Figure 2 is the acquisition flow chart of the patient's vital sign data packet of the present invention;

[0050] Figure 3 is the identification flow chart of abnormal data points of the present invention;

[0051] Figure 4 is the physiological index abnormal analysis record flow chart of the present invention;

[0052] Figure 5 is the execution flow chart of hierarchical processing of the present invention;

[0053] Figure 6 is the acquisition flow chart of remote response information of the present invention;

[0054] Figure 7 is the acquisition flow chart of the remote monitoring dynamic feedback record of the present invention. Detailed implementation mode

[0055] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0056] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more unless otherwise specifically defined.

[0057] Please refer to Figure 1 , a remote monitoring system for critically ill patients includes:

[0058] The life signal acquisition module remotely collects the vital sign data of critically ill patients according to sensors, sends it to the central server through wireless transmission technology, performs data cleaning and sorting on the received data, and obtains the patient's vital sign data packet;

[0059] The physiological state analysis module receives the patient's vital sign data packet, compares the indicators one by one, identifies abnormal data points that exceed the preset safety range, and performs a comprehensive trend analysis based on the identification results combined with the patient's medical record history to generate a physiological index abnormal analysis record;

[0060] The remote monitoring response module performs hierarchical processing based on the physiological index abnormal analysis record according to the urgency level, issues an alarm according to the hierarchical processing result, synchronizes it to the hospital remote monitoring center in combination with the patient's situation, and establishes a direct communication link with medical staff through the medical network to obtain remote response information;

[0061] The remote intervention operation module adjusts the parameters of the treatment device through the remote operation platform according to the remote response information, synchronously updates the patient's treatment plan to match the current physiological state, and outputs a remote monitoring dynamic feedback record.

[0062] The patient's vital sign data packet includes a heartbeat data packet, a blood pressure data packet, and an oxygenation data packet. The physiological index abnormality analysis record includes a heart rate abnormality analysis record, a blood pressure abnormality analysis record, and an oxygen saturation abnormality analysis record. The remote response information includes an alarm level, an alarm content, and an alarm reception confirmation result. The remote monitoring dynamic feedback record includes treatment adjustment details, treatment plan update records, and patient status feedback records.

[0063] Please refer to Figure 2 , the steps for obtaining the patient's vital sign data packet are as follows:

[0064] Continuously monitor the critical vital signs of critically ill patients through sensors, and regularly pack the data to generate a basic data packet;

[0065] The process of monitoring the critical vital signs such as heart rate, blood pressure, and body temperature of critically ill patients through sensors and collecting data to form a real-time vital sign data stream involves multiple key technical steps. First, the sensors need to accurately capture data under different physiological conditions. For example, the heart rate sensor must be able to work stably when the patient is moving or stationary, and the body temperature sensor should have the ability to quickly respond to changes in the ambient temperature. Secondly, during data collection, it is necessary to initially format the collected vital sign data so that the data can be effectively recognized by subsequent systems. This includes, but is not limited to, converting analog signals to digital signals, timestamping the signals, ensuring the integrity and accuracy of the data packet before it is packed and sent. Then, the system also needs to compress the data stream to reduce the amount of data during transmission and improve the transmission efficiency. Finally, through a timing mechanism, the data is regularly packed to ensure that the data contains all necessary vital sign information and the time relationship of the data within the packet is maintained, generating a basic data packet.

[0066] Use an encrypted wireless network to transmit the basic data packet to the central server, confirm the integrity and security during the data transmission process, and generate a transmitted data packet;

[0067] The application of an encrypted wireless network mainly involves two technical aspects: data encryption and secure transmission. First, encrypt the vital sign data to ensure the security of the data during wireless transmission. Common encryption techniques include symmetric encryption and asymmetric encryption. In symmetric encryption, both the data sender and receiver use the same key for encryption and decryption. This method has a fast transmission speed and is suitable for real-time data transmission. Asymmetric encryption uses a pair of public and private keys, where the public key is used for encryption and the private key is used for decryption, enhancing the security of the transmission. After that, transmit the encrypted data packet to the central server through a wireless network. The process needs to ensure the stability and continuity of the network to prevent data loss during transmission. At the same time, the server side needs to configure a corresponding decryption mechanism to ensure that the received data can be correctly interpreted and used, generating a transmitted data packet.

[0068] The central server cleans and organizes the transmitted data packets, removes the noise and outliers in the data, reconstructs the data time series according to the data timestamps, and generates patient vital sign data packets.

[0069] On the central server, in-depth cleaning and organizing of the received vital sign data packets includes several key steps: First, during the data cleaning process, it is necessary to identify and remove the noise and outliers in the data. For example, by setting thresholds, values that do not conform to physiological possibilities in vital sign data such as heart rate and blood pressure can be identified. Then, for the timestamp information in the data packets, data alignment processing is performed to ensure the timeliness and integrity of the data. Next, statistical methods are used to normalize the vital sign data, which helps with subsequent data analysis and pattern recognition. Finally, the data time series is reconstructed by integrating consecutive data points to form meaningful time series information to generate patient vital sign data packets.

[0070] Please refer to Figure 3 , and the steps for identifying abnormal data points are as follows:

[0071] Receive patient vital sign data packets, extract vital sign indicators, attach timestamps to each indicator, organize and store them in a formatted manner to generate formatted vital sign data.

[0072] The received vital sign data packets contain multiple indicators such as heart rate, blood pressure, and blood oxygen saturation. The data is first collected in real-time by high-precision sensors and then sent to the central processing system through an encrypted network protocol. In the system, the data is first marked with timestamps and then undergoes preliminary data cleaning. The cleaning process includes removing obvious incorrect readings and outliers. For example, data points with a heart rate below 40 beats per minute or exceeding 200 beats per minute are automatically excluded, and blood pressure and blood oxygen saturation are processed according to a similar logic. The cleaned data is stored in a highly available database to ensure that the data can be processed quickly. At the same time, the rule engine on the server side standardizes the data, formatting all data into a unified format for subsequent data processing and analysis work. This process ensures the accuracy and timeliness of the data, providing basic data support for the accurate execution of subsequent steps and generating formatted vital sign data.

[0073] Set safety thresholds, compare the formatted vital sign data item by item, identify all indicator data that exceeds the preset range, and obtain the indicator data that exceeds the preset safety range.

[0074] In a data processing center, a set of health thresholds is set according to World Health Organization standards, including the safe ranges of vital signs such as heart rate, blood pressure, and blood oxygen saturation. The system uses an automated data analysis module to compare the received formatted vital sign data. The comparison process utilizes a high-performance computing platform that can handle large amounts of data. Each vital sign indicator is compared with the set threshold. Heart rate data exceeding 100 beats per minute or below 60 beats per minute will be marked as abnormal, and blood pressure data exceeding 140 / 90 mmHg or below 90 / 60 mmHg will be treated the same way. If the blood oxygen saturation is below 92%, it is regarded as a potential health risk. The comparison operation is executed by a data analysis engine that employs multi-threaded technology to accelerate data processing, ensuring real-time and accuracy, and thus obtaining all the indicator data that exceeds the preset safe range.

[0075] Analyze the indicator data that exceeds the preset safe range, mark all abnormal data points, reveal potential health risks, and generate abnormal data point records;

[0076] Once the data analysis module identifies any vital sign data that exceeds the preset safe range, this data will be sent to the next processing stage, namely the marking and analysis of abnormal data points. First, a health risk model for abnormal data is established based on historical data, and then the current abnormal data is compared and analyzed to identify possible health problems. Each abnormal data point will be classified and marked according to its severity and occurrence frequency for doctors and patients to refer to, detailing all abnormal data points and their possible health impacts, providing not only early warnings for patients but also data support for doctors' diagnoses, and generating abnormal data point records.

[0077] Please refer to Figure 4 , and the steps for analyzing physiological index abnormalities and recording are as follows:

[0078] Integrate the abnormal data point records with the patient's medical record history, automatically match the patient's historical health data with the latest abnormal indicators, perform time series analysis on the data, evaluate the changing trend of the patient's health status, and generate a preliminary trend analysis result;

[0079] Based on the abnormal data points extracted from the foregoing process and combined with the patient's medical record history, first, through data integration, the real-time monitored abnormal indicators are automatically matched with the patient's historical health records. After data integration, time series analysis techniques are used to evaluate and predict the changing trend of the patient's health status. The specific operations include normalizing historical and current data points, and then applying autoregressive model (AR) and moving average (MA) techniques to identify long-term trends and periodic fluctuations in the data. By analyzing, the potential changes in the patient's health status are revealed, providing a scientific basis for subsequent medical interventions, and finally generating a preliminary trend analysis result.

[0080] Perform a deep analysis on the preliminary trend analysis results. Combining current medical research and historical similar case data, use the formula:

[0081]

[0082] Calculate the overall health risk assessment score R(t), quantitatively evaluate the potential health risks, and obtain the comprehensive trend analysis results. Among them, P i (t) represents the predicted value of the i-th vital sign indicator at time t, and μ i is the historical average value of indicator i, and σ i is the historical standard deviation of indicator i. n represents the number of indicators;

[0083] At a specific evaluation moment, consider three indicators: heart rate, blood pressure, and blood oxygen saturation.

[0084] There are the following data values:

[0085] Heart rate P HR (t) = 110 bpm, historical average value μ HR = 70 bpm, standard deviation σ HR = 10 bpm.

[0086] Blood pressure P BP (t) = 150 / 100 mmHg.

[0087] Historical average value μ BP = 120 / 80 mmHg.

[0088] Standard deviation σ BP = 15 / 10 mmHg.

[0089] Blood oxygen saturation P SpO2 (t) = 92%, historical average value μ SpO2 = 98%, standard deviation σ SpO2 = 2%.

[0090] Calculate the contribution of each indicator:

[0091] Contribution of heart rate:

[0092]

[0093] Contribution of blood pressure (only considering systolic pressure):

[0094]

[0095] Contribution of blood oxygen saturation:

[0096]

[0097] Sum up the above results to obtain the total risk assessment score:

[0098] R(t) = 4 + 2 + 3 = 9

[0099] The calculated result R(t) = 9 indicates that among the considered indicators, the patient's current overall health risk is relatively high. A higher score (9) means that the patient's current vital sign indicators deviate significantly from their historical normal values, especially the deviations in heart rate and blood oxygen saturation are more prominent, indicating that the patient is experiencing some form of health crisis or requires immediate medical intervention.

[0100] According to the results of the comprehensive trend analysis, analyze the potential health impacts of each abnormal indicator and recommended medical measures, and generate an analysis record of abnormal physiological indicators;

[0101] After completing the comprehensive trend analysis, summarize the data obtained through statistical models and machine learning algorithms, and deeply explain the specific analysis of the impact of each abnormal vital sign indicator on the patient's potential health. During the process, use various data visualization tools, such as scatter plots, line charts, and heat maps, etc., to clearly display the abnormal trends of the indicators and the patient's health risk levels. Each analysis step is accurately recorded to ensure that medical providers can quickly understand and respond. In addition, the record also includes medical measures recommended based on the current health data, such as medication adjustments, suggestions for lifestyle changes, etc., aiming to provide comprehensive decision-making support. Finally, a formatted analysis file is formed, which is convenient for the medical team to review and also convenient for patients to manage themselves, thus playing a key role in medical practice.

[0102] Please refer to Figure 5 , and the execution steps for hierarchical processing are as follows:

[0103] Receive the analysis record of abnormal physiological indicators, mark the degree and type of abnormality of each data point, and generate an analysis record of abnormal data;

[0104] During the initial generation of abnormal data records, the data in the physiological index abnormal analysis records are sent to the hospital's data processing center via Bluetooth or Wi-Fi. The first step in the data center is data cleaning, which includes removing noise data caused by sensor errors, patient movements, or other external factors. For example, abnormal jump values within a short period in the heart rate record are removed, such as records where the heart rate instantaneously jumps above 200 bpm or drops to an abnormal low point below 30 bpm. After data cleaning, the system will perform data screening based on preset health standard thresholds. Values outside the normal range of 40 to 180 bpm in the heart rate data will be marked as abnormal, and the blood pressure and body temperature data will be processed in the same way. The normal range of blood pressure is set at 90 / 60 mmHg to 140 / 90 mmHg, and the body temperature is set between 36.5°C and 37.5°C. Data points outside these ranges are considered abnormal and recorded. The abnormal data points are then incorporated into the preliminary abnormal data record, providing a basis for the next step of data analysis and emergency response decision-making.

[0105] According to the preset emergency level assessment criteria, each piece of data in the abnormal data analysis record is graded for urgency, using the formula

[0106]

[0107] Calculate the emergency level G of each piece of abnormal data i , and generate the grading processing result. Among them, E i represents the abnormal degree value of the abnormal data point, M represents the median of the abnormal degrees of all data points, and V represents the variance of the abnormal degrees of the data points;

[0108] E i represents the value of a specific abnormal data point, for example, 155 bpm (the normal range is 60 - 100 bpm), M is the median of all abnormal data points, calculated to be 120 bpm, and V is the variance, calculated to be 200. Substitute the specific values into the formula. First, calculate the square root of the absolute deviation, that is Then calculate the cube root of the variance, that is 200 1 / 3 ≈5.85, and the finally obtained emergency level is This result indicates that the emergency level of the abnormal data point is slightly higher than 1, meaning it is slightly urgent, requiring attention but not extremely urgent.

[0109] Summarize the grading processing results, analyze the abnormal situations with high emergency levels, conduct priority ranking, determine the pertinence and timeliness of medical responses, and generate an emergency treatment priority list;

[0110] In the stage of summarizing and analyzing the classification results, the system first sorts all the data points marked as abnormal according to their emergency levels, and gives priority to dealing with those with higher emergency levels. For example, cases where the heart rate continuously deviates abnormally below 40 bpm or above 180 bpm will be rated as higher emergency levels. Identifying and sorting high-risk situations not only considers the urgency of individual data points, but also combines the patient's historical health records and possible medical warnings. For example, if the heart rate of a heart disease patient suddenly drops below 50 bpm, this situation will be marked as very urgent and communicated immediately with the medical team. This information will be transmitted to the remote monitoring center through the hospital's information system. The medical team can view the sorted high-risk patient data in real time on the interface of the monitoring center and prepare to take necessary medical measures. This process ensures the timeliness and pertinence of medical responses. The ultimate goal is to reduce the patient's health risks and possible medical accidents through effective data analysis and emergency responses.

[0111] Please refer to Figure 6 , the steps for obtaining remote response information are as follows:

[0112] Receive the emergency treatment priority list, extract the patient's emergency level and key physiological data, automatically issue an alarm according to the emergency level, and give enhanced reminders through sound and visual signals to generate alarm notification information;

[0113] Receive the emergency treatment priority list, which details the patient's emergency level and key physiological data. The system conducts intelligent classification based on the urgency of the data and automatically issues corresponding alarms. Specifically, the system analyzes each patient's data, such as heart rate and blood pressure, compares it with preset critical values, and situations exceeding the critical values will trigger alarms of corresponding levels. The alarms are divided into three levels according to severity, with level 1 alarm being the most serious and requiring immediate response. When the system issues a level 1 alarm, it will simultaneously activate the hospital's internal emergency response protocol, including enhanced reminders of sound and visual signals, to attract the special attention of medical staff. In addition, all alarm information will be recorded in the log file of the medical system for subsequent review and improvement of the early warning system. Through this process, it ensures the real-time monitoring and rapid response to all patient states, greatly improving the efficiency and safety of medical responses.

[0114] Synchronize the alarm notification information to the hospital's remote monitoring center, send the patient's alarm level and real-time physiological data, and adjust the monitoring frequency according to the patient's alarm level to generate a real-time monitoring data stream;

[0115] The system alarm notification information is synchronized to the hospital's remote monitoring center. During the process, first, the alarm information and the patient's critical physiological data are packaged and sent through the internal network. On the large screen of the remote monitoring center, the information is updated and displayed in real time, enabling the medical team to instantly understand the latest situation of the patient. To improve the response efficiency, the system also automatically adjusts the monitoring frequency according to the patient's alarm level. For example, for patients at the highest alarm level, the monitoring frequency can be increased from once every 10 minutes to once every minute, and it will be dynamically adjusted according to the patient's emergency level and previous monitoring data. This not only improves the monitoring coverage but also ensures that rapid actions can be taken in case of emergencies. All operations are recorded in the system's operation log for easy problem tracking and system optimization.

[0116] Establish a direct communication link between medical staff and the monitoring center, receive real-time monitoring data streams, conduct remote diagnosis and decision-making, initiate emergency medical responses, record and output remote response information;

[0117] After establishing a direct communication link between the medical network and medical staff, it ensures that the medical team can receive all key data from the remote monitoring center in real time. This communication link utilizes a high-speed network connection to ensure low latency and high reliability of data transmission. Medical staff receive real-time data streams through specific terminal devices such as tablets or smartphones, including physiological information such as the patient's heart rate and blood pressure and their corresponding alarm levels. In addition, the system also provides an interactive interface that allows medical staff to make decisions quickly based on the received data, such as adjusting the patient's treatment plan or directly having a video call with the on-site first aid team to guide the implementation of first aid measures. The full process coverage from data reception to decision support greatly enhances the response speed and quality of medical services, ensuring that necessary medical assistance can be provided at critical moments, thereby improving the patient's survival rate and treatment effect.

[0118] Please refer to Figure 7 , the steps for obtaining the remote monitoring dynamic feedback record are as follows:

[0119] Receive remote response information, directly retrieve the patient's physiological data, through data analysis, compare the current data with the set parameters of the treatment device, and generate a comparison result;

[0120] The telemedicine team receives the remote response information and immediately activates the remote monitoring system to retrieve the patient's physiological data, including heart rate and blood pressure. The data is collected and transmitted in real-time by the multi-functional physiological monitoring device worn by the patient and transmitted through an encrypted network to the monitoring center of the medical team. The team uses data analysis to evaluate the consistency between the physiological indicators and the current parameters of the device, including comparing the trends of heart rate and blood pressure changes with historical data to identify any abnormal fluctuations and ensure that the device settings still conform to the patient's health status. The analysis process ensures the timeliness and accuracy of the treatment measures. The generated comparison results will determine whether it is necessary to adjust the parameters of the treatment device to better meet the patient's current physiological needs.

[0121] Based on the comparison results, the telemedicine team adjusts the device parameters through the remote operation platform, confirms that the adjusted parameters match the patient's current physiological state, and generates a device parameter adjustment plan.

[0122] The telemedicine team makes precise adjustments to the device parameters through the remote operation platform according to the comparison results. This adjustment process involves evaluating the deviation between the heart rate and blood pressure data and the preset parameters of the treatment device and determining the adjustment amplitude according to the size of the deviation. The difference between the real-time data and the target parameters is displayed on the team operation interface, and the doctor determines the specific value to be adjusted based on the data. The adjustment operations include increasing or decreasing pressure, adjusting the injection rate, etc., ensuring that each parameter modification is based on the latest clinical data. The refined management adjustment strategy ensures the optimization of device operation, greatly enhances the ability to respond immediately to the patient's status, and the comprehensive adjustment plan aims to maximize the treatment effect by precisely controlling the working parameters of the treatment device. The finally generated device parameter adjustment plan is a direct update and optimization of the patient's treatment plan.

[0123] Apply the device parameter adjustment plan to update the patient's treatment plan, fuse the update results and the new device parameters through data synchronization, and output the remote monitoring dynamic feedback record.

[0124] After completing the adjustment of the device parameters, the telemedicine team synchronously updates the patient's treatment plan, including detailed recording and integration of the adjusted parameters into the patient's treatment file. At the same time, using the updated treatment plan, the medical team conducts real-time synchronization through the data process system to ensure that all relevant devices can receive the latest parameter settings. During this process, the medical team also monitors the real-time effect of the treatment through the dynamic data feedback system and adjusts the treatment strategy in a timely manner to respond to any changes in the patient's status, not only improving the adaptability and effectiveness of the treatment but also enhancing the function of telemedicine monitoring. The output remote monitoring dynamic feedback record provides valuable real-time data for the medical team to support continuous evaluation of the treatment effect and necessary medical decisions, ensuring that the patient receives the most appropriate and timely medical services.

[0125] A remote monitoring method for critically ill patients, comprising the following steps:

[0126] S1: Continuously monitor the critical vital signs of critically ill patients through sensors, regularly pack the data, transmit it to the central server, clean and organize the data, and generate a patient vital sign data packet;

[0127] S2: Receive the patient vital sign data packet, extract the vital sign indicators, store them in a formatted manner, identify all indicator data that exceeds the preset range, mark all abnormal data points, reveal potential health risks, and generate an abnormal data point record;

[0128] S3: Integrate the abnormal data point record with the patient's medical record history, perform time series analysis on the data, combine current medical research and historical similar case data, quantitatively evaluate the potential health risks, analyze the potential health impacts of each abnormal indicator, and generate a physiological indicator abnormality analysis record;

[0129] S4: Receive the physiological indicator abnormality analysis record, mark the degree and type of abnormality of each data point, classify the urgency of each data in the abnormal data analysis record, perform priority sorting, and generate an emergency treatment priority list;

[0130] S5: Receive the emergency treatment priority list, extract the patient's emergency level and critical physiological data, automatically issue an alarm, synchronize it to the hospital remote monitoring center and adjust the monitoring frequency, perform remote diagnosis and decision-making, initiate an emergency medical response, record and output remote response information;

[0131] S6: Receive the remote response information, compare the current data with the set parameters of the treatment device, adjust the device parameters, confirm that the adjusted parameters match the patient's current physiological state, update the patient's treatment plan, and output a remote monitoring dynamic feedback record.

[0132] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A remote monitoring system for critically ill patients, characterized by: The system comprises: The life signal acquisition module collects the vital signs data of critically ill patients remotely through sensors, sends it to the central server through wireless transmission technology, performs data cleaning and sorting on the received data, and obtains the patient's vital signs data package; The physiological status analysis module receives the patient's vital sign data packet, compares the indicators one by one, identifies abnormal data points that exceed the preset safety range, performs comprehensive trend analysis based on the identification results and the patient's medical history, and generates a physiological indicator abnormality analysis record; The remote monitoring response module performs graded processing based on the abnormal physiological index analysis records according to the degree of urgency, issues an alarm based on the graded processing results, synchronizes the alarm to the hospital remote monitoring center based on the patient's condition, establishes a direct communication link with medical personnel through the medical network, and obtains remote response information; The remote intervention operation module uses the remote response information and the remote medical team adjusts the treatment equipment parameters through the remote operation platform, synchronously updates the patient's treatment plan to match the current physiological state, and outputs the remote monitoring dynamic feedback record.

2. The remote monitoring system for critically ill patients according to claim 1, characterized in that: The steps for obtaining the patient's vital signs data packet are: Continuously monitor the key vital signs of critically ill patients through sensors, and regularly package the data to generate basic data packets; Using an encrypted wireless network, the basic data packet is transmitted to a central server, the integrity and security of the data transmission process are confirmed, and a transmitted data packet is generated; The central server cleans and organizes the transmitted data packets, removes noise and outliers in the data, reconstructs the data time series according to the data timestamps, and generates a patient vital sign data packet.

3. The remote monitoring system for critically ill patients according to claim 2, characterized in that: The steps for identifying abnormal data points are: Receive the patient's vital sign data packet, extract vital sign indicators, each indicator is accompanied by a timestamp, sort and format the data for storage, and generate formatted vital sign data; Setting a safety threshold, comparing the formatted vital sign data item by item, identifying all indicator data exceeding a preset range, and obtaining indicator data exceeding a preset safety range; Analyze the indicator data that exceeds the preset safety range, mark all abnormal data points, reveal potential health risks, and generate abnormal data point records.

4. The remote monitoring system for critically ill patients according to claim 3, characterized in that: The steps of analyzing and recording abnormal physiological indicators are as follows: Integrate the abnormal data point records with the patient's medical history, automatically match the patient's historical health data with the latest abnormal indicators, perform time series analysis on the data, evaluate the trend of changes in the patient's health status, and generate preliminary trend analysis results; A deeper analysis of the preliminary trend analysis results, combined with current medical research and historical similar case data, uses the formula: Calculate the overall health risk assessment score R(t), conduct a quantitative assessment of potential health risks, and obtain comprehensive trend analysis results, where P i (t) represents the predicted value of the i-th vital sign indicator at time t, μ i is the historical average of indicator i, σ i is the historical standard deviation of indicator i, and n represents the number of indicators; Based on the comprehensive trend analysis results, the potential health impact and recommended medical measures of each abnormal indicator are analyzed, and an abnormal physiological indicator analysis record is generated.

5. The remote monitoring system for critically ill patients according to claim 4, characterized in that: The steps of performing the hierarchical processing are: Receiving the abnormal analysis record of the physiological indicator, marking the abnormal degree and type of each data point, and generating an abnormal data analysis record; According to the preset emergency assessment standard, each data in the abnormal data analysis record is graded for urgency, using the formula: Calculate the emergency level G of each abnormal data i , generate the hierarchical processing results, where E i Represents the abnormal degree value of the abnormal data point, M represents the median of the abnormal degree of all data points, and V represents the variance of the abnormal degree of the data point; The hierarchical processing results are summarized, abnormal situations with high emergency levels are analyzed, and priority is determined to determine the pertinence and timeliness of the medical response, and to generate an emergency treatment priority list.

6. The remote monitoring system for critically ill patients according to claim 5, characterized in that: The steps for obtaining the remote response information are: Receiving the emergency treatment priority list, extracting the patient's emergency level and key physiological data, automatically issuing an alarm according to the emergency level, providing enhanced reminders through sound and visual signals, and generating alarm notification information; Synchronize the alarm notification information to the hospital remote monitoring center, send the patient's alarm level and real-time physiological data, adjust the monitoring frequency according to the patient's alarm level, and generate a real-time monitoring data stream; Establish a direct communication link between medical personnel and the monitoring center, receive the real-time monitoring data stream, perform remote diagnosis and decision-making, initiate emergency medical response, and record and output remote response information.

7. The remote monitoring system for critically ill patients according to claim 6, characterized in that: The steps for obtaining the remote monitoring dynamic feedback record are: Receive the remote response information, directly retrieve the patient's physiological data, and compare the current data with the treatment device setting parameters through data analysis to generate a comparison result; According to the comparison results, the telemedicine team adjusts the equipment parameters through the remote operation platform, confirms that the adjusted parameters match the patient's current physiological state, and generates an equipment parameter adjustment plan; The device parameter adjustment scheme is applied to update the patient treatment plan, and the update results and new device parameters are integrated through data synchronization to output a remote monitoring dynamic feedback record.

8. A remote monitoring method for critically ill patients, characterized in that: The remote monitoring system for critically ill patients according to any one of claims 1 to 7 comprises the following steps: The sensors continuously monitor the key vital signs of critically ill patients, and regularly package and transmit the data to the central server for cleaning and data organization to generate patient vital sign data packets; Receive the patient's vital sign data packet, extract vital sign indicators, format and store them, identify all indicator data that exceed a preset range, mark all abnormal data points, reveal potential health risks, and generate abnormal data point records; Integrate the abnormal data point records with the patient's medical history, perform time series analysis on the data, combine current medical research and historical similar case data, quantitatively evaluate potential health risks, analyze the potential health impact of each abnormal indicator, and generate abnormal physiological indicator analysis records; Receive the abnormal analysis record of the physiological index, mark the abnormal degree and type of each data point, classify the urgency of each data item in the abnormal data analysis record, perform priority sorting, and generate an emergency treatment priority list; Receive the emergency treatment priority list, extract the patient's emergency level and key physiological data, automatically issue an alarm, synchronize to the hospital remote monitoring center and adjust the monitoring frequency, perform remote diagnosis and decision-making, initiate emergency medical response, and record and output remote response information; Receive the remote response information, compare the current data with the treatment equipment setting parameters, adjust the equipment parameters, confirm that the adjusted parameters match the patient's current physiological state, update the patient's treatment plan, and output the remote monitoring dynamic feedback record.

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