Automatic distribution system for medical examination items

Abstract

The invention provides an automatic distribution system for medical examination items. The automatic distribution system for the medical examination items comprises a hardware module which comprises a data acquisition terminal, a patient terminal device, an examination point terminal device, a central server and a network communication module; the software module comprises data acquisition software, path planning and sorting algorithm software, user interaction interface software and push control software, a sorting algorithm supports priority dynamic adjustment rules of patients with special requirements, and personalized requirement priorities of emergency patients can be automatically assigned according to disease levels of the patients. According to the algorithm model, the system achieves dynamic planning and pushing of the checking sequence through the following optimization model. According to the automatic distribution system for the medical examination items, through cooperative work of the hardware module, the software module and the optimization algorithm model, the efficiency and accuracy of examination path planning and queuing information integration are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hospital examination item push, and in particular to an automatic allocation system for medical examination items. Background Art

[0002] The core function of the system is to automatically generate the optimal inspection sequence for the patient's reference based on the patient's inspection item list, combined with the geographical location of each inspection point in the hospital, the real-time busyness (such as the number of queues, equipment availability, etc.). The system first obtains the patient's inspection items through the hospital management system and assigns them to the corresponding inspection points. Subsequently, the system obtains real-time information of each inspection point through the data acquisition module, such as the number of people in the queue, the working status of the equipment, and the arrival distance. The sorting algorithm module generates a dynamically adjusted inspection sequence based on this data. Patients obtain recommended inspection plans through mobile devices or self-service terminals. After completing an inspection, the system updates the status of other inspection points in real time to further optimize the subsequent inspection sequence.

[0003] While this system significantly reduces patient wait times and improves the efficiency of the examination process, some issues remain to be addressed. The complexity of route planning and integrating queue information can lead to lengthy algorithm calculation times, particularly in large patient populations or large hospitals, which can reduce the user experience. The system's handling of special needs, such as emergency patients or specialized examinations, needs further improvement. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides an automatic allocation system for medical examination items, which solves the problem that the complexity of path planning and queuing information integration is high, which may cause the algorithm calculation time to be too long; and the processing of special needs such as emergency patients or special examination items needs to be further enhanced.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A medical examination item automatic allocation system, comprising:

[0006] Hardware modules: including data acquisition terminal, patient terminal equipment, checkpoint terminal equipment, central server and network communication module;

[0007] Software modules: including data acquisition software, path planning and sorting algorithm software, user interface software and push control software. The sorting algorithm supports dynamic adjustment rules for the priority of patients with special needs, and personalized priority of emergency patients. i Can automatically assign values ​​according to the patient's condition level;

[0008] Algorithm model: The system implements dynamic planning and push of inspection sequence through the following optimization model:

[0009] Checkpoint weight formula:

[0010] W i =α·D i +β·Q i +γ·T i +δ·P i +∈·C i ;

[0011] in:

[0012] W i : The comprehensive weight of checkpoint i;

[0013] D i : the distance between the patient's current position and checkpoint i;

[0014] Q i : Real-time queue volume at the checkpoint;

[0015] T i : Time sensitivity factor of the checkpoint;

[0016] P i : Prioritize patients’ personalized needs;

[0017] C i : The current load capacity of the checkpoint;

[0018] α, β, γ, δ,∈: adjustable weight parameters;

[0019] Path planning formula: Dynamic path optimization formula combined with improved A* algorithm:

[0020]

[0021] in:

[0022] C(p): the comprehensive cost of path p;

[0023] H(p): the remaining path cost estimated by the heuristic function;

[0024] The total time spent on patient examination is as follows:

[0025]

[0026] in:

[0027] T travel,i : The time it takes for the patient to travel to checkpoint i,

[0028] T queue,i : the queue time of checkpoint i,

[0029] T check,i : Checkpoint i’s inspection time;

[0030] Innovative mechanism: Introducing a historical data prediction module, using machine learning algorithms to predict future checkpoint queue conditions, and dynamically adjusting the sorting scheme based on real-time data to enhance the system's accuracy and response speed.

[0031] Preferably, the data collection terminal includes an Internet of Things sensor, an RFID reader and a camera module, which are used to collect information about the geographical location of the checkpoint, the number of people in the queue, the equipment status and the ambient temperature.

[0032] Preferably, the patient terminal device includes a mobile application, a self-service terminal and a smart wearable device, supports real-time inspection sequence display and voice feedback functions, and the patient terminal device has a path navigation function, combined with location-based service technology, to provide dynamic navigation and checkpoint location prompts.

[0033] Preferably, the central server combines cloud computing and edge computing modules to distribute tasks with high computational complexity to checkpoint terminal devices to reduce the computational load of the main server.

[0034] Preferably, the time sensitive factor T i Combined with the timeliness requirements of the examination items, for example, imaging examinations and pre-operative examinations are given priority.

[0035] Preferably, the sorting algorithm supports the dynamic adjustment rules of the priority of patients with special needs, and the personalized demand priority of emergency patients P i Values ​​can be automatically assigned based on the patient's condition level.

[0036] Preferably, the historical data prediction module adopts a time series analysis model in machine learning to predict the changing trend of the number of people queuing at the checkpoint, thereby improving the accuracy of the sorting results.

[0037] Preferably, the push control software adopts a priority-based message queue mechanism to ensure that emergency patient information is pushed first. The push system adopts an intelligent fault-tolerant mechanism, which can generate a backup sorting scheme in the event of data collection interruption or partial checkpoint equipment failure. The weight parameters α, β, γ, δ, ∈ are dynamically adjusted according to different scenarios through the system self-learning mechanism to meet the personalized needs of different hospitals.

[0038] The present invention provides a system for automatically allocating medical examination items. It has the following beneficial effects:

[0039] This automatic allocation system for medical examination items significantly improves the efficiency and accuracy of examination path planning and queue information integration through the collaborative work of hardware modules, software modules and optimization algorithm models. The system introduces an improved path planning and sorting algorithm, including a weight calculation formula that comprehensively considers distance, queue volume, time sensitivity, patient individual needs and checkpoint load capacity, combined with a dynamic path optimization algorithm, which effectively reduces the calculation time in complex scenarios. Through the formula for the total time spent on patient examinations, the system can dynamically adjust the order of examinations and optimize patients' waiting time and examination processes. In addition, the innovative introduction of a historical data prediction module predicts future queue conditions at checkpoints based on a machine learning algorithm, and further adjusts the sorting results in combination with real-time data, thereby improving the system's adaptability to dynamic environments.

[0040] While meeting the needs of standard patients, this invention enhances both the processing capabilities and system reliability for patients with special needs. Through dynamic priority adjustment rules, the needs of emergency patients or those with specialized examinations are automatically assigned higher weights, ensuring that their examination plans are prioritized. Furthermore, the system's push control software utilizes a priority message queue mechanism, combined with intelligent fault-tolerant design, to generate backup sorting solutions in the event of data collection interruptions or equipment failures, ensuring continuous system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0043] Example 1

[0044] like Figure 1As shown, an embodiment of the present invention provides an automatic allocation system for medical examination items, including hardware modules: a data acquisition terminal, a patient terminal device, a checkpoint terminal device, a central server and a network communication module. The data acquisition terminal includes an Internet of Things sensor, an RFID reader and a camera module, which is used to collect the geographical location, number of people in the queue, equipment status and ambient temperature information of the checkpoint. The patient terminal device includes a mobile application, a self-service terminal and a smart wearable device, which supports real-time inspection sequence display and voice feedback functions. The patient terminal device has a path navigation function, and combines location-based service technology to provide dynamic navigation and checkpoint location prompts. The central server combines cloud computing and edge computing modules to allocate tasks with high computational complexity to the checkpoint terminal device to reduce the computing load of the main server. Furthermore, the Internet of Things sensor in the data acquisition terminal includes a temperature and humidity sensor, an infrared sensor and a pressure sensor, which is used to comprehensively monitor the equipment status and environmental conditions of the checkpoint. The RFID reader is used to quickly identify patient information and examination items to improve the efficiency of data interaction. The camera module supports high-definition image capture and video stream upload, which is used to monitor the queue dynamics of the checkpoint in real time to ensure the accuracy and real-time nature of the collected information.

[0045] Software modules: including data acquisition software, path planning and sorting algorithm software, user interaction interface software and push control software.

[0046] Algorithm model: The system implements dynamic planning and push of inspection sequence through the following optimization model:

[0047] Checkpoint weight formula:

[0048] W i =α·D i +β·Q i +γ·T i +δ·P i +∈·C i ;

[0049] in:

[0050] W i : The comprehensive weight of checkpoint i.

[0051] D i : The distance between the patient’s current position and checkpoint i.

[0052] Q i : Real-time queue volume at the checkpoint.

[0053] T i : Time sensitivity factor of the checkpoint.

[0054] P i : Prioritize patients’ personalized needs.

[0055] C i : The current load capacity of the checkpoint.

[0056] α, β, γ, δ, ∈: adjustable weight parameters, time-sensitive factor T i Taking into account the timeliness requirements of examination items, such as the prioritization of imaging examinations and pre-operative examinations, the push control software adopts a priority-based message queue mechanism to ensure that emergency patient information is pushed first. The push system adopts an intelligent fault-tolerant mechanism, which can generate a backup sorting scheme in the event of data collection interruption or partial checkpoint equipment failure. The weight parameters α, β, γ, δ, ∈ are dynamically adjusted according to different scenarios through the system's self-learning mechanism to meet the personalized needs of different hospitals. Furthermore, the assignment rules of the time-sensitive factor Ti are dynamically optimized based on the specific scenarios of the hospital. For example, for examinations that need to be linked with subsequent projects, such as the linkage analysis of blood tests and CT scans, the system prioritizes assigning higher time-sensitive factor weights. For regular follow-up visits or non-emergency examinations, the time-sensitive factor weight can be appropriately reduced to optimize the fairness of the overall examination process.

[0057] Path planning formula: Dynamic path optimization formula combined with improved A* algorithm:

[0058]

[0059] in:

[0060] C(p): The comprehensive cost of path p.

[0061] H(p): The remaining path cost estimated by the heuristic function.

[0062] The total time spent on patient examination is as follows:

[0063]

[0064] in:

[0065] T travel,i : The time it takes for the patient to travel to checkpoint i,

[0066] T queue,i : the queue time of checkpoint i,

[0067] T check,i : Checkpoint i’s checkpoint time.

[0068] Innovative mechanism: Introducing a historical data prediction module, using machine learning algorithms to predict future queues at checkpoints, dynamically adjusting sorting schemes based on real-time data, and enhancing the accuracy and responsiveness of the system. The historical data prediction module uses a time series analysis model in machine learning to predict changing trends in the number of people queuing at checkpoints, thereby improving the accuracy of sorting results.

[0069] Example 2

[0070] Hardware module deployment

[0071] Data collection terminal:

[0072] IoT sensors, RFID readers, and camera modules are installed at every checkpoint in the hospital.

[0073] Temperature and humidity sensors are used to monitor the temperature and humidity of the equipment environment in real time to ensure equipment stability.

[0074] Infrared sensors record the flow of patients entering the checkpoint.

[0075] Pressure sensors monitor the operating status of equipment (such as imaging equipment load).

[0076] The camera module uploads real-time queue video stream to identify the current number of people in the queue.

[0077] Patient terminal equipment:

[0078] Patients can use the hospital app to view the queue status, navigation path and estimated time of the checkpoint in real time.

[0079] Self-service terminal devices are distributed in the hospital outpatient hall, where patients can check the examination order and print the route.

[0080] Smart wearable devices (such as smart watches) push inspection notifications and inform the next steps through voice.

[0081] Central server and network communication module:

[0082] The central server integrates cloud computing and edge computing functions to handle high-concurrency sorting tasks.

[0083] The edge computing module is deployed at each checkpoint to process data in real time and communicate with the central server to reduce the load on the main server.

[0084] Software module operation

[0085] Data acquisition software:

[0086] Continuously collect patient information, queue numbers, equipment status, and environmental parameters from checkpoints.

[0087] Path planning and sorting algorithm software:

[0088] The improved A* algorithm is used to calculate the optimal path for the patient to the next checkpoint.

[0089] The priority of each checkpoint is calculated based on the comprehensive weight formula, and the inspection order is dynamically adjusted.

[0090] User interface software:

[0091] Dynamic sorting information is displayed through the patient terminal, and the examination status is pushed in real time.

[0092] Push control software:

[0093] Utilize a priority-based message queue mechanism to ensure that emergency patients are given priority.

[0094] Provides backup sequencing solutions to ensure service continuity in the event of equipment failure.

[0095] Algorithm optimization

[0096] Checkpoint weight calculation formula:

[0097] Take the linked analysis of imaging examinations and blood tests as an example:

[0098] The time-sensitive factor is set higher and is given priority.

[0099] Checkpoints with long queues are given lower weights through the prediction model to disperse the patient flow.

[0100] Path planning formula:

[0101] When patients go from the registration office to the checkpoint, the system calculates the comprehensive cost of the walking path in real time to avoid congested areas.

[0102] The total time spent on patient examination is as follows:

[0103] For a patient who needs blood tests and a CT scan:

[0104] The system predicts that the waiting time for blood test points will be 5 minutes and that for CT test points will be 10 minutes.

[0105] According to the examination time, the patient's path from blood test to CT scan is dynamically adjusted to optimize the total time.

[0106] Application of innovative mechanisms

[0107] Historical data prediction module:

[0108] The system uses the queue data from the past week and predicts through time series analysis that the number of people queuing for blood tests will reach a peak between 10:00 and 12:00 on a certain day.

[0109] On this basis, non-emergency patients are guided in advance to avoid peak hours.

[0110] Intelligent fault-tolerance mechanism:

[0111] When an imaging device temporarily malfunctions, the system reallocates patients to adjacent imaging examination points through a backup sorting plan and notifies relevant patients to change their examination order.

[0112] Actual operation scenario

[0113] Case Patient A:

[0114] Tests to be scheduled: blood tests, CT scans, ultrasounds.

[0115] Initial order: blood test → CT scan → ultrasound examination.

[0116] System push:

[0117] Real-time queue information showed that the number of people queuing at the blood testing point had increased sharply. The system adjusted the order based on the weight formula and recommended that patients complete the CT scan first.

[0118] The navigation function guides patients along the optimal route from the registration area to the CT examination point, which is expected to take 3 minutes.

[0119] After the CT scan is completed, the system predicts that the number of people queuing at the blood testing point has decreased and notifies the patient to return to complete the blood test.

[0120] Implementation Effect

[0121] Efficiency improvement:

[0122] Through dynamic sorting, the average waiting time for patients is reduced by approximately 20%.

[0123] Improved patient satisfaction:

[0124] Real-time information push and voice reminders reduce patients' waiting anxiety.

[0125] System stability enhancements:

[0126] Intelligent fault-tolerant mechanism ensures that the system can still run smoothly in the event of equipment failure.

[0127] Resource optimization:

[0128] The central server load was reduced by approximately 30%, and the operating efficiency of checkpoint equipment was improved.

[0129] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A medical examination item automatic allocation system, characterized in that: include: Hardware modules: including data acquisition terminal, patient terminal equipment, checkpoint terminal equipment, central server and network communication module; Software modules: including data acquisition software, path planning and sorting algorithm software, user interaction interface software and push control software; Algorithm model: The system implements dynamic planning and push of inspection order through the following optimization model: Checkpoint weight formula: W i =α·D i +β·Q i +γ·T i +δ·P i +∈·C i ; in: W i : The comprehensive weight of checkpoint i; D i : The distance between the patient's current position and checkpoint i; Q i : Real-time queue volume at the checkpoint; T i : Time sensitivity factor of the checkpoint; P i :Priority of patients’ individual needs; C i : The current load capacity of the checkpoint; α, β, γ, δ,∈: adjustable weight parameters; Path planning formula: Dynamic path optimization formula combined with improved A* algorithm: in: C(p): the comprehensive cost of path p; H(p): the remaining path cost estimated by the heuristic function; The total time spent on patient examination is as follows: in: T travel,i : The time it takes for the patient to go to checkpoint i, T queue,i : The queue time of checkpoint i, T check,i : Checkpoint i’s check time; Innovative mechanism: Introduce a historical data prediction module, use machine learning algorithms to predict future queue conditions at checkpoints, and dynamically adjust the sorting scheme based on real-time data to enhance the accuracy and response speed of the system.

2. The automatic allocation system for medical examination items according to claim 1, characterized in that: The data collection terminal includes an Internet of Things sensor, an RFID reader and a camera module, which are used to collect the geographical location of the checkpoint, the number of people in the queue, the equipment status and the ambient temperature information.

3. The automatic allocation system for medical examination items according to claim 1, characterized in that: The patient terminal device includes a mobile application, a self-service terminal and a smart wearable device, which supports real-time inspection sequence display and voice feedback functions. The patient terminal device has a path navigation function, combined with location-based service technology, to provide dynamic navigation and checkpoint location prompts.

4. The automatic allocation system for medical examination items according to claim 1, characterized in that: The central server combines cloud computing and edge computing modules to distribute tasks with high computational complexity to checkpoint terminal devices to reduce the computational load of the main server.

5. The automatic allocation system for medical examination items according to claim 1, characterized in that: The time sensitive factor T i Consider the timeliness requirements of the examination items, such as giving priority to imaging examinations and pre-operative examinations.

6. The automatic allocation system for medical examination items according to claim 1, characterized in that: The sorting algorithm supports the dynamic adjustment rules of the priority of patients with special needs, and the personalized priority of emergency patients P i Values ​​can be automatically assigned based on the patient's condition level.

7. The automatic allocation system for medical examination items according to claim 1, characterized in that: The historical data prediction module adopts the time series analysis model in machine learning to predict the changing trend of the number of people queuing at the checkpoint and improve the accuracy of the sorting results.

8. The automatic allocation system for medical examination items according to claim 1, characterized in that: The push control software adopts a priority-based message queue mechanism to ensure that emergency patient information is pushed first. The push system adopts an intelligent fault-tolerant mechanism, which can generate a backup sorting scheme in the event of data collection interruption or partial checkpoint equipment failure. The weight parameters α, β, γ, δ, ∈ are dynamically adjusted according to different scenarios through the system self-learning mechanism to meet the personalized needs of different hospitals.

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