A method and system for monitoring the condition of a patient during an emergency patient transfer procedure

Abstract

The application provides a disease condition monitoring method and system for emergency patient transfer process, relates to the technical field of transfer disease condition monitoring, acquires transfer demand information, performs disease condition data evaluation, processing, classification risk analysis and marking on the patient, acquires disease condition classification risk marking information, acquires patient transfer path information, further generates a relay position, calculates a patient disease condition development coefficient, acquires a disease condition development time sequence of the patient transfer path information, performs abnormal disease condition node marking, further performs abnormal position marking on the transfer path information, acquires development position abnormal marking information, acquires the shortest distance of the development position abnormal marking information and the relay position information, further acquires a first transport distance of the shortest distance, determines a transport path, and performs secondary transfer, and the application can realize comprehensive monitoring and intelligent adjustment of the emergency patient transfer process according to disease condition monitoring data.

Description

Technical Field

[0001] This invention proposes a method and system for monitoring the condition of emergency patients during transport, which relates to the field of condition monitoring technology, specifically to the field of monitoring the condition of emergency patients during transport. Background Technology

[0002] Traditional emergency patient transport often relies on the experience and judgment of medical staff, lacking systematic and intelligent disease monitoring methods. This makes it difficult to detect and effectively respond to changes in the patient's condition during transport. Furthermore, traditional transport routes rarely include relay nodes needed to handle emergencies, making it difficult to flexibly and automatically adjust transport routes according to actual conditions. The lack of real-time monitoring and dynamic assessment of the patient's condition hinders timely detection and intervention of changes. Inadequate transport route planning can lead to excessively long transport times or safety hazards during transport. Inappropriate relay location selection can also prevent patients from receiving timely medical treatment during transport. Summary of the Invention

[0003] This invention provides a method and system for monitoring the condition of emergency patients during transport, in order to solve the above-mentioned problems:

[0004] This invention proposes a method and system for monitoring the condition of emergency patients during transport, the method comprising:

[0005] S1. Obtain transfer demand information, assess and process patient condition data, obtain patient assessment and processing data, perform risk analysis and labeling on patient assessment and processing data, and obtain condition category risk labeling information.

[0006] S2. Obtain patient transfer path information, obtain preset transfer process nodes based on the patient transfer path information, and then generate relay positions to obtain relay position information;

[0007] S3. Calculate the patient's disease development coefficient, obtain the disease development time sequence of the patient's transfer path information based on the patient's disease development coefficient, mark abnormal disease nodes, and then mark abnormal locations in the transfer path information to obtain abnormal location marking information.

[0008] S4. Obtain the shortest distance between the development location anomaly labeling information and the relay location information, then obtain the first transportation distance of the shortest distance, determine the transportation route, and carry out secondary transfer.

[0009] Further, S1 includes:

[0010] Obtain the patient's transport needs information, and trigger a patient assessment instruction based on the transport needs information;

[0011] Based on the patient's assessment instructions, an alarm is triggered to assess the patient's condition, thereby obtaining the patient's condition assessment data;

[0012] The patient's condition assessment data is preprocessed to obtain patient assessment processing data;

[0013] The patient assessment and processing data are classified according to preset disease types to obtain multiple categories of patient disease data.

[0014] The patient's condition category data is compared with a preset category threshold to obtain patient condition comparison information;

[0015] Based on the patient's condition comparison information, the patient's condition risk category is determined, and the patient's condition risk category is labeled to obtain condition category risk labeling information.

[0016] Further, S2 includes:

[0017] Obtain the patient's origin and destination information, set the patient's transport route according to the shortest transport time based on the origin and destination information, and obtain the patient's transport route information.

[0018] The system acquires preset transfer process nodes (hospitals along the way) of the patient's transfer route information, sets the preset transfer process nodes as relay locations, and obtains multiple relay location information.

[0019] Further, S3 includes:

[0020] Acquire information at preset monitoring time points, and at each preset monitoring time point, acquire patient disease category risk labeling information and patient assessment and treatment data;

[0021] The patient's disease progression coefficient is calculated based on the disease category risk labeling information and the patient assessment and treatment data.

[0022] The patient's disease progression coefficients are sorted according to preset monitoring time nodes to obtain the disease progression time sequence;

[0023] Obtain the path location information for each preset monitoring time node, and bind the disease development time sequence with the path location information to obtain location development binding data;

[0024] The disease progression coefficient of each patient in the disease progression time sequence is compared with the preset progression threshold in turn to obtain the disease progression comparison results;

[0025] Based on the comparison results of the disease development, abnormal disease nodes are marked in the disease development sequence;

[0026] Obtain the patient's disease progression coefficient corresponding to the first abnormal disease node in the disease progression timeline;

[0027] The abnormal location information of the path location information in the location development binding data corresponding to the patient's condition development coefficient marked by the first abnormal condition node is obtained by annotating the development location abnormality information.

[0028] Further, S4 includes:

[0029] Obtain anomaly marker information and relay location information for development locations, and calculate the shortest transportation distance between the anomaly marker information for development locations and multiple relay location information;

[0030] Select the first delivery distance from multiple shortest delivery distances;

[0031] Obtain the transportation route corresponding to the first transportation distance;

[0032] The patient is re-shipped via the aforementioned transportation route to obtain secondary shipment information.

[0033] Furthermore, the system includes:

[0034] The disease analysis and annotation module is used to obtain transportation demand information, evaluate and process patient disease data, obtain patient evaluation and processing data, perform risk analysis and annotation on patient evaluation and processing data, and obtain disease category risk annotation information.

[0035] The route relay setting module is used to obtain patient transport route information, obtain preset transport process nodes based on the patient transport route information, and then generate relay positions to obtain relay position information.

[0036] The disease progression monitoring module is used to calculate the patient's disease progression coefficient, obtain the disease progression time sequence of the patient's transfer path information based on the patient's disease progression coefficient, mark abnormal disease nodes, and then mark abnormal locations in the transfer path information to obtain abnormal location marking information.

[0037] The emergency transfer module is used to obtain the shortest distance between the abnormal location marker information and the relay location information, and then obtain the first transportation distance of the shortest distance to determine the transportation route and carry out secondary transfer.

[0038] Furthermore, the disease analysis and annotation module includes:

[0039] The condition assessment and processing module is used to obtain the patient's transfer request information and trigger the patient assessment instruction based on the transfer request information;

[0040] Based on the patient's assessment instructions, an alarm is triggered to assess the patient's condition, thereby obtaining the patient's condition assessment data;

[0041] The patient's condition assessment data is preprocessed to obtain patient assessment processing data;

[0042] The classification, comparison, and labeling module is used to classify the patient assessment and processing data according to preset disease categories to obtain multiple categories of patient disease category data.

[0043] The patient's condition category data is compared with a preset category threshold to obtain patient condition comparison information;

[0044] Based on the patient's condition comparison information, the patient's condition risk category is determined, and the patient's condition risk category is labeled to obtain condition category risk labeling information.

[0045] Furthermore, the path relay setting module includes:

[0046] The route information acquisition module is used to acquire the patient's transfer origin information and transfer destination information, set the patient's transfer route according to the shortest transfer time based on the transfer origin information and transfer destination information, and acquire the patient's transfer route information.

[0047] The relay location determination module is used to obtain preset transfer process nodes (hospitals passed by, etc.) of the patient transfer path information, set the preset transfer process nodes as relay locations, and obtain multiple relay location information.

[0048] Furthermore, the disease progression monitoring module includes:

[0049] The disease progression analysis module is used to obtain information at preset monitoring time points, and at each preset monitoring time point, it obtains the patient's disease category risk labeling information and patient assessment and treatment data;

[0050] The patient's disease progression coefficient is calculated based on the disease category risk labeling information and the patient assessment and treatment data.

[0051] The disease progression time sequence acquisition module is used to sort the patient's disease progression coefficients according to preset monitoring time node information to obtain the disease progression time sequence;

[0052] The disease location binding module is used to obtain the path location information of each preset monitoring time node, bind the disease development sequence with the path location information, and obtain location development binding data.

[0053] The disease progression comparison and annotation module is used to compare the disease progression coefficient of each patient with a preset progression threshold in sequence to obtain the disease progression comparison results.

[0054] Based on the comparison results of the disease development, abnormal disease nodes are marked in the disease development sequence;

[0055] Obtain the patient's disease progression coefficient corresponding to the first abnormal disease node in the disease progression timeline;

[0056] The location anomaly labeling module is used to annotate the path location information of the patient's condition development coefficient corresponding to the first abnormal condition node in the location development binding data, and obtain development location anomaly labeling information.

[0057] Furthermore, the emergency transfer module includes:

[0058] The transportation distance comparison module is used to obtain anomaly marker information and relay location information, and calculate the shortest transportation distance between the anomaly marker information and multiple relay location information.

[0059] The optimal route transfer module is used to select the first transfer distance from multiple shortest transfer distances;

[0060] Obtain the transportation route corresponding to the first transportation distance;

[0061] The patient is re-shipped via the aforementioned transportation route to obtain secondary shipment information.

[0062] The beneficial effects of this invention are as follows: Through automated assessment and risk labeling, the severity of a patient's condition and transport needs can be quickly identified, thereby optimizing the transport process and improving transport efficiency. It can monitor the development of a patient's condition in real time and mark anomalies at key points, enabling timely detection and handling of potential risks and enhancing the safety of the transport process. Based on the patient's condition classification and risk labeling, medical resources can be rationally allocated, such as arranging suitable transport vehicles, medical personnel, and emergency equipment, ensuring efficient resource utilization. Through rapid and accurate transport and treatment, the system can reduce patient waiting time and suffering. When a patient's condition suddenly and rapidly deteriorates during transport, it can automatically generate the optimal relay route to ensure timely treatment. The condition assessment, risk labeling, and transport route information provided by the system can automatically generate a condition control strategy. Attached Figure Description

[0063] Figure 1 This is a schematic diagram of a method for monitoring the condition of an emergency patient during transport.

[0064] Figure 2 This is a schematic diagram of a patient monitoring system during emergency patient transfer. Detailed Implementation

[0065] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0066] In one embodiment of the present invention, a method and system for monitoring the condition of emergency patients during transport are provided, the method comprising:

[0067] S1. Obtain transfer demand information, assess and process patient condition data, obtain patient assessment and processing data, perform risk analysis and labeling on patient assessment and processing data, and obtain condition category risk labeling information.

[0068] S2. Obtain patient transfer path information, obtain preset transfer process nodes based on the patient transfer path information, and then generate relay positions to obtain relay position information;

[0069] S3. Calculate the patient's disease development coefficient, obtain the disease development time sequence of the patient's transfer path information based on the patient's disease development coefficient, mark abnormal disease nodes, and then mark abnormal locations in the transfer path information to obtain abnormal location marking information.

[0070] S4. Obtain the shortest distance between the anomaly marker information and the relay location information, then obtain the first transportation distance of the shortest distance, determine the transportation route, and perform secondary transshipment, such as... Figure 1 As shown.

[0071] The working principle of the above technical solution is as follows: When an emergency patient needs to be transferred, the system acquires transfer request information, including the patient's basic information, preliminary diagnosis, and current vital signs. It then acquires and processes the patient's condition data as assessed by the doctor, generating patient assessment and processing data. Based on this data, it performs risk analysis on the patient's condition, evaluating blood pressure, blood sugar, and heart rate, and labeling categories exceeding corresponding thresholds to generate condition category risk labeling information. The system obtains the patient's transfer route information based on the origin, destination, hospital layout, and traffic conditions. Key nodes are pre-set as relay locations along the transfer route; these locations may include important medical facilities, emergency stations, or traffic junctions. A condition development coefficient is calculated, reflecting the trend of the patient's condition changing over time. Based on this coefficient, nodes on the transfer route that may exhibit abnormal conditions are marked, i.e., abnormal condition node markings. The system calculates the shortest distance between the abnormal condition markings and the relay locations, enabling rapid response and handling of abnormal conditions. Finally, the optimal transport route is determined based on the shortest distance, and a secondary transfer is performed to ensure that the patient receives timely and necessary medical treatment in the event of an emergency.

[0072] The technical effects of the above solution are as follows: Through automated assessment and risk labeling, the severity of a patient's condition and transport needs can be quickly identified, thereby optimizing the transport process and improving transport efficiency. It can monitor the development of a patient's condition in real time and mark anomalies at key points, enabling timely detection and handling of potential risks and enhancing the safety of the transport process. Based on the patient's condition classification and risk labeling, medical resources can be rationally allocated, such as arranging suitable transport vehicles, medical personnel, and emergency equipment, ensuring efficient resource utilization. Through rapid and accurate transport and treatment, the system can reduce patient waiting time and suffering. When a patient's condition suddenly and rapidly deteriorates during transport, it can automatically generate the optimal relay route to ensure timely treatment. The condition assessment, risk labeling, and transport route information provided by the system can automatically generate a condition control strategy.

[0073] In one embodiment of the present invention, S1 includes:

[0074] Obtain the patient's transport needs information, and trigger a patient assessment instruction based on the transport needs information;

[0075] Based on the patient's assessment instructions, an alarm is triggered to assess the patient's condition, thereby obtaining the patient's condition assessment data;

[0076] The patient's condition assessment data is preprocessed to obtain patient assessment processing data;

[0077] The patient assessment and processing data are classified according to preset disease types to obtain multiple categories of patient disease data.

[0078] The patient's condition category data is compared with a preset category threshold to obtain patient condition comparison information;

[0079] Based on the patient's condition comparison information, the patient's condition risk category is determined, and the patient's condition risk category is labeled with risk information. When the patient's condition category data exceeds a preset category threshold, the patient's condition risk category corresponding to the patient's condition category data is labeled with risk information.

[0080] The working principle of the above technical solution is as follows: It receives transfer requests from within or outside the medical institution, which may originate from doctors, nurses, or other medical staff. These requests include the patient's basic information, the destination, and the reason for the transfer. Based on the received transfer request information, the system automatically triggers a patient assessment process. This process may include a series of standardized assessment steps to collect information such as the patient's vital signs, medical history, and current condition. During the assessment, if the system detects abnormalities in one or more of the patient's vital signs or a deterioration in their condition, it will automatically trigger an alarm mechanism. This alerts medical staff to immediately pay attention and take necessary emergency measures. The patient condition assessment data collected by the system may include readings of various vital signs, laboratory test results, imaging data, etc. This data needs to be preprocessed, such as cleaning, formatting, and standardization. The preprocessed data is then categorized according to preset disease types. These categories may be based on factors such as disease type, severity, and treatment needs. The categorized data forms multiple categories of patient condition data. Each category of condition data is compared with preset category thresholds. These thresholds are set based on clinical experience and medical research. Based on the comparison results, the system determines the patient's disease risk category. If a patient's condition category data exceeds the preset category threshold, the system will label the category.

[0081] The technical effects of the above solution are as follows: The automated assessment process reduces the time and error associated with manual assessment, improving accuracy and efficiency. The real-time alarm mechanism can promptly detect and respond to high-risk situations, ensuring patients receive timely and effective medical care during transport. The system can intelligently allocate medical resources based on the patient's condition risk category, such as prioritizing the transport and treatment of high-risk patients. By providing timely and accurate condition assessments and transport arrangements, the system can enhance patient satisfaction and trust. The condition classification and risk labeling information provided by the system can strongly support clinical decision-making by medical staff. Through automated assessment, real-time alarms, intelligent classification, and risk labeling, the safety and efficiency of patient transport are significantly improved, while also optimizing the allocation of medical resources and enhancing patient satisfaction.

[0082] In one embodiment of the present invention, S2 includes:

[0083] Obtain the patient's transfer origin and destination information, set the patient transfer route according to the shortest transfer time based on the transfer origin and destination information, and obtain the patient transfer route information (the patient transfer route is the road's permitted route);

[0084] The system acquires preset transfer process nodes (hospitals along the way) of the patient's transfer route information, sets the preset transfer process nodes as relay locations, and obtains multiple relay location information.

[0085] The working principle of the above technical solution is as follows: The system receives or inputs the specific location information of the patient's transfer origin (e.g., current hospital, emergency scene, etc.) and destination (e.g., target treatment hospital). This information may include address, latitude and longitude coordinates, etc. Based on the acquired origin and destination information, the system uses path planning algorithms (e.g., Dijkstra's algorithm, A* algorithm, etc.) to search for the shortest transfer time path in the current road network data. Factors considered here include road conditions, traffic flow, and possible traffic control to ensure the path is practically feasible and time-optimal. Road accessibility is considered during path planning, excluding roads that are impassable due to construction, closure, etc. Once the optimal path is determined, the system outputs detailed transfer path information, including the roads along the route, intersections, and estimated travel time. On the transfer path, the system identifies and marks preset transfer process nodes, which are typically locations such as hospitals and emergency stations where medical operations or handovers may occur. The system sets these nodes as relay locations. Based on the node distribution along the path, the system ultimately generates a list containing information on multiple relay locations. This information is of great reference value for resource allocation, personnel arrangement, and emergency response during the transfer process.

[0086] The technical effects of the above solution are as follows: Through precise route planning and the application of real-time traffic information, the system can ensure that patients are transferred from the origin to the destination in the shortest possible time, thereby improving transfer efficiency and reducing risks during the transfer process. The system can identify and mark key relay locations, enabling the transfer team to perform effective medical operations or handovers at these locations, thus optimizing the allocation and use of medical resources. In emergency situations, the system can quickly generate optimal transfer routes and relay location information, enhancing emergency response capabilities. This invention improves the intelligence and flexibility of the medical service transfer process.

[0087] In one embodiment of the present invention, S3 includes:

[0088] Acquire information at preset monitoring time points, and at each preset monitoring time point, acquire patient disease category risk labeling information and patient assessment and treatment data;

[0089] The patient's disease progression coefficient is calculated based on the disease category risk labeling information and the patient assessment and treatment data.

[0090] The formula for calculating the patient's disease progression coefficient is as follows:

[0091]

[0092] Where BF is the patient's disease progression coefficient, α is the pre-defined weight data for the categories, β is the pre-defined weight data for the assessment, y is the number of risk labels for each disease category, w is the total number of categories, and Z is the risk factor for each category. i Z represents the patient's disease category data for risk labeling of the i-th disease category. a Here, ΔB represents the patient condition category data for category a, and P represents the current assessment change data. s For initial patient assessment data;

[0093] The initial patient assessment data is the average of the ratios of actual data to thresholds for all categories.

[0094] The patient's disease progression coefficients are sorted according to preset monitoring time nodes to obtain the disease progression time sequence;

[0095] Obtain the path location information for each preset monitoring time node, and bind the disease development time sequence with the path location information to obtain location development binding data;

[0096] The disease progression coefficient of each patient in the disease progression time sequence is compared with the preset progression threshold in turn to obtain the disease progression comparison results;

[0097] Based on the comparison results of the disease development, abnormal disease nodes are marked in the disease development sequence;

[0098] Obtain the patient's disease progression coefficient corresponding to the first abnormal disease node in the disease progression timeline;

[0099] The abnormal location information of the path location information in the location development binding data corresponding to the patient's condition development coefficient marked by the first abnormal condition node is obtained by annotating the development location abnormality information.

[0100] The working principle of the above technical solution is as follows: Preset monitoring time nodes are acquired. These nodes can be fixed time intervals (such as every minute or hour) or time points triggered by specific events (the presence of sudden symptoms). At each preset monitoring time node, the system collects the patient's disease category risk labeling information and also acquires the patient's assessment and treatment data, such as vital sign monitoring data, laboratory test results, and imaging examination reports. This data is used to comprehensively assess the patient's health status. Combining the disease category risk labeling information and the patient assessment and treatment data, the patient's disease progression coefficient is calculated. This coefficient reflects the dynamic changes in the patient's condition at the monitoring time nodes. The formula combines the consideration of disease category risk labeling and the consideration of changes in patient assessment data through two weighting coefficients, α and β, to form a comprehensive disease progression coefficient (BF). The values ​​of α and β can be adjusted according to the actual situation to balance the impact of disease category risk labeling and changes in patient assessment data on the disease progression coefficient. The final disease progression coefficient (BF) is a value between 0 and 1, used to quantitatively assess the patient's disease progression trend. A higher value (or closer to 1) indicates a more severe progression of the patient's condition; a lower value (or closer to 0) indicates a more stable progression of the patient's condition. Relatively speaking... The larger the ΔB is, the greater the patient's disease progression coefficient. The patient's disease progression coefficients at each monitoring time point are sorted chronologically to form a disease progression time series. This time series shows the trend of the patient's condition changing over time. The path location information for each preset monitoring time point is obtained; this could be the patient's actual geographical location, etc. The disease progression time series is bound to the path location information to form location-bound data. In this way, each disease progression coefficient is associated with a specific path location. The disease progression coefficient of each patient in the disease progression time series is compared with preset progression thresholds. These thresholds are usually set based on clinical experience and statistical data. Based on the comparison results, the system marks disease progression coefficients exceeding the thresholds in the disease progression time series as abnormal disease nodes. These markings indicate the time points when the patient's condition may worsen. The location information of the first abnormal disease node is obtained and marked: the system finds the first abnormal disease node marking in the disease progression time series and obtains its corresponding patient disease progression coefficient. The path location information corresponding to this disease progression coefficient is searched in the location-bound data, and abnormal location marking is performed. In this way, the system determines the specific location and time when the patient's condition first becomes abnormal.

[0101] The technical effects of the above solution are as follows: By pre-setting monitoring time nodes and collecting data in real time, the system can monitor changes in the patient's condition in real time and issue timely warnings when abnormalities occur. Binding the disease progression timeline with path location information enables precise location and tracking of the patient's condition. This helps doctors quickly understand the patient's condition and its spatial and temporal changes. By analyzing the disease progression timeline and the location information of abnormal disease nodes, the system can provide patients with suggestions regarding the allocation and scheduling of medical resources. The disease progression timeline and abnormal disease node annotation information provided by the system offer doctors a comprehensive basis for disease assessment. This method can achieve quantitative monitoring of disease progression at each transfer location.

[0102] In one embodiment of the present invention, S4 includes:

[0103] Obtain anomaly marker information and relay location information for development locations, and calculate the shortest transportation distance between the anomaly marker information for development locations and multiple relay location information;

[0104] Obtain the first delivery distance from multiple shortest delivery distances (the shortest distance among multiple shortest delivery distances);

[0105] Obtain the transportation route corresponding to the first transportation distance;

[0106] The patient was re-transported via the aforementioned transportation route, and secondary transport information was obtained. (Relatively speaking, due to the severity of the illness, the patient was chosen to seek medical treatment at the nearest location).

[0107] The working principle of the above technical solution is as follows: When the system detects an abnormality in the patient's condition, it records and marks the location of this abnormality, i.e., the abnormality location marking information. This typically includes the specific location and time of the abnormality. The system maintains a relay location information database, which can be the locations of critical medical facilities within the hospital, such as emergency stations, operating rooms, and intensive care units. These locations are potential sites for patient transfer or further treatment. The system calculates the shortest transport distance from the location in the abnormality location marking information to each relay location. These distances take into account factors such as the physical layout of traffic. Among multiple shortest transport distances, the system identifies the shortest distance, i.e., the first transport distance, and obtains the corresponding transport path. This path is the optimal path from the abnormal location to the nearest critical medical facility. Based on the obtained transport path, medical personnel perform secondary transport of the patient (i.e., transfer them from the abnormal location to the designated relay location). During the transfer, the system records detailed information about the secondary transport, including the start and end times of the transfer, the personnel involved, and changes in the patient's condition.

[0108] The technical effects of the above solution are as follows: By calculating the shortest transport distance and planning the optimal transport route in real time, the system can quickly guide medical staff to re-transport patients, thereby shortening response time and improving the efficiency and quality of medical services. The system can automatically select the nearest relay location for transfer, avoiding unnecessary resource waste. Simultaneously, by recording transfer information, the system can also provide hospitals with data support regarding the allocation and utilization of medical resources. In emergencies, rapid and accurate transfer can reduce patient risks and discomfort. By providing optimal transport routes and re-transport information, the system helps ensure patient safety during transfer. The application of this system demonstrates the potential of medical informatization and intelligence. By integrating functions such as route planning, data recording, and analysis, the system provides hospitals with a comprehensive, efficient, and intelligent medical management platform. The shortest transport distance, transport route, and re-transport information provided by the system offer crucial decision support for doctors. This invention improves emergency response speed and optimizes the utilization of medical resources by calculating the shortest transport distance and planning the optimal transport route in real time.

[0109] According to one embodiment of the present invention, the system includes:

[0110] The disease analysis and annotation module is used to obtain transportation demand information, evaluate and process patient disease data, obtain patient evaluation and processing data, perform risk analysis and annotation on patient evaluation and processing data, and obtain disease category risk annotation information.

[0111] The route relay setting module is used to obtain patient transport route information, obtain preset transport process nodes based on the patient transport route information, and then generate relay positions to obtain relay position information.

[0112] The disease progression monitoring module is used to calculate the patient's disease progression coefficient, obtain the disease progression time sequence of the patient's transfer path information based on the patient's disease progression coefficient, mark abnormal disease nodes, and then mark abnormal locations in the transfer path information to obtain abnormal location marking information.

[0113] The emergency transfer module is used to obtain the shortest distance between the anomaly marker information and the relay location information, and then obtain the first transport distance of the shortest distance to determine the transport route and carry out secondary transfer, such as... Figure 2 As shown.

[0114] The working principle of the above technical solution is as follows: When an emergency patient needs to be transferred, the system acquires transfer request information, including the patient's basic information, preliminary diagnosis, and current vital signs. It then acquires and processes the patient's condition data as assessed by the doctor, generating patient assessment and processing data. Based on this data, it performs risk analysis on the patient's condition, evaluating blood pressure, blood sugar, and heart rate, and labeling categories exceeding corresponding thresholds to generate condition category risk labeling information. The system obtains the patient's transfer route information based on the origin, destination, hospital layout, and traffic conditions. Key nodes are pre-set as relay locations along the transfer route; these locations may include important medical facilities, emergency stations, or traffic junctions. A condition development coefficient is calculated, reflecting the trend of the patient's condition changing over time. Based on this coefficient, nodes on the transfer route that may exhibit abnormal conditions are marked, i.e., abnormal condition node markings. The system calculates the shortest distance between the abnormal condition markings and the relay locations, enabling rapid response and handling of abnormal conditions. Finally, the optimal transport route is determined based on the shortest distance, and a secondary transfer is performed to ensure that the patient receives timely and necessary medical treatment in the event of an emergency.

[0115] The technical effects of the above solution are as follows: Through automated assessment and risk labeling, the severity of a patient's condition and transport needs can be quickly identified, thereby optimizing the transport process and improving transport efficiency. It can monitor the development of a patient's condition in real time and mark anomalies at key points, enabling timely detection and handling of potential risks and enhancing the safety of the transport process. Based on the patient's condition classification and risk labeling, medical resources can be rationally allocated, such as arranging suitable transport vehicles, medical personnel, and emergency equipment, ensuring efficient resource utilization. Through rapid and accurate transport and treatment, the system can reduce patient waiting time and suffering. When a patient's condition suddenly and rapidly deteriorates during transport, it can automatically generate the optimal relay route to ensure timely treatment. The condition assessment, risk labeling, and transport route information provided by the system can automatically generate a condition control strategy.

[0116] In one embodiment of the present invention, the disease analysis and annotation module includes:

[0117] The condition assessment and processing module is used to obtain the patient's transfer request information and trigger the patient assessment instruction based on the transfer request information;

[0118] Based on the patient's assessment instructions, an alarm is triggered to assess the patient's condition, thereby obtaining the patient's condition assessment data;

[0119] The patient's condition assessment data is preprocessed to obtain patient assessment processing data;

[0120] The classification, comparison, and labeling module is used to classify the patient assessment and processing data according to preset disease categories to obtain multiple categories of patient disease category data.

[0121] The patient's condition category data is compared with a preset category threshold to obtain patient condition comparison information;

[0122] Based on the patient's condition comparison information, the patient's condition risk category is determined, and the patient's condition risk category is labeled with risk information. When the patient's condition category data exceeds a preset category threshold, the patient's condition risk category corresponding to the patient's condition category data is labeled with risk information.

[0123] The working principle of the above technical solution is as follows: It receives transfer requests from within or outside the medical institution, which may originate from doctors, nurses, or other medical staff. These requests include the patient's basic information, the destination, and the reason for the transfer. Based on the received transfer request information, the system automatically triggers a patient assessment process. This process may include a series of standardized assessment steps to collect information such as the patient's vital signs, medical history, and current condition. During the assessment, if the system detects abnormalities in one or more of the patient's vital signs or a deterioration in their condition, it will automatically trigger an alarm mechanism. This alerts medical staff to immediately pay attention and take necessary emergency measures. The patient condition assessment data collected by the system may include readings of various vital signs, laboratory test results, imaging data, etc. This data needs to be preprocessed, such as cleaning, formatting, and standardization. The preprocessed data is then categorized according to preset disease types. These categories may be based on factors such as disease type, severity, and treatment needs. The categorized data forms multiple categories of patient condition data. Each category of condition data is compared with preset category thresholds. These thresholds are set based on clinical experience and medical research. Based on the comparison results, the system determines the patient's disease risk category. If a patient's condition category data exceeds the preset category threshold, the system will label the category.

[0124] The technical effects of the above solution are as follows: The automated assessment process reduces the time and error associated with manual assessment, improving accuracy and efficiency. The real-time alarm mechanism can promptly detect and respond to high-risk situations, ensuring patients receive timely and effective medical care during transport. The system can intelligently allocate medical resources based on the patient's condition risk category, such as prioritizing the transport and treatment of high-risk patients. By providing timely and accurate condition assessments and transport arrangements, the system can enhance patient satisfaction and trust. The condition classification and risk labeling information provided by the system can strongly support clinical decision-making by medical staff. Through automated assessment, real-time alarms, intelligent classification, and risk labeling, the safety and efficiency of patient transport are significantly improved, while also optimizing the allocation of medical resources and enhancing patient satisfaction.

[0125] In one embodiment of the present invention, the path relay setting module includes:

[0126] The route information acquisition module is used to acquire the patient's transfer start point information and transfer destination information, set the patient transfer route according to the shortest transfer time based on the transfer start point information and transfer destination information, and acquire the patient transfer route information (the patient transfer route is the road's permitted passage route).

[0127] The relay location determination module is used to obtain preset transfer process nodes (hospitals passed by, etc.) of the patient transfer path information, set the preset transfer process nodes as relay locations, and obtain multiple relay location information.

[0128] The working principle of the above technical solution is as follows: The system receives or inputs the specific location information of the patient's transfer origin (e.g., current hospital, emergency scene, etc.) and destination (e.g., target treatment hospital). This information may include address, latitude and longitude coordinates, etc. Based on the acquired origin and destination information, the system uses path planning algorithms (e.g., Dijkstra's algorithm, A* algorithm, etc.) to search for the shortest transfer time path in the current road network data. Factors considered here include road conditions, traffic flow, and possible traffic control to ensure the path is practically feasible and time-optimal. Road accessibility is considered during path planning, excluding roads that are impassable due to construction, closure, etc. Once the optimal path is determined, the system outputs detailed transfer path information, including the roads along the route, intersections, and estimated travel time. On the transfer path, the system identifies and marks preset transfer process nodes, which are typically locations such as hospitals and emergency stations where medical operations or handovers may occur. The system sets these nodes as relay locations. Based on the node distribution along the path, the system ultimately generates a list containing information on multiple relay locations. This information is of great reference value for resource allocation, personnel arrangement, and emergency response during the transfer process.

[0129] The technical effects of the above solution are as follows: Through precise route planning and the application of real-time traffic information, the system can ensure that patients are transferred from the origin to the destination in the shortest possible time, thereby improving transfer efficiency and reducing risks during the transfer process. The system can identify and mark key relay locations, enabling the transfer team to perform effective medical operations or handovers at these locations, thus optimizing the allocation and use of medical resources. In emergency situations, the system can quickly generate optimal transfer routes and relay location information, enhancing emergency response capabilities. This invention improves the intelligence and flexibility of the medical service transfer process.

[0130] In one embodiment of the present invention, the disease progression monitoring module includes:

[0131] The disease progression analysis module is used to obtain information at preset monitoring time points, and at each preset monitoring time point, it obtains the patient's disease category risk labeling information and patient assessment and treatment data;

[0132] The patient's disease progression coefficient is calculated based on the disease category risk labeling information and the patient assessment and treatment data.

[0133] The disease progression time sequence acquisition module is used to sort the patient's disease progression coefficients according to preset monitoring time node information to obtain the disease progression time sequence;

[0134] The disease location binding module is used to obtain the path location information of each preset monitoring time node, bind the disease development sequence with the path location information, and obtain location development binding data.

[0135] The disease progression comparison and annotation module is used to compare the disease progression coefficient of each patient with a preset progression threshold in sequence to obtain the disease progression comparison results.

[0136] Based on the comparison results of the disease development, abnormal disease nodes are marked in the disease development sequence;

[0137] Obtain the patient's disease progression coefficient corresponding to the first abnormal disease node in the disease progression timeline;

[0138] The formula for calculating the patient's disease progression coefficient is as follows:

[0139]

[0140] Where BF is the patient's disease progression coefficient, α is the pre-defined weight data for the categories, β is the pre-defined weight data for the assessment, y is the number of risk labels for each disease category, w is the total number of categories, and Z is the risk factor for each category. i Z represents the patient's disease category data for risk labeling of the i-th disease category. a Here, ΔB represents the patient condition category data for category a, and P represents the current assessment change data.s For initial patient assessment data;

[0141] The location anomaly labeling module is used to annotate the path location information of the patient's condition development coefficient corresponding to the first abnormal condition node in the location development binding data, and obtain development location anomaly labeling information.

[0142] The working principle of the above technical solution is as follows: Preset monitoring time nodes are acquired. These nodes can be fixed time intervals (such as every minute or hour) or time points triggered by specific events. At each preset monitoring time node, the system collects patient disease category risk labeling information and also acquires patient assessment and treatment data, such as vital sign monitoring data, laboratory test results, and imaging examination reports. This data is used to comprehensively assess the patient's health status. Combining the disease category risk labeling information and patient assessment and treatment data, a patient disease development coefficient is calculated. This coefficient reflects the dynamic changes in the patient's condition at each monitoring time node. The patient disease development coefficients at each monitoring time node are sorted chronologically to form a disease development time series. This time series shows the trend of the patient's condition changing over time. Path location information for each preset monitoring time node is acquired, which may include the patient's actual geographical location. The disease development time series is bound to the path location information to form location-bound development data. In this way, each disease development coefficient is associated with a specific path location. The disease development coefficient of each patient in the disease development time series is compared with preset development thresholds. These thresholds are usually set based on clinical experience and statistical data. Based on the comparison results, the system marks abnormal disease nodes for disease development coefficients exceeding the threshold in the disease development timeline. These markings indicate the time points when the patient's condition may worsen. The system obtains and marks the location information of the first abnormal disease node: it locates the first abnormal disease node in the disease development timeline and obtains its corresponding patient disease development coefficient. It then searches for the path location information corresponding to this disease development coefficient in the location development binding data and marks the abnormal location. In this way, the system determines the specific location and time when the patient's condition first becomes abnormal.

[0143] The technical effects of the above solution are as follows: By pre-setting monitoring time nodes and collecting data in real time, the system can monitor changes in the patient's condition in real time and issue timely warnings when abnormalities occur. Binding the disease progression timeline with path location information enables precise location and tracking of the patient's condition. This helps doctors quickly understand the patient's condition and its spatial and temporal changes. By analyzing the disease progression timeline and the location information of abnormal disease nodes, the system can provide patients with suggestions regarding the allocation and scheduling of medical resources. The disease progression timeline and abnormal disease node annotation information provided by the system offer doctors a comprehensive basis for disease assessment. This method can achieve quantitative monitoring of disease progression at each transfer location.

[0144] In one embodiment of the present invention, the emergency transfer module includes:

[0145] The transportation distance comparison module is used to obtain anomaly marker information and relay location information, and calculate the shortest transportation distance between the anomaly marker information and multiple relay location information.

[0146] The optimal route transfer module is used to obtain the first transfer distance (the shortest distance among multiple shortest transfer distances) from multiple shortest transfer distances;

[0147] Obtain the transportation route corresponding to the first transportation distance;

[0148] The patient is transported a second time through the aforementioned transportation route to obtain secondary transport information (relatively speaking, due to the severity of the illness, the patient is chosen to seek medical treatment nearby).

[0149] The working principle of the above technical solution is as follows: When the system detects an abnormality in the patient's condition, it records and marks the location of this abnormality, i.e., the abnormality location marking information. This typically includes the specific location and time of the abnormality. The system maintains a relay location information database, which can be the locations of critical medical facilities within the hospital, such as emergency stations, operating rooms, and intensive care units. These locations are potential sites for patient transfer or further treatment. The system calculates the shortest transport distance from the location in the abnormality location marking information to each relay location. These distances take into account factors such as the physical layout of traffic. Among multiple shortest transport distances, the system identifies the shortest distance, i.e., the first transport distance, and obtains the corresponding transport path. This path is the optimal path from the abnormal location to the nearest critical medical facility. Based on the obtained transport path, medical personnel perform secondary transport of the patient (i.e., transfer them from the abnormal location to the designated relay location). During the transfer, the system records detailed information about the secondary transport, including the start and end times of the transfer, the personnel involved, and changes in the patient's condition.

[0150] The technical effects of the above solution are as follows: By calculating the shortest transport distance and planning the optimal transport route in real time, the system can quickly guide medical staff to re-transport patients, thereby shortening response time and improving the efficiency and quality of medical services. The system can automatically select the nearest relay location for transfer, avoiding unnecessary resource waste. Simultaneously, by recording transfer information, the system can also provide hospitals with data support regarding the allocation and utilization of medical resources. In emergencies, rapid and accurate transfer can reduce patient risks and discomfort. By providing optimal transport routes and re-transport information, the system helps ensure patient safety during transfer. The application of this system demonstrates the potential of medical informatization and intelligence. By integrating functions such as route planning, data recording, and analysis, the system provides hospitals with a comprehensive, efficient, and intelligent medical management platform. The shortest transport distance, transport route, and re-transport information provided by the system offer crucial decision support for doctors. This invention improves emergency response speed and optimizes the utilization of medical resources by calculating the shortest transport distance and planning the optimal transport route in real time.

[0151] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method of condition monitoring during an emergency patient transport procedure, characterized by, The method comprises: S1, obtaining the transport demand information, evaluating and processing the patient's condition data to obtain the patient evaluation processing data, classifying and risk labeling the patient evaluation processing data to obtain the condition category risk labeling information; S2, obtaining the patient transport path information, obtaining the preset transport process node according to the patient transport path information, and then generating the relay position to obtain the relay position information; S3, calculating the patient condition development coefficient, obtaining the condition development time sequence of the patient transport path information according to the patient condition development coefficient, marking the abnormal condition node, and then marking the abnormal position of the transport path information to obtain the development position abnormal labeling information; S4, obtaining the shortest distance between the development position abnormal labeling information and the relay position information, then obtaining the first transport distance of the shortest distance, determining the transport path, and performing secondary transport.

2. The method of claim 1, wherein, The S1 comprises: Obtain the transport demand information of the patient, and trigger the patient evaluation instruction according to the transport demand information; According to the patient evaluation instruction, the patient condition evaluation alarm is carried out, and then the patient condition evaluation data is obtained; The patient condition evaluation data is preprocessed to obtain the patient evaluation processing data; According to the preset condition category, the patient evaluation processing data is classified to obtain the patient condition category data of multiple categories; The patient condition category data is compared with the preset category condition threshold to obtain the patient condition comparison information; According to the patient condition comparison information, the patient condition risk category is determined, the patient condition risk category is labeled, and the condition category risk labeling information is obtained.

3. The method of claim 1, wherein the method further comprises: The S2 comprises: Obtain the transport starting point information and the transport terminal information of the patient, set the patient transport path according to the shortest transport time according to the transport starting point information and the transport terminal information, and obtain the patient transport path information; Obtain the preset transport process node of the patient transport path information, set the preset transport process node as the relay position, and obtain the multiple relay position information.

4. The method of claim 1, wherein the patient condition is monitored during the emergency patient transport process. The S3 comprises: Obtain the preset monitoring time node information, obtain the patient condition category risk labeling information and patient evaluation processing data at each preset monitoring time node; According to the condition category risk labeling information and the patient evaluation processing data, the patient condition development coefficient is calculated; The patient condition development coefficient is sorted according to the preset monitoring time node information to obtain the condition development time sequence; Obtain the path position information of each preset monitoring time node, bind the condition development time sequence and the path position information to obtain the position development binding data; Each patient condition development coefficient of the condition development time sequence is compared with the preset development threshold in sequence to obtain the condition development comparison result; According to the condition development comparison result, the condition development time sequence is marked with an abnormal condition node; Obtain the patient condition development coefficient corresponding to the first abnormal condition node marking in the condition development time sequence; The corresponding path position information of the first abnormal condition node marking corresponding to the patient condition development coefficient in the position development binding data is marked with an abnormal position to obtain the development position abnormal labeling information.

5. The method of claim 1, wherein the method further comprises: The S4 comprises: Obtain development position abnormality annotation information and relay position information, calculate the shortest transport distance of the development position abnormality annotation information and multiple relay position information; Obtain a first transport distance in multiple shortest transport distances; Obtain a transport path corresponding to the first transport distance; Secondary transport the patient through the transport path, and obtain secondary transport information.

6. A condition monitoring system for an emergency patient transfer process, characterized by The system comprises: A disease analysis annotation module is configured to obtain transport demand information, evaluate and process patient data, obtain patient evaluation processing data, classify and analyze the risk of the patient evaluation processing data, and obtain disease category risk annotation information; A path relay setting module is configured to obtain patient transport path information, obtain a preset transport process node according to the patient transport path information, and then generate a relay position to obtain relay position information; A disease development monitoring module is configured to calculate a patient disease development coefficient, obtain a disease development time sequence of the patient transport path information according to the patient disease development coefficient, perform abnormal disease node annotation, and then perform abnormal position annotation on the transport path information to obtain development position abnormality annotation information; An emergency transport module is configured to obtain the shortest distance of the development position abnormality annotation information and the relay position information, then obtain a first transport distance of the shortest distance, determine a transport path, and perform secondary transport.

7. The system for monitoring the condition of a patient during an emergency patient transport of claim 6, wherein, The disease analysis annotation module comprises: A disease evaluation processing module is configured to obtain patient transport demand information, and trigger a patient evaluation instruction according to the transport demand information; Perform patient disease evaluation alarm according to the patient evaluation instruction, and then obtain patient disease evaluation data; Preprocess the patient disease evaluation data to obtain patient evaluation processing data; A classification comparison annotation module is configured to classify the patient evaluation processing data according to a preset disease category to obtain multiple category patient disease category data; Compare the patient disease category data with a preset category disease threshold to obtain patient disease comparison information; Determine a patient disease risk category according to the patient disease comparison information, perform category risk annotation on the patient disease risk category, and obtain disease category risk annotation information.

8. The system for monitoring the condition of a patient during an emergency patient transport of claim 6, wherein, The path relay setting module comprises: A path information acquisition module is configured to obtain patient transport starting point information and transport end point information, set a patient transport path according to the shortest transport time based on the transport starting point information and the transport end point information, and obtain patient transport path information; A relay position determination module is configured to obtain a preset transport process node of the patient transport path information, set the preset transport process node as a relay position, and obtain multiple relay position information.

9. The system for monitoring the condition of a patient during an emergency patient transport of claim 6, wherein, The disease development monitoring module comprises: A disease development analysis module is configured to obtain preset monitoring time node information, obtain patient disease category risk annotation information and patient evaluation processing data at each preset monitoring time node; Calculate a patient disease development coefficient based on the disease category risk annotation information and the patient evaluation processing data; A development time sequence acquisition module is configured to sort the patient disease development coefficient according to preset monitoring time node information to obtain a disease development time sequence; The disease position binding module is configured to acquire path position information of each preset monitoring time node, bind the disease development time sequence with the path position information, and obtain position development binding data; The disease comparison marking module is configured to sequentially compare each patient disease development coefficient of the disease development time sequence with a preset development threshold, and obtain a disease development comparison result; The disease development time sequence is marked according to the disease development comparison result; A patient disease development coefficient corresponding to a first abnormal disease node marking in the disease development time sequence is acquired; The position abnormal marking module is configured to mark an abnormal position of the patient disease development coefficient corresponding to the first abnormal disease node marking in the corresponding path position information in the position development binding data, and obtain development position abnormal marking information.

10. The system for monitoring the condition of a patient during an emergency patient transport of claim 6, wherein, The emergency transfer module includes: The transport distance comparison module is configured to acquire the development position abnormal marking information and the relay position information, calculate shortest transport distances of the development position abnormal marking information and the plurality of relay position information, and obtain the plurality of shortest transport distances; The optimal path transfer module is configured to acquire a first transport distance from the plurality of shortest transport distances; A transport path corresponding to the first transport distance is acquired; The patient is re-shipped through the transport path, and re-shipment information is obtained.

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