Orthopedic patient data management and remote monitoring platform

Abstract

The invention discloses an orthopedic patient data management and remote monitoring platform, and relates to the technical field of monitoring management, the orthopedic patient data management and remote monitoring platform comprises a monitoring management platform, the monitoring management platform is in communication connection with a data acquisition module, a data processing module, an abnormity monitoring module, an abnormity early warning module and a remote guidance module, all the modules are connected through electric signals. According to the orthopedic patient data management and remote monitoring platform, various monitoring devices and wearable devices are integrated, physiological parameters and motion parameters of a patient are collected, a comprehensive patient health condition view is provided for medical staff, and the medical staff can find abnormal physiological and motion characteristics of the patient in time through real-time monitoring, so that the medical staff can monitor the abnormal physiological and motion characteristics of the patient in time. In addition, the abnormal trend in the patient data is identified, targeted diagnosis and treatment reminding is provided for medical staff through setting of the abnormal early warning coefficient and the abnormal early warning level, and the risk of missed diagnosis and misdiagnosis is remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the field of monitoring management technology, and in particular to an orthopedic patient data management and remote monitoring platform. Background Art

[0002] With the aging of the global population, the incidence of orthopedic diseases is also increasing year by year. Orthopedic diseases such as osteoporosis, arthritis, and fractures are particularly common among the elderly, which has led to an increase in demand for orthopedic medical services and promoted the development of orthopedic patient data management and remote monitoring technology. In recent years, medical technology has made significant progress. Digitalization and intelligence have become important trends in the development of the medical industry. The orthopedic field is no exception. From the traditional experience + procedure + instrument model, it has been rapidly upgraded to a new model of digital + artificial intelligence + personalization. The application of these new technologies has provided strong support for orthopedic patient data management and remote monitoring platforms. Through big data, artificial intelligence and other technical means, it is possible to achieve accurate management and remote monitoring of orthopedic patient data, thereby improving the efficiency and accuracy of diagnosis and treatment.

[0003] In the existing technology, although the remote monitoring platform can monitor the patient's data in real time, the data is relatively complicated. It is difficult for medical staff to carry out targeted diagnosis and treatment based on the patient's abnormal monitoring data, and there is a certain risk of missed diagnosis. Therefore, how to evaluate and analyze the remote monitoring data of orthopedic patients and determine the abnormal monitoring data to increase the attention of medical staff is the problem we need to solve. To this end, an orthopedic patient data management and remote monitoring platform is proposed. Summary of the invention

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: an orthopedic patient data management and remote monitoring platform, including a monitoring management platform, wherein the monitoring management platform is communicatively connected with a data acquisition module, a data processing module, an abnormality monitoring module, an abnormality warning module and a remote guidance module, wherein the modules are connected by electrical signals;

[0005] The data acquisition module collects the health data of physiological parameters and motion parameters of orthopedic patients in real time through monitoring equipment and wearable devices, providing a basis for subsequent analysis and evaluation. The collected data is transmitted to the monitoring management platform and stored in the database to facilitate subsequent data query, analysis and processing, provide data backup and recovery functions, and ensure the security and integrity of the data;

[0006] The data processing module pre-processes the collected data, extracts abnormal physiological characteristics and abnormal motion characteristics from the pre-processed data, and analyzes the physiological parameter trends and motion parameter trends of orthopedic patients;

[0007] The abnormality monitoring module uses big data analysis technology to monitor the patient's data to automatically detect abnormal data, and establish an abnormality early warning model to analyze the severity of the patient's abnormal condition;

[0008] The abnormal warning module sets abnormal warning levels with different warning thresholds in combination with the patient's medical history and clinical manifestation information, and further evaluates and analyzes the analysis results of abnormal monitoring, determines the corresponding abnormal warning level, and implements warning marks of the corresponding abnormal warning level, so as to provide medical staff with targeted diagnosis and treatment reminders and reduce the risk of missed diagnosis;

[0009] The remote guidance module enables medical staff to provide remote guidance to orthopedic patients based on the analysis results of abnormal warning and abnormal monitoring, including the formulation of rehabilitation plans, recommendations on drug use, and precautions for daily care, so as to improve the rehabilitation effect and quality of life of patients.

[0010] Preferably, in the data acquisition module, the process of acquiring physiological parameter and motion parameter health data includes:

[0011] Use monitoring equipment and wearable devices to monitor the patient's physiological parameters and exercise parameters to obtain the health data of orthopedic patients, where the monitoring equipment and wearable devices include heart rate monitors, blood pressure monitors, and activity trackers; physiological parameters include heart rate, blood pressure, body temperature, and blood sugar; exercise parameters include exercise intensity, exercise frequency, and exercise time;

[0012] Using wireless communication technologies such as Bluetooth and Wi-Fi, the collected health data is synchronously transmitted from monitoring devices and wearable devices to the monitoring management platform to ensure that the transmitted data format is unified and standardized to facilitate subsequent data processing and analysis. After receiving the data, the monitoring management platform stores it in a database based on cloud services;

[0013] The data collection time and device number metadata are recorded. The metadata records are associated with the data itself to facilitate subsequent data query and tracing. The health data in the database are backed up regularly. At the same time, access control is performed on the monitoring management platform. Only authorized users can access and operate the health data in the database. The user's access and operation logs are recorded for auditing and tracing.

[0014] Preferably, in the data processing module, the analysis process of the physiological parameter trend and the motion parameter trend of the orthopedic patient includes:

[0015] Preprocessing the health data of physiological parameters and motion parameters collected in the database, the preprocessing operation includes data cleaning and data normalization processing steps;

[0016] Conduct demand analysis on the pre-processed health data to determine the detection features to be analyzed, which are abnormal physiological features and abnormal motion features, and determine the sub-features of each detection feature, where the sub-features of abnormal physiological features are abnormal heart rate, abnormal blood pressure, abnormal body temperature, and abnormal blood sugar, and the sub-features of abnormal motion features are abnormal exercise intensity, abnormal exercise frequency, and abnormal exercise time;

[0017] Setting a threshold range of physiological parameters, extracting abnormal physiological features beyond the normal range by comparing the collected physiological parameters with the set threshold range, i.e., sub-features of abnormal physiological features; setting a threshold range of motion parameters, extracting abnormal motion features beyond the normal range by comparing the collected motion parameters with the set threshold range, i.e., sub-features of abnormal motion features; integrating sub-feature data of each detection feature to obtain an abnormal feature data set;

[0018] Perform time series analysis on the preprocessed physiological parameter data based on the abnormal feature data set, observe the changing trend of the physiological parameters, and predict the future trend of the physiological parameters through linear regression analysis, calculate the abnormal physiological state evaluation index, and evaluate whether the patient's physiological condition is stable or has a deteriorating trend;

[0019] Based on the abnormal feature data set, the preprocessed motion parameter data is analyzed in time series to observe the changing trend of the motion level, analyze the correlation between the motion parameters and the physiological parameters, calculate the abnormal motion state evaluation index, and clarify the impact of the motion level on the patient's physiological condition;

[0020] Summarize the extracted abnormal physiological characteristics and abnormal movement characteristics to form an abnormal characteristic report. According to the results of physiological parameter trend analysis and movement parameter trend analysis, write a trend analysis report to evaluate the patient's health status and exercise habits.

[0021] Preferably, the calculation expression of the abnormal physiological state evaluation index is:

[0022] ;

[0023] ;

[0024] ;

[0025] in, It is an index for evaluating abnormal physiological status. is the physiological parameter deviation function, is the extreme value function of physiological parameters, For real-time heart rate, is the individual baseline heart rate, is the individual's maximum heart rate, For real-time blood pressure, is the individual baseline blood pressure, is the individual's maximum blood pressure, For real-time body temperature, is the individual baseline body temperature, is the individual's maximum body temperature, For real-time blood sugar, is the individual baseline blood sugar, is the individual's maximum blood sugar, The value range is between 0 and 1.

[0026] Preferably, the calculation expression of the abnormal motion state evaluation index is:

[0027] ;

[0028] ;

[0029] ;

[0030] in, Abnormal movement status assessment index, is the motion parameter deviation function, is the motion parameter extreme value function, For real-time exercise intensity, is the individual baseline exercise intensity, is the individual's maximum exercise intensity, is the real-time motion frequency, is the individual baseline movement frequency, is the individual's maximum movement frequency, is the real-time motion time, is the individual benchmark exercise time, is the individual's maximum exercise time, A The value range is between 0 and 1.

[0031] Preferably, in the abnormality monitoring module, the analysis process of the severity of the patient's abnormal condition includes:

[0032] Based on the physiological parameters and motion parameter data of orthopedic patients in the abnormal feature data set, the abnormal physiological characteristics and abnormal motion characteristics are extracted and screened out. The big data analysis technology is used to monitor the patient's data and detect the abnormal data and mark them, and the detailed information of the abnormal data is synchronously stored, including the abnormal type, occurrence time and abnormal value;

[0033] The abnormal feature data set is divided into a training set and a test set. The training set data is combined with a decision tree model to train the abnormal warning model, and the test set data is used to verify the model, evaluate the accuracy and stability of the model, optimize the model according to the verification results, improve the model's early warning ability, and use the trained abnormal warning model to evaluate and analyze the patient's abnormal state;

[0034] Combining the analysis results of abnormal physiological state and abnormal movement state, based on the evaluation of the abnormal warning model, the abnormal warning coefficient is comprehensively calculated to analyze the severity of the patient's abnormal condition.

[0035] Preferably, the calculation expression of the abnormal warning coefficient is:

[0036] ;

[0037] in, is the abnormal warning coefficient, It is an index for evaluating abnormal physiological status. It is an individual baseline abnormal physiological state assessment index. It is the individual's maximum abnormal physiological state assessment index. Abnormal movement status assessment index, is the individual baseline abnormal movement state assessment index, is the individual's maximum abnormal movement state assessment index, The value range is between 0 and 1.

[0038] Preferably, in the abnormal warning module, the process of determining the abnormal warning level includes:

[0039] Collect patients' medical history information, motion parameter information, and physiological parameter information from electronic medical record systems, wearable devices, and monitoring equipment data sources, and analyze the clinical manifestations of orthopedic patients based on the motion parameter information and physiological parameter information;

[0040] Based on the urgency of the clinical manifestations of orthopedic patients and the output value of the abnormal warning coefficient, different abnormal warning levels are divided into low-risk abnormal warning level, medium-risk abnormal warning level and high-risk abnormal warning level, and different warning thresholds are set for each abnormal warning level;

[0041] Set corresponding intervention measures according to the risk level of each abnormal warning level, and further evaluate and analyze the analysis results of abnormal monitoring, including the abnormal type, occurrence time, duration and degree of abnormality, to determine the abnormal warning level of orthopedic patients and comprehensively judge the health status and potential risks of patients;

[0042] Patients with abnormal warning levels are marked, warning information is generated, and the warning information is pushed to relevant medical staff, including the patient's name, warning level, abnormal data details, and recommended intervention measures;

[0043] Based on the determined abnormal warning level and warning information, targeted diagnosis and treatment reminders are provided to medical staff.

[0044] Preferably, the plurality of abnormal warning levels correspond to the plurality of warning thresholds, wherein the warning thresholds include an upper threshold and a lower threshold;

[0045] The multiple abnormal warning levels and the multiple warning thresholds satisfy the following relationship:

[0046] Low-risk abnormal warning level ;

[0047] Medium-risk abnormal warning level ;

[0048] High-risk abnormal warning level ;

[0049] in, is the rehabilitation risk assessment coefficient, is the lower threshold corresponding to the medium-risk abnormal warning level and the upper threshold corresponding to the low-risk abnormal warning level. It is the lower threshold corresponding to the high-risk abnormal warning level and the upper threshold corresponding to the medium-risk abnormal warning level.

[0050] Preferably, in the remote guidance module, the remote guidance process includes:

[0051] Based on the analysis results of abnormal warning and abnormal monitoring, analyze the risk level of the patient's health status, and combine the determined abnormal warning level and warning information to assist medical staff in quickly locating abnormal patients and the patient's abnormal data details;

[0052] Develop personalized rehabilitation plans based on the patient's specific situation and needs, and use remote communication technology to enable medical staff to provide remote guidance to patients, including guidance on personalized rehabilitation plans, medication recommendations, and daily care precautions;

[0053] Continuously monitor the patient's physiological parameters and movement parameters, update the data in real time, and adjust the risk monitoring of the patient's health status based on the latest data, and then adjust the patient's abnormal warning level, so that medical staff can further evaluate and treat the patient based on the warning information, so as to continuously optimize the rehabilitation treatment needs of orthopedic patients.

[0054] The present invention provides an orthopedic patient data management and remote monitoring platform. It has the following beneficial effects:

[0055] 1. The orthopedic patient data management and remote monitoring platform integrates a variety of monitoring devices and wearable devices to collect the patient's physiological parameters and movement parameters, providing medical staff with a comprehensive view of the patient's health status. Through real-time monitoring, medical staff can promptly discover the patient's abnormal physiological and movement characteristics, so as to respond quickly and issue early warnings. In addition, it identifies abnormal trends in patient data and provides medical staff with targeted diagnosis and treatment reminders by setting abnormal warning coefficients and abnormal warning levels, significantly reducing the risk of missed diagnosis and misdiagnosis.

[0056] 2. The orthopedic patient data management and remote monitoring platform can not only monitor the patient's physiological and motor parameters, but also provide personalized rehabilitation guidance based on the patient's specific situation. It can formulate personalized rehabilitation plans based on the analysis results of the patient's abnormal physiological state and abnormal motor state, including drug use recommendations, daily care precautions, etc., which can help patients better participate in their own rehabilitation process, improve the initiative and compliance of rehabilitation, and significantly improve the patient's rehabilitation effect.

[0057] 3. The orthopedic patient data management and remote monitoring platform reduces the workload of medical staff in daily monitoring through automated and intelligent data collection and analysis. It can automatically trigger alarms based on abnormal conditions of patients, allowing medical staff to give priority to patients at high-risk abnormal warning levels, thereby improving the utilization efficiency of medical resources. In addition, remote monitoring reduces the pressure on hospital admissions and reduces medical costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a module diagram of an orthopedic patient data management and remote monitoring platform of the present invention;

[0059] Figure 2 The present invention is a flowchart of analyzing the trend of physiological parameters and movement parameters of orthopedic patients;

[0060] Figure 3 is a flow chart for analyzing the severity of abnormal conditions of patients according to the present invention;

[0061] Figure 4 This is a flow chart for determining the abnormal warning level of the present invention. DETAILED DESCRIPTION

[0062] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for the purpose of illustration and description, and are not intended to be exhaustive or to limit the present invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention, and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

[0063] The first embodiment, as Figure 1 , Figure 2 As shown, the present invention provides a technical solution: an orthopedic patient data management and remote monitoring platform, including a monitoring management platform, the monitoring management platform is communicatively connected with a data acquisition module, a data processing module, an abnormal monitoring module, an abnormal warning module and a remote guidance module, wherein the modules are connected by electrical signals;

[0064] The data acquisition module collects the health data of physiological parameters and motion parameters of orthopedic patients in real time through monitoring equipment and wearable devices, providing a basis for subsequent analysis and evaluation. The collected data is transmitted to the monitoring management platform and stored in the database to facilitate subsequent data query, analysis and processing. It provides data backup and recovery functions to ensure data security and integrity. Monitoring equipment and wearable devices are used to monitor the physiological parameters and motion parameters of patients to obtain the health data of orthopedic patients. Among them, monitoring equipment and wearable devices include heart rate monitors, blood pressure monitors, activity trackers, physiological parameters include heart rate, blood pressure, body temperature and blood sugar, and motion parameters include exercise intensity, exercise frequency and exercise time. Wireless communication technologies such as Bluetooth and Wi-Fi are used to synchronously transmit the collected health data from monitoring devices and wearable devices to the monitoring management platform, ensuring that the transmitted data format is unified and standardized to facilitate subsequent data processing and analysis. After receiving the data, the monitoring management platform stores it in a database based on cloud services, records the data collection time and device number metadata, and associates the metadata records with the data itself for subsequent data query and tracing. The health data in the database is backed up regularly, and access control is performed on the monitoring management platform. Only authorized users can access and operate the health data in the database, and the user's access and operation logs are recorded for auditing and tracing;

[0065] The data processing module preprocesses the collected data, extracts abnormal physiological characteristics and abnormal motion characteristics from the preprocessed data, analyzes the physiological parameter trends and motion parameter trends of orthopedic patients, and preprocesses the collected physiological parameters and motion parameter health data in the database. The preprocessing operation includes data cleaning and data normalization processing steps, among which data cleaning is used to check whether there are missing values, outliers and duplicate values ​​in the data set, use interpolation methods (mean, median, mode interpolation or Lagrange interpolation method) to process missing values, process outliers according to the box plot analysis method, delete outliers or perform Correct and remove duplicate records, normalize the data, scale the data to the same range to eliminate the dimensional differences between different features, discretize the continuous data as needed, classify the data according to different intervals, perform demand analysis on the pre-processed health data, determine the detection features to be analyzed, which are abnormal physiological features and abnormal motion features, and determine the sub-features of each detection feature, among which the sub-features of abnormal physiological features are abnormal heart rate, abnormal blood pressure, abnormal body temperature and abnormal blood sugar, and the sub-features of abnormal motion features are abnormal exercise intensity, abnormal exercise frequency and abnormal exercise time, set the production The threshold range of physiological parameters is set, and abnormal physiological features beyond the normal range are extracted by comparing the collected physiological parameters with the set threshold range, which are sub-features of abnormal physiological features. The threshold range of motion parameters is set, and abnormal motion features beyond the normal range are extracted by comparing the collected motion parameters with the set threshold range, which are sub-features of abnormal motion features. The sub-feature data of each detection feature are integrated to obtain an abnormal feature data set. Based on the abnormal feature data set, a time series analysis is performed on the preprocessed physiological parameter data, and the changing trend of the physiological parameters is observed. The future trend of the physiological parameters is predicted by linear regression analysis. The abnormal physiological state evaluation index is calculated to evaluate whether the patient's physiological condition is stable or has a deteriorating trend. Based on the abnormal feature data set, a time series analysis is performed on the preprocessed motion parameter data, and the changing trend of the exercise level is observed. The correlation between the motion parameters and the physiological parameters is analyzed, and the abnormal motion state evaluation index is calculated to clarify the impact of the exercise level on the patient's physiological condition. The extracted abnormal physiological features and abnormal motion features are summarized to form an abnormal feature report. According to the results of the physiological parameter trend analysis and the motion parameter trend analysis, a trend analysis report is written to evaluate the patient's health status and exercise habits.

[0066] Furthermore, the calculation expression of the abnormal physiological state evaluation index is:

[0067] ;

[0068] ;

[0069] ;

[0070] in, It is an index for evaluating abnormal physiological status. is the physiological parameter deviation function, which indicates the relative deviation between the real-time physiological parameters and the reference values. is the extreme value function of physiological parameters, ensuring that when any physiological parameter deviates from its maximum value, will increase significantly. For real-time heart rate, is the individual baseline heart rate, is the individual's maximum heart rate, For real-time blood pressure, is the individual baseline blood pressure, is the individual's maximum blood pressure, For real-time body temperature, is the individual baseline body temperature, is the individual's maximum body temperature, For real-time blood sugar, is the individual baseline blood sugar, is the individual's maximum blood sugar, The value range is between 0 and 1. When all physiological parameters are within the normal range, Close to 0, when any physiological parameter exceeds the normal range, Increase;

[0071] The calculation expression of abnormal motion state evaluation index is:

[0072] ;

[0073] ;

[0074] ;

[0075] in, Abnormal movement status assessment index, is the motion parameter deviation function, is the motion parameter extreme value function, For real-time exercise intensity, is the individual baseline exercise intensity, is the individual's maximum exercise intensity, is the real-time motion frequency, is the individual baseline movement frequency, is the individual's maximum movement frequency, is the real-time motion time, is the individual benchmark exercise time, is the individual's maximum exercise time, A The value range of is between 0 and 1. When all motion parameters are within the normal range, Close to 0, when any motion parameter exceeds the normal range, Increase;

[0076] The abnormality monitoring module uses big data analysis technology to monitor the patient's data to automatically detect abnormal data, and establish an abnormality warning model to analyze the severity of the patient's abnormal condition;

[0077] The abnormal warning module combines the patient's medical history and clinical manifestation information to set abnormal warning levels with different warning thresholds, and further evaluates and analyzes the analysis results of abnormal monitoring to determine the corresponding abnormal warning level, implements the corresponding abnormal warning level warning mark, and provides medical staff with targeted diagnosis and treatment reminders to reduce the risk of missed diagnosis;

[0078] The remote guidance module allows medical staff to provide remote guidance to orthopedic patients based on the analysis results of abnormal warning and abnormal monitoring, including the formulation of rehabilitation plans, recommendations on drug use, and precautions for daily care, to improve patients' rehabilitation effects and quality of life.

[0079] The second embodiment is based on the first embodiment. Figure 3 , Figure 4 As shown, in the abnormal monitoring module, the analysis process of the severity of the patient's abnormal condition includes:

[0080] Based on the physiological parameters and motion parameter data of orthopedic patients in the abnormal feature data set, the selected abnormal physiological characteristics and abnormal motion characteristics are extracted, and the patient's data is monitored and the abnormal data is detected and marked using big data analysis technology. The detailed information of the abnormal data, including the abnormal type, occurrence time and abnormal value, is synchronously stored. The abnormal feature data set is divided into a training set and a test set. The training set data is combined with the decision tree model to train the abnormal warning model, and the test set data is used to verify the model. The accuracy and stability of the model are evaluated. The model is optimized according to the verification results to improve the warning ability of the model. The trained abnormal warning model is used to evaluate and analyze the abnormal state of the patient. Combined with the analysis results of the abnormal physiological state and abnormal motion state, on the basis of the evaluation of the abnormal warning model, the abnormal warning coefficient is comprehensively calculated to analyze the severity of the patient's abnormal condition.

[0081] Furthermore, the calculation expression of the abnormal warning coefficient is:

[0082] ;

[0083] in, is the abnormal warning coefficient, It is an index for evaluating abnormal physiological status. It is an individual baseline abnormal physiological state assessment index. It is the individual's maximum abnormal physiological state assessment index. Abnormal movement status assessment index, is the individual baseline abnormal movement state assessment index, is the individual's maximum abnormal movement state assessment index, The value range is between 0 and 1. When all evaluation indexes are within the normal range, Close to 0, when any evaluation index is beyond the normal range, Increase, when the value of any fraction in the numerator increases (that is, the degree to which the evaluation index deviates from the benchmark value increases), will increase. The greater the deviation between the real-time evaluation index and the benchmark value, It will also increase, The patient's abnormal state is comprehensively assessed by calculating the relative deviation between the real-time abnormal physiological state assessment index and the abnormal movement state assessment index and the baseline value, and combining the individual's maximum assessment index;

[0084] In the abnormal warning module, the process of determining the abnormal warning level includes:

[0085] Collect the patient's medical history information, motion parameter information and physiological parameter information from the electronic medical record system, wearable devices and monitoring equipment data sources, analyze the clinical manifestations of orthopedic patients by integrating the motion parameter information and physiological parameter information, divide different abnormal warning levels into low-risk abnormal warning level, medium-risk abnormal warning level and high-risk abnormal warning level based on the urgency of the clinical manifestations of orthopedic patients and the output value of the abnormal warning coefficient, set different warning thresholds for each abnormal warning level, set corresponding intervention measures according to the risk level of each abnormal warning level, and further evaluate and analyze the analysis results of abnormal monitoring, including the abnormal type, occurrence time, duration and degree of abnormality, to determine the abnormal warning level of orthopedic patients, comprehensively judge the health status and potential risks of patients, mark patients with determined abnormal warning levels, generate warning information, and push the warning information to relevant medical staff, including the patient's name, warning level, abnormal data details and recommended intervention measures, and provide targeted diagnosis and treatment reminders to medical staff according to the determined abnormal warning level and warning information;

[0086] Further, multiple abnormal warning levels correspond to multiple warning thresholds, wherein the warning threshold includes an upper threshold and a lower threshold;

[0087] Multiple abnormal warning levels and multiple warning thresholds satisfy the following relationship:

[0088] Low-risk abnormal warning level ; Indicates that the patient's abnormal condition is mild and requires basic monitoring and observation, but not necessarily immediate medical intervention;

[0089] Medium-risk abnormal warning level ; indicates that the patient's abnormality is moderate and requires further evaluation and possible medical intervention;

[0090] High-risk abnormal warning level ; Indicates that the patient's abnormal condition is serious and requires immediate medical attention and intervention;

[0091] in, is the rehabilitation risk assessment coefficient, is the lower threshold corresponding to the medium-risk abnormal warning level and the upper threshold corresponding to the low-risk abnormal warning level. The lower threshold corresponding to the high-risk abnormal warning level and the upper threshold corresponding to the medium-risk abnormal warning level;

[0092] In the remote guidance module, the remote guidance process includes:

[0093] Based on the analysis results of abnormal warning and abnormal monitoring, the risk level of the patient's health status is analyzed, and combined with the determined abnormal warning level and warning information, medical staff are assisted to quickly locate abnormal patients and the patient's abnormal data details, and develop personalized rehabilitation plans according to the patient's specific situation and needs. Using remote communication technology, medical staff can provide remote guidance to patients. The guidance content includes personalized rehabilitation plan guidance, drug use recommendations, and daily care precautions. The patient's physiological parameters and movement parameters are continuously monitored, data is updated in real time, and risk monitoring of the patient's health status is adjusted according to the latest data, and then the patient's abnormal warning level is adjusted, so that medical staff can further evaluate and treat the patient based on the warning information, so as to continuously optimize the rehabilitation treatment needs of orthopedic patients.

[0094] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without creative work should fall within the scope of protection of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention are implemented according to the conventional means in the field unless otherwise specified and limited.

Claims

1. An orthopedic patient data management and remote monitoring platform, including a monitoring management platform, characterized in that: The monitoring and management platform is communicatively connected with a data acquisition module, a data processing module, an abnormality monitoring module, an abnormality warning module and a remote guidance module, wherein electrical signals are connected between the modules; The data acquisition module collects the health data of physiological parameters and motion parameters of orthopedic patients in real time through monitoring devices and wearable devices, and stores them in the database; The data processing module pre-processes the collected data, extracts abnormal physiological characteristics and abnormal motion characteristics from the pre-processed data, and analyzes the physiological parameter trends and motion parameter trends of orthopedic patients; The abnormality monitoring module uses big data analysis technology to monitor the patient's data to automatically detect abnormal data, and establish an abnormality early warning model to analyze the severity of the patient's abnormal condition; The abnormal warning module sets abnormal warning levels with different warning thresholds in combination with the patient's medical history and clinical manifestation information, and further evaluates and analyzes the analysis results of abnormal monitoring to determine the corresponding abnormal warning level; The remote guidance module enables medical staff to provide remote guidance to orthopedic patients based on the analysis results of abnormal warning and abnormal monitoring.

2. The orthopedic patient data management and remote monitoring platform according to claim 1, characterized in that: In the data acquisition module, the process of acquiring physiological parameter and motion parameter health data includes: Use monitoring equipment and wearable devices to monitor the patient's physiological parameters and exercise parameters to obtain the health data of orthopedic patients, where the monitoring equipment and wearable devices include heart rate monitors, blood pressure monitors, and activity trackers; physiological parameters include heart rate, blood pressure, body temperature, and blood sugar; exercise parameters include exercise intensity, exercise frequency, and exercise time; The collected health data is synchronously transmitted from the monitoring devices and wearable devices to the monitoring management platform. After the monitoring management platform receives the data, it is stored in a database based on cloud services; The data collection time and device number metadata are recorded. The metadata records are associated with the data itself. The health data in the database is backed up regularly. At the same time, access control is performed on the monitoring management platform, and user access and operation logs are recorded.

3. The orthopedic patient data management and remote monitoring platform according to claim 2, characterized in that: In the data processing module, the analysis process of the physiological parameter trend and the motion parameter trend of orthopedic patients includes: Preprocessing the health data of physiological parameters and motion parameters collected in the database, the preprocessing operation includes data cleaning and data normalization processing steps; Conduct demand analysis on the pre-processed health data to determine the detection features to be analyzed, which are abnormal physiological features and abnormal motion features, and determine the sub-features of each detection feature, where the sub-features of abnormal physiological features are abnormal heart rate, abnormal blood pressure, abnormal body temperature, and abnormal blood sugar, and the sub-features of abnormal motion features are abnormal exercise intensity, abnormal exercise frequency, and abnormal exercise time; Setting a threshold range of physiological parameters, extracting abnormal physiological features beyond the normal range by comparing the collected physiological parameters with the set threshold range, i.e., sub-features of abnormal physiological features; setting a threshold range of motion parameters, extracting abnormal motion features beyond the normal range by comparing the collected motion parameters with the set threshold range, i.e., sub-features of abnormal motion features; integrating sub-feature data of each detection feature to obtain an abnormal feature data set; Perform time series analysis on the preprocessed physiological parameter data based on the abnormal feature data set, observe the changing trend of the physiological parameters, and predict the future trend of the physiological parameters through linear regression analysis, calculate the abnormal physiological state evaluation index, and evaluate whether the patient's physiological condition is stable or has a deteriorating trend; Based on the abnormal feature data set, the preprocessed motion parameter data is analyzed in time series to observe the changing trend of the motion level, analyze the correlation between the motion parameters and the physiological parameters, calculate the abnormal motion state evaluation index, and clarify the impact of the motion level on the patient's physiological condition; Summarize the extracted abnormal physiological characteristics and abnormal movement characteristics to form an abnormal characteristic report. According to the results of physiological parameter trend analysis and movement parameter trend analysis, write a trend analysis report to evaluate the patient's health status and exercise habits.

4. The orthopedic patient data management and remote monitoring platform according to claim 3, characterized in that: The calculation expression of the abnormal physiological state evaluation index is: ； ； ； in, It is an index for evaluating abnormal physiological status. is the physiological parameter deviation function, is the extreme value function of physiological parameters, For real-time heart rate, is the individual baseline heart rate, is the individual's maximum heart rate, For real-time blood pressure, is the individual baseline blood pressure, is the individual's maximum blood pressure, For real-time body temperature, is the individual baseline body temperature, is the individual's maximum body temperature, For real-time blood sugar, is the individual baseline blood sugar, is the individual's maximum blood sugar, The value range is between 0 and 1.

5. The orthopedic patient data management and remote monitoring platform according to claim 4, characterized in that: The calculation expression of the abnormal motion state evaluation index is: ； ； ； in, Abnormal movement status assessment index, is the motion parameter deviation function, is the motion parameter extreme value function, For real-time exercise intensity, is the individual baseline exercise intensity, is the individual's maximum exercise intensity, is the real-time motion frequency, is the individual baseline movement frequency, is the individual's maximum movement frequency, is the real-time motion time, is the individual benchmark exercise time, is the individual's maximum exercise time, A The value range is between 0 and 1.

6. The orthopedic patient data management and remote monitoring platform according to claim 5, characterized in that: In the abnormality monitoring module, the analysis process of the severity of the patient's abnormal condition includes: Based on the physiological parameters and motion parameter data of orthopedic patients in the abnormal feature data set, the abnormal physiological characteristics and abnormal motion characteristics are extracted and screened out. The big data analysis technology is used to monitor the patient's data and detect the abnormal data and mark them, and the detailed information of the abnormal data is synchronously stored, including the abnormal type, occurrence time and abnormal value; The abnormal feature data set is divided into a training set and a test set. The training set data is combined with a decision tree model to train the abnormal warning model, and the test set data is used to verify the model, evaluate the accuracy and stability of the model, optimize the model according to the verification results, improve the model's early warning ability, and use the trained abnormal warning model to evaluate and analyze the patient's abnormal state; Combining the analysis results of abnormal physiological state and abnormal movement state, based on the evaluation of the abnormal warning model, the abnormal warning coefficient is comprehensively calculated to analyze the severity of the patient's abnormal condition.

7. The orthopedic patient data management and remote monitoring platform according to claim 6, characterized in that: The calculation expression of the abnormal warning coefficient is: ； in, is the abnormal warning coefficient, It is an index for evaluating abnormal physiological status. It is an individual baseline abnormal physiological state assessment index. It is the individual's maximum abnormal physiological state assessment index. Abnormal movement status assessment index, is the individual baseline abnormal movement state assessment index, is the individual's maximum abnormal movement state assessment index, The value range is between 0 and 1.

8. The orthopedic patient data management and remote monitoring platform according to claim 7, characterized in that: In the abnormal warning module, the process of determining the abnormal warning level includes: Collect patients' medical history information, motion parameter information, and physiological parameter information from electronic medical record systems, wearable devices, and monitoring equipment data sources, and analyze the clinical manifestations of orthopedic patients based on the motion parameter information and physiological parameter information; Based on the urgency of the clinical manifestations of orthopedic patients and the output value of the abnormal warning coefficient, different abnormal warning levels are divided into low-risk abnormal warning level, medium-risk abnormal warning level and high-risk abnormal warning level, and different warning thresholds are set for each abnormal warning level; Set corresponding intervention measures according to the risk level of each abnormal warning level, and further evaluate and analyze the analysis results of abnormal monitoring, including the abnormal type, occurrence time, duration and degree of abnormality, to determine the abnormal warning level of orthopedic patients and comprehensively judge the health status and potential risks of patients; Mark patients with abnormal warning levels, generate warning information, and push the warning information to relevant medical staff; Based on the determined abnormal warning level and warning information, targeted diagnosis and treatment reminders are provided to medical staff.

9. The orthopedic patient data management and remote monitoring platform according to claim 8, characterized in that: A plurality of the abnormal warning levels correspond to a plurality of the warning thresholds, wherein the warning thresholds include an upper threshold and a lower threshold; The multiple abnormal warning levels and the multiple warning thresholds satisfy the following relationship: Low-risk abnormal warning level ; Medium-risk abnormal warning level ; High-risk abnormal warning level ; in, is the rehabilitation risk assessment coefficient, is the lower threshold corresponding to the medium-risk abnormal warning level and the upper threshold corresponding to the low-risk abnormal warning level. It is the lower threshold corresponding to the high-risk abnormal warning level and the upper threshold corresponding to the medium-risk abnormal warning level.

10. The orthopedic patient data management and remote monitoring platform according to claim 9, characterized in that: In the remote guidance module, the remote guidance process includes: Based on the analysis results of abnormal warning and abnormal monitoring, analyze the risk level of the patient's health status, and combine the determined abnormal warning level and warning information to assist medical staff in quickly locating abnormal patients and the patient's abnormal data details; Develop personalized rehabilitation plans based on the patient's specific situation and needs, and use remote communication technology to enable medical staff to provide remote guidance to patients, including guidance on personalized rehabilitation plans, medication recommendations, and daily care precautions; Continuously monitor the patient's physiological parameters and movement parameters, update the data in real time, and adjust the risk monitoring of the patient's health status based on the latest data, and then adjust the patient's abnormal warning level, so that medical staff can further evaluate and treat the patient based on the warning information, so as to continuously optimize the rehabilitation treatment needs of orthopedic patients.

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