County-level medical care dispatching system and method suitable for elderly patients

Abstract

According to the county-level medical care dispatching system and method suitable for the elderly patients, an intelligent terminal supports multiple interaction modes, and real-time health data and request data of the elderly patients are transmitted to an Ai dispatching center module in an encrypted mode through a 5G / Internet of Things protocol; dynamically adjusting an execution strategy, generating a corresponding service scheme, and transmitting the service scheme to an execution module for medical care distribution; the county medical cloud platform optimization decision model realizes update of medical rules, promotes evolution of the Ai scheduling center module, re-deploys Ai triage priority weights, maintains terminal security and sets personalized parameters, and remotely manages intelligent terminals; and the execution module records the whole medical service process and feeds back the whole medical service process to the county medical cloud platform through block chain evidence storage to complete a closed loop. According to the scheme, resources of hospitals, communities and third-party institutions are integrated, a complete closed loop is formed from data acquisition to service evaluation in the aging-suitable design, the system performs automatic iteration according to the execution effect, and the service quality is continuously improved.

Description

Technical Field

[0001] This invention relates to a medical care dispatching system, and more particularly to a county-level medical care dispatching system and method suitable for elderly patients, belonging to the field of medical allocation technology. Background Technology

[0002] A county-level hospital medical staff dispatch system is an intelligent and information-based platform specifically designed for hospitals within a county-level administrative region to manage and dispatch medical resources. This system integrates various medical resource data within the hospital to optimize the work arrangements and task allocation for medical staff, thereby improving the efficiency and quality of medical services. Its goal is to address the common problems faced by county hospitals, such as insufficient medical staff and low dispatch efficiency, through digital means, thereby improving the speed of medical service response, optimizing the patient experience, and reducing the workload of medical staff.

[0003] With the increasing aging of my country's population, the latest statistics show that the elderly population aged 60 and above has exceeded 260 million, of which approximately 44 million are disabled to varying degrees. However, existing county-level hospital medical dispatch systems typically have the following shortcomings when serving the elderly: 1) Terminal pages often use standardized designs with small fonts and cumbersome operation processes, making it difficult for the elderly to use independently due to vision impairment, cognitive decline, or digital divide; 2) Many elderly people suffer from chronic diseases (such as diabetes and hypertension), but dispatch systems often focus on acute illnesses, lacking functions such as long-term follow-up and medication reminders; 3) When elderly patients living alone experience sudden illness, they may not be able to quickly trigger an emergency response through the system, or inaccurate location information may delay treatment; 4) The elderly's past medical history and medication records are scattered across different institutions, and the system does not achieve data interoperability within the county, affecting the efficiency of diagnosis and treatment; 5) The system's processes are mechanical and do not consider communication barriers for the elderly (e.g., hearing loss, dialect differences), leading to poor doctor-patient communication.

[0004] Therefore, it is necessary to propose a more effective county-level medical and nursing dispatch system and method suitable for elderly patients, so as to better meet the medical and nursing needs of disabled elderly people. Summary of the Invention

[0005] The purpose of this invention is to solve the above-mentioned problems and provide a county-level medical and nursing dispatch system and method suitable for elderly patients. This system serves as a core link in medical resource management, rationally allocating medical and nursing personnel, improving medical efficiency, and ensuring that elderly patients receive timely, accurate, and safe medical treatment.

[0006] The technical solution of this invention is: a county-level medical care dispatch system suitable for elderly patients, comprising a smart terminal with a hierarchical interactive architecture, an AI dispatch central module, a county-level medical cloud platform, and an execution module. The smart terminal, with multi-channel access, supports various interaction methods such as voice, buttons, and gestures, and integrates functions including but not limited to fall detection and heart rate monitoring. It transmits real-time health data and elderly patient request data to the AI ​​dispatch central module via 5G / IoT protocols with encryption. The AI ​​dispatch central module dynamically adjusts execution strategies and generates corresponding service plans, which are then transmitted to the execution module for medical care allocation. The county-level medical cloud platform optimizes the decision-making model to update medical rules, thereby promoting the evolution of the AI ​​dispatch central module and reallocating AI triage priority weights. The county-level medical cloud platform maintains terminal security (e.g., vulnerability patching) and sets personalized parameters (e.g., adjusting the monitoring frequency for elderly people living alone) for remote management of the smart terminal. The execution module records the entire medical care service process, stores the data on a blockchain, and then feeds it back to the county-level medical cloud platform to complete the closed loop.

[0007] Furthermore, in the aforementioned county-level medical dispatch system for elderly patients, the intelligent terminal and the execution module form an emergency bypass, and the two are directly connected via local Bluetooth. This ensures reliability and timely response in life-threatening situations for elderly patients (e.g., direct connection to the ambulance in case of cardiac arrest).

[0008] Furthermore, the aforementioned county-level medical and nursing dispatch system for elderly patients includes: the intelligent terminal comprising a data collection section and a data processing center unit consisting of a physiological monitoring unit, an interactive control unit, a communication transmission unit, an emergency response unit, and an energy management unit. The physiological monitoring unit monitors the health data of elderly patients; the interactive control unit facilitates the flow of collected data; the communication transmission unit transmits data; the emergency response unit provides timely alarms for emergency or abnormal events; and the energy management unit provides power. The data processing center unit receives data, performs lightweight compression processing, and performs detection. The data processing center unit employs a multimodal physiological signal fusion algorithm and constructs a state-space model based on Kalman filtering sensor data fusion. This state-space model integrates multi-source heterogeneous data from physiological monitoring units (e.g., smart bracelets, mattress sensors) to improve measurement accuracy (including but not limited to heart rate and respiratory rate parameters), thereby achieving highly accurate real-time health monitoring and emergency response, and improving safety. The state-space model is as follows:

[0009]

[0010] In the formula: x t z is the vector of the actual physiological state at time t (e.g., [heart rate, blood oxygen]); tFor sensor observations; w t ~N(0,Q),v t ~N(0, R) represents process noise and observation noise; Additionally, this formula uses the Kalman gain K... t Dynamically weighted confidence scores for each sensor: K t =P t -H T HP t - H T +R) -1 .

[0011] To further reduce false positives caused by "over-fusion", clinical prior knowledge can be introduced to constrain the evidence space required for calculation, thus improving the DS evidence theory. The formula is as follows:

[0012]

[0013] In the formula: m1 and m2 are the basic probability assignment functions for different sensors; A is the hypothesis of abnormal events (e.g., an elderly patient falls); and the denominator is used to normalize conflicting evidence.

[0014] Specifically, the aforementioned county-level medical and nursing dispatch system for elderly patients includes: the physiological monitoring unit comprising wearable devices (including but not limited to smart bracelets and fall detection belts), environmental sensors (including but not limited to mattress pressure pads and indoor millimeter-wave radar), and portable medical devices (including but not limited to Bluetooth blood pressure monitors and blood glucose meters).

[0015] Furthermore, in the aforementioned county-level medical and nursing dispatch system applicable to elderly patients, the AI ​​dispatch center module adopts multimodal decision-making. The input unit uniformly receives data (physiological signals / voice requests / environmental data) from the smart terminal. After the support unit processes the sensor noise and missing values ​​of the data, it outputs it to the core decision-making unit for multi-objective optimization. Finally, the dispatch results are simulated through a digital twin and transmitted to the output unit for data transmission.

[0016] Furthermore, the aforementioned county-level medical and nursing dispatch system for elderly patients includes: a county-level medical cloud platform comprising a data unit, a business unit, an AI capability unit, and an application access unit; the data unit integrates health data of elderly patients from hospitals, communities, and families within the county; the business unit facilitates multi-level referrals and simultaneously connects to the medical insurance system to achieve billing for home care services and special reimbursement for chronic disease medications; the AI ​​capability unit predicts disease models based on inpatient data to generate personalized treatment plans for elderly patients based on liver and kidney function and drug interactions; and the application access unit supports elderly patients or medical staff to log in via biometrics.

[0017] Furthermore, the aforementioned county-level medical and nursing dispatch system for elderly patients includes the following: the execution module comprises a service execution unit, a resource management unit, a quality monitoring unit, and a feedback optimization unit. The service execution unit is used for the rational dispatch and allocation of medical and nursing personnel; the resource management unit is used for the rational dispatch and allocation of medical and nursing staff; the quality monitoring unit is used for the rational recording of the medical and nursing process for blockchain traceability; and the feedback optimization unit is used for the rational accumulation of data for AI model iteration, while generating improvement work orders for manual follow-up within 24 hours.

[0018] This invention also provides a county-level medical care dispatching method suitable for elderly patients, comprising the following steps:

[0019] Step (1) The physiological monitoring unit (e.g., smart bracelet and / or mattress device) continuously collects heart rate, blood oxygen and activity data of elderly patients and performs real-time analysis. When an abnormality is detected (e.g., fall, heart arrhythmia), the emergency response unit is triggered. The elderly patient actively initiates a request through voice commands (e.g., "call a doctor for me" and / or "I need a doctor") or emergency button. The smart terminal device automatically attaches location information and basic health data. After the data processing center unit encrypts the data packet (including but not limited to timestamp, device ID, health indicators, event level) and outputs it to the AI ​​dispatch center module.

[0020] Step (II) After the NLP engine of the support unit parses the voice request and combines it with the knowledge graph, the core decision unit determines the type of need of the elderly patient (emergency / medication / life assistance), calculates the best handling method, generates a structured work order (including service SLA and operation instructions), and outputs the dispatch instruction to the county medical cloud platform. If an emergency occurs, the three-level response mechanism L1-L3 will be automatically activated.

[0021] Step (3) The data unit retrieves the complete health record (i.e., past medical history, allergic drugs) based on the elderly patient's ID, and simultaneously checks the medical insurance balance and reimbursement policy. The business unit records key operation nodes (e.g., dispatch time, nurse check-in) and multimedia evidence of the service process (e.g., before and after nursing photos) through the blockchain. The AI ​​capability unit learns and aggregates new data from various hospitals to update the corresponding disease prediction model. The encrypted data stream and model parameters are output to the execution module.

[0022] Step (IV) The work order is received by the hospital, community or third-party team. The service execution unit's APP automatically plans the optimal route and prepares the necessary equipment (e.g., oxygen cylinder, catheterization kit). The resource management unit confirms the identity of medical staff through biometric authentication (including but not limited to face and / or voiceprint) and records the entire process using a law enforcement recorder (including but not limited to privacy desensitization processing). The quality monitoring unit allows elderly patients or their families to give a 1-5 star rating via voice or APP and automatically detects the standardization of the service (e.g., whether the dressing change operation meets video standards). Finally, a service completion report is output (including but not limited to consumable usage, execution time, and effect evaluation).

[0023] Step (5) The execution results of the execution module (e.g., blood pressure recovery value) are updated to the health records of the county medical cloud platform. Then, the AI ​​scheduling center module readjusts the scheduling strategy. If an abnormal event occurs (e.g., service timeout), the root cause analysis record of the county medical cloud platform is automatically triggered.

[0024] The technical solution of this invention utilizes a system that combines "online application and offline service" to achieve real-time location tracking of medical staff, task assignment, and remote consultation. This provides comprehensive nursing services for elderly patients suffering from illnesses and with limited mobility, meeting their diverse health needs and enabling effective connection between nursing services from hospitals to homes. It establishes a sound model for elderly care services throughout the entire life cycle. Based on AI-adaptive priorities, the system dynamically and in real-time assesses changes in the elderly patient's condition, automatically adjusts service priorities, and coordinates medical staff, equipment, and service processes in medical institutions to achieve efficient resource allocation and resolve resource misallocation caused by static priorities.

[0025] Compared with existing technologies, the technical solution of this invention forms a complete closed loop from data collection to service evaluation for age-friendly design. The system automatically iterates based on the execution effect, continuously improving service quality. It integrates resources from hospitals, communities, and third-party institutions, opening up the "hospital-community-family" service chain, solving the problems of insufficient primary healthcare resources and the key health needs of people staying at home. Furthermore, it dynamically allocates resources through a digital work order system, effectively improving the utilization rate of idle resources. Primary healthcare workers can take on more home care needs through the system. It maximizes the effectiveness of existing resources through AI intelligent scheduling. Utilizing a multi-dimensional matching algorithm, it comprehensively considers the skill matrix of healthcare workers, the needs and characteristics of elderly patients, and geographical location, and can adapt to changes in needs in real time, effectively improving the accuracy of matching healthcare workers with elderly patients and reducing unnecessary referrals. Attached Figure Description

[0026] Figure 1 This is a system framework diagram of the present invention;

[0027] Figure 2 This is a structural framework diagram of the intelligent terminal of the present invention;

[0028] Figure 3 This is a structural framework diagram of the Ai scheduling hub module of the present invention;

[0029] Figure 4 This is a structural framework diagram of the county-level medical cloud platform of the present invention;

[0030] Figure 5 This is a structural framework diagram of the execution module of the present invention;

[0031] Figure 6 This is a flowchart illustrating the actual usage of the present invention;

[0032] Figure 7 This is a schematic diagram illustrating the operation of the system response for disabled elderly patients according to the present invention;

[0033] Figure 8 This is a schematic diagram illustrating the enhanced performance of tasks for disabled elderly patients according to the present invention;

[0034] Figure 9 This is a schematic diagram illustrating the system response of the present invention for non-disabled elderly patients.

[0035] Figure 10 This is a schematic diagram illustrating the work performed by a non-disabled elderly patient using the present invention. Detailed Implementation

[0036] The technical solutions of the present invention will be further described below with reference to the accompanying drawings to make them easier to understand and master. The technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. The smart bracelets, fall detection belts, mattress pressure pads, indoor millimeter-wave radars, Bluetooth blood pressure monitors, and blood glucose meters involved are all commonly used devices generally recognized by those skilled in the art, and this application does not have any special requirements for them.

[0037] like Figure 1 As shown, the present invention provides a county-level medical and nursing dispatch system suitable for elderly patients, comprising a smart terminal with a hierarchical interactive architecture, an AI dispatch central module, a county-level medical cloud platform, and an execution module.

[0038] According to the technical solution of this invention, the smart terminal with multi-channel access supports multiple interaction methods such as voice, buttons, and gestures, and integrates functions including but not limited to fall detection and heart rate monitoring. It transmits real-time health data and elderly patient request data to the AI ​​scheduling center module via 5G / IoT protocol encryption. The AI ​​scheduling center module dynamically adjusts the execution strategy and generates corresponding service plans, which are then transmitted to the execution module for medical staff allocation. The county-level medical cloud platform optimizes the decision-making model to update medical rules, thereby promoting the evolution of the AI ​​scheduling center module and reallocating AI triage priority weights. The county-level medical cloud platform maintains terminal security (e.g., vulnerability patching) and sets personalized parameters (e.g., adjusting the monitoring frequency for elderly people living alone) for remote management of the smart terminal. The execution module records the entire medical service process, stores the data on a blockchain, and then feeds it back to the county-level medical cloud platform to complete the closed loop. The smart terminal and the execution module form an emergency bypass, connected directly via local Bluetooth. This ensures reliability and timely responsiveness in life-threatening scenarios for elderly patients (e.g., direct connection to an ambulance in case of cardiac arrest).

[0039] Preferably, such as Figure 2 As shown in the above structure: the intelligent terminal includes a data collection section and a data processing center unit composed of a physiological monitoring unit, an interactive control unit, a communication transmission unit, an emergency response unit, and an energy management unit. The physiological monitoring unit is used to monitor the health data of elderly patients; the interactive control unit is used to facilitate the flow of collected data; the communication transmission unit is used to transmit data; the emergency response unit is used for timely alarms of emergency or abnormal events; and the energy management unit is used for power supply. The data processing center unit is used to receive data, perform lightweight compression processing, and perform detection. The data processing center unit adopts a multimodal physiological signal fusion algorithm and constructs a state space model based on Kalman filtering sensor data fusion. The state space model is used to integrate multi-source heterogeneous data from physiological monitoring units (e.g., smart bracelets, mattress sensors, etc.) to improve measurement accuracy (including but not limited to heart rate, respiratory rate parameters, etc.), thereby achieving highly accurate real-time health monitoring and emergency response, and improving safety. The state space model is as follows:

[0040]

[0041] In the formula: x t z is the vector of the actual physiological state at time t (e.g., [heart rate, blood oxygen]); t For sensor observations; w t ~N(0,Q),v t ~N(0, R) represents process noise and observation noise; Additionally, this formula uses the Kalman gain K... t Dynamically weighted confidence scores for each sensor: K t =Pt -H T HP t - H T +R) -1 .

[0042] To further reduce false positives caused by "over-fusion", clinical prior knowledge can be introduced to constrain the evidence space required for calculation, thus improving the DS evidence theory. The formula is as follows:

[0043]

[0044] In the formula: m1 and m2 are the basic probability assignment functions for different sensors; A is the hypothesis of abnormal events (e.g., an elderly patient falls); and the denominator is used to normalize conflicting evidence.

[0045] Simultaneously, fall detection is performed in real time for elderly patients to facilitate timely response to emergencies. This is achieved using a spatiotemporal graph convolutional network algorithm (emphasizing Tucker decomposition to compress weight tensors, reducing computational load and improving computational efficiency).

[0046]

[0047] In the formula: l i (v j ) is the joint v j Relative to v i The dividing label; Z ij This is the cardinality normalization term for the subset.

[0048] Specifically, the aforementioned county-level medical and nursing dispatch system for elderly patients includes: the physiological monitoring unit comprising wearable devices (including but not limited to smart bracelets and fall detection belts), environmental sensors (including but not limited to mattress pressure pads and indoor millimeter-wave radar), and portable medical devices (including but not limited to Bluetooth blood pressure monitors and blood glucose meters).

[0049] Preferably, such as Figure 3 As shown in the above structure: the Ai scheduling hub module adopts multimodal decision-making. The input unit uniformly receives data (physiological signals / voice requests / environmental data) from the smart terminal. After the support unit processes the sensor noise and missing values ​​of the data, it outputs it to the core decision-making unit for multi-objective optimization. Finally, the scheduling result is simulated by a digital twin and sent to the output unit for data transmission.

[0050] Preferably, such as Figure 4As shown in the diagram, the above structure includes a county-level medical cloud platform comprising a data unit, a business unit, an AI capability unit, and an application access unit. The data unit integrates health data of elderly patients from hospitals, communities, and families within the county. The business unit facilitates multi-level referrals and connects to the medical insurance system to enable billing for home care services and reimbursement for medications used in chronic diseases. The AI ​​capability unit predicts disease models based on inpatient data to generate personalized treatment plans for elderly patients based on liver and kidney function and drug interactions. The application access unit supports elderly patients or medical staff to log in via biometrics.

[0051] Preferably, such as Figure 5 As shown in the above structure: the execution module includes a service execution unit, a resource management unit, a quality monitoring unit, and a feedback optimization unit. The service execution unit is used to reasonably schedule and allocate medical staff; the resource management unit is used to reasonably schedule and allocate medical staff; the quality monitoring unit is used to reasonably record the medical process for blockchain traceability; and the feedback optimization unit is used to reasonably accumulate data for AI model iteration and generate improvement work orders for manual follow-up within 24 hours.

[0052] like Figure 6 As shown, the present invention also provides a county-level medical care dispatching method suitable for elderly patients, comprising the following steps:

[0053] 1) The physiological monitoring unit (e.g., smart bracelet and / or mattress device) continuously collects heart rate, blood oxygen and activity data of elderly patients and performs real-time analysis. When abnormalities are detected (e.g., falls, heart arrhythmia), the emergency response unit is triggered. The elderly patient actively initiates a request through voice commands (e.g., "call a doctor for me" and / or "I need a doctor") or emergency button. The smart terminal device automatically attaches location information and basic health data. After the data processing center unit encrypts the data packet (including but not limited to timestamp, device ID, health indicators, event level) and outputs it to the AI ​​dispatch center module.

[0054] 2) After the NLP engine of the support unit parses the voice request and combines it with the knowledge graph, the core decision unit determines the type of need of the elderly patient (emergency / medication / life assistance), calculates the best handling method, generates a structured work order (including service SLA and operation instructions), and outputs the dispatch instructions to the county medical cloud platform. If an emergency occurs, the three-level response mechanism L1-L3 will be automatically activated.

[0055] 3) The data unit retrieves the complete health records (i.e., past medical history, allergic drugs) based on the elderly patient's ID, and simultaneously checks the medical insurance balance and reimbursement policy. The business unit records key operation nodes (e.g., dispatch time, nurse check-in) and multimedia evidence of the service process (e.g., before and after nursing photos) through the blockchain. The AI ​​capability unit learns and aggregates new data from various hospitals to update the corresponding disease prediction model. The encrypted data stream and model parameters are output to the execution module.

[0056] 4) Upon receiving a work order from a hospital, community, or third-party team (e.g., a nursing company), the service execution unit's app automatically plans the optimal route and prepares the necessary equipment (e.g., oxygen cylinders, catheterization kits). The resource management unit verifies the identity of medical staff through biometric authentication (including but not limited to facial and / or voiceprint recognition) and records the entire process using a law enforcement recorder (including but not limited to privacy anonymization). The quality monitoring unit assesses service compliance (e.g., whether the dressing change procedure meets video standards) by having elderly patients or their families rate the service from 1 to 5 stars via voice or the app, and automatically checks the service's standardization (e.g., whether the dressing change procedure meets video standards). Finally, a service completion report is generated (including but not limited to consumable usage, execution time, and effectiveness evaluation).

[0057] 5) The execution results of the execution module (e.g., blood pressure recovery value) are updated to the health records of the county medical cloud platform, and then the AI ​​scheduling center module readjusts the scheduling strategy. If an abnormal event occurs (e.g., service timeout), the root cause analysis record of the county medical cloud platform is automatically triggered.

[0058] Example 1

[0059] like Figure 7 and Figure 8 The system responds to and executes medical requests from disabled elderly patients as follows: When the smart mattress detects that the patient has not turned over for two consecutive hours, an L2 alarm is triggered; the AI ​​dispatch center module matches the patient with a community nurse holding a "wound care certificate" within a 5-kilometer radius; the county-level medical cloud platform pushes the elderly patient's past pressure ulcer records and medication contraindications; the nurse delivers specialized dressings to the patient's home, scanning a QR code to confirm compliance; the family receives before-and-after photos of the care via an app and provides a satisfaction evaluation; the system can also mark the nurse's pressure ulcer care skill proficiency with a corresponding coefficient.

[0060] Example 2

[0061] like Figure 9 and Figure 10The system, in response to and execution of medical requests from non-disabled elderly patients, includes: follow-up visits for chronic diseases; automatic generation of follow-up tasks after three consecutive days of elevated blood pressure detected by a health bracelet; allocation of contracted family doctors by the AI ​​scheduling center module, prioritizing those familiar with the patient's medical history; automatic generation of personalized questionnaires by the county-level medical cloud platform, focusing on dietary aspects; data synchronization to pharmacies for medication dispensing after video consultations by community doctors; and the issuance of corresponding health points, which can be used to redeem physical examination items, etc.

[0062] The key technology of this invention lies in the combination of AI-based multimodal interaction and multi-medical resource collaboration mechanism. Figure 1 and Figure 5 The highlight of this invention is its hierarchical interactive architecture system, which combines AI technology to analyze regional health data and conduct targeted health education or screening programs, providing services such as home visits by doctors, nurses, and pharmacists, as well as online consultations for specific populations.

[0063] Thus, by adopting the technical solution of this invention, a comprehensive dispatch system is built through the "Internet + Medical Dispatch" model, enabling real-time location tracking of medical staff, task assignment, and remote consultation. Based on AI-adaptive priorities, the system dynamically and in real-time assesses changes in the elderly patient's condition, automatically adjusts service priorities, and coordinates medical staff, equipment, and service processes within medical institutions to achieve efficient resource allocation, thus resolving resource misallocation caused by static priorities. Using an "online application, offline service" model, comprehensive nursing services are provided for elderly patients suffering from illnesses and with limited mobility, meeting multi-level health needs, effectively connecting nursing services from hospitals to homes, and establishing a comprehensive elderly care service system covering the entire life cycle.

[0064] As can be seen from the above description, compared with the existing technology, after adopting the technical solution of this invention, the age-friendly design forms a complete closed loop from data collection to service evaluation. The system automatically iterates based on the execution effect (e.g., optimizing the nurse scheduling model) to continuously improve service quality. It integrates resources from hospitals, communities, and third-party institutions, and opens up the "hospital-community-family" service chain, solving the problems of insufficient primary healthcare resources and the key health needs of people staying at home. Furthermore, through the dynamic allocation of digital work orders, the utilization rate of idle resources is effectively improved, and primary healthcare workers can take on more home care needs through the system. By maximizing the effectiveness of existing resources through AI intelligent scheduling, and using a multi-dimensional matching algorithm, it comprehensively considers the skill matrix of medical staff, the needs and characteristics of elderly patients, and geographical location, and can adapt to changes in needs in real time, effectively improving the accuracy of matching medical staff with elderly patients and reducing unnecessary referrals.

[0065] The technical solution, working process, and implementation effects of the present invention have been described in detail above. It should be noted that the described examples are only typical examples of the present invention. In addition, the present invention may have many other specific implementation methods. All technical solutions formed by equivalent substitution or equivalent transformation fall within the scope of protection claimed by the present invention.

Claims

1. A county-level medical care dispatch system suitable for elderly patients, characterized in that: The system includes a layered interactive architecture for smart terminals, an AI scheduling hub module, a county-level medical cloud platform, and an execution module. The smart terminals, accessible through multiple channels, support various interaction methods. They transmit real-time health data and elderly patient requests to the AI ​​scheduling hub module via 5G / IoT protocols with encryption. The AI ​​scheduling hub module dynamically adjusts execution strategies and generates corresponding service plans, which are then transmitted to the execution module for allocation of medical staff. The county-level medical cloud platform optimizes decision-making models to update medical rules, promoting the evolution of the AI ​​scheduling hub module and reallocating AI triage priority weights. The platform also maintains terminal security and sets personalized parameters for remote management of the smart terminals. The execution module records the entire medical service process, stores the data on a blockchain, and then feeds it back to the county-level medical cloud platform to complete the closed loop.

2. The county-level medical and nursing dispatch system for elderly patients according to claim 1, characterized in that: The smart terminal and the execution module form an emergency bypass, and the two are directly connected via local Bluetooth.

3. The county-level medical and nursing dispatch system for elderly patients according to claim 2, characterized in that: The intelligent terminal includes a data collection section and a data processing center unit, comprising a physiological monitoring unit, an interactive control unit, a communication transmission unit, an emergency response unit, and an energy management unit. The physiological monitoring unit monitors the health data of elderly patients; the interactive control unit facilitates the flow of collected data; the communication transmission unit transmits data; the emergency response unit provides timely alarms for emergency or abnormal events; and the energy management unit provides power. The data processing center unit receives data, performs lightweight compression processing, and performs detection. The data processing center unit employs a multimodal physiological signal fusion algorithm and constructs a state-space model based on Kalman filtering sensor data fusion. This state-space model integrates multi-source heterogeneous data from the physiological monitoring unit to improve measurement accuracy. The state-space model is as follows: In the formula: x t z is the true physiological state vector at time t; t For sensor observations; w t ~N(0,Q),v t ~N(0, R) represents process noise and observation noise; Additionally, this formula uses the Kalman gain K... t Dynamically weighted confidence scores for each sensor: K t =P t -H T HP t - H T +R) -1 .

4. The county-level medical and nursing dispatch system for elderly patients according to claim 2 or 3, characterized in that: The physiological monitoring unit includes wearable devices, environmental sensors, and portable medical devices.

5. The county-level medical and nursing dispatch system for elderly patients according to claim 1, characterized in that: The AI ​​scheduling hub module adopts multimodal decision-making. The input unit uniformly receives data from the smart terminal, and the data is processed by the support unit to remove sensor noise and missing values ​​before being output to the core decision-making unit for multi-objective optimization. Finally, the scheduling results are simulated by a digital twin and sent to the output unit for data transmission.

6. The county-level medical and nursing dispatch system for elderly patients according to claim 1, characterized in that: The county-level medical cloud platform includes a data unit, a business unit, an AI capability unit, and an application access unit. The data unit integrates health data of elderly patients from hospitals, communities, and homes within the county. The business unit facilitates multi-level referrals and connects to the medical insurance system to enable billing for home care services and special reimbursement for medications for chronic diseases. The AI ​​capability unit predicts disease models based on inpatient data to generate personalized treatment plans for elderly patients based on liver and kidney function and drug interactions. The application access unit supports elderly patients or medical staff to log in via biometrics.

7. The county-level medical and nursing dispatch system for elderly patients according to claim 1, characterized in that: The execution module includes a service execution unit, a resource management unit, a quality monitoring unit, and a feedback optimization unit. The service execution unit is used to rationally schedule and allocate medical staff; the resource management unit is used to rationally schedule and allocate medical supplies; the quality monitoring unit is used to rationally record the medical process for blockchain traceability; and the feedback optimization unit is used to rationally accumulate data for AI model iteration and generate improvement work orders for manual follow-up within 24 hours.

8. A county-level medical and nursing dispatching method suitable for elderly patients, characterized in that, Includes the following steps: Step S1: The physiological monitoring unit continuously collects heart rate, blood oxygen, and activity data of elderly patients and performs real-time analysis. When an abnormality is detected, the emergency response unit is triggered. The elderly patient actively initiates a request through voice command or emergency button. The smart terminal device automatically attaches location information and basic health data. After the data processing center unit encrypts the data packet, it is output to the AI ​​dispatch center module. Step S2: After the NLP engine of the support unit parses the voice request and combines it with the knowledge graph, the core decision unit determines the type of need of the elderly patient, calculates the best processing method, generates a structured work order, and outputs the scheduling instruction to the county medical cloud platform. Step S3: The data unit retrieves the complete health record based on the elderly patient's ID, simultaneously checks the medical insurance balance and reimbursement policy, records key operation nodes and service process multimedia evidence through the blockchain of the business unit, learns and aggregates new data from various hospitals through the AI ​​capability unit to update the corresponding disease prediction model, and outputs encrypted data streams and model parameters to the execution module. Step S4: The work order is received by the hospital, community or third-party team. The service execution unit's APP automatically plans the optimal route and prepares the necessary equipment at the same time. The resource management unit confirms the identity of medical staff through biometric authentication and records the whole process with a law enforcement recorder. The quality monitoring unit automatically detects the standardization of the service by having the elderly patient or his / her family give a 1-5 star rating through voice or APP. Finally, a service completion report is output. Step S5: The execution results of the execution module are updated to the health records of the county-level medical cloud platform, and then the AI ​​scheduling center module readjusts the scheduling strategy.

9. The county-level medical and nursing dispatching method for elderly patients according to claim 8, characterized in that: In step S2, if an emergency occurs, the three-level response mechanism L1-L3 is automatically activated.

10. The county-level medical and nursing dispatching method for elderly patients according to claim 8, characterized in that: In step S5, if an abnormal event occurs, the root cause analysis record of the county-level medical cloud platform will be automatically triggered.

Images