Medical resource dynamic allocation method and system based on patient state data driving

Abstract

The invention provides a medical resource dynamic allocation method and system based on patient state data driving. The method comprises the following steps: receiving a patient state data set of a target user; generating target recommended medical resources; generating a candidate hospital sequence; acquiring a first adjustable medical resource of the first hospital in a first preset time; generating a first adaptation index; and when the first adaptation index is greater than or equal to a preset adaptation index, triggering a resource allocation instruction, and sending the target recommended medical resource to a first hospital. The technical problem that the medical resource preparation time is long due to the fact that data cannot be transmitted to a hospital in real time and medical resource scheduling analysis is lacked in the prior art can be solved, the vital sign data of the patient is transmitted in real time, the hospital is matched through intelligent analysis, the medical resource allocation scheme is generated, and the medical resource allocation efficiency is improved. Before the patient arrives at the hospital, the hospital is prepared correspondingly, so that the technical effect of shortening the waiting time of the patient is achieved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for dynamically allocating medical resources driven by patient status data. Background Art

[0002] In medical rescue, the ambulance is a very important part.

[0003] Most traditional ambulances are equipped with basic medical equipment, such as electrocardiogram monitors, blood pressure monitors, etc. These devices can provide limited vital signs monitoring for patients on site, but the data collected in the ambulance cannot be transmitted to the hospital in real time. When the patient arrives at the emergency clinic, it takes time to dispatch a more professional doctor, and a series of examinations are performed after seeing the patient, which prolongs the diagnosis time and prevents the patient from enjoying efficient and high-quality medical services.

[0004] Existing ambulances have technical problems such as the inability to transmit data to hospitals in real time and the lack of medical resource scheduling analysis, which leads to a long time for medical resource preparation.

[0005] Therefore, in response to the above problems, it is necessary to propose a method to integrate patients' medical resources and allocate them reasonably. Summary of the invention

[0006] The present invention provides a method and system for dynamic resource allocation driven by patient status data, which is used to solve the defects in the prior art that the medical resource preparation time is long due to the inability to transmit data to the hospital in real time and the lack of medical resource scheduling analysis.

[0007] In a first aspect, the present invention provides a method for dynamic resource allocation driven by patient status data, comprising: Connect to the target emergency vehicle and receive the patient status data set of the target user; Sending the patient status data set to a service decision center, which performs medical resource adaptation analysis and generates target recommended medical resources; According to the target recommended medical resources, searching hospitals within a preset range is performed to obtain a candidate hospital sequence; Extracting a first candidate hospital from the candidate hospital sequence, connecting to a hospital management platform of the first candidate hospital, and acquiring a first available medical resource of the first candidate hospital at a first preset time; Performing an adaptation analysis on the first callable medical resource and the target recommended medical resource to generate a first adaptation index; If it is determined that the first adaptation index is greater than or equal to the preset adaptation index, a resource allocation instruction is triggered to push the target recommended medical resources to the first candidate hospital.

[0008] According to a patient status data-driven dynamic resource allocation method provided by the present invention, the patient status data set is transmitted to a service decision center, a medical resource adaptation analysis is performed, and target recommended medical resources are generated, including: Calling a medical resource adaptation database through the service decision center to identify a plurality of medical resource adaptation samples having a preset similarity between the medical resource adaptation database and the patient status data set; A frequency analysis is performed on the multiple medical resource adaptation samples, and the medical resource adaptation sample with the maximum frequency is extracted as the target recommended medical resource.

[0009] According to a patient status data-driven dynamic resource allocation method provided by the present invention, the patient status data set is sent to a service decision center, comprising: Establishing a plurality of data communication channels between the target emergency vehicle and the service decision center; Transmitting the patient status data set through the multiple data communication channels to obtain a multi-channel data transmission result received by the service decision center; After parsing the multi-channel data transmission results, consistency identification is performed. If it is determined that the consistency index is greater than a preset index, a medical resource adaptation analysis is performed based on the parsed results.

[0010] According to a patient status data-driven dynamic resource allocation method provided by the present invention, the medical resource adaptation database includes multiple groups of resource adaptation samples; Among them, any group of resource adaptation samples includes patient status data samples and medical resource adaptation samples, and the medical resource adaptation samples include medical staff configuration constraints and medical equipment configuration constraints.

[0011] According to a patient status data-driven dynamic resource allocation method provided by the present invention, according to the target recommended medical resources, hospitals within a preset range are searched to obtain a candidate hospital sequence, including: Searching for a set of hospitals within the preset range; Obtaining each emergency distance from each hospital in the hospital set to the target ambulance and each hospital grade; After the indicators of each emergency distance are positively processed, they are weighted by normalized average with each hospital grade to generate multiple weight coefficients; According to the multiple weight coefficients, the hospitals in the hospital set are arranged in descending order to generate the candidate hospital sequence.

[0012] According to a patient status data-driven dynamic resource allocation method provided by the present invention, a first candidate hospital is extracted from the candidate hospital sequence, a hospital management platform of the first candidate hospital is connected, and a first available medical resource of the first candidate hospital at a first preset time is obtained, including: Acquire first location information of the first candidate hospital; Connecting to the navigation system of the target emergency vehicle, performing navigation route analysis based on the first location information, and generating the first preset time; Connect to the hospital management platform, obtain the on-duty medical staff and vacant medical equipment at the first preset time, and generate the first adjustable medical resources.

[0013] A patient status data-driven dynamic resource allocation method provided by the present invention further includes: If it is determined that the first adaptation index is less than the preset adaptation index, extracting a second candidate hospital from the candidate hospital sequence for adaptation analysis; If the candidate hospital sequence is traversed and the adaptation indexes of all hospitals in the candidate hospital sequence are less than the preset adaptation index, a temporary scheduling instruction is generated; A target hospital with the largest adaptation index is obtained based on the temporary scheduling instruction, and the temporary scheduling instruction is sent to the target hospital.

[0014] According to a patient status data-driven dynamic resource allocation method provided by the present invention, an intelligent biological detection cabin is arranged in the target emergency vehicle, and the cabin of the intelligent biological detection cabin is equipped with a variety of biological detection equipment.

[0015] In a second aspect, the present invention further provides a medical resource dynamic allocation system driven by patient status data, comprising: A data receiving module, used to connect to a target emergency vehicle and receive a patient status data set of a target user; An adaptation analysis module, used to send the patient status data set to a service decision center, which performs medical resource adaptation analysis and generates target recommended medical resources; A search module, used to search hospitals within a preset range according to the target recommended medical resources to obtain a candidate hospital sequence; A resource acquisition module, configured to extract a first candidate hospital from the candidate hospital sequence, connect to the hospital management platform of the first candidate hospital, and acquire a first available medical resource of the first candidate hospital at a first preset time; A generating module, configured to perform a matching analysis on the first callable medical resource and the target recommended medical resource to generate a first matching index; The resource allocation module is used to trigger a resource allocation instruction if it is determined that the first adaptation index is greater than or equal to a preset adaptation index, and push the target recommended medical resources to the first candidate hospital.

[0016] In a third aspect, the present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for dynamic allocation of medical resources based on patient status data as described above is implemented.

[0017] The method and system for dynamic allocation of medical resources driven by patient status data provided by the present invention solve the technical problem in the prior art that the data cannot be transmitted to the hospital in real time and there is a lack of medical resource scheduling analysis, resulting in a long medical resource preparation time. By transmitting the patient's vital signs data in real time and matching the hospital through intelligent analysis, a medical resource allocation plan is generated. Before the patient arrives at the hospital, the hospital has made corresponding preparations, thereby achieving the technical effect of shortening the patient's waiting time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a flow chart of a method for dynamic allocation of medical resources driven by patient status data provided by the present invention; Figure 2 It is a structural schematic diagram of a medical resource dynamic allocation system driven by patient status data provided by the present invention; Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Figure 1 FIG. 1 is a flow chart of a method for dynamic allocation of medical resources driven by patient status data provided by an embodiment of the present invention. Figure 1As shown, including: Step 100: Connect to the target emergency vehicle and receive the patient status data set of the target user; Step 200: Send the patient status data set to a service decision center, which performs medical resource adaptation analysis and generates target recommended medical resources; Step 300: searching hospitals within a preset range according to the target recommended medical resources to obtain a candidate hospital sequence; Step 400: extracting a first candidate hospital from the candidate hospital sequence, connecting to the hospital management platform of the first candidate hospital, and acquiring a first available medical resource of the first candidate hospital at a first preset time; Step 500: performing adaptation analysis on the first callable medical resource and the target recommended medical resource to generate a first adaptation index; Step 600: If it is determined that the first adaptation index is greater than or equal to a preset adaptation index, a resource allocation instruction is triggered to push the target recommended medical resources to the first candidate hospital.

[0022] Specifically, the method for dynamic allocation of medical resources driven by patient status data provided by an embodiment of the present invention includes the following steps: The first step is to connect to the target emergency vehicle and receive the patient status dataset of the target user.

[0023] The target emergency vehicle is equipped with an intelligent biological detection cabin, which is a smart device cabin specially designed for emergency environments. It integrates a variety of biological detection equipment, such as electrocardiogram (ECG) sensors, blood pressure monitors, blood oximeters, body temperature detectors, etc. It can automatically and efficiently complete the patient's various vital signs monitoring tasks, and the equipment coordinates with each other to achieve real-time data collection and upload. The target user is the patient who currently needs emergency treatment, and all test data are collected in real time around this user. The patient status data set refers to the patient's physiological parameter data collected by a variety of biological detection equipment in the intelligent biological detection cabin, such as heart rate, blood pressure, blood oxygen saturation, body temperature, etc. These data constitute complete patient health status information and are quickly transmitted to the emergency command center or hospital through the 5G network. For example, in an emergency experiment, a target user (patient) enters the target ambulance, and the sensors in the intelligent biological detection cabin are immediately activated to start collecting various biological data of the patient. The following data are recorded in the experiment: electrocardiogram: the patient's heart rate is monitored in real time as 76 beats per minute; blood pressure: after detection by the automatic blood pressure monitor, the patient's blood pressure is 120 / 80 mmHg; blood oxygen saturation: the finger-clip oximeter shows that the patient's blood oxygen saturation is 98%; body temperature: the intelligent body temperature monitor records the patient's body temperature as 36.5°C.

[0024] The second step is to send the patient status data set to a service decision center, which performs medical resource adaptation analysis and generates target recommended medical resources.

[0025] When the intelligent biological detection cabin completes the patient's vital signs monitoring, the patient status data set is transmitted to the service decision center in real time through the 5G network. The service decision center is responsible for quickly analyzing the patient's emergency situation, using big data technology to perform medical resource adaptation analysis, and generating target recommended medical resources. The patient status data set contains all physiological parameters collected from the target user, including but not limited to heart rate, blood pressure, blood oxygen saturation, body temperature, respiratory rate, etc. The service decision center is the core hub of the emergency medical network. Its main function is to receive data from the target ambulance, combine artificial intelligence algorithms and big data analysis, quickly generate medical resource allocation plans for patients, and dispatch relevant resources. Medical resource adaptation analysis compares the patient's status data with historical medical data to identify the most appropriate combination of medical resources, such as selecting an appropriate medical team and equipping the required emergency equipment or drugs. Target recommended medical resources refer to the medical resource plan generated after analyzing the patient's status data. It includes medical staff in specific fields, special emergency equipment (such as ventilators, defibrillators), and specific drugs or treatment equipment.

[0026] For example, in an emergency, the status data set (heart rate, blood pressure, blood oxygen saturation, body temperature, etc.) of a target user was immediately transmitted to the service decision center through the 5G network after collection. The following data were recorded in the experiment: heart rate: 120 beats per minute; blood pressure: 160 / 100mmHg; blood oxygen saturation: 92%; body temperature: 38.1°C. After receiving the data, the service decision center used AI algorithms to analyze historical data samples, quickly compared the historical cases most similar to the current patient's condition, and recommended a medical team including cardiovascular specialists, nurses, and necessary equipment (such as portable ventilators, emergency antihypertensive drugs) through medical resource adaptation analysis. The plan was recommended as a target medical resource and pushed to the hospital at the same time.

[0027] Furthermore, in the second step, the patient status data set is transmitted to the service decision center to perform medical resource adaptation analysis and generate target recommended medical resources, including: The service decision center calls a medical resource adaptation database, identifies multiple medical resource adaptation samples in the medical resource adaptation database whose similarity with the patient status data set meets a preset similarity; performs frequency analysis on the multiple medical resource adaptation samples, and generates a medical resource adaptation sample with the greatest frequency as the target recommended medical resource.

[0028] The medical resource adaptation database is a database containing a large number of historical emergency cases and corresponding medical resource configurations. Each case will record the patient's status data and the configured medical staff, emergency equipment and other resources for reference in future emergency treatment. After receiving the patient status data set, the service decision center immediately calls the medical resource adaptation database, calculates the similarity between the patient status data set and the historical cases in the medical resource adaptation database, and identifies multiple medical resource adaptation samples that meet the preset similarity requirements. The similarity can be based on existing similarity calculation methods, such as by comparing data differences.

[0029] For the multiple identified medical resource adaptation samples, frequency analysis is performed. The purpose of frequency analysis is to evaluate the number of times these samples have been successfully used in similar emergency scenarios. The higher the frequency, the better the effect of the medical resource allocation plan in similar emergency scenarios. Through frequency analysis, the highest frequency adaptation sample is finally selected as the target recommended medical resource, which includes the best medical resource allocation plan for the patient's current emergency needs, including the required medical staff, emergency equipment, and drugs. Through this process, the service decision center can quickly and intelligently generate the most appropriate medical resource recommendation plan for patients, ensuring that the relevant medical team and equipment are ready before the patient arrives at the hospital, thereby shortening the patient's waiting time.

[0030] It should be noted that the medical resource adaptation database includes multiple groups of resource adaptation samples; wherein any group of resource adaptation samples includes patient status data samples and medical resource adaptation samples, and the medical resource adaptation samples include medical staff configuration constraints and medical equipment configuration constraints.

[0031] The medical resource adaptation database stores a large amount of resource configuration information about historical emergency cases. These historical data not only include the patient's physiological condition, but also the corresponding medical resource configuration plan. They are constructed by professional and technical personnel in this field by analyzing historical data and can be updated in real time. In the medical resource adaptation database, each set of resource adaptation samples consists of two parts: patient status data samples and medical resource adaptation samples. The patient status data sample records the patient's physiological parameters and conditions, such as heart rate, blood pressure, blood oxygen saturation, body temperature, etc., as the basis for matching medical resources. The medical resource adaptation sample includes the medical resource configuration plan scheduled for the corresponding patient status. The first type of important information in the medical resource adaptation sample is the medical staff configuration constraint, which refers to the recommendation of a suitable combination of medical staff according to the patient's condition. For example, for patients with cardiovascular problems, cardiologists, emergency nurses and auxiliary personnel will be configured. The configuration of these personnel is based on historical data and the current patient's status to ensure that the most suitable medical team can respond quickly. Medical equipment configuration constraints refer to the equipment and instruments needed during the emergency process. For example, for patients with respiratory failure, portable ventilators or oxygen equipment may be required. After receiving the patient status data set, the service decision center will extract the corresponding resource adaptation samples from the medical resource adaptation database, combine the current patient's condition, analyze similar historical cases, and match the patient status data samples with the target user's physiological condition to determine the corresponding medical staff configuration constraints and medical equipment configuration constraints, ensuring that the optimal combination of medical resources is provided to patients, enabling ambulances and hospitals to respond quickly.

[0032] Sending the patient status data set to a service decision center includes: Establishing a plurality of data communication channels between the target emergency vehicle and the service decision center; Transmitting the patient status data set through the multiple data communication channels to obtain a multi-channel data transmission result received by the service decision center; After parsing the multi-channel data transmission results, consistency identification is performed. If it is determined that the consistency index is greater than a preset index, a medical resource adaptation analysis is performed based on the parsed results.

[0033] When the patient enters the target ambulance, the intelligent biological detection equipment in the vehicle will start to collect the patient's physiological parameters and generate a patient status data set. In order to ensure that this data can be stably transmitted to the service decision center, multiple data communication channels will be configured for the ambulance and the service decision center. These channels usually include 5G networks, satellite communications, and backup wireless local area networks (Wi-Fi) and other transmission methods to ensure that even if a channel has problems, other channels can maintain smooth data transmission. At the same time, the patient status data set is transmitted to the service decision center through multiple communication channels. This multi-channel transmission method can effectively avoid data loss or delay due to unstable signals, network congestion, and other problems in a single channel. The data transmitted by each channel will be synchronized to ensure the consistency and integrity of all data points during the transmission process. After receiving the patient data (multi-channel data transmission results) transmitted from different communication channels, the service decision center will read and parse the multi-channel data transmission results. The purpose of the analysis is to compare the data content transmitted by each channel to ensure that all data is consistent and correct. Compare the key parameters such as heart rate, blood pressure, and blood oxygen transmitted by each channel to see if they are consistent within the specified time window. After parsing the data of each channel, consistency identification is performed. Consistency identification refers to comparing the data of different channels and calculating the differences between them. When the consistency index is greater than the preset index (that is, the degree of data matching is high enough), it is confirmed that the data is valid and accurate. It is used for medical resource adaptation analysis to ensure that even in complex emergency environments, the patient's data can be accurately transmitted to the service decision center, maximizing the accuracy and timeliness of emergency resource allocation.

[0034] The third step is to search hospitals within a preset range based on the target recommended medical resources to obtain a candidate hospital sequence.

[0035] In the process of emergency treatment, it is crucial to ensure that the target user can quickly reach the most suitable hospital for treatment. In order to achieve this goal, a hospital search within a preset range is performed to generate a candidate hospital sequence, thereby providing the patient with the best treatment options. First, a preset range of emergency services needs to be determined. This range is usually defined based on the geographical location of the ambulance, the urgency of the patient's condition, and the traffic conditions. The preset range can be a circular area where the ambulance is currently located, or a time threshold, such as the need to reach the hospital within one hour. The specific setting can be quickly uploaded by the professional staff on the ambulance. After determining the preset range, a hospital search is performed through an integrated geographic information system. During the search, all hospitals within the range will be considered, and each emergency distance from each hospital in the hospital set to the target emergency vehicle and each hospital grade will be obtained; each emergency distance is processed by index positive and then normalized and averaged weighted with each hospital grade to generate multiple weight coefficients; according to the multiple weight coefficients, each hospital in the hospital set is arranged in order from large to small to generate the candidate hospital sequence.

[0036] Selecting the right hospital is the key to optimizing the allocation of emergency resources. In order to improve the efficiency of emergency, the following steps are used to screen and rank candidate hospitals. First, set a geographical range (such as 10 kilometers, 20 kilometers) as the search area to ensure that the target ambulance can reach the target hospital within an acceptable time. The preset range refers to an acceptable distance or area set in advance. Find all qualified hospitals within the search range to form a hospital set. Example: Assuming that the ambulance is located in the city center and a range of 10 kilometers is set, this range includes Hospital A, Hospital B, and Hospital C. In the hospital set, calculate the distance from each hospital to the target ambulance to form each emergency distance. In addition, extract the hospital grade of each hospital, which is usually divided according to factors such as facilities, emergency capacity, medical staffing, and the number of specialties. The higher the grade, the stronger the comprehensive ability of the hospital. Example: The distances from the ambulance to hospitals A, B, and C are 5 kilometers, 8 kilometers, and 3 kilometers respectively; assuming that the hospital grades are Hospital A (Level 3), Hospital B (Level 2), and Hospital C (Level 1).

[0037] In order to comprehensively consider the impact of emergency distance and hospital grade, the emergency distance is processed positively, so that the shorter the distance, the higher the priority of the hospital. Usually, the minimum-maximum positive formula can be used, which is a common technical means for technicians in this field and will not be expanded here. After this treatment, the value of the hospital with a closer distance will be higher, which is convenient for subsequent calculations. Then, the normalized emergency distance and hospital grade are weighted by average, and the weight coefficient is calculated. According to each weight coefficient, the hospitals in the hospital set are sorted from large to small according to the weight to generate a candidate hospital sequence. The sorted hospital set can guide the ambulance to give priority to the hospital with the highest weight coefficient, thereby effectively improving the timeliness of the emergency response.

[0038] The fourth step is to extract the first candidate hospital from the candidate hospital sequence, connect to the hospital management platform of the first candidate hospital, and obtain the first available medical resources of the first candidate hospital at the first preset time.

[0039] The first hospital ranked first in the candidate hospital sequence is extracted, and connected to the hospital management platform of the hospital to obtain the medical resources that can be allocated by the hospital within the first preset time. This process ensures that when the ambulance arrives at the hospital, the required medical resources (such as medical staff and equipment) are ready to provide the fastest treatment for patients. The hospital management platform usually contains the real-time medical resource information of the hospital, including the available medical staff, equipment status and the current occupancy of the beds. By connecting to the hospital management platform, the current medical resource configuration information of the hospital can be obtained to prepare for the subsequent resource allocation. The first preset time refers to the estimated time required for the target ambulance to arrive at the first hospital. This time is calculated by the navigation module of the system based on the current location of the ambulance, road traffic conditions and navigation path. The first preset time is used to determine whether the hospital can allocate the required medical resources before the ambulance arrives at the hospital. For example, if it is estimated that the ambulance will take 10 minutes to arrive at the hospital, this is used as the first preset time, and the hospital is required to prepare the necessary resources within this period. In this way, the first adjustable medical resources of the hospital within the first preset time can be obtained in real time.

[0040] Extracting a first candidate hospital from the candidate hospital sequence, connecting to a hospital management platform of the first candidate hospital, and acquiring a first available medical resource of the first candidate hospital at a first preset time, including: Acquire first location information of the first candidate hospital; Connecting to the navigation system of the target emergency vehicle, performing navigation route analysis based on the first location information, and generating the first preset time; Connect to the hospital management platform, obtain the on-duty medical staff and vacant medical equipment at the first preset time, and generate the first adjustable medical resources.

[0041] After the first hospital is determined from the candidate hospital sequence, the location information of the first hospital is first obtained, which usually includes the precise geographic coordinates of the hospital, such as latitude and longitude data, or other address information. These data will be used in the navigation system to ensure that the ambulance can find the optimal path to quickly reach the hospital. Once the location information of the first hospital is obtained, this information will be input into the navigation system of the target ambulance. The navigation system generates the fastest driving route based on the current location of the ambulance and the real-time traffic conditions to avoid delay factors such as traffic congestion, thereby obtaining the driving time required for the driving route, that is, the first preset time. For example, if the ambulance is 5 kilometers away from the hospital and the road is smooth, a route with a preset time of 10 minutes may be generated; if there is traffic congestion or special road conditions, the preset time will be recalculated and adjusted. While generating the first preset time, it is connected to the management platform of the first hospital to obtain the on-duty medical staff and vacant medical equipment (such as ECG monitors, portable ventilators, defibrillators, etc.) of the first hospital within the first preset time, so as to generate the first adjustable medical resources. Through this full-process coordination and resource preparation, patients are guaranteed to receive the best medical services in the shortest time.

[0042] The fifth step is to perform adaptation analysis on the first callable medical resources and the target recommended medical resources to generate a first adaptation index.

[0043] After the first adjustable medical resource is generated, the first adjustable medical resource is compared with the target recommended medical resource required by the patient. The target recommended medical resource is generated based on the patient's current vital signs data and historical emergency cases, reflecting the medical staff, equipment, and drugs required by the patient during the emergency treatment process. For example, for a patient with acute myocardial infarction, the target recommended medical resources may include a cardiovascular specialist with emergency PCI capabilities, a portable ECG monitor, an automatic external defibrillator, an idle catheter room, and related emergency drugs such as antiplatelet, anticoagulant, and vasodilator. By comparing each configuration of the first adjustable medical resource with the target recommended medical resource one by one, the first adaptation index is calculated. The adaptation index is a quantitative value that measures the degree of resource matching, reflecting whether the current resources of the hospital can fully meet the needs of the patient. The dimensions of adaptation analysis may include: medical staff matching, medical equipment matching, and bed resource matching. Specifically, through comparison, it is determined whether the first adjustable medical resource can fully cover the target recommended medical resource. If so, the first fitness index is 100%. Assuming that the target recommended medical resource requires 1 emergency physician, 2 cardiovascular physicians with emergency PCI capabilities, 1 vacant catheter room, corresponding equipment and materials, 1 ECG monitor and 1 bed, and the resources provided by the first hospital can fully meet these requirements, then the fitness index will reach 100%. If there is no complete coverage, it is necessary to conduct a detailed analysis of the uncovered resources to identify the missing medical staff, medical equipment, bed resources, etc., and calculate the fitness index according to the difference. The greater the difference, the smaller the fitness index. For example, a patient with previous heart failure is identified as having severe hemorrhagic stroke by a small on-board CT scan on the target ambulance. Assuming that the target recommended medical resources require 1 emergency physician, 2 neurosurgeons with emergency brain hematoma removal capabilities, 1 cardiologist, 1 anesthesiologist, 1 ECG monitor, 1 oxygen device, 1 vacant operating room, 1 intensive care bed, and related emergency drugs such as dehydration and intracranial pressure reduction and blood pressure reduction. If the cardiologist is dealing with other emergency cases and cannot participate in the consultation and cardiac function assessment of stroke patients within the first preset time, the missing resource is 1 cardiologist. According to the patient's medical needs and urgency, the missing resources are prioritized and the first fitness is calculated. Assuming the first fitness is 85% and the preset value is 85%, the first adjustable medical resources cannot completely cover the target recommended medical resources, but if the first fitness index is equal to or exceeds the preset value, resource allocation can still be triggered. The preset adaptation index is a threshold value set by professional and technical personnel in this field. This threshold value represents the minimum standard for resource matching. Resource allocation will be triggered only when the first adaptation index is equal to or exceeds the preset value.

[0044] In the sixth step, if it is determined that the first adaptation index is greater than or equal to the preset adaptation index, a resource allocation instruction is triggered to push the target recommended medical resources to the first candidate hospital.

[0045] When the first adaptation index is greater than or equal to the preset adaptation index, the resource allocation instruction is immediately triggered to send the target recommended medical resources to the first hospital. This instruction ensures that the hospital management system can quickly call relevant resources (such as medical staff and equipment) and arrange for them to be ready when the ambulance arrives. Through this resource adaptation analysis and dynamic instruction triggering mechanism, the resource coordination between the ambulance and the hospital is ensured to be efficient, and the rescue preparation time is shortened to the greatest extent.

[0046] When the first adaptation index is less than the preset adaptation index, a second hospital is extracted from the candidate hospital sequence for adaptation analysis; if the candidate hospital sequence is traversed and the adaptation indexes of all hospitals in the candidate hospital sequence are less than the preset adaptation index, a temporary scheduling instruction is generated; the target hospital with the largest adaptation index is obtained by using the temporary scheduling instruction, and the temporary scheduling instruction is sent to the target hospital.

[0047] It is understandable that when the first adaptation index (i.e., the degree of match between the current medical resources of the hospital and the patient's needs) is less than the preset adaptation index, the patient cannot be immediately arranged to the hospital. This may be due to insufficient medical resources in the first hospital, such as the absence of cardiovascular specialists, full beds in the emergency room, or unavailability of key emergency equipment. Therefore, it is necessary to continue to look for other feasible hospitals in the candidate hospital sequence. The second hospital is extracted from the candidate hospital sequence, and a new adaptation analysis is performed on its available resources and the target recommended medical resources for patient needs. This process is the same as the analysis of the first hospital. The management platform of the second hospital will be connected to obtain its current medical resource information and calculate the second adaptation index. If the adaptation index of the second hospital is higher than the preset adaptation index, the target user will be arranged to go to the second hospital. If the adaptation index of the second hospital is still lower than the preset standard, each hospital in the candidate hospital sequence will continue to be traversed in turn, and adaptation analysis will be performed one by one. This process will continue until a hospital is found whose adaptation index meets or exceeds the preset standard, or until all hospitals in the candidate hospital sequence have been adapted.

[0048] If all candidate hospitals are traversed, but the fitness index of each hospital is lower than the preset index, a temporary dispatch instruction will be generated. The purpose of the temporary dispatch instruction is to give priority to sending patients to the hospital closest to the matching requirements when resources are insufficient. At this time, the one with the highest fitness index is selected as the target hospital from all candidate hospitals, even if its fitness index does not fully meet the preset standard. Once the target hospital is selected, a temporary dispatch instruction will be sent to the hospital, which includes the patient's basic information, emergency needs, expected arrival time, etc. At the same time, the hospital is notified to allocate existing medical resources as much as possible and make emergency preparations. After receiving the temporary dispatch instruction, the hospital will immediately adjust its internal resources and try to prepare the necessary medical staff and equipment before the ambulance arrives. For example, in a simulated emergency, the target ambulance carries a heart patient, and the preset fitness index is 85%. The first hospital was selected first, but after adaptation analysis, the first hospital's fitness index was only 75%, because the emergency beds were full and the ECG monitoring equipment was temporarily unavailable. Then the second hospital was extracted from the candidate hospital sequence for adaptation analysis, and its fitness index was 80%, which was still lower than the preset standard. Continuing to traverse the candidate hospitals, it was found that the adaptation index of all hospitals did not reach the preset standard of 85%. Finally, a temporary dispatch instruction was generated, and the second hospital with the highest adaptation index (80%) was selected as the target hospital. The temporary dispatch instruction was then sent to the second hospital, notifying the hospital to prepare the existing resources, including a cardiovascular specialist and some emergency equipment, waiting for the patient to arrive. Through this traversal and temporary dispatch mechanism, it is possible to find the relatively optimal treatment plan for patients under limited resources, ensuring the maximum response speed and treatment effect during the emergency process.

[0049] The following describes the medical resource dynamic allocation system driven by patient status data provided by the present invention. The medical resource dynamic allocation system driven by patient status data described below and the medical resource dynamic allocation method driven by patient status data described above can refer to each other.

[0050] Figure 2 FIG. 1 is a schematic diagram of a medical resource dynamic allocation system driven by patient status data provided by an embodiment of the present invention. Figure 2 As shown, it includes: a data receiving module 21, an adaptation analysis module 22, a search module 23, a resource acquisition module 24, a generation module 25 and a resource allocation module 26, wherein: The data receiving module 21 is used to connect to the target ambulance and receive the patient status data set of the target user; the adaptation analysis module 22 is used to send the patient status data set to the service decision center, and the service decision center performs medical resource adaptation analysis to generate target recommended medical resources; the search module 23 is used to search for hospitals within a preset range according to the target recommended medical resources to obtain a candidate hospital sequence; the resource acquisition module 24 is used to extract the first candidate hospital from the candidate hospital sequence, connect to the hospital management platform of the first candidate hospital, and obtain the first available medical resource of the first candidate hospital at the first preset time; the generation module 25 is used to perform adaptation analysis on the first available medical resource and the target recommended medical resource to generate a first adaptation index; the resource allocation module 26 is used to trigger a resource allocation instruction if it is determined that the first adaptation index is greater than or equal to the preset adaptation index, and push the target recommended medical resource to the first candidate hospital.

[0051] Figure 3 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 3 As shown, the electronic device may include: a processor (processor) 310, a communication interface (Communications Interface) 320, a memory (memory) 330 and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other through the communication bus 340. The processor 310 can call the logic instructions in the memory 330 to execute a medical resource dynamic allocation method driven by patient status data, the method comprising: connecting to a target ambulance and receiving a patient status data set of a target user; sending the patient status data set to a service decision center, and the service decision center performs a medical resource adaptation analysis to generate target recommended medical resources; searching for hospitals within a preset range according to the target recommended medical resources to obtain a candidate hospital sequence; extracting a first candidate hospital from the candidate hospital sequence, connecting to the hospital management platform of the first candidate hospital, and obtaining a first available medical resource of the first candidate hospital at a first preset time; performing an adaptation analysis on the first available medical resource and the target recommended medical resource to generate a first adaptation index; if it is determined that the first adaptation index is greater than or equal to the preset adaptation index, triggering a resource allocation instruction to push the target recommended medical resource to the first candidate hospital.

[0052] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0053] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0054] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamic allocation of medical resources based on patient status data, characterized in that: include: Connect to the target emergency vehicle and receive the patient status data set of the target user; Sending the patient status data set to a service decision center, which performs medical resource adaptation analysis and generates target recommended medical resources; According to the target recommended medical resources, searching hospitals within a preset range is performed to obtain a candidate hospital sequence; Extracting a first candidate hospital from the candidate hospital sequence, connecting to a hospital management platform of the first candidate hospital, and acquiring a first available medical resource of the first candidate hospital at a first preset time; Performing an adaptation analysis on the first callable medical resource and the target recommended medical resource to generate a first adaptation index; If it is determined that the first adaptation index is greater than or equal to the preset adaptation index, a resource allocation instruction is triggered to push the target recommended medical resources to the first candidate hospital.

2. The method for dynamic allocation of medical resources based on patient status data drive according to claim 1, characterized in that: The patient status data set is transmitted to the service decision center for medical resource adaptation analysis to generate target recommended medical resources, including: Calling a medical resource adaptation database through the service decision center to identify a plurality of medical resource adaptation samples having a preset similarity between the medical resource adaptation database and the patient status data set; A frequency analysis is performed on the multiple medical resource adaptation samples, and the medical resource adaptation sample with the maximum frequency is extracted as the target recommended medical resource.

3. The method for dynamic allocation of medical resources based on patient status data drive according to claim 2, characterized in that: Sending the patient status data set to a service decision center includes: Establishing a plurality of data communication channels between the target emergency vehicle and the service decision center; Transmitting the patient status data set through the multiple data communication channels to obtain a multi-channel data transmission result received by the service decision center; After parsing the multi-channel data transmission results, consistency identification is performed. If it is determined that the consistency index is greater than a preset index, a medical resource adaptation analysis is performed based on the parsed results.

4. The method for dynamic allocation of medical resources based on patient status data drive according to claim 2, characterized in that: The medical resource adaptation database includes multiple groups of resource adaptation samples; Among them, any group of resource adaptation samples includes patient status data samples and medical resource adaptation samples, and the medical resource adaptation samples include medical staff configuration constraints and medical equipment configuration constraints.

5. The method for dynamic allocation of medical resources based on patient status data drive according to claim 1, characterized in that: According to the target recommended medical resources, hospitals within a preset range are searched to obtain a candidate hospital sequence, including: Searching for a set of hospitals within the preset range; Obtaining each emergency distance from each hospital in the hospital set to the target ambulance and each hospital grade; After the indicators of each emergency distance are positively processed, they are weighted by normalized average with each hospital grade to generate multiple weight coefficients; According to the multiple weight coefficients, the hospitals in the hospital set are arranged in descending order to generate the candidate hospital sequence.

6. The method for dynamic allocation of medical resources based on patient status data drive according to claim 1, characterized in that: Extracting a first candidate hospital from the candidate hospital sequence, connecting to a hospital management platform of the first candidate hospital, and acquiring a first available medical resource of the first candidate hospital at a first preset time, including: Acquire first location information of the first candidate hospital; Connecting to the navigation system of the target emergency vehicle, performing navigation route analysis based on the first location information, and generating the first preset time; Connect to the hospital management platform, obtain the on-duty medical staff and vacant medical equipment at the first preset time, and generate the first adjustable medical resources.

7. The method for dynamic allocation of medical resources based on patient status data drive according to claim 1, characterized in that: Also includes: If it is determined that the first adaptation index is less than the preset adaptation index, extracting a second candidate hospital from the candidate hospital sequence for adaptation analysis; If the candidate hospital sequence is traversed and the adaptation indexes of all hospitals in the candidate hospital sequence are less than the preset adaptation index, a temporary scheduling instruction is generated; A target hospital with the largest adaptation index is obtained based on the temporary scheduling instruction, and the temporary scheduling instruction is sent to the target hospital.

8. The method for dynamic allocation of medical resources based on patient status data drive according to any one of claims 1 to 7, characterized in that: An intelligent biological detection cabin is arranged in the target emergency vehicle, and the cabin of the intelligent biological detection cabin is equipped with a variety of biological detection equipment.

9. A medical resource dynamic allocation system driven by patient status data, characterized in that: include: A data receiving module, used to connect to a target emergency vehicle and receive a patient status data set of a target user; An adaptation analysis module, used to send the patient status data set to a service decision center, which performs medical resource adaptation analysis and generates target recommended medical resources; A search module, used to search hospitals within a preset range according to the target recommended medical resources to obtain a candidate hospital sequence; A resource acquisition module, configured to extract a first candidate hospital from the candidate hospital sequence, connect to the hospital management platform of the first candidate hospital, and acquire a first available medical resource of the first candidate hospital at a first preset time; A generating module, configured to perform a matching analysis on the first callable medical resource and the target recommended medical resource to generate a first matching index; The resource allocation module is used to trigger a resource allocation instruction and push the target recommended medical resources to the first candidate hospital if it is determined that the first adaptation index is greater than or equal to a preset adaptation index.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for dynamic allocation of medical resources driven by patient status data as described in any one of claims 1 to 8 is implemented.

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