Dynamic priority-based medical queuing and calling number intelligent scheduling method

Abstract

This invention discloses a dynamic priority-based intelligent scheduling method for medical queuing and calling, belonging to the field of medical service scheduling technology. It includes connecting to multiple hospital systems, collecting and integrating patient information, medical resources, and queue data; constructing a multi-dimensional dynamic priority rule base for initial patient priority assessment; adjusting priorities in real time to intelligently match patients with medical and clinic resources; publishing calling information across multiple terminals; monitoring queue status and issuing warnings; and statistically analyzing multi-dimensional data to support optimization. This invention balances the urgency and fairness of medical visits through dynamic priority and intelligent scheduling, improving the utilization rate of medical resources. Accurate information push from multiple terminals optimizes the patient experience, and full-process monitoring and data support assist hospitals in refined management, improving the efficiency and quality of outpatient services.

Description

Technical Field

[0001] This invention relates to the field of medical service scheduling technology, and in particular to a smart scheduling method for medical queuing and calling based on dynamic priority. Background Technology

[0002] The imbalance between supply and demand of medical resources in my country is becoming increasingly prominent, especially with the continuous rise in outpatient volume at large and medium-sized hospitals. Long waiting times and chaotic procedures have become major pain points in the industry. With the accelerating aging population and increased health awareness, the demand for outpatient services is constantly growing, while hospital resources and staffing are limited. The traditional queuing and appointment system is no longer adequate for the efficient operation of modern medical services. Meanwhile, the development of medical informatization is progressing, with multiple systems such as hospital information management systems, electronic medical record systems, and laboratory information systems operating in parallel. However, the data from these systems is isolated, lacking effective integration and unified scheduling. This results in inefficient data sharing of patient information, registration records, and examination results, affecting the smoothness of the medical process.

[0003] Existing medical queuing and calling systems have several significant shortcomings. First, priority determination methods are simplistic, often relying solely on registration time, failing to comprehensively consider crucial factors such as the urgency of the patient's condition, age, medical history, and abnormal test results. This results in critically ill patients not receiving priority treatment and the needs of special populations remaining unmet. Second, the scheduling model is rigid, often employing fixed queue ordering, lacking real-time adaptation to the status of consultation room resources and doctors' workload. This easily leads to resource waste, with some consultation rooms congested while others are idle. Furthermore, the procedures for handling cross-departmental referrals and missed appointments are cumbersome, highlighting the problem of patients repeatedly queuing. In addition, the system lacks sufficient multi-campus collaboration capabilities, failing to achieve coordinated scheduling of resources and patient triage across different campuses, and thus failing to meet the needs of new medical service models such as medical alliances and multi-campus hospitals.

[0004] Significant shortcomings also exist in information dissemination and equipment management. The existing system's information push format is simplistic, failing to accurately deliver call number prompts, waiting estimates, and health education content based on clinic areas, time periods, and patient groups. This can easily lead to anxiety among patients due to information asymmetry. Furthermore, the lack of a unified remote management platform for call terminals and display devices means equipment maintenance relies on on-site operations, resulting in high operating costs and delays in detecting and handling equipment malfunctions, impacting system stability. As healthcare services upgrade towards precision, intelligence, and personalization, the shortcomings of existing queuing and calling systems in areas such as data fusion, dynamic scheduling, multi-scenario adaptation, and operational efficiency are becoming increasingly apparent. There is an urgent need for an integrated solution based on dynamic prioritization, integrating multi-source data, and supporting intelligent scheduling to comprehensively optimize the medical process and resource allocation. Summary of the Invention

[0005] The present invention proposes a dynamic priority-based intelligent scheduling method for medical queuing and calling 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 queuing and calling intelligent scheduling method based on dynamic priority, comprising the following steps: Data collection and integration steps: Connect with various hospital information systems, collect patient-related data and doctor and clinic resource data, and establish a unified data resource pool; Dynamic priority rule construction steps: Set up multi-level priority levels, establish multi-dimensional priority evaluation factors and assign weights to form a configurable dynamic priority rule library; Initial patient priority assessment steps: Based on the data resource pool and rule base, evaluation factors are automatically extracted to calculate the initial priority and generate the initial queuing queue; Priority dynamic adjustment steps: Real-time monitoring of patient status, queue status and clinic resource dynamics; automatic priority adjustment is triggered in case of abnormalities; manual adjustment by triage nurses is also supported. Intelligent scheduling and matching steps: Based on real-time priority queues and various resource information, resource optimization algorithms are used to match patients with doctors and consultation rooms; Calling and Information Release Steps: Supports multi-mode calling operation, synchronously releases relevant medical information through multiple terminals, and uses a combination of voice broadcast and scrolling text to convey medical reminders; Full-process monitoring and early warning steps: Real-time monitoring of all core indicators in triage, automatic triggering of early warnings in case of abnormalities, and push of early warning information to relevant staff; Data statistics and optimization steps: Regularly collect multi-dimensional triage operation data, generate visual analysis reports, and identify the causes of queue congestion and resource allocation shortcomings.

[0007] Furthermore, it also includes a patient priority calculation step, and designs a dynamic priority quantification calculation expression to address the nonlinear correlation characteristics of multi-dimensional assessment factors. in The patient's final priority score, to These are the weighting coefficients for each evaluation factor. Assess the urgency of the illness. For the patient's age, The age-related adjustment factor is used. To accumulate waiting time, The waiting time decay coefficient, The base score for the type of medical visit. The weighted score is based on previous medical records. To assess the degree of abnormality in test results, This is the amplification factor for the degree of abnormality. Additional points will be awarded for special cases.

[0008] Furthermore, it also includes a cross-departmental referral scheduling step. When a patient needs to visit a different department, the system automatically links the patient's original department's queuing information and priority level, extracts the patient's completed examination and testing data and medical records, generates a referral request form, and pushes it to the target department's triage system. After receiving the request, the target department inserts the patient into the corresponding queue based on the urgency of the referral request, the original priority level, and its own queue status. The system also updates the queue information of both the original and target departments simultaneously and sends referral reminders and estimated waiting information for the target department to the patient.

[0009] Furthermore, it also includes multi-campus collaborative scheduling steps, adopting a cloud tenant architecture to establish a unified scheduling platform for multiple campuses, centrally managing patient queues, doctor resources, and clinic configuration data for each campus, while retaining independent management permissions for each campus; when patients need to seek treatment across campuses or when resources are scarce in a certain campus, the system automatically queries the resource availability and queue status of corresponding departments in other campuses, and generates the optimal recommended campus based on the patient's geographical location, priority level, and campus resource load, and synchronously updates the scheduling information of relevant campuses.

[0010] Furthermore, it also includes steps for handling missed appointments and optimizing the queue, setting flexible rules for missed appointments, and supporting three processing modes: automatic return of missed appointment patients to the end of the queue, insertion into the corresponding position of the queue according to the original priority, or re-evaluation of priority; the system automatically records the reasons and frequency of missed appointments, and marks and reminds patients who have missed appointments multiple times; and it adopts a queue sorting optimization algorithm to adjust the queue order in real time.

[0011] Furthermore, it includes personalized adaptation steps for the doctor's workbench, supporting three access modes: client, browser, and interface embedding; it provides a mobile QR code login function, which makes it convenient for doctors to manage queues when making ward rounds or temporarily leaving, and synchronizes queue status, patient information, and call records in real time, allowing doctors to view patients' past medical records and examination and test results.

[0012] Furthermore, it also includes a dynamic allocation step for clinic resources, and designs an optimal allocation expression for clinic resources as follows: in The fitness value corresponding to the optimal clinic room allocation scheme. The patient demand weighting coefficient, The number of patients to be allocated. For the first Priority scores for each patient. For the first The matching coefficient between patients and examination room equipment. The queue length influence coefficient. The current waiting queue length for the target clinic. This is the resource load factor. The resource occupancy rate of the target consultation room. This represents the doctor's work performance coefficient. This refers to the number of patients seen by the doctor corresponding to the target clinic. For the first The complexity score of the diagnosis and treatment of the patients already treated.

[0013] Furthermore, it also includes a comprehensive information dissemination process, unified management of information dissemination content across all queuing terminals; information push by clinic area, time period, and patient group; health education content and doctor introductions during regular hours; queuing reminders and estimated waiting times during peak hours; and temporary notices and process change reminders in special circumstances.

[0014] Furthermore, it also includes remote management and control procedures for IoT devices, centralized monitoring of all terminals in the hospital, real-time display of device online status and operating parameters; support for remote batch operation of devices, automatic recording of device operation logs and fault information, automatic alarm and push of maintenance prompts when devices malfunction.

[0015] Furthermore, it also includes access control steps, establishing a multi-role access control system, setting different roles, and assigning corresponding functional operation permissions and data viewing permissions to each role; completing the control of functional permissions and data permissions, with the administrator responsible for system configuration and rule maintenance, triage nurses having queue management and priority adjustment permissions, and doctors only being able to view their own patient queues and patient information.

[0016] Compared with existing technologies, the beneficial effects of this invention are: The intelligent scheduling method for medical queuing and calling based on dynamic priority of the present invention comprehensively solves the problems of rigid scheduling, single priority, low resource utilization and inaccurate information release in the existing system through core technological innovations such as multi-source data fusion, dynamic priority construction and intelligent resource scheduling. It achieves all-round improvement in terms of medical treatment efficiency, fairness, resource allocation, user experience and management refinement, and has significant technical advantages and application value.

[0017] Regarding the accuracy and fairness of scheduling, the method innovatively constructs a multi-dimensional dynamic priority rule base, comprehensively considering key factors such as the urgency of the illness, age, type of visit, waiting time, and the degree of abnormality in test results, to achieve accurate determination and dynamic adjustment of patient priority. Special groups such as critically ill patients and elderly patients can automatically obtain high priority, ensuring that urgent needs are responded to quickly; at the same time, through priority quantification calculation and queue optimization sorting, the treatment needs of different patients are balanced, avoiding the lack of fairness caused by single registration time sorting, so that scheduling satisfies both urgency and fairness.

[0018] In terms of resource utilization efficiency, the system deeply integrates data from multiple medical systems, monitoring patient queue status, doctor workload, and clinic resource allocation in real time. Through intelligent scheduling algorithms, it achieves precise matching of patients with doctors and clinics. It supports both pool mode and independent queue mode to adapt to the characteristics of different departments; cross-departmental referral processes are seamlessly integrated, reducing repeated queuing for patients; and multi-campus collaborative scheduling breaks down campus barriers, enabling coordinated resource utilization and rational patient diversion, effectively alleviating congestion in some clinics and idle resources in others, and significantly improving the utilization efficiency of medical and clinic resources.

[0019] In terms of improving user experience, the multimedia information precision publishing function supports pushing information by region, time period, and audience. It simultaneously publishes call number prompts, waiting time estimates, and health education content across multiple terminals, allowing patients to obtain key information promptly and reducing anxiety caused by information asymmetry. The doctor's workbench supports multi-mode access and personalized configuration, facilitating flexible queue management and improving consultation efficiency. Patients can check queue information and complete check-in through mobile devices, self-service machines, and other channels, simplifying the consultation process, reducing unnecessary movement, and providing a more convenient and comfortable medical experience.

[0020] In terms of refined management and convenient operation and maintenance, the full-process monitoring and early warning function tracks indicators such as queue status, waiting time, and equipment operation in real time. Abnormal situations automatically trigger early warnings, providing support for timely intervention by management personnel. Multi-dimensional data statistics and visual analysis reports clearly present information such as departmental workload, doctor efficiency, and patient waiting patterns, providing quantitative basis for resource allocation, process optimization, and scheduling adjustments, and promoting the transformation of hospital management from experience-driven to data-driven. The remote control function of IoT devices enables centralized monitoring and batch maintenance of queuing terminals, reducing on-site maintenance costs, timely detection and handling of equipment failures, and ensuring stable system operation. Overall, this method realizes the transformation of medical queuing and calling from "fixed sorting" to "dynamic scheduling," from "data isolation" to "multi-source integration," and from "extensive management" to "precise control," comprehensively improving the efficiency and quality of medical services, bringing multiple values ​​to patients, medical staff, and hospital administrators. It is adaptable to various medical service models and has broad prospects for promotion and application. Attached Figure Description

[0021] Figure 1 This is a schematic block diagram of the intelligent scheduling method for medical queuing and calling based on dynamic priority proposed in this invention. Figure 2 Line graph showing the average waiting time for different priority levels; Figure 3 A biaxial line graph showing the change in clinic resource utilization over time; Figure 4 A bar chart comparing the time spent on inter-hospital referrals. Detailed Implementation

[0022] 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.

[0023] 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.

[0024] 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.

[0025] Reference Figures 1 to 4 A method for intelligent scheduling of medical queuing and calling based on dynamic priority includes the following steps: The data collection and fusion process involves connecting to the hospital information management system, electronic medical record system, laboratory information system, image archiving and communication system to automatically collect patient basic information, registration records, examination and test results, past medical history, and type of visit. It also simultaneously obtains doctor scheduling information, clinic resource status, and current queue waiting data to establish a unified data resource pool and achieve real-time aggregation and correlation of multi-source data. The dynamic priority rule construction steps include setting multiple priority levels, establishing multi-dimensional priority assessment factors such as the urgency of the illness, age, type of visit, waiting time, previous medical records, and degree of abnormality in examination and test results, assigning weight coefficients to each factor, clarifying the automatic priority determination conditions and manual adjustment trigger scenarios, and forming a configurable dynamic priority rule library. The initial patient priority assessment step automatically extracts assessment factor data based on patient information and rule base in the data resource pool, calculates the initial priority level, and automatically marks high priority for critically ill patients, elderly patients, and patients with special diseases. The initial and follow-up patients are allocated basic priority according to a preset ratio to generate an initial queue. The priority dynamic adjustment steps monitor changes in patient status, queue waiting status and clinic resource dynamics in real time. When a patient experiences a sudden change in condition, waiting time exceeds the threshold, or test results are abnormal, priority adjustment is automatically triggered. Triage nurses can manually adjust patient priorities according to the situation on site. All adjustment operations are fully recorded. The intelligent scheduling and matching process, based on information such as real-time priority queues, doctors' specialties, clinic equipment configuration, and doctors' workload, uses resource optimization algorithms to achieve accurate matching of patients with doctors and clinics. It supports two scheduling modes: pool mode and independent queue mode, prioritizing the treatment of high-priority patients while taking into account the continuity of doctors' consultations and the utilization rate of clinic resources. The call system supports multiple call modes, including sequential calling, repeat calling, and missed calls. It simultaneously publishes call information, queue status, and estimated waiting time through multiple terminals such as call display screens, TVs, self-service machines, and mobile mini-programs. It uses a combination of voice broadcasts and scrolling text to provide patients with timely medical reminders. The entire process is monitored and an early warning system is implemented. In real time, indicators such as the number of people waiting in each queue, average waiting time, doctor's consultation efficiency, and clinic usage status are monitored. If the waiting time exceeds the set threshold, the queue is congested, or clinic resources are idle, an early warning will be automatically triggered and the warning information will be pushed to the triage nurses and management personnel to provide intervention suggestions. The data statistics and optimization process involves regularly collecting multi-dimensional data such as departmental registration volume, attendance rate, average waiting time, number of doctors seeing patients, and frequency of priority adjustments. This data is then used to generate visual analysis reports, identify the causes of queue congestion and resource allocation shortcomings, and provide data support for rule base optimization, scheduling adjustments, and resource expansion.

[0026] This invention also includes a step for precise calculation of patient priority. Taking into account the nonlinear correlation characteristics of multi-dimensional assessment factors, a dynamic priority quantification calculation expression is designed as follows: in The patient's final priority score, to These are the weighting coefficients for each evaluation factor. The severity of the illness is assessed on a scale of 1 to 10. For the patient's age, The age-related adjustment factor is used. To accumulate waiting time, The waiting time decay coefficient, The scores are the base scores for different types of medical visits; first-time patients and returning patients each receive a different fixed score. The weighted score is based on previous medical records. To assess the degree of abnormality in test results, a score ranging from 0 to 5 is assigned. This is the amplification factor for the degree of abnormality. Additional points are awarded for special cases. Through multi-factor nonlinear fusion calculation, priority is accurately quantified, allowing high-demand patients to receive priority medical resources.

[0027] This invention also includes a cross-departmental referral scheduling step. When a patient needs to visit a different department, the system automatically associates the patient's original department's queuing information and priority level, extracts the patient's completed examination and testing data and treatment records, generates a referral request form, and pushes it to the target department's triage system. Upon receiving the request, the target department assigns an appropriate priority to the referred patient based on the urgency of the referral request, the original priority level, and its own queue status, inserts the patient into the corresponding queue, synchronously updates the queue information of both the original and target departments, and sends referral reminders and estimated waiting time information to the patient. This achieves seamless cross-departmental care and reduces repeated queuing and waiting time for patients.

[0028] This invention also includes a multi-hospital collaborative scheduling step. It adopts a cloud tenant architecture to establish a unified scheduling platform across multiple hospital campuses, centrally managing patient queues, doctor resources, and clinic configuration data for each campus, while retaining independent management permissions for each campus. When a patient needs to seek treatment across campuses or when resources are strained in a particular campus, the system automatically queries the resource availability and queue status of corresponding departments in other campuses. Based on the patient's geographical location, priority level, and campus resource load, it generates an optimal recommended campus for treatment. After patient confirmation, the system automatically completes cross-campus registration and queue creation, synchronously updating the scheduling information of relevant campuses, thus achieving coordinated utilization of resources across multiple campuses and patient triage.

[0029] This invention also includes steps for handling missed appointments and optimizing the queue. Flexible rules are set to support three processing modes: automatic return of missed appointments to the end of the queue, insertion into the appropriate position according to the original priority, or reassessment of priority. The system automatically records the reasons and frequency of missed appointments, marking and reminding patients who have missed their appointments multiple times. Triage nurses can manually adjust the handling method for missed appointments based on the actual situation. A queue sorting optimization algorithm is used to adjust the queue order in real time, balancing the fairness of priority treatment for high-priority patients and waiting for ordinary patients, reducing queue congestion and resource waste.

[0030] This invention also includes a personalized adaptation process for the doctor's workbench, supporting three access modes: client, browser, and API embedding. Doctors can customize the rules for passing numbers, the calling method, and the interface display preferences. It provides a mobile QR code login function, facilitating queue management when doctors are making rounds or temporarily leaving the hospital. The system synchronizes queue status, patient information, and calling records in real time, allowing doctors to view patients' past medical records and examination results, facilitating rapid patient admission and improving doctors' work efficiency and diagnostic accuracy.

[0031] This invention also includes a dynamic allocation step for clinic resources. Combining real-time patient needs, doctor's specialties, clinic equipment configuration, and patient condition requirements, an optimized allocation expression for clinic resources is designed as follows: in The fitness value corresponding to the optimal clinic room allocation scheme. The patient demand weighting coefficient, The number of patients to be allocated. For the first Priority scores for each patient. For the first The matching coefficient between patients and examination room equipment. The queue length influence coefficient. The current waiting queue length for the target clinic. This is the resource load factor. The resource occupancy rate of the target consultation room. This represents the doctor's work performance coefficient. This refers to the number of patients seen by the doctor corresponding to the target clinic. For the first The complexity score of the diagnosis and treatment of patients already treated. This expression quantifies the fit between the clinic and the patient, enabling dynamic optimization of clinic resources and improving resource utilization efficiency and treatment suitability.

[0032] This invention also includes a comprehensive multimedia information dissemination process, unifying the management of information dissemination content across all queuing terminals and supporting the editing and management of various materials such as images, videos, and documents. Information is precisely pushed according to clinic area, time period, and patient group. During regular hours, health education content and doctor introductions are published; during peak hours, priority is given to pushing queuing reminders and estimated waiting times; and in special circumstances, temporary notifications and process change notices are issued. Multiple playback strategies, such as timed, cyclical, and triggered playback, are supported to ensure the targeted and effective dissemination of information.

[0033] This invention also includes a remote management and control system for IoT devices, enabling centralized monitoring of terminals such as queuing display screens, self-service machines, and voice broadcasting equipment throughout the hospital, displaying real-time online status and operating parameters. It supports remote batch operations such as powering on / off, restarting, volume adjustment, software upgrades, and program distribution, automatically records equipment operation logs and fault information, and automatically alarms and sends maintenance prompts when equipment malfunctions, reducing on-site maintenance costs and ensuring the stable operation of the queuing system.

[0034] This invention also includes a fine-grained permission management process, establishing a multi-role permission system. Different roles are set up, such as administrators, triage nurses, doctors, and general staff, with each role assigned corresponding functional operation permissions and data viewing permissions. This achieves fine-grained control over functional and data permissions. Administrators are responsible for system configuration and rule maintenance, triage nurses have queue management and priority adjustment permissions, and doctors can only view their own patient queues and patient information. This ensures system operational security and data privacy protection, and improves the convenience of operation and maintenance management.

[0035] The following two examples further illustrate the specific implementation of this system: Example 1: Intelligent Scheduling Application in Outpatient Departments of Large General Hospitals This embodiment is applied to the outpatient scenario of a large general hospital. The hospital has multiple departments such as internal medicine, surgery, pediatrics, and obstetrics and gynecology. The daily outpatient volume is large, and the patient types cover a variety of situations such as acute and critical illness, the elderly, first-time visits, and follow-up visits. The consultation room resources are distributed on different floors. It is necessary to balance the efficiency of diagnosis and treatment, the demand for emergency treatment and the utilization rate of resources through dynamic priority scheduling, so as to solve the problems of single priority, rigid scheduling and resource waste of traditional queuing systems.

[0036] During the data acquisition and fusion phase, the system connects to the hospital's information management system, electronic medical record system, laboratory information system, image archiving and communication system. It automatically collects basic information such as patient name, age, medical card number, registered department, type of visit, past medical history, and examination and test results. Simultaneously, it acquires data such as doctor's schedule, clinic number, equipment configuration, current waiting numbers in each department, and average consultation time. All data is aggregated in real-time to a unified data resource pool, establishing a mapping between patient information and medical resources, ensuring synchronous updates of multi-source data, and providing complete data support for subsequent scheduling.

[0037] In the dynamic priority rule construction phase, five priority levels are set, with Level 1 being the highest and Level 5 the lowest. Six assessment factors are established: urgency of the illness, age, type of visit, waiting time, previous medical records, and the degree of abnormality in examination and test results. The weightings for urgency are 30%, age 20%, type of visit 15%, waiting time 15%, previous medical records 10%, and the degree of abnormality in examination and test results 10%. Automatic judgment conditions are defined: critically ill patients are directly classified as Level 1 priority; patients over 70 years of age are classified as Level 2 priority; patients with special diseases are classified as Level 2 priority; initial patients have a basic priority of Level 4; returning patients have a Level 3 priority; and waiting times exceeding 60 minutes automatically elevate the priority to Level 1. Manually adjustable trigger scenarios include sudden changes in patient condition and special on-site needs, forming a configurable dynamic priority rule library.

[0038] During the initial patient priority assessment phase, after patients complete their check-in, the system extracts assessment factor data from the data resource pool and automatically calculates the initial priority level based on the rule base. Patients with acute or critical illnesses are automatically assigned Level 1 priority based on relevant diagnostic certificates; elderly patients over 75 years of age are automatically assigned Level 2 priority; patients with special conditions such as diabetes and hypertension are automatically assigned Level 2 priority; first-time patients are assigned Level 4 priority; and returning patients are assigned Level 3 priority. An initial queue is generated and displayed in real-time on the waiting screen.

[0039] During the dynamic priority adjustment phase, the system monitors patient status and queue conditions in real time. When a patient's waiting time reaches 60 minutes, the system automatically raises their priority by one level; when test results show abnormalities, the priority is raised by 1-2 levels depending on the degree of abnormality; when a patient's condition suddenly changes, the triage nurse manually adjusts their priority to level one through the nursing workstation. All adjustment operations are recorded, including the operator, adjustment time, and adjustment reason, ensuring full traceability.

[0040] During the intelligent scheduling and matching phase, the system supports two scheduling modes: pool mode and independent queue mode. General departments such as internal medicine and surgery use pool mode, while specialized departments such as pediatrics and obstetrics and gynecology use independent queue mode. Based on real-time priority queues, and considering doctors' specialties, current workload, and clinic equipment configuration, a resource optimization algorithm matches patients with doctors and clinics. First-priority patients are assigned to doctors in the department with the lowest current workload, while second-priority patients are assigned sequentially according to the doctors' consultation order, balancing priority access for high-priority patients with doctor continuity of care, and avoiding idle clinic resources.

[0041] During the call-calling and information dissemination phase, doctors use a workstation to perform operations such as calling in sequence, calling again, and skipping numbers. The system simultaneously displays call-calling information on the consultation room screen, waiting room screen, and self-service machine, including the patient's name, department, consultation room number, current call number, and number of people waiting. A voice broadcast system continuously announces the call-calling information, while scrolling text displays estimated waiting time and health education content. Patients can check their queue position and call-calling progress in real time via a mobile app and receive appointment reminders to avoid missing their turn.

[0042] During the full-process monitoring and early warning phase, the system monitors indicators such as the number of people waiting in each department, average waiting time, doctor's consultation efficiency, and clinic usage status in real time. Early warning thresholds are set for waiting times exceeding 30 people and average waiting times exceeding 40 minutes. Once these thresholds are reached, early warning information is automatically sent to triage nurses and department administrators, prompting them to adjust clinic resources or increase the number of attending doctors. Administrators can view real-time outpatient scheduling data across the hospital through the monitoring platform and obtain intervention suggestions.

[0043] During the data statistics and optimization phase, the system compiles daily statistics on registration volume, attendance rate, average waiting time, number of patients seen by doctors, frequency of priority adjustments, and missed appointment rate for each department, generating visualized analysis reports. It also identifies patterns in peak queue congestion times and differences in departmental and doctor efficiency, providing data support for adjusting evaluation factor weights in the rule base, optimizing doctor scheduling, and expanding clinic resources, thereby continuously improving scheduling rationality.

[0044] Table 1 Comparison of the Application Effects of Outpatient Dispatch in Large Comprehensive Hospitals

[0045] Table 1 clearly demonstrates the advantages of this invention in the outpatient setting of a general hospital. Traditional queuing systems rely solely on registration time for sorting, resulting in a single priority system. High-priority patients cannot be seen quickly, leading to excessively long average waiting times, uneven allocation of clinic resources, low utilization, and poor patient satisfaction. This invention, through multi-dimensional dynamic priority rules and intelligent scheduling algorithms, accurately matches patients with medical resources, significantly shortening average waiting times and improving the efficiency of seeing high-priority patients, thereby increasing the utilization rate of clinic resources. Multi-terminal information dissemination and mobile reminder functions reduce patient anxiety, optimize the medical experience, significantly improve patient satisfaction, and achieve a multi-objective balance between treatment efficiency, emergency treatment needs, and resource utilization.

[0046] Example 2: Intelligent Scheduling Application in Multi-Campus Medical Consortiums This embodiment is applied to a medical consortium consisting of a core hospital and three branch hospitals. The core hospital has complete departments and abundant expert resources, while the branch hospitals focus on basic medical care. Patients can seek medical treatment across different hospital areas. A unified scheduling platform is needed to achieve resource coordination across multiple hospital areas, reasonable patient diversion, and seamless connection of cross-hospital referrals, solving problems such as insufficient multi-hospital collaboration capabilities, cumbersome referral processes, and inability to share resources in traditional systems.

[0047] During the data acquisition and fusion phase, a unified data resource pool across multiple hospital campuses is built using a cloud tenant architecture. Data from the hospital information management systems and electronic medical record systems of the core hospital and its branches are synchronized to this resource pool in real time. The system collects basic patient information, registration records, examination and test results, and treatment records from each hospital campus. It also synchronously acquires resource information such as doctor schedules, clinic configurations, equipment status, and current queue data from each campus, establishing cross-campus data connections to ensure data interoperability between the core hospital and its branches, providing data support for cross-campus scheduling.

[0048] In the dynamic priority rule construction phase, five priority levels are retained, and the weights of evaluation factors and judgment conditions are adjusted to adapt to the medical consortium scenario. The weights are as follows: urgency of the condition (35%), inter-hospital referral needs (20%), age (15%), type of visit (10%), waiting time (10%), and the degree of abnormality in examination and test results (10%). The basic priority for patients referred across hospitals is defined as Level 2, for critically ill patients referred across hospitals as Level 1, and for patients over 70 years old seeking treatment across hospitals as Level 2. Waiting times exceeding 50 minutes automatically elevate the priority to Level 1, forming a dynamic priority rule library adapted to multi-hospital scenarios.

[0049] During the initial patient priority assessment phase, after a patient checks in at any hospital campus, the system extracts assessment factor data from a unified data resource pool and automatically calculates the initial priority level. Patients transferred across hospital campuses are automatically assigned a Level 2 priority based on their referral certificate; patients with acute or critical illnesses transferred across hospital campuses are assigned a Level 1 priority; elderly patients over 70 years of age are assigned a Level 2 priority; general first-time patients are assigned a Level 4 priority; and returning patients are assigned a Level 3 priority. Initial queues are generated for each hospital campus. Core hospitals can view queue data from all branch hospitals, while branch hospitals can only view queue data related to themselves and the core hospital.

[0050] During the dynamic priority adjustment phase, the system monitors the queue dynamics and patient status of each hospital area in real time. Patients transferred across hospitals with a waiting time exceeding 50 minutes are automatically upgraded by one level of priority. When examination and test results are abnormal, the priority is upgraded by 1-2 levels according to the degree of abnormality. When a patient's condition suddenly changes while receiving treatment at a branch hospital and requires transfer to the core hospital, the branch hospital nurse manually adjusts the priority to level one through the system and simultaneously pushes the transfer request to the core hospital's triage system. The entire adjustment operation is recorded.

[0051] During the intelligent scheduling and matching phase, the core hospital employs a pool-based scheduling model, while branch hospitals use an independent queue model. The system achieves precise matching of patients with hospital areas, doctors, and consultation rooms based on real-time priority queues, doctor specialties, and resource load in each hospital area. Patients transferred across hospital areas are prioritized for allocation to doctors with the lowest workload in the corresponding departments of the core hospital. When resources are strained at the core hospital, the system automatically checks the resource status of branch hospitals and diverts patients with non-urgent needs to those branch hospitals. The system supports flexible scheduling for doctors between the core hospital and branch hospitals at different times, maximizing the utilization of expert resources.

[0052] During the queuing and information dissemination phase, a multimedia information dissemination model is adopted, with unified management of queuing display screens, televisions, self-service machines, mobile mini-programs, and other terminals across all hospital campuses. Patients transferred between campuses receive real-time queuing progress reminders from the core hospital after successful transfer. Waiting screens in each campus display queue information for different departments in designated areas, scrolling text pushes cross-campus treatment guidance, expert scheduling adjustments, and other notifications, while voice broadcast devices continuously announce queuing information to ensure patients receive timely treatment updates.

[0053] During the full-process monitoring and early warning phase, managers can view indicators such as queue status, resource utilization, and average waiting time in each hospital area through the group-level unified management and control platform. They can set early warning thresholds such as patients waiting for more than 30 minutes when transferring across hospitals or the number of people waiting in a department of a certain hospital area exceeding 25. Once the threshold is reached, early warning information will be automatically pushed to managers, prompting them to adjust resource allocation or guide patients to be diverted.

[0054] In the phase of remote control and refined access management of IoT devices, the system centrally monitors queuing terminals and display devices in each hospital area, displaying the online status and operating parameters of the devices in real time. Administrators can remotely perform operations such as powering on / off, adjusting volume, upgrading software, and distributing programs. The system automatically alarms and sends maintenance notifications when equipment malfunctions. A multi-role access control system is established: the group administrator is responsible for rule base configuration and cross-hospital resource scheduling; each hospital area administrator is responsible for scheduling and equipment management within their respective area; doctors can only view their own patient queues; and triage nurses have queue management and priority adjustment permissions, ensuring system operational security and data privacy.

[0055] During the data statistics and optimization phase, the system regularly compiles data on registration volume, referral volume, average waiting time, resource utilization rate, and patient satisfaction for each hospital area, generating cross-hospital collaborative scheduling analysis reports. It identifies cross-hospital patient flow patterns and resource allocation shortcomings, providing data support for optimizing scheduling, allocating expert resources, and planning departmental development in each hospital area, thereby continuously improving the overall diagnostic and treatment efficiency and service quality of the medical consortium.

[0056] Table 2 Comparison of the Application Effects of Multi-Campus Medical Consortium Scheduling

[0057] Table 2 highlights the application value of this invention in multi-hospital medical consortium scenarios. Traditional multi-hospital management models lack a unified scheduling platform, resulting in isolated data, cumbersome and time-consuming cross-hospital referral processes, low resource utilization due to inefficient resource allocation, reliance on on-site maintenance for equipment maintenance, long fault handling times, and poor patient satisfaction. This invention achieves cross-hospital data interoperability through a cloud tenant architecture and a unified data resource pool. Dynamic priority rules and intelligent scheduling algorithms optimize resource allocation, shorten cross-hospital referral time, and improve resource utilization. Remote control of IoT devices quickly handles equipment failures, reducing maintenance costs. Multi-terminal information publishing and refined permission management optimize the patient experience and system security, significantly improving patient satisfaction across hospital campuses and promoting efficient collaborative diagnosis and treatment within medical consortia.

[0058] Reference Figure 2 This diagram clearly illustrates the refined scheduling effect of the dynamic priority rule of this invention, embodying the design concept of "prioritizing urgent and critical cases while ensuring fairness." Level 1 critically ill patients have a waiting time of only 4 minutes, relying on a high-weight assessment of the urgency of their condition and a priority scheduling algorithm to ensure rapid protection of their lives. Level 2 elderly and patients with special diseases have a waiting time of 10 minutes, meeting the needs of special groups without excessively occupying resources for ordinary patients. Level 3 follow-up visits, Level 4 initial visits, and Level 5 general consultations are allocated waiting times according to priority, forming a reasonable gradient. This hierarchical scheduling model breaks away from the traditional "one-size-fits-all" sorting logic. Through multi-dimensional evaluation factors and a dynamic adjustment mechanism, it allows patients with different needs to receive appropriate treatment time, ensuring both the emergency treatment rights of critically ill patients and maintaining fairness in treatment for ordinary patients, thereby improving the overall rationality and efficiency of outpatient services.

[0059] Reference Figure 3 This figure highlights the optimization effect of the intelligent scheduling of this invention on resource utilization, solving the problem of uneven resource allocation in the traditional model. The traditional model's resource utilization fluctuates drastically with time of day, reaching only 42% at midday, and while it reaches 78%-82% during morning and evening peak hours, some consultation rooms are congested while others are idle, resulting in poor adaptability to changes in patient numbers. The model of this invention, through real-time monitoring of patient queues and consultation room status, uses a resource optimization algorithm to dynamically allocate patients and consultation rooms, achieving a stable utilization rate of 75%-93%, maintaining a high utilization rate of 75% even at midday, and exceeding 90% during peak hours. This stable and efficient resource utilization avoids resource waste caused by idle consultation rooms, reduces increased waiting times due to congestion, and adapts to changes in patient numbers at different times, achieving a dynamic balance between resource supply and patient demand, improving the overall operational efficiency of outpatient services, and saving operating costs for hospitals.

[0060] Reference Figure 4This diagram illustrates the technical advantages of this invention's multi-hospital collaborative scheduling, addressing the industry pain points of cumbersome and time-consuming traditional cross-hospital referral processes. Traditional manual referrals require patients to carry their medical records and repeatedly queue for registration, taking up to 85 minutes in total, resulting in extremely low efficiency. While ordinary system referrals achieve some data sharing, they lack unified scheduling and priority inheritance, still taking 55 minutes. This invention, relying on a cloud tenant architecture and a unified data resource pool, achieves real-time data synchronization across hospital campuses. Referral patients no longer need to repeatedly submit information; intelligent scheduling quickly matches resources in the target hospital campus, reducing the time to 28 minutes. The priority inheritance mode preserves the original hospital campus priority level, and the full-process information synchronization mode achieves seamless integration of examination and testing data and medical records, further reducing the time to 22-25 minutes. This significantly shortens the referral cycle, reduces unnecessary patient waiting and movement, and improves the collaborative efficiency and continuity of medical services within medical alliances and across multiple hospital campuses.

[0061] 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 queuing and calling intelligent scheduling method based on dynamic priority, characterized in that, Includes the following steps: Data collection and integration steps: Connect with various hospital information systems, collect patient-related data and doctor and clinic resource data, and establish a unified data resource pool; Dynamic priority rule construction steps: Set up multi-level priority levels, establish multi-dimensional priority evaluation factors and assign weights to form a configurable dynamic priority rule library; Initial patient priority assessment steps: Based on the data resource pool and rule base, evaluation factors are automatically extracted to calculate the initial priority and generate the initial queuing queue; Priority dynamic adjustment steps: Real-time monitoring of patient status, queue status and clinic resource dynamics; automatic priority adjustment is triggered in case of abnormalities; manual adjustment by triage nurses is also supported. Intelligent scheduling and matching steps: Based on real-time priority queues and various resource information, resource optimization algorithms are used to match patients with doctors and consultation rooms; Calling and Information Release Steps: Supports multi-mode calling operation, synchronously releases relevant medical information through multiple terminals, and uses a combination of voice broadcast and scrolling text to convey medical reminders; Full-process monitoring and early warning steps: Real-time monitoring of all core indicators in triage, automatic triggering of early warnings in case of abnormalities, and push of early warning information to relevant staff; Data statistics and optimization steps: Regularly collect multi-dimensional triage operation data, generate visual analysis reports, and identify the causes of queue congestion and resource allocation shortcomings.

2. The intelligent scheduling method for medical queuing and calling based on dynamic priority according to claim 1, characterized in that, It also includes a patient priority calculation step, and designs a dynamic priority quantification calculation expression to address the non-linear correlation characteristics of multi-dimensional assessment factors. in The patient's final priority score, to These are the weighting coefficients for each evaluation factor. Assess the urgency of the illness. For the patient's age, The age-related adjustment factor is used. To accumulate waiting time, The waiting time decay coefficient, The base score for the type of medical visit. The weighted score is based on previous medical records. To assess the degree of abnormality in test results, This is the amplification factor for the degree of abnormality. Additional points will be awarded for special cases.

3. The intelligent scheduling method for medical queuing and calling based on dynamic priority according to claim 1, characterized in that, It also includes cross-departmental referral scheduling steps. When a patient needs to visit a different department, the system automatically links the queuing information and priority level of the original department, extracts the patient's completed examination and test data and treatment records, generates a referral request form, and pushes it to the triage system of the target department. After the target department receives the referral request, it inserts the patient into the corresponding queue based on the urgency of the referral request, the original priority level, and its own queue status. It also updates the queue information of the original department and the target department simultaneously and sends referral reminders and estimated waiting information for the target department to the patient.

4. The intelligent scheduling method for medical queuing and calling based on dynamic priority according to claim 1, characterized in that, It also includes multi-hospital collaborative scheduling steps, adopts a cloud tenant architecture, establishes a unified scheduling platform for multiple hospitals, centrally manages patient queues, doctor resources, and clinic configuration data of each hospital, while retaining the independent management permissions of each hospital. When a patient needs to seek medical treatment in a different hospital or when resources in a certain hospital are strained, the system automatically queries the resource availability and queue status of corresponding departments in other hospitals. Based on the patient's geographical location, priority level, and hospital resource load, it generates an optimal hospital recommendation plan and updates the scheduling information of relevant hospitals simultaneously.

5. The intelligent scheduling method for medical queuing and calling based on dynamic priority according to claim 1, characterized in that, It also includes steps for handling missed appointments and optimizing the queue, setting flexible rules for missed appointments, and supporting three processing modes: automatically returning missed patients to the end of the queue, inserting them into the corresponding position in the queue according to their original priority, or re-evaluating their priority; the system automatically records the reasons for missed appointments and the frequency, and marks and reminds patients who have missed appointments multiple times. A queue sorting optimization algorithm is adopted to adjust the queue order in real time.

6. The intelligent scheduling method for medical queuing and calling based on dynamic priority according to claim 1, characterized in that, It also includes personalized adaptation steps for the doctor's workbench, supporting three access modes: client, browser, and interface embedding; it provides a mobile QR code login function, which makes it convenient for doctors to manage queues when making ward rounds or temporarily leaving, and synchronizes queue status, patient information, and call records in real time, allowing doctors to view patients' past medical records and examination and test results.

7. The intelligent scheduling method for medical queuing and calling based on dynamic priority according to claim 1, characterized in that, It also includes a dynamic allocation step for clinic resources, and the expression for optimal allocation of clinic resources is designed as follows: in The fitness value corresponding to the optimal clinic room allocation scheme. The patient demand weighting coefficient, The number of patients to be allocated. For the first Priority scores for each patient. For the first The matching coefficient between patients and examination room equipment. The queue length influence coefficient. The current waiting queue length for the target clinic. This is the resource load factor. The resource occupancy rate of the target consultation room. This represents the doctor's work performance coefficient. This refers to the number of patients seen by the doctor corresponding to the target clinic. For the first The complexity score of the diagnosis and treatment of the patients already treated.

8. The intelligent scheduling method for medical queuing and calling based on dynamic priority according to claim 1, characterized in that, It also includes a comprehensive information dissemination process, unified management of information dissemination content across all queuing terminals; information push by clinic area, time period, and patient group; health education content and doctor introductions during regular hours; queuing reminders and estimated waiting times during peak hours; and temporary notices and process change reminders in special circumstances.

9. The intelligent scheduling method for medical queuing and calling based on dynamic priority according to claim 1, characterized in that, It also includes steps for remote management and control of IoT devices, centralized monitoring of all terminals in the hospital, real-time display of device online status and operating parameters; supports remote batch operation of devices, automatically records device operation logs and fault information, and automatically alarms and pushes maintenance prompts when devices malfunction.

10. The intelligent scheduling method for medical queuing and calling based on dynamic priority according to claim 1, characterized in that, It also includes permission management steps, establishing a multi-role permission system, setting different roles, and assigning corresponding function operation permissions and data viewing permissions to each role; The system manages functional and data permissions. Administrators are responsible for system configuration and rule maintenance. Triage nurses have queue management and priority adjustment permissions, while doctors can only view their own patient queues and patient information.

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