A healthcare information integrated management system

Abstract

The application discloses a kind of medical care information comprehensive management systems, it is related to medical care management technical field, to solve the problem of more quickly, more accurate medical judgment cannot be carried out according to the health data of patient.The specific attribute of non-health data is confirmed by the application, and the system can accurately transmit data to the corresponding medical personnel diagnosis center.This precision ensures that data can be analyzed by medical personnel with relevant expertise, improving the accuracy and efficiency of diagnosis, can automatically determine whether to take written diagnosis or home visit, provide more personalized and appropriate medical services for patients, improve patient satisfaction and treatment effect, according to the residual capacity of each channel and the transmission amount of patient health data Intelligent matching ensures that the selected channel can meet the data transmission requirements and will not waste too many network resources, which helps to improve the operation efficiency of the entire medical care system.

Description

Technical Field

[0001] This invention relates to the field of healthcare management technology, specifically to a comprehensive healthcare information management system. Background Technology

[0002] Healthcare management refers to the process of planning, organizing, guiding, and controlling the operations, finances, human resources, and service quality of healthcare institutions or systems.

[0003] Patent application CN114144844A discloses a system and method for reducing the actions a patient needs to take to advance their healthcare. The system primarily involves a health management system receiving patient health data; the health management system analyzing the patient health data to determine the healthcare products the patient needs; the health management system determining the specific provider of the healthcare products; the health management system generating a healthcare product ordering interface, which includes information about the healthcare products and a no-order control; and, in response to determining that the no-order control is not activated, generating a product order and transmitting it to the provider's system. While this solution addresses the issues of healthcare management, the following problems remain in practical operation:

[0004] 1. The lack of comprehensive monitoring and collection of patients' health data has resulted in inaccurate patient health data.

[0005] 2. After obtaining the patient's health data, it was not transmitted to the medical institution in a timely manner so that professionals could make a professional judgment, which led to the patient being misdiagnosed.

[0006] 3. Failure to make targeted decisions based on the patient's health data led to poor treatment outcomes. Summary of the Invention

[0007] The purpose of this invention is to provide a comprehensive healthcare information management system that identifies the specific attributes of non-health data and accurately transmits the data to the corresponding medical personnel's diagnostic center. This precision ensures that the data can be analyzed by medical personnel with relevant expertise, improving the accuracy and efficiency of diagnosis. It can automatically determine whether to provide a written diagnosis or a home consultation, offering more personalized and appropriate medical services to patients, thus improving patient satisfaction and treatment outcomes. The system intelligently matches the remaining capacity of each channel with the amount of patient health data to ensure that the selected channel meets data transmission needs without wasting excessive network resources, thereby improving the overall operational efficiency of the healthcare system and solving problems in existing technologies.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A comprehensive healthcare information management system, comprising:

[0010] The patient health monitoring unit is used for:

[0011] Patients undergo health monitoring and health inquiries, and their health data is categorized according to the data types of the health monitoring and health inquiries. After categorization, a unique code is assigned, and the patient's health data is obtained after the unique code is assigned.

[0012] The medical institution docking unit is used for:

[0013] The patient's health data is transmitted to the connected medical institution, and the fastest transmission channel is selected when transmitting the patient's health data.

[0014] The monitoring data consultation unit is used for:

[0015] Medical institutions receive patients' health data, monitor and assess the received data, make consultation decisions based on the assessment results, generate health reports based on the assessment results and consultation decisions, and transmit the health reports to the patients' mobile terminals for display.

[0016] The health and wellness push notification unit is used for:

[0017] Patients can view their health status based on the generated health report on their mobile devices, and also conduct online consultations based on the health report.

[0018] Preferably, the patient health monitoring unit includes:

[0019] Health data acquisition module:

[0020] Health data of patients can be collected using intelligent measuring devices or by staff through regular inquiries.

[0021] Health data includes blood pressure data, blood glucose data, heart rate data, body temperature data, electrocardiogram data, and blood oxygen saturation data;

[0022] Among them, intelligent measuring devices include intelligent blood pressure monitors, intelligent blood glucose meters, smartwatches, intelligent thermometers, and intelligent electrocardiogram monitoring devices;

[0023] Smart blood pressure monitors collect blood pressure data; smart blood glucose meters collect blood glucose data; smartwatches collect blood oxygen saturation and heart rate data; smart thermometers collect body temperature data; and smart ECG monitoring devices collect ECG data.

[0024] Staff members conduct regular home visits to collect data at set times, and staff members wear monitoring devices themselves.

[0025] The intelligent measuring device and the monitoring equipment worn by the staff collect the patients' health data and label it as the raw health data.

[0026] Preferably, the patient health monitoring unit further includes:

[0027] The health data classification and labeling module is used for:

[0028] The raw health data is classified according to its attributes, and then a unique code is assigned after classification.

[0029] The process for unique identification codes is as follows:

[0030] The raw health data is encoded according to the rules, which include a prefix, timestamp, and sequence number.

[0031] The prefix is ​​a fixed name generated based on the attributes categorized from the original health data; the timestamp is the time when the original health data was generated; the sequence number is the number that distinguishes different records of the original health data under the same timestamp.

[0032] Once the coding structure rules are confirmed, a unique code is obtained for the original health data, and the original health data with the unique code is labeled as patient health data.

[0033] Preferably, the medical institution docking unit includes:

[0034] The monitoring data import module is used for:

[0035] Patient health data is imported into the patient's mobile device. Data collected by the smart measuring device is transmitted to the patient's mobile device via Bluetooth, while data collected by staff during regular inquiries is entered into the patient's mobile device by the staff.

[0036] The patient's mobile device establishes a signal connection with the medical institution;

[0037] After signal docking is completed, prepare for the transmission of patient health data.

[0038] Preferably, the medical institution docking unit further includes:

[0039] The monitoring data transmission module is used for:

[0040] When patient health data is transmitted from the patient's mobile terminal to the medical institution, the fastest transmission channel is automatically selected;

[0041] First, the patient's health data is divided into segments of equal length.

[0042] The amount of patient health data transmitted is calculated based on the segment data.

[0043] There shall be no fewer than four transmission channels from the patient's mobile terminal to the medical institution;

[0044] Confirm the remaining capacity of each transmission channel;

[0045] The transmission channel for transmitting patient health data from the mobile terminal to the medical institution is selected when the amount of patient health data transmitted is less than the remaining channel capacity.

[0046] Preferably, the monitoring data transmission module includes:

[0047] The real-time monitoring module is used to monitor the data transmission operation parameters of the patient's health data from the mobile terminal to the medical institution in real time.

[0048] The data transmission operation parameters include the amount of data transmitted in parallel with the patient's health data in the transmission channel during the patient's health data transmission process, as well as the data transmission rate of the patient's health data and the data transmission rate of the other data transmitted in parallel with the patient's health data.

[0049] The data volume information extraction module is used to extract the data volume of other data transmitted in parallel besides the transmitted patient health data for each unit of time during the transmission of patient health data; wherein, the unit of time is 1 second;

[0050] The first data transmission evaluation coefficient acquisition module is used to obtain the first data transmission evaluation coefficient by combining the amount of data of other data transmitted in parallel with the transmission of patient health data corresponding to each unit time during the patient health data transmission process with the amount of data transmitted per unit time during the patient health data transmission process.

[0051] The first data transmission evaluation coefficient is obtained by the following formula:

[0052]

[0053] Among them, R 01 denoted as the first data transmission evaluation coefficient; n represents the number of unit time intervals that have elapsed during the patient's health data transmission process; C 01i C represents the amount of patient health data transmitted in the i-th unit of time; 02i C represents the amount of data transmitted in parallel, excluding patient health data, for the i-th unit of time;yi C represents the remaining capacity of the transmission channel corresponding to the i-th unit of time; 02b C represents the standard deviation of the amount of data transmitted in parallel with other data besides patient health data, corresponding to n units of time elapsed during the patient health data transmission process; 01b The standard deviation of the data volume corresponding to n units of time that the patient's health data transmission process has taken; r represents the first adjustment coefficient, which is obtained by the following formula:

[0054]

[0055] Where r represents the first adjustment coefficient; n represents the number of unit times that the patient's health data transmission process has gone through; C 01i C represents the amount of patient health data transmitted in the i-th unit of time; 02i C represents the amount of data transmitted in parallel, excluding patient health data, for the i-th unit of time; yi This represents the remaining capacity of the transmission channel corresponding to the i-th unit of time;

[0056] The first comparison module is used to compare the first data transmission evaluation coefficient with a preset first coefficient threshold.

[0057] The quality evaluation module is used to evaluate the data transmission quality of the patient health data by using the data transmission rate of the patient health data and the data transmission rate of other data transmitted in parallel in addition to transmitting the patient health data when the first data transmission evaluation coefficient exceeds the preset first coefficient threshold.

[0058] Preferably, the quality evaluation module includes:

[0059] The first data transmission rate information extraction module is used to extract the data transmission rate corresponding to each unit of time during the patient's health data transmission process.

[0060] The second data transmission rate information extraction module is used to extract the data transmission rate per unit time for other data transmitted in parallel besides patient health data.

[0061] The second data transmission evaluation coefficient acquisition module is used to acquire the second data transmission evaluation coefficient by utilizing the data transmission rate of patient health data and the data transmission rate of other data transmitted in parallel in addition to the transmission of patient health data.

[0062] The second data transmission evaluation coefficient is obtained using the following formula:

[0063]

[0064] Among them, R 02 V represents the second data transmission evaluation coefficient; n represents the number of unit times that the patient health data transmission process has gone through; m represents the number of other data transmitted in parallel besides the patient health data for each unit time; V 01i V represents the data transmission rate of patient health data transmission corresponding to the i-th unit of time; 02ij=1 V 02ij=2 ... V 02ij=m V represents the data transmission rate corresponding to the 1st, 2nd, ..., mth data points transmitted in parallel (excluding patient health data) within the i-th unit of time; 02ip V represents the average data transmission rate of m data points transmitted in parallel, excluding patient health data, within the i-th unit of time; 01bi and V 02bi Let f represent the standard deviation of the data transmission rate of the patient's health data in the i-th unit time and the average standard deviation of the data transmission rate of the m other data transmitted in parallel besides the patient's health data; f represents the second adjustment coefficient, which is obtained by the following formula:

[0065]

[0066] Where f represents the second adjustment coefficient; V 01bi V represents the standard deviation of the data transmission rate of the patient's health data in the i-th unit of time; 02bimax V represents the maximum standard deviation of the data transmission rate for m data items transmitted in parallel besides patient health data; 02bimin This represents the minimum standard deviation of the data transmission rate of m data items transmitted in parallel besides patient health data.

[0067] The second comparison module is used to compare the second data transmission evaluation coefficient with a preset second coefficient threshold.

[0068] The anomaly detection and alarm module is used to determine that there is an anomaly in the data transmission of the patient's health data when the second data transmission evaluation coefficient exceeds a preset second coefficient threshold, and to issue an anomaly alarm.

[0069] Preferably, the monitoring data consultation unit includes:

[0070] The health data assessment module is used for:

[0071] Medical institutions receive patients' health data and then make an anomaly assessment.

[0072] Specifically, the blood pressure, blood sugar, heart rate, body temperature, electrocardiogram, and blood oxygen saturation data in the patient's health data are converted into curve data, and the converted data are used to obtain curve health data.

[0073] Compare and overlap the health curve data with the standard curve data;

[0074] Based on the overlap comparison results, qualified data, low data, and high data are obtained;

[0075] Among them, low data refers to the data region where the curve health data is lower than the standard curve data when the data is overlapped; high data refers to the data region where the curve health data is higher than the standard curve data when the data is overlapped.

[0076] Low and high data are labeled as unhealthy data and stored separately.

[0077] Preferably, the monitoring data consultation unit further includes:

[0078] The data analysis module is used for:

[0079] Medical institutions will confirm the specific attributes of non-health data;

[0080] Based on the specific attributes of the non-health data, the non-health data will be transmitted to the corresponding medical personnel's diagnostic center for corresponding diagnosis;

[0081] Medical personnel make diagnostic decisions based on abnormal thresholds of non-healthy data;

[0082] Diagnostic decisions are made based on the degree of abnormality in unhealthy data.

[0083] If the abnormal threshold of low and high data in unhealthy data does not exceed 20%, a written diagnosis is made; if the abnormal threshold of low and high data in unhealthy data exceeds 20%, an on-site consultation is made.

[0084] A health report is generated based on the diagnostic decision and then transmitted to the patient's mobile device.

[0085] Preferably, the health and wellness push unit is further used for:

[0086] Patients can view their health reports on their mobile devices and confirm the final treatment plan based on the results.

[0087] Among these measures, medication is purchased based on written diagnostic results, and consultation times are confirmed based on the results of in-home consultations.

[0088] Meanwhile, patients can conduct online consultations on their mobile devices based on their health reports.

[0089] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0090] 1. This invention provides a comprehensive healthcare information management system. The use of intelligent measuring devices automates the data collection process, enabling the immediate acquisition of patients' health data, reducing manual intervention and waiting time, and improving data collection efficiency. By establishing a unified coding structure rule for the raw health data, relevant information can be quickly retrieved, greatly improving work efficiency. Through the timestamp and sequence number in the coding structure, the generation time and source of the data can be traced, enhancing data security and credibility.

[0091] 2. This invention provides a comprehensive healthcare information management system that intelligently matches channels based on their remaining capacity and the amount of patient health data to be transmitted, ensuring that the selected channels meet data transmission needs without wasting excessive network resources. This optimized resource utilization helps improve the operational efficiency of the entire healthcare system. Through signal interfacing between patient mobile terminals and medical institutions, remote transmission and sharing of patient health data are achieved.

[0092] 3. This invention provides a comprehensive healthcare information management system. By confirming the specific attributes of non-health data, the system can accurately transmit data to the corresponding medical personnel's diagnostic center. This precision ensures that the data can be analyzed by medical personnel with relevant expertise, improving the accuracy and efficiency of diagnosis. It can automatically determine whether to provide a written diagnosis or a home consultation, offering patients more personalized and appropriate medical services. This customized approach enhances patient satisfaction and treatment outcomes. Attached Figure Description

[0093] Figure 1 This is a schematic diagram of the integrated healthcare information management unit of the present invention;

[0094] Figure 2 This is a schematic diagram of the integrated healthcare information management process of the present invention. Detailed Implementation

[0095] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0096] To address the issue of inaccurate patient health data due to the lack of diversified monitoring and collection methods in existing technologies, please refer to [link to relevant documentation]. Figure 1 and Figure 2 This embodiment provides the following technical solution:

[0097] A comprehensive healthcare information management system, comprising:

[0098] The patient health monitoring unit is used for:

[0099] Patients undergo health monitoring and health inquiries, and their health data is categorized according to the data types of the health monitoring and health inquiries. After categorization, a unique code is assigned, and the patient's health data is obtained after the unique code is assigned.

[0100] The medical institution docking unit is used for:

[0101] The patient's health data is transmitted to the connected medical institution, and the fastest transmission channel is selected when transmitting the patient's health data.

[0102] The monitoring data consultation unit is used for:

[0103] Medical institutions receive patients' health data, monitor and assess the received data, make consultation decisions based on the assessment results, generate health reports based on the assessment results and consultation decisions, and transmit the health reports to the patients' mobile terminals for display.

[0104] The health and wellness push notification unit is used for:

[0105] Patients can view their health status based on the generated health report on their mobile devices, and also conduct online consultations based on the health report.

[0106] Specifically, the patient health monitoring unit allows for the tracing of data generation time and source, enhancing data security and reliability. The medical institution docking unit connects the patient's mobile terminal with the medical institution's signal, enabling remote transmission and sharing of patient health data. The monitoring data consultation unit ensures that the data can be analyzed by medical personnel with relevant expertise, improving the accuracy and efficiency of diagnosis. The health care push unit allows patients to view detailed health reports, including diagnostic results and examination data, helping them to gain a more comprehensive understanding of their health status.

[0107] The patient health monitoring unit includes:

[0108] Health data acquisition module:

[0109] Health data of patients can be collected using intelligent measuring devices or by staff through regular inquiries.

[0110] Health data includes blood pressure data, blood glucose data, heart rate data, body temperature data, electrocardiogram data, and blood oxygen saturation data;

[0111] Among them, intelligent measuring devices include intelligent blood pressure monitors, intelligent blood glucose meters, smartwatches, intelligent thermometers, and intelligent electrocardiogram monitoring devices;

[0112] Smart blood pressure monitors collect blood pressure data; smart blood glucose meters collect blood glucose data; smartwatches collect blood oxygen saturation and heart rate data; smart thermometers collect body temperature data; and smart ECG monitoring devices collect ECG data.

[0113] Staff members conduct regular home visits to collect data at set times, and staff members wear monitoring devices themselves.

[0114] The intelligent measuring device and the monitoring equipment worn by the staff collect the patients' health data and label it as the raw health data.

[0115] The health data classification and labeling module is used for:

[0116] The raw health data is classified according to its attributes, and then a unique code is assigned after classification.

[0117] The process for unique identification codes is as follows:

[0118] The raw health data is encoded according to the rules, which include a prefix, timestamp, and sequence number.

[0119] The prefix is ​​a fixed name generated based on the attributes categorized from the original health data; the timestamp is the time when the original health data was generated; the sequence number is the number that distinguishes different records of the original health data under the same timestamp.

[0120] Once the coding structure rules are confirmed, a unique code is obtained for the original health data, and the original health data with the unique code is labeled as patient health data.

[0121] Specifically, the health data acquisition module monitors patients' health data. The use of intelligent measuring devices automates the data collection process, enabling real-time acquisition of patient health data, reducing manual intervention and waiting time, and improving data collection efficiency. Real-time monitoring functions (such as continuous monitoring of blood oxygen saturation and heart rate by smartwatches) help to promptly detect abnormal health conditions, gaining valuable time for timely intervention and treatment. Intelligent measuring devices typically have user-friendly interfaces and simple usage procedures, allowing patients or their families to easily learn and use them, lowering the barrier to entry. After the raw health data is collected, it can be further analyzed and mined through a comprehensive healthcare information management system, providing doctors with key information such as trend analysis of patient health status and abnormal warnings. The monitored data is further analyzed and processed through a health data classification and labeling module. By establishing unified coding structure rules for the raw health data, data standardization and normalization are achieved. This not only facilitates data storage, retrieval, and management but also improves data consistency and comparability, laying a solid foundation for subsequent data analysis and mining. Unique coding labels ensure that each piece of patient health data can be quickly and accurately located. When querying or analyzing the health data of a specific patient, relevant information can be quickly retrieved simply by using the code, greatly improving work efficiency. The timestamp and sequence number in the code structure allow for tracing the data's generation time and source, enhancing data security and reliability. At the same time, the unique code also reduces the risk of data tampering or misuse.

[0122] To address the problem in existing technologies where patient health data is acquired but not promptly transmitted to medical institutions for professional assessment, leading to misdiagnosis, please refer to [link to relevant documentation]. Figure 1 and Figure 2 This embodiment provides the following technical solution:

[0123] The medical institution docking unit includes:

[0124] The monitoring data import module is used for:

[0125] Patient health data is imported into the patient's mobile device. Data collected by the smart measuring device is transmitted to the patient's mobile device via Bluetooth, while data collected by staff during regular inquiries is entered into the patient's mobile device by the staff.

[0126] The patient's mobile device establishes a signal connection with the medical institution;

[0127] After signal docking is completed, prepare for the transmission of patient health data.

[0128] The monitoring data transmission module is used for:

[0129] When patient health data is transmitted from the patient's mobile terminal to the medical institution, the fastest transmission channel is automatically selected;

[0130] First, the patient's health data is divided into segments of equal length.

[0131] The amount of patient health data transmitted is calculated based on the segment data.

[0132] There shall be no fewer than four transmission channels from the patient's mobile terminal to the medical institution;

[0133] Confirm the remaining capacity of each transmission channel;

[0134] The transmission channel for transmitting patient health data from the mobile terminal to the medical institution is selected when the amount of patient health data transmitted is less than the remaining channel capacity.

[0135] Specifically, by automatically selecting the fastest transmission channel, the system ensures that patient health data is transmitted from the patient's mobile terminal to the medical institution at the fastest speed. This dynamic selection of the optimal channel reduces data transmission latency, enabling medical institutions to obtain the latest patient health data more quickly and make diagnostic or treatment decisions faster. Among multiple available channels, the system intelligently matches the remaining capacity of each channel with the amount of patient health data to be transmitted, ensuring that the selected channel meets data transmission needs without wasting excessive network resources. This optimized resource utilization helps improve the operational efficiency of the entire healthcare system. Dividing patient health data into several segments of uniform length and transmitting them segment by segment effectively reduces the risk of data loss or corruption due to transmission interruptions or errors. Even if a segment of data encounters a problem during transmission, only that segment can be retransmitted instead of the entire data packet, thus ensuring data integrity and accuracy. Through signal interfacing between the patient's mobile terminal and the medical institution, remote transmission and sharing of patient health data are achieved. This provides strong support for new medical service models such as telemedicine and remote consultation, enabling patients to enjoy high-quality medical services even in remote areas.

[0136] Specifically, the monitoring data transmission module includes:

[0137] The real-time monitoring module is used to monitor the data transmission operation parameters of the patient's health data from the mobile terminal to the medical institution in real time.

[0138] The data transmission operation parameters include the amount of data transmitted in parallel with the patient's health data in the transmission channel during the patient's health data transmission process, as well as the data transmission rate of the patient's health data and the data transmission rate of the other data transmitted in parallel with the patient's health data.

[0139] The data volume information extraction module is used to extract the data volume of other data transmitted in parallel besides the transmitted patient health data for each unit of time during the transmission of patient health data; wherein, the unit of time is 1 second;

[0140] The first data transmission evaluation coefficient acquisition module is used to obtain the first data transmission evaluation coefficient by combining the amount of data of other data transmitted in parallel with the transmission of patient health data corresponding to each unit time during the patient health data transmission process with the amount of data transmitted per unit time during the patient health data transmission process.

[0141] The first data transmission evaluation coefficient is obtained by the following formula:

[0142]

[0143] Among them, R 01 denoted as the first data transmission evaluation coefficient; n represents the number of unit time intervals that have elapsed during the patient's health data transmission process; C 01i C represents the amount of patient health data transmitted in the i-th unit of time; 02i C represents the amount of data transmitted in parallel, excluding patient health data, for the i-th unit of time; yi C represents the remaining capacity of the transmission channel corresponding to the i-th unit of time; 02b C represents the standard deviation of the amount of data transmitted in parallel with other data besides patient health data, corresponding to n units of time elapsed during the patient health data transmission process; 01b The standard deviation of the data volume corresponding to n units of time that the patient's health data transmission process has taken; r represents the first adjustment coefficient, which is obtained by the following formula:

[0144]

[0145] Where r represents the first adjustment coefficient; n represents the number of unit times that the patient's health data transmission process has gone through; C 01i C represents the amount of patient health data transmitted in the i-th unit of time; 02i C represents the amount of data transmitted in parallel, excluding patient health data, for the i-th unit of time; yiThis represents the remaining capacity of the transmission channel corresponding to the i-th unit of time;

[0146] The first comparison module is used to compare the first data transmission evaluation coefficient with a preset first coefficient threshold.

[0147] The quality evaluation module is used to evaluate the data transmission quality of the patient health data by using the data transmission rate of the patient health data and the data transmission rate of other data transmitted in parallel in addition to transmitting the patient health data when the first data transmission evaluation coefficient exceeds the preset first coefficient threshold.

[0148] The technical effects of the above solution are as follows: Through the real-time monitoring module, various operational parameters during the data transmission process of patient health data from the mobile terminal to the medical institution can be tracked in real time, including the data volume of other data transmitted in parallel and the data transmission rate of patient health data. This provides a detailed data foundation for subsequent data transmission quality evaluation. With the help of the first data transmission evaluation coefficient acquisition module and the first comparison module, the first data transmission evaluation coefficient can be calculated using the real-time collected data and compared with a preset threshold, thereby achieving accurate evaluation of data transmission quality.

[0149] The first adjustment coefficient in this technical solution is derived through a complex calculation formula, taking into account multiple factors such as the amount of data per unit time during patient health data transmission, the amount of other data transmitted in parallel, and the remaining capacity of the transmission channel. This allows the evaluation coefficient to dynamically adapt to different data transmission scenarios and conditions. This dynamic adaptability helps improve the accuracy and applicability of the evaluation, more accurately reflecting the actual situation during data transmission. When the first data transmission evaluation coefficient exceeds a preset threshold, the quality evaluation module immediately intervenes, evaluating the data transmission quality of the patient health data using the data transmission rate of the patient health data and the data transmission rate of other data transmitted in parallel. This evaluation method considers not only the data transmission rate but also the potential impact of other data transmitted in parallel on data transmission quality, thus enabling a more comprehensive and accurate assessment of data transmission quality. This technical solution can promptly identify problems in the data transmission process, such as decreased transmission rate or channel congestion caused by excessive data volume, providing strong support for optimizing the data transmission process. Medical institutions can adjust their data transmission strategies based on the evaluation results, such as increasing channel capacity or optimizing data transmission algorithms, to improve the efficiency and quality of data transmission. Meanwhile, the application of this technology helps improve the management of patient health data, ensuring accurate and timely data transmission and providing reliable data support for clinical decision-making and scientific research analysis in medical institutions. Furthermore, by optimizing the data transmission process, it can reduce error and loss rates during data transmission, thereby improving the integrity and usability of patient health data.

[0150] In summary, this technical solution effectively improves the efficiency and quality of patient health data transmission by real-time monitoring of data transmission operation parameters, dynamic calculation of evaluation coefficients, efficient evaluation of data transmission quality, and optimization of data transmission processes, providing medical institutions with more reliable and accurate data support.

[0151] Specifically, the quality evaluation module includes:

[0152] The first data transmission rate information extraction module is used to extract the data transmission rate corresponding to each unit of time during the patient's health data transmission process.

[0153] The second data transmission rate information extraction module is used to extract the data transmission rate per unit time for other data transmitted in parallel besides patient health data.

[0154] The second data transmission evaluation coefficient acquisition module is used to acquire the second data transmission evaluation coefficient by utilizing the data transmission rate of patient health data and the data transmission rate of other data transmitted in parallel in addition to the transmission of patient health data.

[0155] The second data transmission evaluation coefficient is obtained using the following formula:

[0156]

[0157] Among them, R 02 V represents the second data transmission evaluation coefficient; n represents the number of unit times that the patient health data transmission process has gone through; m represents the number of other data transmitted in parallel besides the patient health data for each unit time; V 01i V represents the data transmission rate of patient health data transmission corresponding to the i-th unit of time; 02ij=1 V 02ij=2 ... V 02ij=m V represents the data transmission rate corresponding to the 1st, 2nd, ..., mth data points transmitted in parallel (excluding patient health data) within the i-th unit of time; 02ip V represents the average data transmission rate of m data points transmitted in parallel, excluding patient health data, within the i-th unit of time; 01bi and V 02bi Let f represent the standard deviation of the data transmission rate of the patient's health data in the i-th unit time and the average standard deviation of the data transmission rate of the m other data transmitted in parallel besides the patient's health data; f represents the second adjustment coefficient, which is obtained by the following formula:

[0158]

[0159] Where f represents the second adjustment coefficient; V 01bi V represents the standard deviation of the data transmission rate of the patient's health data in the i-th unit of time; 02bimax V represents the maximum standard deviation of the data transmission rate for m data items transmitted in parallel besides patient health data; 02bimin This represents the minimum standard deviation of the data transmission rate of m data items transmitted in parallel besides patient health data.

[0160] The second comparison module is used to compare the second data transmission evaluation coefficient with a preset second coefficient threshold.

[0161] The anomaly detection and alarm module is used to determine that there is an anomaly in the data transmission of the patient's health data when the second data transmission evaluation coefficient exceeds a preset second coefficient threshold, and to issue an anomaly alarm.

[0162] The technical effects of the above solution are as follows: The first and second data transmission rate information extraction modules can extract the data transmission rates of patient health data and other data transmitted in parallel within each unit of time, providing accurate data support for subsequent data transmission evaluation. The second data transmission evaluation coefficient acquisition module utilizes the data transmission rates of patient health data and other data transmitted in parallel, combined with other relevant parameters (such as data transmission rate standard deviation, average value, etc.), to derive the second data transmission evaluation coefficient through a complex calculation formula. This coefficient considers not only the data transmission rate but also its stability and fluctuation, thus enabling a more comprehensive and accurate evaluation of data transmission quality. The calculation formula for the second adjustment coefficient considers the maximum and minimum values ​​of the data transmission rate standard deviation of patient health data and the data transmission rate standard deviation of other data transmitted in parallel. This allows the evaluation coefficient to dynamically adapt to different data transmission scenarios and conditions, improving the accuracy and applicability of the evaluation.

[0163] When the second data transmission evaluation coefficient exceeds the preset second coefficient threshold, the anomaly detection and alarm module will immediately determine that there is an anomaly in the data transmission and issue an alarm. This helps to promptly identify and resolve potential problems during data transmission, ensuring accurate and timely data transmission. Through this technical solution, medical institutions can more accurately grasp the actual situation of data transmission and optimize the data transmission process based on the evaluation results, such as adjusting the data transmission rate and optimizing the data transmission algorithm, to improve the efficiency and quality of data transmission. The application of this technical solution helps to improve the management level of patient health data, ensuring the integrity, accuracy, and usability of the data. This helps medical institutions better utilize patient health data for clinical decision-making and scientific research analysis, improving the quality and efficiency of medical services.

[0164] In summary, this technical solution effectively improves the efficiency and quality of patient health data transmission through measures such as detailed data transmission rate monitoring, comprehensive data transmission quality evaluation, the introduction of dynamic adjustment coefficients, timely anomaly detection and alarms, and optimization of data transmission processes and strategies, providing medical institutions with more reliable and accurate data support.

[0165] To address the issue of poor treatment outcomes caused by the lack of targeted decision-making based on patient health data in existing technologies, please refer to [link to relevant documentation]. Figure 1 and Figure 2 This embodiment provides the following technical solution:

[0166] The monitoring data consultation unit includes:

[0167] The health data assessment module is used for:

[0168] Medical institutions receive patients' health data and then make an anomaly assessment.

[0169] Specifically, the blood pressure, blood sugar, heart rate, body temperature, electrocardiogram, and blood oxygen saturation data in the patient's health data are converted into curve data, and the converted data are used to obtain curve health data.

[0170] Compare and overlap the health curve data with the standard curve data;

[0171] Based on the overlap comparison results, qualified data, low data, and high data are obtained;

[0172] Among them, low data refers to the data region where the curve health data is lower than the standard curve data when the data is overlapped; high data refers to the data region where the curve health data is higher than the standard curve data when the data is overlapped.

[0173] Low and high data are labeled as unhealthy data and stored separately.

[0174] The data analysis module is used for:

[0175] Medical institutions will confirm the specific attributes of non-health data;

[0176] Based on the specific attributes of the non-health data, the non-health data will be transmitted to the corresponding medical personnel's diagnostic center for corresponding diagnosis;

[0177] Medical personnel make diagnostic decisions based on abnormal thresholds of non-healthy data;

[0178] Diagnostic decisions are made based on the degree of abnormality in unhealthy data.

[0179] If the abnormal threshold of low and high data in unhealthy data does not exceed 20%, a written diagnosis is made; if the abnormal threshold of low and high data in unhealthy data exceeds 20%, an on-site consultation is made.

[0180] A health report is generated based on the diagnostic decision and then transmitted to the patient's mobile device.

[0181] Specifically, by converting key health indicators such as blood pressure, blood sugar, heart rate, body temperature, electrocardiogram, and blood oxygen saturation into curve data and comparing them with a standard curve, the system can more intuitively and accurately reflect whether a patient's health status is within the normal range. Compared to single numerical judgments, this curve comparison-based method can better capture subtle changes and trends in health data, thus providing a more accurate health assessment. By comparing patients' health data with the standard curve in real time or periodically, the system can promptly identify abnormal data (low or high data) and mark them as unhealthy data for separate storage. This helps medical personnel obtain timely warning information, intervene early in potential health problems, prevent the condition from worsening, and improve treatment outcomes. Since the standard curve may differ for different patients and at different stages of disease, this solution allows for setting or adjusting the standard curve according to the patient's specific situation, thereby achieving personalized health management. This helps provide patients with more accurate and effective medical advice and care plans. By confirming the specific attributes of unhealthy data, the system can accurately transmit data to the corresponding medical personnel's diagnostic center. This precision ensures that data can be analyzed by qualified medical personnel, improving diagnostic accuracy and efficiency. The entire process, from data verification and transmission to diagnostic decision-making, is automated or semi-automated, significantly reducing manual operation time and potential errors. This improves the speed and efficiency of healthcare services. Based on the degree of abnormality in non-healthy data (e.g., low and high data abnormality thresholds), the system can automatically decide whether to provide a written diagnosis or a home consultation, offering patients more personalized and appropriate medical services. This customized approach enhances patient satisfaction and treatment outcomes. When non-healthy data is abnormal, especially when the abnormality threshold exceeds preset standards, the system can quickly trigger corresponding response mechanisms (e.g., home consultation), ensuring timely resolution of problems and reducing the risk of disease deterioration. The introduction of home consultations promotes the application of telemedicine services, allowing patients to receive professional medical services without having to travel to the hospital, which is particularly significant for patients with mobility issues or those living in remote areas.

[0182] The health and wellness push notification unit is also used for:

[0183] Patients can view their health reports on their mobile devices and confirm the final treatment plan based on the results.

[0184] Among these measures, medication is purchased based on written diagnostic results, and consultation times are confirmed based on the results of in-home consultations.

[0185] Meanwhile, patients can conduct online consultations on their mobile devices based on their health reports.

[0186] Specifically, patients are no longer limited by location and time and can view their health reports anytime, anywhere via mobile devices, greatly improving the convenience of medical treatment. Patients can immediately make preliminary treatment decisions based on their health reports, such as whether to purchase specific medications or confirm consultation times, thereby speeding up the diagnosis and treatment process. Patients can view their health reports in detail, including diagnostic results and test data, which helps them to understand their health status more comprehensively. By viewing their health reports and conducting online consultations, patients can participate more actively in their treatment process, improving treatment satisfaction and compliance.

[0187] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0188] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A comprehensive healthcare information management system, characterized in that, include: The patient health monitoring unit is used for: Patients undergo health monitoring and health inquiries, and their health data is categorized according to the data types of the health monitoring and health inquiries. After categorization, a unique code is assigned, and the patient's health data is obtained after the unique code is assigned. The medical institution docking unit is used for: The patient's health data is transmitted to the connected medical institution, and the fastest transmission channel is selected when transmitting the patient's health data. The monitoring data consultation unit is used for: Medical institutions receive patients' health data, monitor and assess the received data, make consultation decisions based on the assessment results, generate health reports based on the assessment results and consultation decisions, and transmit the health reports to the patients' mobile terminals for display. The health and wellness push notification unit is used for: Patients can view their health status based on the generated health report on their mobile devices, and also conduct online consultations based on the health report on their mobile devices. The medical institution docking unit also includes: The monitoring data transmission module is used for: When patient health data is transmitted from the patient's mobile terminal to the medical institution, the fastest transmission channel is automatically selected; First, the patient's health data is divided into segments of equal length. The amount of patient health data transmitted is calculated based on the segment data. There shall be no fewer than four transmission channels from the patient's mobile terminal to the medical institution; Confirm the remaining capacity of each transmission channel; Select a transmission channel where the amount of patient health data transmitted is less than the remaining channel capacity as the transmission channel for transmitting patient health data from the mobile terminal to the medical institution. The monitoring data transmission module includes: The real-time monitoring module is used to monitor the data transmission operation parameters of the patient's health data from the mobile terminal to the medical institution in real time. The data transmission operation parameters include the amount of data transmitted in parallel with the patient's health data in the transmission channel during the patient's health data transmission process, as well as the data transmission rate of the patient's health data and the data transmission rate of the other data transmitted in parallel with the patient's health data. The data volume information extraction module is used to extract the data volume of other data transmitted in parallel besides the transmitted patient health data for each unit of time during the transmission of patient health data; wherein, the unit of time is 1 second; The first data transmission evaluation coefficient acquisition module is used to obtain the first data transmission evaluation coefficient by combining the amount of data of other data transmitted in parallel with the transmission of patient health data corresponding to each unit time during the patient health data transmission process with the amount of data transmitted per unit time during the patient health data transmission process. The first data transmission evaluation coefficient is obtained by the following formula: Among them, R 01 denoted as the first data transmission evaluation coefficient; n represents the number of unit time intervals that have elapsed during the patient's health data transmission process; C 01i C represents the amount of patient health data transmitted in the i-th unit of time; 02i C represents the amount of data transmitted in parallel, excluding patient health data, for the i-th unit of time; yi C represents the remaining capacity of the transmission channel corresponding to the i-th unit of time; 02b C represents the standard deviation of the amount of data transmitted in parallel with other data besides patient health data, corresponding to n units of time elapsed during the patient health data transmission process; 01b The standard deviation of the data volume corresponding to n units of time that the patient's health data transmission process has taken; r represents the first adjustment coefficient, which is obtained by the following formula: Where r represents the first adjustment coefficient; n represents the number of unit times that the patient's health data transmission process has gone through; C 01i C represents the amount of patient health data transmitted in the i-th unit of time; 02i C represents the amount of data transmitted in parallel, excluding patient health data, for the i-th unit of time; yi This represents the remaining capacity of the transmission channel corresponding to the i-th unit of time; The first comparison module is used to compare the first data transmission evaluation coefficient with a preset first coefficient threshold. The quality evaluation module is used to evaluate the data transmission quality of the patient health data by using the data transmission rate of the patient health data and the data transmission rate of other data transmitted in parallel in addition to transmitting the patient health data when the first data transmission evaluation coefficient exceeds the preset first coefficient threshold.

2. The integrated medical and healthcare information management system according to claim 1, characterized in that, The patient health monitoring unit includes: Health data acquisition module: Health data of patients can be collected using intelligent measuring devices or by staff through regular inquiries. Health data includes blood pressure data, blood glucose data, heart rate data, body temperature data, electrocardiogram data, and blood oxygen saturation data; Among them, intelligent measuring devices include intelligent blood pressure monitors, intelligent blood glucose meters, smartwatches, intelligent thermometers, and intelligent electrocardiogram monitoring devices; Smart blood pressure monitors collect blood pressure data; smart blood glucose meters collect blood glucose data; smartwatches collect blood oxygen saturation and heart rate data; smart thermometers collect body temperature data; and smart ECG monitoring devices collect ECG data. Staff members conduct regular home visits to collect data at set times, and staff members wear monitoring devices themselves. The intelligent measuring device and the monitoring equipment worn by the staff collect the patients' health data and label it as the raw health data.

3. The integrated medical and healthcare information management system according to claim 2, characterized in that, The patient health monitoring unit also includes: The health data classification and labeling module is used for: The raw health data is classified according to its attributes, and then a unique code is assigned after classification. The process for unique identification codes is as follows: The raw health data is encoded according to the rules, which include a prefix, timestamp, and sequence number. The prefix is ​​a fixed name generated based on the attributes categorized from the original health data; the timestamp is the time when the original health data was generated; the sequence number is the number that distinguishes different records of the original health data under the same timestamp. Once the coding structure rules are confirmed, a unique code is obtained for the original health data, and the original health data with the unique code is labeled as patient health data.

4. The integrated healthcare information management system according to claim 3, characterized in that, The medical institution docking unit includes: The monitoring data import module is used for: Patient health data is imported into the patient's mobile device. Data collected by the smart measuring device is transmitted to the patient's mobile device via Bluetooth, while data collected by staff during regular inquiries is entered into the patient's mobile device by the staff. The patient's mobile device establishes a signal connection with the medical institution; After signal docking is completed, prepare for the transmission of patient health data.

5. A comprehensive healthcare information management system according to claim 4, characterized in that, The quality evaluation module includes: The first data transmission rate information extraction module is used to extract the data transmission rate corresponding to each unit of time during the patient's health data transmission process. The second data transmission rate information extraction module is used to extract the data transmission rate per unit time for other data transmitted in parallel besides patient health data. The second data transmission evaluation coefficient acquisition module is used to acquire the second data transmission evaluation coefficient by utilizing the data transmission rate of patient health data and the data transmission rate of other data transmitted in parallel in addition to the transmission of patient health data. The second data transmission evaluation coefficient is obtained using the following formula: Among them, R 02 V represents the second data transmission evaluation coefficient; n represents the number of unit times that the patient health data transmission process has gone through; m represents the number of other data transmitted in parallel besides the patient health data for each unit time; V 01i V represents the data transmission rate of patient health data transmission corresponding to the i-th unit of time; 02ij=1 V 02ij=2 ... V 02ij=m V represents the data transmission rate corresponding to the 1st, 2nd, ..., mth data points transmitted in parallel (excluding patient health data) within the i-th unit of time; 02ip V represents the average data transmission rate of m data points transmitted in parallel, excluding patient health data, within the i-th unit of time; 01bi and V 02bi Let f represent the standard deviation of the data transmission rate of the patient's health data in the i-th unit time and the average standard deviation of the data transmission rate of the m other data transmitted in parallel besides the patient's health data; f represents the second adjustment coefficient, which is obtained by the following formula: Where f represents the second adjustment coefficient; V 01bi V represents the standard deviation of the data transmission rate of the patient's health data in the i-th unit of time; 02bimax V represents the maximum standard deviation of the data transmission rate for m data items transmitted in parallel besides patient health data; 02bimin This represents the minimum standard deviation of the data transmission rate of m data items transmitted in parallel besides patient health data. The second comparison module is used to compare the second data transmission evaluation coefficient with a preset second coefficient threshold. The anomaly detection and alarm module is used to determine that there is an anomaly in the data transmission of the patient's health data when the second data transmission evaluation coefficient exceeds a preset second coefficient threshold, and to issue an anomaly alarm.

6. A comprehensive healthcare information management system according to claim 5, characterized in that, The monitoring data consultation unit includes: The health data assessment module is used for: Medical institutions receive patients' health data and then make an anomaly assessment. Specifically, the blood pressure, blood sugar, heart rate, body temperature, electrocardiogram, and blood oxygen saturation data in the patient's health data are converted into curve data, and the converted data are used to obtain curve health data. Compare and overlap the health curve data with the standard curve data; Based on the overlap comparison results, qualified data, low data, and high data are obtained; Among them, low data refers to the data region where the curve health data is lower than the standard curve data when the data is overlapped; high data refers to the data region where the curve health data is higher than the standard curve data when the data is overlapped. Low and high data are labeled as unhealthy data and stored separately.

7. A comprehensive healthcare information management system according to claim 6, characterized in that, The monitoring data consultation unit also includes: The data analysis module is used for: Medical institutions will confirm the specific attributes of non-health data; Based on the specific attributes of the non-health data, the non-health data will be transmitted to the corresponding medical personnel's diagnostic center for corresponding diagnosis; Medical personnel make diagnostic decisions based on abnormal thresholds of non-healthy data; Diagnostic decisions are made based on the degree of abnormality in unhealthy data. If the abnormal threshold of low and high data in unhealthy data does not exceed 20%, a written diagnosis is made; if the abnormal threshold of low and high data in unhealthy data exceeds 20%, an on-site consultation is made. A health report is generated based on the diagnostic decision and then transmitted to the patient's mobile device.

8. A comprehensive healthcare information management system according to claim 7, characterized in that, The health and wellness push unit is also used for: Patients can view their health reports on their mobile devices and confirm the final treatment plan based on the results. Among these measures, medication is purchased based on written diagnostic results, and consultation times are confirmed based on the results of in-home consultations. Meanwhile, patients can conduct online consultations on their mobile devices based on their health reports.

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