Intelligent hospital accompanying management system and method supporting user portrait and multi-terminal cooperation
By collecting data through IoT devices to build dynamic profiles and using digital twin models for intelligent scheduling, the problem of data isolation and information silos in existing care management systems has been solved. This has enabled precise task allocation and efficient collaboration, thereby improving the quality of care and management efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- THE FIRST AFFILIATED HOSPITAL OF HENAN UNIV OF TCM
- Filing Date
- 2026-03-18
- Publication Date
- 2026-06-16
Smart Images
Figure CN122224445A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent medical management technology, specifically to an intelligent hospital care management system and method that supports user profiling and multi-terminal collaboration. Background Technology
[0002] Currently, some existing hospital caregiver management systems have attempted to optimize service processes using technology, such as electronically recording some caregiver information. However, these systems typically have significant limitations. Their data sources are often relatively singular, relying mainly on manual entry or isolated medical information systems. They struggle to obtain detailed physiological and behavioral data about patients in real time, as well as the real-time location and workload of caregivers. This results in a lack of comprehensive and dynamic data support for assessing patients' true needs and caregiver service capabilities.
[0003] In addition, most existing task scheduling mechanisms rely on manual dispatching or simple polling rules, with limited intelligence. They cannot comprehensively consider multiple factors such as the urgency of the task, the required skills, the actual location of the caregiver, and the current workload. This can easily lead to uneven task allocation, response delays, and inaccurate matching of caregivers' professional expertise with patients' personalized needs, thereby affecting the overall service efficiency and quality.
[0004] On the other hand, internal collaboration within hospitals is generally insufficient, with information barriers existing between caregivers, medical staff, patients' families, and administrators. Caregivers cannot quickly and structurally report any abnormal situations encountered during their duties to medical staff; medical staff also struggle to easily translate professional guidance into actionable tasks; and families lack effective channels to understand the patient's real-time condition, easily leading to anxiety. This information silo phenomenon hinders efficient collaboration among multiple roles and is detrimental to forming a closed-loop care management system. Summary of the Invention
[0005] The purpose of this invention is to provide an intelligent hospital care companion management system and method that supports user profiling and multi-terminal collaboration. It integrates data from IoT devices and the hospital information system through a data acquisition module, generates and continuously updates dynamic profiles reflecting patient status and caregiver capabilities using a profile calculation module, constructs a real-time mirror of the hospital environment through a digital twin model, intelligently assigns care companion tasks based on real-time data and profile information using an intelligent scheduling module, and achieves task execution and information collaboration through a care companion work order management module and a multi-terminal collaboration module. This enables precise, intelligent management and efficient collaboration of care companion services, effectively improving nursing quality, optimizing human resource allocation, and enhancing management efficiency, thus solving the problems mentioned in the background art.
[0006] To achieve the above objectives, the present invention provides the following technical solution:
[0007] A smart hospital care management system that supports user profiling and multi-terminal collaboration includes:
[0008] The data acquisition module is used to acquire dynamic perception data of patients through a network of IoT devices deployed in the hospital environment, and to acquire static medical record data of patients, as well as caregiver files and task data through integration with the hospital information system.
[0009] The profile calculation module, connected to the data acquisition module, is used to perform multi-dimensional feature extraction and fusion analysis on the acquired data, generate and continuously update dynamic profiles of patients and caregivers. The dynamic profile of patients includes quantitative labels of physiological status, behavioral habits and risk index, and the dynamic profile of caregivers includes dynamic labels of professional skills, work efficiency and service quality.
[0010] The digital twin model, connected to the portrait calculation module and the data acquisition module, is used to construct a three-dimensional spatial model that is synchronized with the physical hospital environment in real time, and to map real-time data and dynamic portraits to the corresponding digital entities of patients and caregivers.
[0011] The intelligent scheduling module is connected to the digital twin model and the profile calculation module. It is used to respond to the needs of caregiving tasks and match and assign suitable caregivers to tasks based on real-time status, profile data and preset optimization goals.
[0012] The caregiver work order management module is connected to the intelligent scheduling module. It is used to generate digital caregiver work orders containing operation instructions and specific patient information, and push them to the caregiver's mobile terminal to support task execution and record feedback.
[0013] The multi-terminal collaboration module is connected to the caregiver work order management module and the digital twin model. It is used to provide medical staff, patients' families and hospital administrators with data views and interaction interfaces corresponding to their roles, so as to realize information sharing and collaborative operation.
[0014] Preferably, the IoT device network in the data acquisition module includes a smart mattress sensor installed on the hospital bed, a medical-grade wearable device worn by the patient, an environmental sensor, and a positioning tag based on Bluetooth beacon technology, used to collect vital signs, body movement data, environmental parameters, and centimeter-level location information; the integration with the hospital information system is used to obtain diagnostic information, medical orders, nursing levels, nurse qualifications, training history, and service evaluation data.
[0015] Preferably, the intelligent scheduling module is configured to perform the following operations: analyze task requirements to determine constraints and optimization objectives; calculate the arrival time of candidate caregivers based on real-time location in a digital twin model; calculate the matching degree between the dynamic profile of candidate caregivers and task requirements using a multi-factor scoring model with configurable weights, wherein the factors include skill matching degree, path distance, current workload, and historical evaluation; and assign the task to the caregiver with the highest comprehensive score.
[0016] Preferably, when the intelligent scheduling module calculates the matching degree, it also refers to the dynamic profile of the target patient. If the specific need tag in the patient profile has a significant weight, then the caregiver with a high score in that need will be matched first.
[0017] Preferably, the digital caregiver work order generated by the caregiver work order management module is a contextualized task package, which highlights key precautions related to the patient's dynamic profile and embeds standard operation videos or graphic instructions for professional operations; caregivers record task execution nodes through mobile terminal applications by selecting preset options, voice input, or taking photos, and the recorded data is transmitted back in real time to update the relevant dynamic profile.
[0018] Preferably, the mobile terminal application has an abnormal situation reporting function, which allows caregivers to select abnormal type tags and attach descriptions or photos to form high-priority events that are sent to the responsible nurse's terminal and the nurse station monitoring system.
[0019] Preferably, the multi-terminal collaboration module provides an interactive interface for medical staff that integrates medical information system data, IoT vital sign trends, caregiver records, and risk warning scores, and supports the conversion of medical guidance into structured task instructions that can be dispatched.
[0020] Preferably, the multi-terminal collaboration module provides family members with terminal applications that support viewing patient status, nursing logs, and periodic reports, and allows for video visits or message sending with permission; the backend interface provided to administrators uses a digital twin model to visualize the busy / idle status of nursing assistants throughout the hospital, patient risk distribution, and abnormal events, and provides multi-dimensional operational analysis reports.
[0021] Compared with the prior art, the beneficial effects of the present invention are:
[0022] 1. This invention integrates dynamic sensing data from the Internet of Things with static data from the hospital information system, and constructs continuously updated dynamic profiles of patients and caregivers based on this. The system realizes the digital and quantitative representation of all elements of care services, fundamentally changing the traditional decision-making model that relies on isolated, static information.
[0023] 2. This invention applies digital twin technology to the caregiver management scenario, creating a virtual model that is synchronized in real time with the physical hospital environment. It not only provides a global visualization view, but also serves as the decision-making basis for the intelligent scheduling module. This allows task allocation to comprehensively consider multi-dimensional dynamic factors such as real-time location, caregiver workload, skill matching degree, and patient's personalized needs, thereby achieving a leap from manual dispatch to intelligent matching, significantly improving the accuracy and efficiency of resource allocation.
[0024] 3. This invention breaks down information barriers between caregivers, medical staff, family members, and managers through structured digital work orders and an integrated multi-terminal collaborative platform. The caregiver's execution process is conveniently recorded and fed back in real time. Medical staff can provide professional guidance based on a panoramic view. Family members gain a transparent information channel, and managers can conduct overall monitoring and make scientific decisions. This forms a complete process from task triggering, execution, feedback to optimization, which not only improves the quality and safety of care services but also effectively enhances trust among all parties and optimizes the overall operational management efficiency of the hospital. Attached Figure Description
[0025] Figure 1 This is a flowchart of the system modules of the present invention. Detailed Implementation
[0026] 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.
[0027] To address the issues of inaccurate matching of supply and demand for care services, low scheduling efficiency, and poor collaboration among various roles caused by information silos in existing technologies, please refer to [link to relevant documentation]. Figure 1 This embodiment provides the following technical solution:
[0028] The system is equipped with a data acquisition module, which includes a dynamic sensing layer and a static acquisition layer. Specifically:
[0029] The dynamic sensing layer consists of a heterogeneous network of IoT devices deployed in the specific environment of the hospital, including device nodes and dedicated IoT gateways, wherein:
[0030] The equipment nodes include smart mattress sensors installed on the hospital bed to continuously collect data on the patient's position, heart rate, respiratory rate, and body movement; medical-grade wearable devices worn on the patient's wrist to monitor blood oxygen saturation, body surface temperature, and basic activity levels; environmental sensors deployed in the ward to collect parameters such as ambient temperature, humidity, and light intensity; and positioning tags based on Bluetooth beacon technology, worn by patients and caregivers to achieve real-time, centimeter-level positioning of personnel's indoor location.
[0031] All device nodes encrypt and preliminarily encapsulate the raw data streams collected through dedicated IoT gateways, and stably transmit them to the system's cloud data access service. The cloud data access service is used to clean, denoise, and standardize the massive amounts of time-series data, converting them into standardized and unified time-series data, providing a good dynamic sensing data foundation for subsequent fusion analysis and computation.
[0032] The static data acquisition layer, while aggregating dynamic sensing data in real time, integrates with the hospital's medical information system and human resource management system through application programming interfaces (APIs) to acquire: patient medical record data, including but not limited to diagnostic information, valid medical orders, nursing levels, and drug allergy history; and caregiver data, including but not limited to: basic identity information, professional qualification certificates, training history and assessment results, currently executing task queues, types of historically completed tasks, time spent and completion quality, as well as periodic service evaluations from patients' families and medical staff. The static data acquisition layer provides a solid foundation for subsequent fusion analysis and computation.
[0033] The aforementioned statically collected data and dynamically perceived data serve as dual data sources for constructing and generating dynamic patient and caregiver profiles. These data are input into the profile calculation module within the system. The profile calculation module performs multi-dimensional feature extraction and fusion analysis on the input data to construct the dynamic patient and caregiver profiles. Specifically:
[0034] Data feature extraction and fusion analysis of patients includes, but is not limited to: analyzing patients' sleep cycles and daytime activity patterns from continuous body movement and bed-walking event data; identifying baseline fluctuation patterns and abnormal deviation trends from vital sign time series; and calculating patients' medication adherence risk scores, fall risk indices, or pressure ulcer probability by combining medical order information. All the features derived from these analyses are quantified into weighted labels and dynamically linked to each patient's dynamic profile, thus forming a comprehensive digital representation reflecting the patient's physiological state, behavioral habits, nursing needs, and safety risks.
[0035] Data feature extraction and fusion analysis of caregivers include, but are not limited to: caregiver experience values for patients of different specialties, proficiency scores in various nursing procedures, average response time in emergency call tasks, and the overall trend of the evaluations. Based on the above analysis results, a series of dynamic labels characterizing caregivers' professional skills, work efficiency, service attitude, and real-time workload are generated and dynamically linked to each caregiver's dynamic profile, thus forming a multi-dimensional digital representation that comprehensively reflects the continuous evolution of caregivers' overall service capabilities and real-time status, facilitating subsequent quantitative assessment and personnel deployment.
[0036] The system also established a digital twin model that is synchronized in real time with the physical hospital environment. This model, based on a 3D spatial map, not only maps physical structures such as wards, corridors, and functional areas, but also labels the aforementioned dynamic perception data and dynamic profile data as state attributes in real time within the digital twin model. In the digital twin model, each patient digital entity is associated with its continuously updated patient dynamic profile, and each caregiver digital entity is associated with its corresponding caregiver dynamic profile. The positional movement of all digital entities in the digital twin model is synchronized with their movement in the real world. The digital twin model provides the entire system with a unified spatial context and a global real-time data view.
[0037] The system has set up an intelligent scheduling module as the core decision-making module. When a new caregiving task is required, whether it is a periodic task triggered by the nurse workstation according to the standard nursing plan, a temporary caregiving order issued by the doctor, or an intervention task triggered by the risk warning automatically identified by the system through analysis of the patient's dynamic profile, the caregiving task requirement will be submitted to the intelligent scheduling module.
[0038] First, the intelligent scheduling module analyzes the new task requirements and transforms them into a set of explicit constraints and optimization objectives. Specifically:
[0039] The constraints include the professional skills required for the task, the urgency and time window for expected completion, and specific requirements regarding the gender of caregivers; the optimization objective is to maximize the expected quality of task completion, minimize the caregiver's response path and overall workload, and improve patient satisfaction with past services as much as possible, while satisfying all constraints.
[0040] Next, the intelligent scheduling module, based on real-time location data, calculates the estimated time for all digital entities of caregivers that are currently idle or lightly loaded to reach the task initiation location within the digital twin model.
[0041] Subsequently, the set of task constraints is matched with the set of tags in each candidate caregiver's dynamic profile. The matching algorithm configured in the intelligent scheduling module employs a multi-factor scoring model with configurable weights. Factors such as skill matching degree, path distance, current workload index, and historical cooperation evaluation are assigned different weights, and these weight coefficients can be adjusted by hospital administrators according to operational strategies. The matching algorithm calculates a comprehensive matching score for each candidate caregiver and assigns the task to the caregiver with the highest score.
[0042] In addition, during the matching process, the intelligent scheduling module will also refer to the dynamic profile of the target patient. For example, if the label "high communication needs" has a significant weight in the patient profile, the matching algorithm will tend to prioritize matching caregivers with higher scores in the "communication ability" label in historical evaluations, thereby achieving a deeper level of personalized service matching.
[0043] The system includes a caregiver work order management module connected to the intelligent dispatch module. After task matching, this module automatically generates a structured digital caregiver work order and immediately pushes it to the assigned caregiver's mobile smart terminal. This digital work order is a contextualized task package integrating operational guidelines, patient-specific information, and medical requirements. The work order highlights key precautions related to the patient's dynamic profile, such as "The patient's dynamic profile indicates a high risk of fall; a walking aid should be used and constant monitoring is required during movement." For specialized operations derived from medical orders, such as "axial turning" or "wound exudation observation," the digital work order embeds standard operating video guidelines or graphic key points pre-reviewed and entered by the hospital's nursing department to ensure the standardized execution of nursing procedures.
[0044] The caregiver arrives at the patient's bedside and begins executing the digital work order. At this point, their mobile smart terminal becomes the primary interactive tool for task execution and process recording. The mobile smart terminal is equipped with an application software featuring an efficient data entry process. Within the application software, the caregiver records key milestones in task execution by selecting preset options, making voice input, or taking photos of the scene when necessary. For example, after assisting with feeding, the caregiver can select "Feeding Completed" and choose the estimated percentage of intake. This setup significantly reduces the data entry burden on caregivers while ensuring the timeliness and structured nature of the records.
[0045] The data submitted by caregivers will be immediately sent back to the system's backend to trigger updates to the relevant dynamic profiles. For example, a successful "assisting with bedside walking" record will enrich the "daytime activities" data in the patient's dynamic profile; while a "refusal to take medication" record will trigger an increase in the "medication adherence risk" index in the patient's profile.
[0046] During the process, if caregivers discover any unusual situations, such as unexpected pain from the patient, abnormal wound discharge, or sudden extreme depression, they can quickly select the abnormality type tag and attach a brief text description or photo using the application's abnormality reporting function. This report will be sent as a high-priority event, along with the patient's identification and the latest vital signs data, to the responsible nurse's mobile collaborative terminal and simultaneously displayed in the alarm list of the central monitoring system at the nursing station. This forms a rapid processing flow from discovery to alarm, ensuring that medical personnel can intervene and handle the situation promptly.
[0047] The system also interacts with mobile collaborative terminals or workstations used by nurses or doctors, presenting a panoramic view of the patient's health status on the interactive interface. This view integrates test results from the medical information system, displays side-by-side vital sign trend curves from the Internet of Things, and summarizes of behavior and care logs from caregivers. It also highlights various risk warning scores calculated by the system based on all data sources. This allows medical staff to obtain a continuous, multi-dimensional, and intuitive panoramic view of the patient's status and care during ward rounds or patient assessments. Guidance from medical staff related to patient care can also be easily converted into structured task instructions that the system can recognize and assign, directly integrating into the aforementioned intelligent scheduling process.
[0048] The system also facilitates data interaction with patients' families through a family collaboration terminal application. Families can use this application to view the patient's status indicators, such as "resting in bed" or "vital signs within normal range," browse nursing logs generated by caregivers, and receive periodic reports automatically generated by the system regarding the patient's daily activities, nutritional intake, and recovery progress. Furthermore, with the permission of medical staff, families can initiate video visitation requests or send text messages to the on-duty caregiver via messaging channels. This feature effectively alleviates family anxiety, establishes trust based on information transparency, and serves as a means of medical supervision.
[0049] VIII. Administrator Access and Collaboration
[0050] The system's backend provides hospital administrators with a holistic operational monitoring perspective through a visual interface based on a digital twin model. It dynamically displays the distribution of busy / idle status of caregivers across all areas of the hospital, the clustering of patient risk levels, and the density of abnormal events. Administrators can gain a comprehensive understanding of the overall layout and dynamic load of caregiver resources throughout the hospital, and can view detailed information for specific wards, rooms, and even individual beds.
[0051] The backend also provides a series of multi-dimensional operational analysis reports, covering key performance indicators including caregiver work efficiency, task completion quality, average response time for various events, and satisfaction trends based on feedback. This provides managers with a direct basis for making scientific decisions, such as dynamically adjusting human resource allocation during peak hours or organizing specialized skills training for recurring operational problems.
[0052] Working Principle: The system first collects patients' vital signs, behavioral data, and location information in real time through a network of IoT devices deployed in the hospital environment. Simultaneously, it integrates with the hospital's information system via an interface to obtain static data such as medical records and nurse qualifications, forming a unified foundation of dynamic and static data. The collected data is input into the profiling module for multi-dimensional feature extraction and analysis, generating continuously updated dynamic patient and caregiver profiles. The patient profile quantifies physiological status, risk index, and behavioral habits, while the caregiver profile reflects skill level, workload, and service quality. This profile data is synchronized to a digital twin model, which maps the hospital's physical environment in real time in the form of a 3D map, associating the digital entities of patients and caregivers with their profiles.
[0053] When a caregiving task is requested, the intelligent scheduling module analyzes the task constraints and optimization objectives, such as professional skills, urgency, and path distance. Combining this with real-time location data from a digital twin model, it uses a multi-factor scoring algorithm to match the most suitable caregiver. The matching process considers the personalized needs of the patient profile; for example, it prioritizes caregivers with strong communication skills to serve high-needs patients. After task matching, the system automatically generates a structured digital work order and pushes it to the caregiver's mobile terminal. The work order includes operation instructions and specific precautions for the patient. The caregiver records the execution nodes through the terminal application, and the data is transmitted back in real time to update the profile. Abnormal situations can be quickly reported, triggering medical personnel intervention.
[0054] Medical staff can obtain a panoramic view of the patient through the terminal and issue guidance tasks; family members can check the patient's status and nursing logs and conduct video visits; managers can use the digital twin interface to monitor the overall operation, analyze key indicators, and optimize resource allocation.
[0055] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.
[0056] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.
Claims
1. A smart hospital care management system supporting user profiling and multi-terminal collaboration, characterized in that: include: The data acquisition module is configured to acquire patients' dynamic perception data and static medical record data, as well as caregivers' static files and dynamic task data. The profile calculation module is configured to perform multi-dimensional feature extraction and fusion analysis on the acquired data to generate and continuously update dynamic profiles of patients and caregivers. The digital twin model is configured to build and maintain a three-dimensional spatial model that is synchronized with the physical hospital environment in real time, and to map the dynamic profiles of patients, caregivers and real-time location data to the corresponding digital entities; The intelligent scheduling module is configured to respond to the needs of caregiving tasks by assigning appropriate caregivers to tasks based on real-time status and profile data in the digital twin model, as well as preset constraints and optimization goals. The caregiver work order management module is configured to generate structured digital caregiver work orders and push them to the mobile terminal of the assigned caregiver, and to receive and process task execution data fed back by the caregiver. The multi-terminal collaboration module is configured to provide medical staff, hospital administrators, and patients' families with data views and interactive interfaces corresponding to their roles, enabling information sharing and collaborative operations.
2. The intelligent hospital care management system supporting user profiling and multi-terminal collaboration according to claim 1, characterized in that, The data acquisition module includes a dynamic sensing layer and a static acquisition layer; The dynamic sensing layer includes a smart mattress sensor deployed on the hospital bed, a medical-grade wearable device worn by the patient, environmental sensors placed in the ward, and a positioning tag based on Bluetooth beacon technology, used to collect the patient's vital signs, behavioral data, environmental parameters, and centimeter-level location information. The static data acquisition layer is integrated with the hospital's medical information system and human resource management system through an application programming interface. It is used to obtain patients' medical records, medical orders, nursing level information, as well as caregivers' identity, qualifications, training history, task records and evaluation information.
3. The intelligent hospital care management system supporting user profiling and multi-terminal collaboration according to claim 1, characterized in that, The profiling and fusion analysis of patient data by the profiling and calculation module includes: analyzing sleep cycles and activity patterns from body movement and bed-leaving event data; identifying baseline fluctuations and abnormal trends from vital sign time series; calculating medication adherence risk scores, fall risk indices, or pressure ulcer occurrence probabilities by combining medical order information; and quantifying these into weighted labels to form a dynamic patient profile. The profile calculation module performs feature extraction and fusion analysis on caregiver data, including: calculating caregivers' service experience values for patients of different specialties, proficiency scores for various nursing operations, average response time and evaluation trends for emergency tasks, and generating dynamic labels that characterize their professional skills, work efficiency, service attitude and real-time workload to form a dynamic profile of the caregiver.
4. The intelligent hospital care management system supporting user profiling and multi-terminal collaboration according to claim 1, characterized in that, The digital twin model uses a three-dimensional spatial map as a base to map the physical structure of the hospital, and associates real-time updated dynamic perception data, dynamic patient portraits, and dynamic caregiver portraits as state attributes to the corresponding digital patient and digital caregiver entities, so that the position movement of the digital entities is synchronized with reality.
5. The intelligent hospital care management system supporting user profiling and multi-terminal collaboration according to claim 1, characterized in that, When responding to a caregiving task request, the intelligent scheduling module performs the following operations: The task requirements are analyzed and transformed into a set of constraints including essential professional skills, urgency, time window, and gender requirements for caregivers, as well as optimization objectives that aim to maximize the expected quality of task completion and minimize the caregiver response path and overall workload. In the digital twin model, the estimated time for an idle or lightly-loaded caregiver to arrive at the task initiation location is calculated based on real-time location data; A multi-factor scoring model with configurable weights is used to calculate the matching degree between task constraints and dynamic profile labels of candidate caregivers. The multi-factors include skill matching degree, path distance, current workload index and historical cooperation evaluation. The task will be assigned to the caregiver with the highest overall matching score.
6. The intelligent hospital care management system supporting user profiling and multi-terminal collaboration according to claim 5, characterized in that, When calculating the matching degree, the intelligent scheduling module also refers to the dynamic profile of the target patient. If the specific need tag in the patient profile has a significant weight, then the caregiver with a high score in that need will be matched first.
7. The intelligent hospital care management system supporting user profiling and multi-terminal collaboration according to claim 1, characterized in that, The digital caregiver work order generated by the caregiver work order management module is a contextualized task package that integrates operation instructions, patient-specific information and medical requirements, and highlights key precautions related to the patient's dynamic profile. For professional operations, the work order contains pre-approved standard operation video instructions or graphic key points.
8. The intelligent hospital care management system supporting user profiling and multi-terminal collaboration according to claim 1, characterized in that, The caregiver executes digital work orders through a mobile terminal application. The application supports recording key nodes of task execution by selecting preset options, voice input, or taking photos, and the recorded data is transmitted back in real time to update the relevant dynamic profile. The application also has an abnormal situation reporting function, which allows caregivers to select abnormal type tags and attach descriptions or photos to form high-priority events that are sent to the responsible nurse's terminal and the nurse station monitoring system.
9. A method for intelligent hospital care management supporting user profiling and multi-terminal collaboration, implemented based on the intelligent hospital care management system supporting user profiling and multi-terminal collaboration as described in any one of claims 1-8, characterized in that, Includes the following steps: The data acquisition module continuously acquires dynamic perception data and static medical record data of patients, as well as static files and dynamic task data of caregivers. The profiling calculation module performs multi-dimensional feature extraction and fusion analysis on the acquired data to generate and continuously update dynamic patient profiles and dynamic caregiver profiles. Establish and maintain a digital twin model to synchronously map the physical hospital environment, real-time sensing data, dynamic patient profiles, and dynamic caregiver profiles. The intelligent scheduling module responds to the needs of caregiving tasks and assigns suitable caregivers to tasks based on real-time status, profile data and preset rules in the digital twin model. The caregiver's work order management module generates and pushes structured digital caregiver work orders to the caregiver's mobile terminal, and receives feedback on the execution of the processing tasks. The multi-terminal collaboration module provides medical staff, managers, and their families with corresponding data views and interactive interfaces to achieve information sharing and collaboration.
10. The intelligent hospital care management system supporting user profiling and multi-terminal collaboration according to claim 9, characterized in that, The multi-terminal collaboration module specifically executes the following: The interactive interface provided for medical staff integrates medical information system data, IoT vital sign trends, nursing assistant record summaries, and risk warning scores calculated by the system, and supports the conversion of medical and nursing guidance into structured task instructions; The terminal application provided to family members allows them to view patient status indicators, nursing logs, and periodic briefings, and to conduct video visits or send messages with permission; The back-end interface provided to managers is based on the visualization of digital twin models, dynamically displaying the busy and idle status of nursing assistants throughout the hospital, the distribution of patient risks and the density of abnormal events, and providing multi-dimensional operational analysis reports.