A medical resource auxiliary matching system for critical patients

Abstract

The application discloses a kind of medical resource auxiliary matching system for critical patients, it is related to medical care information technology field, the application includes the physiological parameter of real-time acquisition patient, medical history, first-aid demand and field environment data by multi-source data acquisition module;Disease intelligent evaluation module fusion algorithm and clinical guideline, quantize illness and divide four priority levels;Medical resource global search module obtains the dynamic information of medical institutions, beds, equipment and the like in region;Dynamic matching decision module constructs multi-objective model, generates optimal scheme;Real-time scheduling coordination module pushes scheme and establishes multi-party communication;Data security protection module guarantees data security by encryption, authority management;System self-optimization module is iterated and upgraded based on historical data.The application integrates multi-source medical data, realizes intelligent assessment of illness and accurate matching of medical resource, improves the success rate of critical patient treatment and patient satisfaction, adapts to the needs of different medical scenarios.

Description

Technical Field

[0001] This invention relates to the field of healthcare informatics technology, and in particular to a medical resource matching system for critically ill patients. Background Technology

[0002] The treatment outcomes for critically ill patients are highly time-sensitive; the effective utilization of the golden treatment window directly determines patient survival rates. Rapid and accurate matching of medical resources is a core element in shortening treatment time and improving treatment quality. While the total amount of medical resources within a region has gradually increased with the development of medical technology, the dispersed patient data, diverse resource types, and complex collaborative processes involved in critical care lead to inefficient resource matching, making it difficult to meet clinical needs. Currently, medical resource matching in critical care primarily relies on manual scheduling, which is limited by the experience, information access range, and judgment speed of the scheduling personnel, making it difficult to achieve globally optimized resource allocation.

[0003] In existing technologies, some medical resource scheduling systems only target a single resource type or a local area, lacking the ability to integrate and process multi-dimensional patient data. Patient physiological parameters, medical history, and emergency needs are scattered across different medical systems, such as electronic medical record systems, emergency equipment terminals, and laboratory information systems. This heterogeneous data format and inconsistent interfaces make it difficult to quickly integrate multi-source data, failing to provide comprehensive support for disease assessment. Furthermore, disease assessment relies heavily on traditional scoring standards, lacking dynamic quantitative analysis combined with real-time data. The assessment results are highly subjective and inaccurate, affecting the rationality of priority allocation. Medical resource retrieval is limited to basic information such as hospital beds and medical personnel, failing to cover the dynamic status of key resources such as specialized medical equipment, transport resources, and emergency medications. Moreover, resource status updates are not timely, easily leading to situations where "matching is successful but the resource is already occupied."

[0004] Furthermore, existing systems often rely on single factors like distance for matching, failing to comprehensively consider multi-objective optimization factors such as patient priority, matching capacity, resource load, and traffic conditions. This results in unscientific matching schemes, potentially leading to excessively long transport distances for high-priority patients, over-concentration of resources, or underutilization of resources. Regarding cross-institutional collaboration, information silos are prominent between different medical institutions, emergency centers, and transport teams, hindering data sharing and preventing coordinated scheduling throughout the entire treatment process, thus delaying preoperative preparation time. Simultaneously, the system lacks emergency resource backup and dynamic optimization mechanisms, making it difficult to quickly switch to alternative plans in the face of sudden resource shortages or patient deterioration, impacting the continuity of treatment. These problems collectively result in insufficient timeliness, accuracy, and reliability in matching medical resources for critically ill patients, restricting the improvement of treatment success rates. Therefore, a medical resource auxiliary matching system based on multi-source data fusion, intelligent assessment, and global optimization is urgently needed to overcome these challenges. Summary of the Invention

[0005] This invention proposes a medical resource matching system for critically ill patients to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a medical resource matching system for critically ill patients, comprising the following modules: Patient multi-source data acquisition module: Real-time acquisition of patient physiological parameters, medical history information, emergency needs and on-site environmental information. Data is collected through multiple channels, pre-processed and stored in the system database in a standardized format. Intelligent disease assessment and priority classification module: Based on multi-source data, it integrates algorithms and clinical guidelines to quantitatively analyze the severity of the disease, extract key features, classify patients into four priorities, and synchronize the assessment results to the matching module in real time; Medical resource retrieval module: Retrieves available medical institutions, beds, medical teams, medical equipment, emergency medicines and transportation resources within the region, connects with multiple systems to obtain dynamic information on resources, and establishes a dynamic database of resources across the entire region; Dynamic matching decision module: Constructs a multi-objective optimization matching model, solves the optimal matching scheme by comprehensively considering multiple factors, receives data updates in real time to dynamically adjust the results, and generates a matching report; Real-time scheduling and coordination module: pushes matching reports, establishes real-time communication channels among multiple parties, shares data and updates transfer progress, coordinates medical institutions to prepare in advance, and activates alternative plans to reschedule resources in case of emergencies; Data security and privacy protection module: It adopts encrypted transmission and storage methods, anonymizes patient information, establishes a role-based access control mechanism, and leaves full traces of operation logs; System self-optimization module: Based on historical data and feedback, it uses reinforcement learning algorithms to optimize the evaluation model and matching decision model, regularly updates indicator weights, constraints and parameters, and collects feedback to iteratively upgrade system functions.

[0007] Furthermore, it also includes a module for quantifying the urgency of the illness. This module constructs an urgency scoring function based on multi-dimensional data, and the expression for the scoring function is as follows: E represents the comprehensive score indicating the urgency of the condition. For abnormal physiological parameters, weights Weighting of disease risk levels For the treatment window option, For complication risk weights, and P represents the degree of abnormality of physiological parameters, D represents the risk level of the illness, which is assigned according to the classification criteria for acute and critical illnesses, T represents the urgency of the treatment window, and C represents the risk of complications. The weight parameters are obtained by training and optimizing the algorithm with a large amount of clinical case data.

[0008] Furthermore, it also includes a dynamic optimization module for medical resources. This module constructs a resource matching degree calculation model, the expression of which is: Where M is the resource matching degree value, a is the resource saturation weight, b is the transfer distance weight, c is the resource real-time availability weight, d is the treatment capacity matching weight, S is the medical institution resource saturation value, L is the transfer distance adaptation value, calculated based on the distance between the patient's current location and the medical institution and traffic conditions, U is the resource real-time availability value, assigned according to the real-time idle status of required equipment, beds and medicines, and K is the treatment capacity matching value, calculated based on the success rate of treating similar cases in the past and the professional level of the medical team.

[0009] Furthermore, it also includes a multimodal data fusion processing module. This module is used to fuse and process patients' textual, numerical, image, and audio data. The textual data includes medical history descriptions and doctor's orders from electronic medical records; the numerical data includes physiological parameters and laboratory test results; the image data includes CT images, ultrasound images, electrocardiogram scans; and the audio data includes verbal descriptions of patients' symptoms by medical staff and on-site emergency voice recordings. Abnormal features in the image data are analyzed through image processing algorithms, and the audio data is converted into text and core information is extracted using speech recognition technology. After all modal data are fused, a unified comprehensive dataset of patients' conditions is generated.

[0010] Furthermore, it also includes a cross-institutional remote collaboration module. This module builds a multi-institutional collaborative work platform, supporting real-time remote collaboration between emergency center transport teams involved in treatment and hospitals and expert teams. The platform has built-in high-definition video conferencing function, medical data sharing whiteboard, image co-annotation tools, and real-time message push function. Expert teams can remotely view patients' real-time data and image materials through the platform, receiving hospitals can obtain complete patient condition information in advance through the platform and formulate treatment plans, and transport teams can provide real-time feedback on patients' status during the journey and receive remote guidance. The module supports access and adaptation to existing systems of different medical institutions.

[0011] Furthermore, it also includes an emergency resource backup and scheduling module. This module is used to deal with sudden resource shortages or emergencies, establish a regional emergency medical resource reserve, and reserve resources include spare ICU beds, mobile emergency rescue equipment, emergency medical teams, and emergency medicines. The status of reserve resources is monitored in real time. When resources in the main matching plan suddenly become unavailable or a patient's condition suddenly deteriorates and requires higher-level resources, the module automatically retrieves suitable resources from the reserve, generates an emergency matching plan, and triggers reserve resource scheduling instructions. The module also supports adjusting the type and quantity distribution of reserve resources in advance according to the regional incidence patterns of acute and critical illnesses.

[0012] Furthermore, it also includes a patient treatment process tracking module. This module is used to track the entire process information of patients from the start of data collection to the completion of treatment. It records key time nodes at each stage, including data collection start time, condition assessment completion time, matching plan generation time, transfer departure time, arrival time at the hospital, treatment start time, and treatment end time. It also records the condition changes, data resource scheduling, medical and nursing operation information, and treatment effect evaluation results at each stage, forming a complete patient treatment trajectory file. At the same time, the module supports pushing key treatment node information to the patient's family.

[0013] Furthermore, it also includes a medical resource demand forecasting module. This module is based on the seasonal variation patterns of historical acute and critical illness incidence data, climate factors, major event arrangements, and public health event early warning information in the region. It uses a forecasting algorithm that combines time series analysis and machine learning to predict the number and disease distribution of acute and critical illness patients in the region and the types and quantities of medical resources required in the future. It generates a resource demand forecasting report and pushes it to the regional medical resource management department and various medical institutions.

[0014] Furthermore, it also includes a multi-system interface adaptation module. This module adopts a standardized interface design and supports interfacing with various medical-related systems, including hospital information systems (HIS), electronic medical record systems (EMR), laboratory information systems (LIS), medical image archiving and communication systems (PACS), emergency center dispatch systems, wearable medical device data interfaces, and regional medical and health information platforms. The interface supports bidirectional data transmission, and the module supports flexible configuration of interface parameters, which can be adapted and adjusted according to the interface specifications of different interfacing systems.

[0015] Furthermore, it includes a privacy protection enhancement module. Building upon the data security and privacy protection modules, this module employs differential privacy technology to process patient identity information and sensitive medical data. Without affecting data availability, it establishes a data access audit and traceability mechanism, meticulously recording all data access operations, including access time, content, operation type, and purpose. Audit records are retained for at least the legally required time, allowing regulatory authorities to access and verify them at any time. The module also features data leakage monitoring, promptly identifying abnormal access and data leakage risks by monitoring data transmission and access behavior in real time, triggering early warning mechanisms, and taking blocking measures. Additionally, it supports patient authorization management, allowing patients to authorize specific medical institutions or personnel to access their medical data through designated channels.

[0016] Compared with existing technologies, the beneficial effects of this invention are: The medical resource matching system for critically ill patients of this invention focuses on the medical and health care information data processing and resource management needs of the G16H classification number. Through multi-module collaborative design, it comprehensively solves many pain points of existing technologies, and its core beneficial effects are significant.

[0017] The system, through its multi-source patient data acquisition module, comprehensively integrates physiological parameters, medical history, emergency needs, and on-site environmental data. It supports real-time transmission and standardized processing of multiple data types, eliminating information fragmentation caused by data heterogeneity across different systems. This provides comprehensive and accurate data source support for disease assessment and matching decisions. The intelligent disease assessment and prioritization module integrates deep learning algorithms and clinical guidelines, enabling objective quantitative analysis of disease severity. Prioritization more closely aligns with actual treatment needs, avoiding the subjectivity and limitations of manual assessment and providing a scientific basis for prioritizing resource allocation.

[0018] The comprehensive medical resource retrieval module covers all types of resources, including medical institutions, beds, medical teams, specialized equipment, and transportation resources. By connecting with multiple systems to obtain real-time dynamic status of resources and establishing a comprehensive resource database, it solves the problems of narrow search scope and information lag in traditional searches, ensuring the comprehensiveness and timeliness of resource information. The dynamic matching decision module constructs a multi-objective optimization model, which comprehensively considers multiple influencing factors for global optimization, realizing refined and dynamic matching of medical resources. This not only improves the response speed of high-priority patients but also optimizes the overall resource utilization rate, avoiding resource waste and uneven distribution.

[0019] The real-time dispatch and collaboration module establishes a cross-institutional multi-party communication and data sharing platform, breaking down information silos and enabling efficient collaboration between emergency centers, medical institutions, and transport teams. This shortens pre-operative preparation time and improves the efficiency of the entire treatment process. The emergency resource backup and dispatch module establishes a reserve resource database and a seamless switching mechanism, effectively responding to sudden resource shortages or worsening conditions, ensuring the continuity of treatment, and reducing medical risks caused by resource interruptions.

[0020] The data security and privacy protection module, along with the privacy protection enhancement module, comprehensively safeguards the security and privacy of patient medical data through multiple encryption methods, access control, and audit traceability, complying with relevant medical data security regulations. The system self-optimization module continuously iterates and upgrades based on historical data and feedback, improving the system's adaptability to different acute and critical care scenarios and regional medical resource distributions, ensuring the system maintains good operational performance in the long term.

[0021] Overall, through its comprehensive design encompassing data processing, intelligent assessment, optimized matching, collaborative scheduling, and security assurance, this system significantly improves the timeliness, accuracy, and reliability of matching medical resources for critically ill patients. It effectively shortens treatment time, optimizes the allocation of medical resources, and increases the success rate of treatment, providing strong technical support for the treatment of critically ill patients and aligning with the development direction of the field of healthcare informatics. Attached Figure Description

[0022] Figure 1 This is a schematic block diagram of a medical resource auxiliary matching system for critically ill patients proposed in this invention; Figure 2 Line graph showing the time consumption for resource matching under different disease complexities; Figure 3 Pie chart comparing the effects of optimizing medical resource allocation; Figure 4 A bar chart showing the trend of the operating efficiency of each module of the system over time. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0025] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0026] Reference Figures 1 to 4A medical resource matching system for critically ill patients, comprising the following modules: The multi-source patient data acquisition module is used to collect multi-dimensional medical data of critically ill patients in real time. The data types include physiological parameters, medical history information, emergency needs information, and on-site environmental information. Physiological parameters include heart rate, blood pressure, blood oxygen saturation, body temperature, respiratory rate, and electrocardiogram signals. Medical history information is obtained by connecting to the electronic medical record system to obtain past medical history, allergy history, surgical history, and basic disease information. Emergency needs information includes the types of diseases requiring emergency intervention, the special medical equipment required, surgical needs, and transportation requirements. On-site environmental information includes the patient's current location, traffic conditions, and information on available emergency vehicles. Data acquisition supports real-time transmission from wearable medical devices, direct connection to emergency equipment interfaces to medical staff's mobile terminals for manual input, and cross-hospital system data exchange. After preprocessing to remove outliers and fill in missing data, the collected data is stored in the system database in a standardized format. The intelligent disease assessment and priority classification module, based on collected multi-source data, uses deep learning algorithms and clinical emergency and critical care assessment guidelines to quantitatively analyze the severity of patients' conditions. It extracts key disease characteristics, including the degree of abnormality in physiological parameters, the rate of disease progression, the risk of complications, and the treatment window. Based on the analysis results, patients are classified into four priority levels: Special Level, Level 1, Level 2, and Level 3. Special Level corresponds to an endangered state requiring immediate treatment, Level 1 corresponds to a critical state requiring treatment within 1 hour, Level 2 corresponds to an emergency state requiring treatment within 3 hours, and Level 3 corresponds to a severe state requiring treatment within 6 hours. The assessment results are synchronized to the subsequent matching module in real time. The medical resource retrieval module is used to retrieve information on available medical resources within the region. Resource types include medical institutions with the ability to treat critical and severe illnesses, ICU beds, specialist medical teams, special medical equipment, emergency medicines, and transport resources. Medical institution information includes hospital level, treatment specialty qualifications, current number of beds, ICU load rate, number of available operating rooms, and medical staff configuration. Special medical equipment includes extracorporeal membrane oxygenation (ECMO) equipment, ventilators, defibrillators, etc. Transport resources include ambulances, helicopter rescue resources, and transport medical staff configuration. The system connects to the HIS systems of various hospitals in the regional medical resource dispatch center and the emergency center resource management platform to obtain real-time dynamic status information of resources and establish a dynamic database of resources across the entire region. The dynamic matching decision module receives the patient's condition assessment results and resource retrieval information, and constructs a multi-objective optimization matching model. With the goals of maximizing the success rate of treatment, minimizing the rescue time, and optimizing resource utilization, it comprehensively considers the patient's condition priority, the medical institution's treatment capacity, the matching degree, the current resource load, the transfer distance, and the transportation time. It solves the optimal matching scheme through a heuristic algorithm. During the matching process, it receives the patient's condition update data and resource status change information in real time, dynamically adjusts the matching results, and generates a matching report that includes recommended treatment hospitals, alternative hospitals, transfer plans, and resource scheduling instructions. The real-time dispatch and coordination module pushes matching reports to the corresponding medical institution's emergency center and transport team, establishes a multi-party real-time communication channel, supports text, voice and video collaboration and medical data sharing, updates the transport progress in real time, including vehicle location, estimated arrival time and changes in the patient's condition, coordinates medical institutions to make preparations for treatment in advance, including reserving beds, allocating medical teams, and preparing surgical equipment and medicines. When there is a sudden resource shortage or a worsening of the patient's condition, the backup plan activation mechanism is automatically triggered to reschedule resources. The data security and privacy protection module uses an encrypted transmission protocol to protect the medical data transmission process. During the storage stage, it adopts a partitioned encrypted storage method to anonymize patient identity information. It establishes a role-based access control mechanism, dividing different permission levels such as medical staff, dispatchers, and managers. Only authorized personnel can access data at the corresponding level. The system operation log is fully traced, recording all operations such as data access, modification, and transmission, in order to meet the relevant regulatory requirements for medical data security. The system's self-optimization module, based on historical matching data, treatment effect feedback, and resource scheduling records, uses reinforcement learning algorithms to continuously optimize the accuracy of the disease assessment model and the rationality of the matching decision model. It regularly updates the weights of critical care assessment indicators, resource matching constraints, and algorithm parameters, while collecting feedback from medical staff and medical institutions to iteratively upgrade the system's functional modules and improve the system's ability to adapt to different critical care scenarios and regional medical resource distributions.

[0027] This invention also includes a disease urgency quantification scoring module. This module constructs an urgency scoring function based on multi-dimensional data to accurately quantify the urgency of a patient's condition, providing data support for priority classification and matching decisions. The scoring function expression is as follows: E represents the overall urgency score of the condition, ranging from 0 to 100 points, with higher scores indicating greater urgency. This represents the weighting for abnormal physiological parameters, with a value range of 0.3-0.4. This represents the weighting of the disease risk level, with a value ranging from 0.25 to 0.35. The treatment window option weight is 0.15-0.25. This is the risk weight for complications, with a value ranging from 0.1 to 0.15. P represents the degree of abnormality of physiological parameters, calculated by weighting the degree of deviation of various physiological parameters from the normal range, with a value range of 0-100. D represents the risk level of the condition, assigned according to the classification standards for acute, critical and severe illnesses: 100 for endangered state, 80 for critical state, 60 for acute state, and 40 for severe state. T represents the urgency value of the treatment window period; the shorter the treatment window period, the higher the score, with a value range of 0-100. C represents the risk value of complications, predicted based on medical history and current condition, with a value range of 0-100. The weight parameters are obtained by training and optimizing a large amount of clinical case data using machine learning algorithms to meet the needs of actual treatment scenarios.

[0028] This invention also includes a dynamic optimization module for medical resources. This module constructs a resource matching degree calculation model to comprehensively evaluate the degree of fit between medical institutions and patient needs, thereby achieving refined resource matching. The expression for the matching degree calculation model is as follows: Where M is the resource matching score, ranging from 0 to 100, with higher scores indicating better matching; a is the resource saturation weight, ranging from 0.25 to 0.35; b is the transfer distance weight, ranging from 0.2 to 0.3; c is the resource availability weight, ranging from 0.2 to 0.3; d is the treatment capacity matching weight, ranging from 0.15 to 0.25, and a+b+c+d=1; S is the medical institution resource saturation score, calculated based on the current bed utilization rate, ICU load rate, and the workload of medical staff, ranging from 0 to 100, with lower values ​​indicating lower saturation. L is the transfer distance adaptation value, calculated based on the distance between the patient's current location and the medical institution and traffic conditions. The closer the distance and the smoother the traffic, the higher the score. The value ranges from 0 to 100. U is the resource availability value, assigned according to the real-time idle status of the required equipment, beds and medicines. 100 is fully available, 60 is partially available, and 0 is unavailable. K is the treatment capacity matching value, calculated based on the success rate of treating similar cases in the past and the professional level of the medical team. The value ranges from 0 to 100. This model can dynamically balance resource load and patient treatment needs, avoiding excessive concentration or waste of resources.

[0029] This invention also includes a multimodal data fusion processing module. This module is used to fuse and process patient textual, numerical, image, and audio data. The textual data includes medical history descriptions and doctor's orders from electronic medical records; the numerical data includes physiological parameters and laboratory test results; the image data includes CT images, ultrasound images, and electrocardiogram scans; and the audio data includes verbal descriptions of patient symptoms by medical staff and on-site emergency audio recordings. The module uses natural language processing technology to extract key information from the text, analyzes abnormal features in the image data through image processing algorithms, and uses speech recognition technology to convert audio data into text and extract core information. After all modal data are fused, a unified comprehensive dataset of patient conditions is generated, eliminating assessment bias caused by data heterogeneity and providing more comprehensive data source support for condition assessment and matching decisions.

[0030] This invention also includes a cross-institutional remote collaboration module. This module builds a multi-institutional collaborative work platform, supporting real-time remote collaboration between emergency center transport teams and hospitals and expert teams involved in treatment. The platform has built-in high-definition video conferencing, medical data sharing, whiteboard, image annotation tools, and real-time message push functions. Expert teams can remotely view patients' real-time data and images through the platform, providing remote diagnostic suggestions and treatment guidance to frontline medical staff. Receiving hospitals can obtain complete patient information in advance through the platform and formulate targeted treatment plans. Transport teams can provide real-time feedback on patients' status during transport and receive remote guidance. The module supports access and adaptation to existing systems of different medical institutions, breaking down information silos and realizing cross-institutional collaboration throughout the entire process of emergency and critical care treatment.

[0031] This invention also includes an emergency resource backup and scheduling module. This module is used to respond to sudden resource shortages or emergencies by establishing a regional emergency medical resource reserve. The reserve resources include backup ICU beds, mobile emergency rescue equipment, emergency medical teams, and emergency medicines. The module monitors the status of the reserve resources in real time. When resources in the main matching plan suddenly become unavailable or a patient's condition suddenly deteriorates and requires higher-level resources, the module automatically retrieves suitable resources from the reserve, calculates the fastest scheduling path and time, generates an emergency matching plan, and triggers a reserve resource scheduling command to coordinate the rapid arrival of reserve resources. The emergency plan and the main plan can be seamlessly switched to ensure that the patient's treatment process is not interrupted. The module also supports adjusting the type and quantity distribution of reserve resources in advance according to the regional incidence patterns of acute and critical illnesses to improve emergency response capabilities.

[0032] This invention also includes a patient treatment process tracking module. This module tracks the entire process of a patient's treatment from data collection to completion, recording key time nodes at each stage, including data collection start time, condition assessment completion time, matching plan generation time, transfer departure time, arrival time at the hospital, treatment start time, and treatment end time. It also records changes in the patient's condition, data resource scheduling, medical and nursing operation information, and treatment effect evaluation results at each stage, forming a complete patient treatment trajectory file. The file allows medical and management personnel to review and review the treatment process, and is used for medical quality assessment and system optimization analysis. At the same time, the module supports pushing key treatment node information to the patient's family members to ensure their right to know. The content and frequency of the push can be set according to the family's needs and hospital regulations.

[0033] This invention also includes a medical resource demand prediction module. Based on historical data on the incidence of acute and critical illnesses in the region, seasonal variation patterns, climate factors, major event arrangements, and public health event early warning information, this module uses a prediction algorithm combining time series analysis and machine learning to predict the number and disease distribution of acute and critical patients in the region within a certain future time period, as well as the types and quantities of medical resources required. It generates a resource demand prediction report, which is then pushed to the regional medical resource management department and various medical institutions. This provides data support for medical institutions to adjust resource allocation in advance, including medical staff scheduling, equipment maintenance, and supplementary drug procurement. It helps medical institutions balance resource supply and demand, avoid resource shortages or idleness, and improve the overall regional capacity to respond to acute and critical illnesses.

[0034] This invention also includes a multi-system interface adaptation module. This module adopts a standardized interface design and supports interfacing with various medical-related systems, including hospital information systems (HIS), electronic medical record systems (EMR), laboratory information systems (LIS), medical image archiving and communication systems (PACS), emergency center dispatch systems, wearable medical device data interfaces, and regional medical and health information platforms. The interface supports bidirectional data transmission, enabling rapid acquisition of patient data and real-time push of system matching results and dispatch instructions. The interface adopts medical data standard protocols such as HL7 and DICOM to ensure the compatibility and accuracy of data transmission. The module supports flexible configuration of interface parameters and can be adapted and adjusted according to the interface specifications of different interfacing systems, reducing the difficulty of system integration and improving the system's versatility and scalability.

[0035] This invention also includes a privacy protection enhancement module. Building upon the data security and privacy protection modules, this module further strengthens patient privacy protection measures. It employs differential privacy technology to process patient identity information and sensitive medical data, preventing the leakage of patient personal information without affecting data availability. A data access audit and traceability mechanism is established, meticulously recording all data access operations, including access time, content, operation type, and purpose. Audit records are retained for at least the legally required timeframe, allowing regulatory authorities to access and verify them at any time. The module also features data leakage monitoring, promptly identifying abnormal access and data leakage risks by monitoring data transmission and access behavior in real time, triggering an early warning mechanism, and taking blocking measures. Furthermore, it supports patient authorization management, allowing patients to authorize specific medical institutions or personnel to access their medical data through designated channels. The scope and validity period of authorization can be flexibly set, fully protecting patients' data sovereignty.

[0036] The following two examples further illustrate the specific implementation of this system: Example 1: City-level Regional Emergency and Critical Care Medical Resource Matching System This embodiment is applied to large cities with a population of over 10 million, covering 12 tertiary hospitals, 28 secondary hospitals, 56 emergency stations, and over 300 ambulances. The system is connected to the city's electronic medical record system, regional medical resource dispatch center, HIS / LIS / PACS systems of various hospitals, and wearable medical device platform. For patients with acute and critical illnesses such as stroke, myocardial infarction, and severe trauma, it enables rapid and accurate matching and collaborative dispatch of medical resources across the entire region, fully implementing the functions of each module of this invention.

[0037] I. Implementation Details of Core Modules Patient Multi-Source Data Acquisition Module: The system supports three data acquisition methods. Wearable medical devices transmit patient heart rate, blood pressure, blood oxygen saturation, body temperature, respiratory rate, and ECG signals in real time, with a sampling frequency of 1 time / second. The data is transmitted to the emergency terminal via Bluetooth 5.0 and then simultaneously uploaded to the system. The emergency equipment interface directly connects to defibrillators, ventilators, and other devices to acquire treatment-related data such as ECG waveforms and respiratory support parameters. Medical staff can input patient symptom descriptions, emergency intervention measures, and required special treatments via mobile terminals, supporting rapid voice-to-text input. The system also integrates with the city's electronic medical record system to automatically acquire patients' past medical history, allergy history, surgical history, and basic disease information such as hypertension and diabetes. It also integrates with the traffic management department platform to obtain the patient's current location, real-time traffic conditions, and the location information of nearby available emergency vehicles. The collected data is filtered by Kalman filtering to remove outliers in physiological parameters, and the K-nearest neighbor algorithm is used to complete missing data. The data is then stored in the system's distributed database in HL7 standard format.

[0038] The intelligent patient assessment and priority classification module, based on collected multi-source data, integrates a CNN-LSTM deep learning model with APACHE II and SOFA clinical acute and critical care assessment guidelines. It extracts key features such as the degree of physiological parameter abnormalities (e.g., percentage of blood pressure deviating from the normal range), the rate of disease progression (e.g., the increase in heart rate within 10 minutes), complication risks (e.g., the risk of heart failure in myocardial infarction patients), and the remaining treatment window (e.g., the remaining time of the golden window for thrombolysis in stroke patients). The model, trained on data from over 100,000 acute and critical care cases, achieves an assessment accuracy of over 95%. Based on the output results, patients are classified into four priority levels. For example, an acute myocardial infarction patient, assessed with a heart rate of 130 bpm, blood pressure of 85 / 50 mmHg, and a remaining treatment window of 1.5 hours, is classified as a Level 1 priority patient requiring treatment within one hour. The assessment results are synchronized in real-time to the dynamic matching decision module.

[0039] The comprehensive medical resource retrieval module connects to the municipal medical resource dispatch center to obtain real-time dynamic information on medical resources within the region. Medical institution information includes hospital level, specialist certifications such as cardiology and neurosurgery, current number of open beds, ICU load rate updated every 5 minutes, number of available operating rooms, and staffing including chief physicians, nurses, and their specialties. Special medical equipment includes the real-time availability and location of 28 extracorporeal membrane oxygenation (ECMO) devices, 156 ventilators, and 89 defibrillators. Transport resources include over 300 ambulances with onboard equipment, current location, estimated dispatch time, 3 helicopter rescue resources, and the qualifications of transport medical personnel. Emergency medications include the inventory status of over 20 commonly used emergency and critical care medications such as thrombolytic drugs and vasopressors. The system establishes a comprehensive dynamic resource database, categorized and indexed by resource type, geographical location, and availability, supporting millisecond-level retrieval.

[0040] The dynamic matching decision module receives assessment results and resource retrieval information for patients with Level 1 priority acute myocardial infarction. It constructs a multi-objective optimization matching model with the goals of maximizing treatment success rate, minimizing transport time, and optimizing resource utilization. The model comprehensively considers factors such as patient condition priority (weighted at 0.4), medical institution treatment capacity matching degree (weighted at 0.3), current resource load (weighted at 0.15), and transport distance and travel time (weighted at 0.15). A genetic algorithm is used to solve for the optimal matching scheme. Three tertiary hospitals qualified for emergency myocardial infarction surgery were found. Hospital A, with an ICU load rate of 35%, is 8 kilometers from the patient's current location, has an estimated transport time of 20 minutes, and a 92% success rate in treating similar cases. It has the highest overall score and is selected as the recommended hospital. Hospitals B and C are considered as backups. The system generates a matching report including the recommended hospital, backup hospitals, emergency vehicle dispatch instructions, and transport route planning.

[0041] Real-time dispatch and collaboration module: This module pushes matching reports to the emergency department of Hospital A, the municipal emergency medical center, and nearby ambulance terminals, establishing a 5G real-time communication channel. After receiving dispatch instructions, the ambulance departs, uploading real-time dynamic data such as vehicle location, patient's heart rate, and blood pressure. Hospital A uses the system to obtain complete patient information, cardiac imaging, and medical history in advance, reserving ICU beds, allocating the cardiology medical team, and preparing DSA surgical equipment and thrombolytic drugs. Medical staff communicate via voice and video through the system, synchronizing patient treatment progress. If the patient's blood pressure suddenly drops during transport, the system automatically triggers a condition update, adjusting the matching plan in real time and coordinating with Hospital A to initiate emergency preparations in advance.

[0042] Data security and privacy protection module: Data transmission uses the TLS 1.3 encryption protocol, and storage employs AES-256 partitioned encryption. Patient ID numbers, names, and other personal information are anonymized using hash algorithms. A role-based access control mechanism is established: medical staff can only access patient-related medical and treatment data, dispatchers can only view resource scheduling information, and administrators can only obtain statistical data, without data modification permissions. The system operation log records all data access, modification, and transmission operations, including details such as the operator, time, and content. The log is retained for 10 years, meeting the requirements of medical data security regulations.

[0043] Other modules implemented include: a multimodal data fusion processing module that extracts key textual information from patients' electronic medical records, vascular stenosis features from CT images, and symptom details from medical staff's voice descriptions to generate a unified comprehensive dataset of patient conditions; a cross-institutional remote collaboration module that supports video conferencing between experts from Hospital A and medical staff in ambulances to remotely guide emergency measures en route; an emergency resource backup and scheduling module that establishes an emergency reserve of 5 spare ICU beds and 3 mobile ventilators, with real-time status monitoring; a patient treatment process tracking module that records the time of each node in data collection, evaluation, matching, transfer, and treatment to form a complete treatment trajectory archive; a medical resource demand prediction module that predicts the peak incidence of myocardial infarction in the following month based on historical data and pushes resource adjustment suggestions to each hospital; a multi-system interface adaptation module that achieves seamless integration with the existing systems of each hospital through standardized interfaces; and a privacy protection enhancement module that uses differential privacy technology to process sensitive data and establishes an access audit and traceability mechanism.

[0044] Table 1 Comparison of the effects of city-level systems and traditional manual dispatching

[0045] Table 1 clearly demonstrates the advantages of this invention in city-level scenarios. Traditional manual dispatching relies on the experience of dispatchers to retrieve resources, resulting in time-consuming matching processes and difficulties in cross-institutional data sharing. This leads to long response times, low resource utilization, and limited success rates for high-priority patients. The system of this invention achieves rapid matching through automatic multi-source data collection and standardized processing, combined with intelligent algorithms. Resource matching time is reduced to 3 minutes, and cross-institutional data sharing takes only 15 seconds. The response time is compressed to 28 minutes, medical resource utilization is increased to 85%, and the success rate for critically ill patients is improved by 13 percentage points. This fully demonstrates the significant improvement in timeliness, accuracy, and coordination of the system, effectively maximizing the overall efficiency of city-level medical resources and securing golden treatment time for critically ill patients.

[0046] Example 2: County-level Emergency and Critical Care Medical Resource Matching System This embodiment is applied to a county-level area, covering one county-level tertiary hospital, eight township health centers, twelve village-level medical points, and fifteen ambulances. The system is connected to the county's medical and health information platform, the information systems of various medical institutions, and the emergency dispatch center. It aims to integrate and efficiently match medical resources at the county, township, and village levels for common acute and critical illnesses in the county, such as stroke, severe trauma, and obstetric emergencies, with a focus on strengthening grassroots resource coordination and emergency dispatch capabilities.

[0047] I. Implementation Details of Core Modules The patient multi-source data acquisition module: Village-level medical points collect basic physiological parameters such as heart rate, blood pressure, and blood oxygen saturation from patients using portable medical devices and upload them to the system via a 4G network; township health centers directly connect to ECG monitors and ultrasound equipment through device interfaces to obtain ECG signals, ultrasound images, and other data; medical staff enter patient symptoms, past medical history, allergy history, and emergency needs through mobile terminals, supporting offline entry and automatic synchronization after network recovery; the system connects to the county medical and health information platform to obtain patients' past medical records, surgical history, and underlying disease information at county-level hospitals; on-site environmental information is obtained through the positioning module to obtain the patient's current village / township location, combined with data from the county transportation department to obtain rural road conditions and the location of available emergency vehicles. The collected data is processed by an outlier detection algorithm to remove interfering data, and missing fields are filled in before being stored in a standardized format in a local database and simultaneously backed up to the municipal cloud platform.

[0048] The intelligent disease assessment and prioritization module integrates a lightweight deep learning model with county-level assessment guidelines for common acute and critical illnesses, such as the NIHSS score for stroke and the ISS score for trauma. It extracts key features including the degree of physiological parameter abnormalities, the rate of disease progression, complication risks (e.g., postpartum hemorrhage risk in obstetric emergencies), and the treatment window. The model is optimized for county-level case data, adapting to the characteristics of primary healthcare data, achieving an assessment accuracy of over 93%. For example, a stroke patient reported by a village-level medical point, assessed as having altered consciousness, left-sided hemiplegia, 90% oxygen saturation, and a remaining 2 hours of the treatment window, is classified as a Level 1 priority patient requiring transfer to a county-level hospital within one hour. The assessment results are synchronized in real-time to the dynamic matching decision-making module.

[0049] The comprehensive medical resource retrieval module connects to the county-level medical resource dispatch center, enabling the retrieval of all types of medical resources within the county. County-level hospital information includes the number of ICU beds, neurology specialty qualifications, available operating rooms, staffing levels, and thrombolytic drug inventory. Township health center information includes the status of emergency equipment such as ventilators and defibrillators, and the number of temporary treatment beds. Transport resources include 15 ambulances with onboard emergency equipment, current location, estimated dispatch time, and vehicle type information suitable for rural roads. Emergency medications cover the inventory and distribution routes of commonly used acute and critical care drugs in various medical institutions within the county. The system establishes a dynamic database of county-level resources, indexed by a three-tiered system of county, township, and village levels to ensure comprehensive coverage of grassroots resource information.

[0050] The dynamic matching decision module receives the assessment results of first-priority stroke patients and resource retrieval information, and constructs a multi-objective optimization matching model adapted to the county's traffic characteristics. The model aims to minimize transport time, maximize the adaptability of treatment capabilities, and optimize the coordination of primary care resources. It comprehensively considers the patient's condition priority (weighted at 0.35), the county hospital's treatment capacity (weighted at 0.3), the transport road conditions (weighted at 0.2), and the primary care resource cooperation degree (weighted at 0.15), and solves the optimal solution using a simulated annealing algorithm. If a county hospital is found to have stroke thrombolysis qualifications, an ICU load of 28%, is 35 kilometers from the patient's village, has an estimated transport time of 45 minutes on rural roads, and can provide emergency support en route, the hospital with the highest comprehensive score is selected as the recommended treatment hospital. The system generates a matching report including the recommended hospital, the township health center's collaborative plan, emergency vehicle dispatch instructions, and the optimal transport route.

[0051] The real-time dispatch and collaboration module pushes matching reports to county-level hospital emergency departments, township health centers, ambulances, and village-level medical points. Township health center medical staff arrive at the patient's location in advance with emergency equipment to conduct initial emergency intervention; ambulances depart according to the planned route, uploading their location and patient's condition data en route in real time; county-level hospitals obtain complete patient information in advance through the system, reserve ICU beds in the neurology department, and allocate thrombolysis teams and related equipment and medications. The cross-institutional remote collaboration module supports video conferencing between county-level hospital experts and township health center medical staff to remotely guide emergency measures en route; the real-time dispatch module synchronously updates the transport progress and coordinates with county-level hospitals to prepare for surgery.

[0052] Data security and privacy protection module: Data transmission uses SSL encryption protocol, and storage employs partitioned encryption. Patient identity information is anonymized, with only the patient's ID retained for related queries. A role-based access control mechanism is established: county-level hospital medical staff can access complete patient data, township and village-level medical staff can only access information relevant to their own responsibilities, and dispatchers can only view resource scheduling data. System operation logs record all data operation behaviors, including operator, time, content, and device information. The logs are retained for 8 years, complying with county-level medical data security management requirements.

[0053] Other modules implemented include: a multimodal data fusion processing module that extracts abnormal features from patient ultrasound images, key information from electronic medical record text, and symptom details from medical staff's voice descriptions to generate a unified disease dataset; an emergency resource backup and scheduling module that establishes a county-level emergency reserve including two backup ICU beds and two mobile ventilators, monitors its status in real time, and automatically triggers backup resource scheduling when county-level hospital beds are suddenly occupied; a patient treatment process tracking module that records the time and key information of each node from village-level data collection to county-level hospital treatment, forming a traceable treatment record; a medical resource demand prediction module that predicts the peak season for stroke in winter based on the county's seasonal disease patterns and pushes resource adjustment suggestions to county-level hospitals; a multi-system interface adaptation module that enables integration with county-level hospital HIS, LIS, PACS systems and township health center information systems; and a privacy protection enhancement module that uses differential privacy technology to process sensitive data and establishes an access audit mechanism to protect patient privacy.

[0054] Table 2 Comparison of the effects of county-level system and traditional manual dispatching

[0055] Table 2 highlights the application value of this invention in county-level scenarios. Traditional manual dispatching suffers from problems such as poor coordination of county, township, and village resources, unreasonable transfer route planning, and low utilization of grassroots resources, resulting in long coordination and transfer times and low patient satisfaction. This invention's system integrates county, township, and village-level resources through comprehensive resource retrieval, reducing coordination time to 5 minutes and average transfer time to 42 minutes. Grassroots resource utilization is increased to 78%, fully leveraging the pre-emptive emergency role of township and village-level medical points. Emergency resource response time is shortened to 8 minutes, effectively addressing sudden resource shortages. Patient and family satisfaction is increased to 93%, demonstrating the system's significant effects in improving treatment efficiency, optimizing resource allocation, and enhancing the medical experience, adapting to the resource distribution characteristics and coordination needs of county-level emergency and critical care.

[0056] Reference Figure 2 This figure visually demonstrates the superior matching efficiency of this invention across different levels of disease complexity. Traditional manual scheduling relies on human experience to retrieve resources; the more complex the condition, the longer the information integration and judgment take. Matching at level 5 complexity can take up to 50 minutes, delaying crucial treatment time. This invention's system automatically integrates physiological parameters, medical history, and other information through a multi-source patient data acquisition module; the intelligent disease assessment module quickly quantifies the condition; the medical resource global retrieval module obtains resource status in milliseconds; and the dynamic matching decision module uses algorithms to find the optimal solution. Matching at level 2 complexity takes only 2.5 minutes, and at level 5 complexity, only 4.5 minutes, with a significantly lower time increase than traditional methods. The chart data verifies the high efficiency of the system's multi-module collaboration, especially its ability to maintain rapid matching even under complex conditions, saving valuable time for the treatment of critically ill patients.

[0057] Reference Figure 3 This figure highlights the optimization capabilities of this invention in medical resource allocation. Traditional manual scheduling lacks a global optimization mechanism, resulting in irrational resource allocation. ICU beds account for only 30%, and general ward beds account for 15%, failing to meet the needs of critically ill patients for intensive care resources. The dynamic matching decision module of this invention aims to maximize treatment success rate and optimize resource utilization. It comprehensively considers patient priority and resource needs, increasing the allocation ratio of ICU beds to 45% and ambulances to 30%, ensuring that high-priority patients can receive critical resources promptly. Simultaneously, it reduces the allocation ratio of general ward beds and other resources to 5%, avoiding resource idleness. The chart data verifies the scientific nature of the system's resource allocation model, enabling resources to be tilted towards high-value treatment stages, improving the overall efficiency of medical resource utilization, and better serving the treatment of critically ill patients.

[0058] Reference Figure 4This figure illustrates the trend of efficiency improvement of each module in the system over time. In the initial stage of system operation, each module was in a break-in phase, with data acquisition efficiency at 120 records / second, disease assessment efficiency at 80 cases / second, resource matching efficiency at 60 times / second, and scheduling and coordination efficiency at 40 times / second. As the system's self-optimization module continuously optimized algorithm parameters and processes based on historical data, the efficiency of each module gradually improved. After 12 months of operation, data acquisition efficiency increased to 220 records / second, thanks to the optimization of multi-source data interfaces and improvements in transmission protocols; disease assessment efficiency reached 150 cases / second, attributed to the continuous iteration of the assessment model and the enrichment of training data; resource matching efficiency improved to 110 times / second, and scheduling and coordination efficiency reached 90 times / second, reflecting the optimization effects of the matching algorithm and communication mechanism. The data in the figure validates the system's self-optimization capability, ensuring high and stable performance during long-term operation and adapting to constantly changing medical needs and resource conditions.

[0059] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A medical resource matching system for critically ill patients, characterized in that, Includes the following modules: Patient multi-source data acquisition module: Real-time acquisition of patient physiological parameters, medical history information, emergency needs and on-site environmental information. Data is collected through multiple channels, pre-processed and stored in the system database in a standardized format. Intelligent disease assessment and priority classification module: Based on multi-source data, it integrates algorithms and clinical guidelines to quantitatively analyze the severity of the disease, extract key features, classify patients into four priorities, and synchronize the assessment results to the matching module in real time; Medical resource retrieval module: Retrieves available medical institutions, beds, medical teams, medical equipment, emergency medicines and transportation resources within the region, connects with multiple systems to obtain dynamic information on resources, and establishes a dynamic database of resources across the entire region; Dynamic matching decision module: Constructs a multi-objective optimization matching model, solves the optimal matching scheme by comprehensively considering multiple factors, receives data updates in real time to dynamically adjust the results, and generates a matching report; Real-time scheduling and coordination module: pushes matching reports, establishes real-time communication channels among multiple parties, shares data and updates transfer progress, coordinates medical institutions to prepare in advance, and activates alternative plans to reschedule resources in case of emergencies; Data security and privacy protection module: It adopts encrypted transmission and storage methods, anonymizes patient information, establishes a role-based access control mechanism, and leaves full traces of operation logs; System self-optimization module: Based on historical data and feedback, it uses reinforcement learning algorithms to optimize the evaluation model and matching decision model, regularly updates indicator weights, constraints and parameters, and collects feedback to iteratively upgrade system functions.

2. The medical resource matching system for critically ill patients according to claim 1, characterized in that, It also includes a module for quantifying the urgency of a patient's condition. This module constructs an urgency scoring function based on multi-dimensional data. The expression for the scoring function is as follows: E represents the comprehensive score indicating the urgency of the condition. For abnormal physiological parameters, weights Weighting of disease risk levels For the treatment window option, For complication risk weights, and P represents the degree of abnormality of physiological parameters, D represents the risk level of the illness, which is assigned according to the classification criteria for acute and critical illnesses, T represents the urgency of the treatment window, and C represents the risk of complications. The weight parameters are obtained by training and optimizing the algorithm with a large amount of clinical case data.

3. The medical resource matching system for critically ill patients according to claim 1, characterized in that, It also includes a dynamic optimization module for medical resources. This module constructs a resource matching degree calculation model, the expression of which is: Where M is the resource matching degree value, a is the resource saturation weight, b is the transfer distance weight, c is the resource real-time availability weight, d is the treatment capacity matching weight, S is the medical institution resource saturation value, L is the transfer distance adaptation value, calculated based on the distance between the patient's current location and the medical institution and traffic conditions, U is the resource real-time availability value, assigned according to the real-time idle status of required equipment, beds and medicines, and K is the treatment capacity matching value, calculated based on the success rate of treating similar cases in the past and the professional level of the medical team.

4. The medical resource matching system for critically ill patients according to claim 1, characterized in that, It also includes a multimodal data fusion processing module, which is used to fuse and process patients' textual, numerical, image, and audio data. The textual data includes medical history descriptions and doctor's orders from electronic medical records; the numerical data includes physiological parameters and laboratory test results; the image data includes CT images, ultrasound images, electrocardiogram scans; and the audio data includes verbal descriptions of patients' symptoms by medical staff and on-site emergency voice recordings. The module analyzes abnormal features in the image data through image processing algorithms, uses speech recognition technology to convert audio data into text and extract core information, and generates a unified comprehensive dataset of patients' conditions after fusing all the modal data.

5. A medical resource matching system for critically ill patients according to claim 1, characterized in that, It also includes a cross-institutional remote collaboration module, which builds a multi-institutional collaborative work platform to support real-time remote collaboration between emergency center transport teams and hospitals and expert teams involved in treatment. The platform has built-in high-definition video conferencing function, medical data sharing whiteboard, image co-annotation tool and real-time message push function. Expert teams can remotely view patients' real-time data and image data through the platform, and receiving hospitals can obtain complete patient condition information in advance through the platform to formulate treatment plans. Transport teams can provide real-time feedback on patients' status during the journey and receive remote guidance. The module supports access and adaptation to the existing systems of different medical institutions.

6. A medical resource matching system for critically ill patients according to claim 1, characterized in that, It also includes an emergency resource backup and scheduling module, which is used to deal with sudden resource shortages or emergencies. It establishes a regional emergency medical resource reserve, which includes backup ICU beds, mobile emergency equipment, emergency medical teams, and emergency medicines. The module monitors the status of the reserve resources in real time. When the resources in the main matching plan suddenly become unavailable or the patient's condition suddenly deteriorates and requires higher-level resources, the module automatically searches for suitable resources in the reserve, generates an emergency matching plan, and triggers reserve resource scheduling instructions. The module also supports adjusting the type and quantity distribution of reserve resources in advance according to the regional incidence patterns of acute and critical illnesses.

7. A medical resource matching system for critically ill patients according to claim 1, characterized in that, It also includes a patient treatment process tracking module, which tracks the entire process of a patient's treatment from the start of data collection to the completion of treatment. It records key time nodes at each stage, including the start time of data collection, the completion time of condition assessment, the time of matching plan generation, the departure time of transfer, the arrival time at the hospital, the start time of treatment, and the end time of treatment. It also records the changes in the patient's condition, data resource scheduling, medical and nursing operation information, and the evaluation results of treatment effectiveness at each stage, forming a complete patient treatment trajectory file. At the same time, the module supports pushing key treatment node information to the patient's family.

8. A medical resource matching system for critically ill patients according to claim 1, characterized in that, It also includes a medical resource demand forecasting module. This module is based on the seasonal variation patterns of historical acute and critical illness incidence data, climate factors, major event arrangements, and public health event early warning information in the region. It uses a forecasting algorithm that combines time series analysis and machine learning to predict the number and disease distribution of acute and critical patients in the region and the types and quantities of medical resources required in the future. It generates a resource demand forecasting report and pushes it to the regional medical resource management department and various medical institutions.

9. A medical resource matching system for critically ill patients according to claim 1, characterized in that, It also includes a multi-system interface adaptation module, which adopts a standardized interface design and supports interface with a variety of medical-related systems, including hospital information systems (HIS), electronic medical record systems (EMR), laboratory information systems (LIS), medical image archiving and communication systems (PACS), emergency center dispatch systems, wearable medical device data interfaces, and regional medical and health information platforms. The interface supports bidirectional data transmission, and the module supports flexible configuration of interface parameters, which can be adapted and adjusted according to the interface specifications of different interface systems.

10. A medical resource matching system for critically ill patients according to claim 1, characterized in that, It also includes a privacy protection enhancement module. Building upon the data security and privacy protection modules, this module employs differential privacy technology to process patient identity information and sensitive medical data. Without affecting data availability, it establishes a data access audit and traceability mechanism, recording all data access operations in detail, including the access personnel, access time, access content, operation type, and access purpose. The audit records are retained for no less than the legally required period, supporting regulatory authorities to retrieve and verify them at any time. The module also has a data leakage monitoring function, which promptly detects abnormal access and data leakage risks by monitoring data transmission and access behavior in real time, triggering an early warning mechanism and taking blocking measures. It also supports patient authorization management, allowing patients to authorize specific medical institutions or personnel to access their medical data through designated channels.

Images