Mobile red zone emergency unit system

Abstract

The application relates to the technical field of medical health information, and discloses a mobile red zone emergency unit system, which comprises a disease condition assessment and prediction module, a hospital screening module and the like. The disease condition assessment and prediction module is used for comparing current patient data with a historical case database to select matched cases, and a factor analysis model is used to calculate a high medical resource demand probability, and according to a set threshold, patients are divided into high medical resource demand and ordinary medical resource demand. The hospital screening module determines a target hospital in a medical resource database through preset screening rules. For patients with high medical resource demand, a screening unit compares matched cases with a medical resource database to select a hospital to form a preselected list, and a hospital in the preselected list is preferentially selected as the target hospital. The application solves the problem that in the prior art, path planning during first-aid and hospital transportation is only based on passing time, and the specific conditions of patients and hospital resources are not comprehensively considered, thereby affecting the overall rescue efficiency.

Description

Technical Field

[0001] This invention relates to the field of medical and health information technology, specifically to a mobile red zone emergency rescue unit system. Background Technology

[0002] With accelerating urbanization and an aging population, the demand for emergency medical rescue is increasing daily, and traditional medical transport systems are struggling to meet the need for efficient and accurate rescue. Modern emergency medical transport systems integrate various advanced technologies, such as real-time location tracking, big data analytics, the Internet of Things, artificial intelligence, cloud computing, and 5G communication, aiming to build a rapid-response, precisely dispatchable medical emergency network. These systems, through real-time monitoring, intelligent analysis, and efficient dispatch, ensure that patients receive professional treatment in the shortest possible time, thereby improving treatment success rates and patient prognosis.

[0003] For example, the Chinese patent "A Rapid Passage Guidance System for Ambulances" with authorization announcement number CN 116798233 B discloses a method that combines real-time traffic data and machine learning prediction models to dynamically adjust route planning and find the shortest route at present.

[0004] However, when optimizing routes, this system primarily focuses on travel time, neglecting the importance of transporting patients to hospitals that match their condition. This could result in patients being sent to suboptimal hospitals, meaning that even if they arrive at the hospital as quickly as possible, they may not receive effective treatment, thus impacting treatment outcomes and prognosis. Summary of the Invention

[0005] The present invention aims to provide a mobile red zone emergency rescue unit system to solve the problem that the existing technology only considers the travel time when transporting patients to hospitals, without taking into account the specific situation of the patients and the resources of the hospitals, which affects the overall rescue efficiency.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a mobile red zone emergency unit system, including a patient monitoring module, a data management module, a condition assessment and prediction module, and a hospital screening module;

[0007] The patient monitoring module is used to monitor and collect patients' vital signs data as well as input patients' clinical symptoms, preliminary diagnostic information, and special condition information;

[0008] The data management module is used to track and record all patients' treatment information and upload it to the historical case database in the cloud.

[0009] The disease assessment and prediction module includes a patient information processing unit and a disease assessment unit. The patient information processing unit is used to integrate the data collected by the patient monitoring module to form the current patient data. The disease assessment unit is used to compare the current patient data with the historical case database, select one or more historical cases as matching cases for the patient, and calculate the probability of high medical resource demand using a factor analysis model based on the matching historical cases and the current patient data. Based on the set threshold, the patient is divided into high medical resource demand and ordinary medical resource demand.

[0010] The hospital screening module includes a medical resource management unit and a screening unit. The medical resource management unit collects and integrates information from various hospitals to form a medical resource database. The screening unit is used to determine the target hospital in the medical resource database based on the calculation results of the disease assessment and prediction module and through preset screening rules.

[0011] For patients with high demand for medical resources, the screening unit will match cases with the medical resource database and select one or more hospitals to form a pre-selection list, prioritizing the hospitals in the pre-selection list as target hospitals.

[0012] The principles and advantages of this solution are as follows: In practical applications, through real-time monitoring and intelligent analysis, the system can quickly assess a patient's condition and match them with the most suitable hospital, thereby shortening treatment time and improving treatment efficiency. Utilizing a historical case database and factor analysis model, the system can accurately assess the probability of a patient having a high demand for medical resources and select the most suitable hospital accordingly. This avoids the problem of traditional route planning that only considers travel time and ignores hospital resource matching, ensuring that patients are sent to hospitals capable of effective treatment. Through precise hospital matching, the system reduces the risk of patients needing to be transferred due to complex conditions or insufficient hospital resources, improving the success rate of treatment and prognosis.

[0013] Preferably, as an improvement, the disease assessment and prediction module further includes a disease prediction unit, which calculates the optimal time for the patient to be sent to the hospital based on the current patient data and the relationship between the time spent in sending the patient to the hospital and the cure and prognosis in the matched cases.

[0014] For patients with basic medical resource needs, the screening unit prioritizes the hospital that the patient can reach fastest from their current location as the target hospital; for patients with high medical resource needs, the screening unit should select the hospital that can be reached from the pre-selected list within the optimal time for medical treatment as the target hospital.

[0015] The beneficial effects of this improvement are: by adding a disease prediction unit to the disease assessment and prediction module, the system can further refine the emergency medical transport strategy, significantly improving emergency efficiency and patient prognosis quality.

[0016] For patients with basic medical resource needs, such as those experiencing sudden angina or minor stroke, whose conditions are relatively mild, rapid response and timely basic medical support are crucial. Therefore, the system prioritizes the hospital that can be reached from the patient's current location as quickly as possible, ensuring that the patient receives treatment in the shortest possible time and reducing potential risks caused by prolonged transport to the hospital. For patients with high medical resource needs, such as those with limb amputations, the system ensures that patients receive high-level treatment commensurate with their condition within a relatively generous time window, avoiding the mismatch of medical resources that may result from being sent to the nearest hospital. This significantly reduces the risk of patients needing to be transferred to another hospital due to insufficient medical resources at their first hospital visit. This not only saves valuable treatment time but also reduces the physical and mental burden on patients and improves overall treatment efficiency.

[0017] Preferably, as an improvement, the screening unit includes a transfer threshold time. For patients with high demand for medical resources, when there is no hospital in the pre-selected list that can be reached within the optimal delivery time, the screening unit selects a hospital in the pre-selected list that can be reached within the optimal delivery time plus the transfer threshold time as the target hospital. At the same time, the screening unit needs to select a transfer hospital for initial treatment and stabilizing the patient's condition so that the patient can be transferred to the target hospital in the end. The transfer hospital is the hospital that can be reached within the optimal delivery time and takes the least amount of time to transfer the patient from the transfer hospital to the target hospital. The patient is first sent to the transfer hospital and then transferred from the transfer hospital to the target hospital.

[0018] The beneficial effects of this improvement are as follows: By setting a transfer threshold time, it allows patients to choose a hospital that is slightly farther away but can be reached within an acceptable timeframe as the target hospital. This improvement ensures that even in the most unfavorable circumstances, patients can receive professional treatment in a relatively short period of time, avoiding deterioration of their condition due to excessive waiting time. It also considers the selection of intermediate hospitals, choosing those that can arrive at the optimal time of arrival and have the shortest transfer time to the target hospital. This strategy not only shortens the total time from the onset of illness to receiving professional treatment but also reduces the uncertainty and risks during the transfer process. Patients can receive initial treatment and have their condition stabilized at the intermediate hospital, laying a good foundation for subsequent transfer treatment.

[0019] Preferably, as an improvement, for patients with high demand for medical resources, if there is no hospital in the pre-selected list that can be reached within the optimal hospitalization time plus the transfer threshold, the filtering unit will prioritize selecting the hospital with the highest medical resources from among the multiple hospitals that can be reached within the optimal hospitalization time as the target hospital.

[0020] The beneficial effects of this improvement are: in situations where time is limited and medical resources are scarce, selecting the hospital with the highest medical resources as the target hospital can maximize the chances of ensuring that patients receive the most professional and comprehensive treatment. Optimal medical resources within a limited scope can shorten the gap with the ideal hospital, and further remote collaborative treatment can lead to a higher success rate.

[0021] Preferably, as an improvement, the parameters used by the disease assessment unit for assessment include: patient age, heart rate, blood pressure, blood saturation from current patient data, and disease grade, acute physiological and chronic health scores from matched cases, complexity of treatment plan, probability of complication, special characteristics of treatment equipment, and special characteristics of treatment method.

[0022] The beneficial effects of this improvement are that by comprehensively considering parameters from multiple dimensions, the disease assessment unit can more comprehensively and accurately assess a patient's current condition and future development trend. This not only helps doctors formulate more precise treatment plans but also provides patients with more personalized medical services.

[0023] Preferably, as an improvement, the historical case database is used to store, manage, and retrieve case data of all past patients; the historical case database covers the entire process of patient information from admission, treatment, and rehabilitation.

[0024] The beneficial effect of this improvement is that by retrieving similar cases from historical case databases and referencing their diagnoses, treatment plans, and prognoses, more accurate and scientific medical referral decisions can be made. This helps provide patients with medical resources that better meet their needs.

[0025] Preferably, as an improvement, the data information in the medical resource database includes the number of hospital beds, bed occupancy rate, number of available hospital beds, hospital address, number of cases, case type, treatment results, diagnosis and treatment process, doctor team, nurse team, medical equipment and commonly used medicines.

[0026] The beneficial effects of this improvement are that it makes medical resource information more comprehensive, which helps in matching and screening target hospitals with matching cases.

[0027] Preferably, as an improvement, the system also includes a vehicle management module for real-time monitoring of vehicle driving status and geographical location information, and for providing optimal route planning and navigation suggestions by combining real-time traffic data and road condition predictions.

[0028] The beneficial effects of this improvement are: it guides ambulances and other emergency vehicles to reach the patient's location quickly and accurately, shortens emergency response time, and improves treatment efficiency.

[0029] Preferably, as an improvement, the system also includes a communication module that utilizes a 5G network to achieve ultra-high-speed data transmission between the ambulance and the hospital, ensuring the real-time transmission and reception of patient vital sign monitoring data, image data, video calls, and other information.

[0030] The benefits of this improvement are: doctors can remotely guide medical staff in ambulances to provide initial treatment or conduct real-time consultations with expert teams in hospitals, thereby improving treatment outcomes; real-time transmission of patient vital sign monitoring data enables doctors to keep abreast of changes in the patient's condition, adjust treatment plans in a timely manner, and improve the success rate of treatment and the patient's survival rate. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the structure of an embodiment of the present invention. Detailed Implementation

[0032] The following detailed description illustrates the specific implementation method:

[0033] Example

[0034] The basics are as follows: Figure 1 As shown, a mobile red zone emergency medical unit system is integrated into an ambulance. The mobile red zone emergency medical unit system includes a patient monitoring module, a condition assessment and prediction module, a hospital screening module, a data management module, a vehicle management module, and a communication module.

[0035] The patient monitoring module includes a vital signs monitoring unit and a symptom and diagnosis input unit;

[0036] The vital signs monitoring unit integrates vital signs monitoring equipment found in ambulances, such as extracorporeal membrane oxygenation (ECMO), multi-functional integrated defibrillator monitors, advanced transport ventilators, mobile X-ray imaging systems (mobile DR), and integrated ultrasound machines. It enables emergency treatment and advanced life support for patients in the ambulance, while simultaneously monitoring and collecting various vital signs data. Vital signs data include electrocardiograms, blood pressure, blood oxygen saturation, X-rays, and ultrasound images, providing fundamental data support for subsequent condition assessment and prediction.

[0037] The symptom and diagnosis input unit allows emergency personnel to input and collect the patient's current clinical symptoms, preliminary diagnostic information, and special medical condition information through a human-computer interaction interface, ensuring the comprehensiveness and accuracy of the patient's information.

[0038] Clinical symptoms refer to abnormal manifestations that patients subjectively experience or that emergency personnel can observe through examination. These include pain, fever, cough, difficulty breathing, nausea, vomiting, altered consciousness, seizures, bleeding, rash, numbness or weakness in the limbs, speech impairment, and blurred vision. These symptoms are important for assessing a patient's health status and diagnosing diseases.

[0039] Preliminary diagnostic information is a preliminary judgment made by emergency responders based on their professional knowledge and experience after an initial assessment of the patient's clinical symptoms. This may be a specific disease name (such as myocardial infarction or stroke) or a broader disease category (such as cardiovascular disease or neurological disease). Preliminary diagnostic information helps guide subsequent emergency measures and hospital selection.

[0040] When a patient's family is present during treatment, special information about the patient's condition can be obtained from them. This special information refers to specific circumstances in the patient's current condition that require special attention or management. This includes the patient's allergy history, past medical history, medication use, family medical history, and any unusual lifestyle habits. This information is crucial for developing personalized emergency treatment plans and predicting the patient's prognosis.

[0041] The disease assessment and prediction module includes a patient information integration unit, a disease assessment unit, and a disease prediction unit;

[0042] The patient information integration unit combines the data collected by the vital signs monitoring unit and the symptom and diagnosis input unit to form the current patient data, and sends an access request to the cloud to obtain the historical case database. If the patient's identity is clear and they have a history of medical treatment, the unit further accesses the patient's historical case data to assist in the current condition assessment and prediction.

[0043] The historical case database is used to store, manage, and retrieve case data for all past patients. This data covers the entire process of a patient's journey, from admission to treatment and rehabilitation. For example, the stored data in the historical case database includes basic information such as the patient's age and gender. If the patient has a history of emergency medical transport, it also includes the time taken to reach the hospital, patient data collected in the ambulance by the vital signs monitoring unit and the symptom and diagnosis input unit, including the duration of transport, treatment process, transfer records, and prognosis.

[0044] The disease assessment unit uses big data analytics to compare current patient data with a historical case database, finds one or more historical cases with high matching scores as matching cases for the patient, and assesses the level of medical resource needs for the patient's current hospitalization based on the matching historical cases.

[0045] The disease assessment unit extracts key features, such as symptom combinations, vital signs, and preliminary diagnoses, from current patient data and historical case databases. It then uses methods such as cosine similarity, Euclidean distance, and Jaccard similarity to calculate the similarity between the current patient and historical cases. Based on the similarity ranking, one or more historical cases with the highest matching scores are selected as the matched cases.

[0046] The patient's current level of medical resource demand can be determined by calculating the probability of high medical resource demand. This probability is calculated using a factor analysis model, which includes:

[0047] Analyze current patient data and matched cases to obtain multiple observed variables X1, X2, X3, ..., X p The observed variables include patient age, heart rate, blood pressure, blood saturation, disease grade, acute physiological and chronic health score, treatment complexity, probability of complications, specificity of treatment equipment, and specificity of treatment methods. Patient age, heart rate, blood pressure, and blood saturation are derived from current patient data collected by the patient monitoring module. Disease grade, acute physiological and chronic health score, treatment complexity, probability of complications, specificity of treatment equipment, and specificity of treatment methods are derived from matched cases. These data are based on physician assessments of historical cases following existing standardized medical evaluation systems. Each historical case includes the physician's evaluation results from various aspects. Standardized medical evaluation systems include, for example, the TNM staging system for cancer, the NYHA classification for heart disease, and APACHE II / III.

[0048] Factor analysis models are used to extract latent common factors. The goal of factor analysis models is to identify several unobservable latent factors from the observed variables that can explain the correlations between the observed variables.

[0049] The factor loading matrix (A) contains the correlation (i.e., weight) between each observed variable and each factor, and can be represented in matrix form as follows:

[0050]

[0051] Among them, a ij This represents the correlation between the i-th observed variable and the j-th factor.

[0052] The factor score matrix (F) represents the score of each sample on each factor, and can be represented as:

[0053]

[0054] Among them, f ij This represents the score of the i-th sample on the j-th factor.

[0055] The composite score (S) can be calculated based on the factor scores F and a weight vector W. The weight vector W is set according to the explanatory power or importance of the factors and can be expressed as: W = [w1, w2, ..., w m ] T ;

[0056] The formula for calculating the overall score (S) is: W = w1f1 + w2f2 + ... + w m f m ;

[0057] S = w T F;

[0058] Here, F refers to the factor score vector of a single sample, not the entire factor score matrix.

[0059] To ensure the composite score (S) is within a reasonable range and can be compared across different datasets, the factor scores f need to be optimized. j and weight w j Standardization is performed. Standardization is accomplished using the Z-score method:

[0060]

[0061] Where, μ f and σ f These are the mean and standard deviation of the factor scores, respectively, μ w and σ w These are the mean and standard deviation of the weight vector, respectively.

[0062] The overall score (S) is mapped to a probability value, representing the likelihood of high demand for medical resources.

[0063] Suppose there exists a mapping function (g) such that: probability of high demand for medical resources = g(S);

[0064] The mapping function (g) can be determined using a logistic regression model. Therefore, the mapping function can be expressed as:

[0065]

[0066] Where α and β are model parameters estimated from the training dataset.

[0067] The disease assessment unit includes a threshold for categorizing the probability of high medical resource demand. This threshold was determined by experts through extensive data and theoretical analysis. If the calculated probability of high medical resource demand is greater than the threshold, the patient is considered to require a high level of medical resources; if the calculated probability is less than the threshold, the patient is considered to require a moderate level of medical resources.

[0068] The disease prediction unit calculates the optimal time for a patient to be admitted to the hospital based on current patient data and the comparison results between current patient data and historical case databases, combined with the relationship between the time taken to get to the hospital and the cure rate and prognosis in matching historical cases, to ensure that the patient can receive treatment at the best time.

[0069] Determining the optimal time for medical treatment is not only influenced by the patient's current vital signs, but also by factors such as the success rate of treatment, prognosis, and minimizing potential adverse consequences. The optimal time for medical treatment must ensure that the patient receives effective treatment before the disease progresses to an irreversible stage, and avoid significant reductions in treatment effectiveness and increased risks of complications and sequelae due to delays in accessing the hospital.

[0070] For example, patients with acute myocardial infarction are characterized by rapid onset and progression. In the early stages of the disease, although patients may maintain relatively stable vital signs, myocardial cells are continuously dying, leading to a gradual decline in cardiac function. If patients can be transported to the hospital and receive interventional treatment before extensive myocardial cell necrosis, myocardial damage can be significantly reduced, cardiac function improved, and survival rate and prognosis increased. Conversely, if the time to hospital is too long, even if successful treatment is ultimately achieved, the patient's cardiac function may be severely impaired, resulting in a poor prognosis.

[0071] The hospital screening module includes a medical resource management unit and a screening unit;

[0072] The medical resource management unit, based on hospitals, collects and integrates information such as bed availability, geographical location, and historical treatment cases from each hospital to build a comprehensive medical resource database, providing data support for hospital selection.

[0073] The medical resource database includes: Number of beds, recording the total number of beds in different departments and levels (e.g., general wards, intensive care units, etc.) of each hospital; Bed occupancy rate, reflecting the real-time usage and historical trends of beds in each department or the entire hospital, helping to assess the hospital's capacity; Number of available beds, updated in real-time for each department, facilitating patients' quick access to available hospitals and departments; Hospital addresses, detailing the geographical location information of each hospital; Number of cases, statistically analyzing the number of various cases received by each hospital historically, providing insights into the hospital's specialties and treatment capabilities; Case types, meticulously categorizing and recording case types such as internal medicine, surgery, pediatrics, gynecology, etc., along with specific disease names; and Treatment outcomes, recording the treatment results of each case. Treatment outcomes, including key indicators such as cure rate, improvement rate, and mortality rate, assess the hospital's treatment effectiveness; the diagnosis and treatment process, briefly describing the diagnosis and treatment process of each case, including diagnostic methods, treatment plans, and medication use, providing a reference for the treatment of similar cases; the medical team, introducing the main medical teams of each hospital, including the names, professional fields, and clinical experience of experts and professors; the nursing team, recording basic information about the nursing team, such as the number of nurses, their nursing level, and professional skills; medical equipment, detailing the types, quantities, and performance of the medical equipment possessed by the hospital, providing an understanding of the hospital's diagnostic and treatment capabilities; commonly used medicines, listing the hospital's commonly stocked medicines, including names, specifications, and inventory levels, ensuring that patients can obtain the medicines they need in a timely manner; etc.

[0074] The screening unit selects hospitals based on the results of the disease assessment and prediction module and the data from the medical resource management unit.

[0075] Patients with a low probability of high medical resource demand and relatively low treatment difficulty are classified as having general medical resource demand. This refers to patients whose probability of high medical resource demand, calculated by the disease assessment unit, is less than a threshold. These patients may have relatively critical vital signs, but typically do not require highly specialized medical equipment or complex treatment procedures, such as sudden angina or minor stroke. Rapid response and timely basic medical support are crucial for these cases. For these patients, priority should be given to the hospital that is reachable fastest from their current location to minimize transport time and ensure that the selected hospital has available beds so that the patient can receive immediate treatment.

[0076] Patients with a high probability of needing high medical resources and those facing significant treatment challenges are classified as having a high demand for medical resources. This is defined as patients whose probability of needing high medical resources, calculated by the disease assessment unit, is greater than or equal to a certain threshold. Compared to patients with acute illnesses, these patients have a relatively longer window for medical intervention, typically receiving medical assistance within one to half an hour to maintain stable vital signs. These patients often involve non-immediately life-threatening injuries or illnesses, but the "golden treatment time" is crucial; appropriate and professional treatment within this period can significantly improve prognosis.

[0077] For example, in patients with limb amputation injuries: the primary task is to quickly and effectively stop the bleeding, bandage, and properly preserve the severed limb on-site, followed by transport to the hospital within a relatively stable timeframe (e.g., half an hour to an hour). Although the lack of replantation surgery in a short period will not immediately endanger life, high-level microsurgery within the "golden treatment time" is crucial for improving the success rate of limb replantation and functional recovery.

[0078] For patients with a high probability of needing medical resources, focusing solely on getting them to the nearest hospital to shorten the journey without considering the quality of available resources could result in them not receiving effective treatment after initial admission. This would necessitate a transfer to a hospital with the appropriate medical expertise, thus delaying their crucial treatment window. The key is for these patients to receive high-quality medical care from the appropriate department within a relatively generous timeframe.

[0079] For these types of patients, matching cases are obtained by comparing them with historical case databases based on the condition assessment unit. The required level of medical care and specialty direction for these patients are determined. A pre-selection list is formed by searching for hospitals that have effectively treated similar cases in the medical resource management unit. If there are one or more hospitals in the pre-selection list that can arrive at the optimal time of medical treatment, the hospital that can be reached the fastest among these candidate hospitals is selected. The selected hospital has available beds. The target hospital is then selected.

[0080] If no hospital in the pre-selected list can reach the patient within the optimal transport time, a transfer threshold time is set within the filtering unit. The filtering unit re-evaluates the hospitals in the pre-selected list based on this threshold time, selecting the hospital that can be reached within the optimal transport time plus the transfer threshold time as the target hospital. This ensures that even if the patient needs to undergo a transfer, they receive appropriate treatment within or close to the "golden treatment time." At this point, the filtering unit again selects a transfer hospital—the one that can reach the patient within the optimal transport time and minimizes the time required to transfer the patient from the transfer hospital to the target hospital. The transfer time is affected not only by the distance and traffic conditions between the transfer and target hospitals but also by historical transfer data stored in the medical resource management unit, including information such as process complexity (transfer procedures, patient handover), and procedure duration. Analyzing this data can predict and reduce potential delays, ensuring an efficient and smooth transfer process.

[0081] If no hospital in the pre-selected list is reachable within the optimal transport time plus the transfer threshold, the hospital with the highest medical resources among the multiple hospitals reachable within the optimal transport time will be prioritized as the target hospital. Medical resources include medical equipment, medical technology, and the professional level and experience of medical staff. Although the target hospital may not be the initial first choice, selecting the hospital with the highest medical resources can, to some extent, shorten the gap with the ideal level of medical care. Based on this, remote communication technology will be fully utilized to connect with hospitals with high medical resources. Through remote consultations, remote surgical guidance, and real-time transmission of patient data, hospitals with high medical resources can provide technical support and decision-making assistance to the target hospital, thereby improving the success rate of remote collaborative treatment.

[0082] The data management module is responsible for continuously tracking and recording the patient's treatment progress, examination results, diagnostic results, and final treatment outcomes. This information includes the patient's basic information, a description of their condition, and changes in key indicators during the treatment process.

[0083] All collected data will be automatically uploaded to a cloud-based historical case database for centralized storage and backup. This not only ensures data security but also facilitates data sharing and access across multiple locations and devices. Data encryption and access control measures are implemented to guarantee the security and privacy of patient information. Big data and artificial intelligence technologies are used to conduct in-depth analysis and mining of the historical case database, extracting valuable information and patterns to improve the accuracy of future case assessments, optimize treatment recommendations, and support medical research and decision-making.

[0084] The vehicle management module monitors the ambulance's driving status (such as speed, fuel consumption, and fault alarms) and geographical location information in real time through the onboard system, ensuring vehicle safety and controllability. Combining real-time traffic data and road condition predictions, it provides ambulances with optimal route planning and navigation suggestions to reach the target hospital in the fastest and safest way. In the event of an emergency or vehicle breakdown, it automatically triggers an emergency dispatch mechanism to replan the route or dispatch other nearby ambulances for support.

[0085] The communication module utilizes a 5G network to achieve ultra-high-speed data transmission between the ambulance and the hospital, ensuring the real-time transmission and reception of patient vital sign monitoring data, image data, video calls, and other information. It establishes a stable real-time communication link, supporting voice calls, video consultations, and text message exchanges between medical staff, ensuring rapid communication of patient conditions and coordination of treatment plans during transport. Through high-definition video calls and real-time data transmission, remote experts can directly participate in the patient's treatment process, providing remote medical guidance and support. It supports access from various terminal devices (such as smartphones, tablets, and computers), ensuring that medical staff can conveniently access patient information and conduct communication in different scenarios.

[0086] The above descriptions are merely embodiments of the present invention, and common knowledge such as specific technical solutions and / or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solutions of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A mobile red zone emergency rescue unit system, characterized in that, It includes a patient monitoring module, a data management module, a disease assessment and prediction module, and a hospital screening module; The patient monitoring module is used to monitor and collect patients' vital signs data as well as input patients' clinical symptoms, preliminary diagnostic information, and special condition information; The data management module is used to track and record all patients' treatment information and upload it to the historical case database in the cloud. The disease assessment and prediction module includes a patient information processing unit and a disease assessment unit. The patient information processing unit is used to integrate the data collected by the patient monitoring module to form the current patient data. The disease assessment unit is used to compare the current patient data with the historical case database, select one or more historical cases as matching cases for the patient, and calculate the probability of high medical resource demand using a factor analysis model based on the matching historical cases and the current patient data. Based on the set threshold, the patient is divided into high medical resource demand and ordinary medical resource demand. The hospital screening module includes a medical resource management unit and a screening unit. The medical resource management unit collects and integrates information from various hospitals to form a medical resource database. The screening unit is used to determine the target hospital in the medical resource database based on the calculation results of the disease assessment and prediction module and through preset screening rules. For patients with high demand for medical resources, the screening unit will match cases with the medical resource database and select one or more hospitals to form a pre-selection list, prioritizing the hospitals in the pre-selection list as target hospitals.

2. The mobile red zone emergency rescue unit system according to claim 1, characterized in that, The disease assessment and prediction module also includes a disease prediction unit, which calculates the optimal time for the patient to be sent to the hospital based on the current patient data and the relationship between the time spent in sending the patient to the hospital and the cure and prognosis in the matched cases. For patients with basic medical resource needs, the screening unit prioritizes the hospital that the patient can reach fastest from their current location as the target hospital; for patients with high medical resource needs, the screening unit should select the hospital that can be reached from the pre-selected list within the optimal time for medical treatment as the target hospital.

3. The mobile red zone emergency rescue unit system according to claim 2, characterized in that: The screening unit has a transfer threshold time. For patients with high medical resource needs, when there is no hospital in the pre-selected list that can be reached within the optimal time of arrival, the screening unit selects a hospital in the pre-selected list that can be reached within the optimal time of arrival plus the transfer threshold time as the target hospital. At the same time, the screening unit needs to select a transfer hospital for initial treatment and stabilizing the patient's condition so that the patient can be transferred to the target hospital in the end. The transfer hospital is the hospital that can be reached within the optimal time of arrival and takes the least amount of time to transfer the patient from the transfer hospital to the target hospital. The patient is first sent to the transfer hospital and then transferred from the transfer hospital to the target hospital.

4. The mobile red zone emergency rescue unit system according to claim 3, characterized in that: For patients with high demand for medical resources, if there is no hospital in the pre-selected list that can be reached within the optimal time for hospitalization plus the transfer threshold, the filtering unit will prioritize selecting the hospital with the highest medical resources from among the multiple hospitals that can be reached within the optimal time for hospitalization as the target hospital.

5. The mobile red zone emergency rescue unit system according to claim 4, characterized in that: The parameters used by the disease assessment unit include: patient age, heart rate, blood pressure, blood saturation from current patient data, and disease grade, acute physiological and chronic health scores, treatment plan complexity, probability of complications, special characteristics of treatment equipment, and special characteristics of treatment methods from matched cases.

6. The mobile red zone emergency rescue unit system according to claim 5, characterized in that: The historical case database is used to store, manage, and retrieve case data for all past patients; the historical case database covers the entire process of patient information from admission, treatment, and rehabilitation.

7. The mobile red zone emergency rescue unit system according to claim 6, characterized in that: The data information in the medical resource database includes the number of hospital beds, bed occupancy rate, number of available hospital beds, hospital address, number of cases, case type, treatment results, diagnosis and treatment process, doctor team, nurse team, medical equipment and commonly used medicines.

8. The mobile red zone emergency rescue unit system according to claim 7, characterized in that: The system also includes a vehicle management module, which monitors the vehicle's driving status and geographical location information in real time, and provides optimal route planning and navigation suggestions by combining real-time traffic data and road condition predictions.

9. The mobile red zone emergency rescue unit system according to claim 8, characterized in that: The system also includes a communication module that uses a 5G network to achieve ultra-high-speed data transmission between the ambulance and the hospital, ensuring the real-time transmission and reception of patient vital sign monitoring data, image data, video calls, and other information.

Images