Artificial intelligence chronic disease management system and method based on medical resource perception

Abstract

The invention discloses an artificial intelligence chronic disease management system and method based on medical resource perception, and relates to the technical field of medical management, and the system comprises a patient information collection module, a sickbed information collection module, a priority calculation module, a sickbed matching module, and a scheduling execution module. The priority of patient medical resource allocation is calculated through the priority calculation module, sickbed matching is carried out on the patient through the sickbed matching module, and medical resource arrangement can be carried out on the chronic disease patient to the maximum extent; a to-be-matched set Q3 is created, and the in-use sickbeds can be reallocated, so that the utilization rate of medical resources is improved; and the scheduling execution module can perform scheduling arrangement on the patients and also can perform real-time information acquisition, so that the management reliability is improved.

Description

Technical Field

[0001] The present invention relates to the field of medical management technology, and in particular to an artificial intelligence chronic disease management system and method based on medical resource perception. Background Art

[0002] Medical resource perception refers to the process of using modern information technology and sensor technology to monitor, analyze and manage the distribution, utilization and operation status of medical resources in real time. It aims to improve the utilization efficiency of medical resources, optimize the allocation of medical resources and provide data support for medical decision-making.

[0003] In the current chronic disease management system, according to patent number: CN116612846A, reference patent: Intelligent chronic disease management system and device based on health monitoring, which records the contents of "health monitoring terminal module (1), monitoring data aggregation module (2), chronic disease management decision configuration module (3), chronic disease management execution module (4), chronic disease management resource service system (5)". From this, those skilled in the art can know that the existing technology for chronic disease management is to monitor and manage its treatment, but there is no targeted medical resource perception management in the hospital.

[0004] Therefore, an artificial intelligence chronic disease management system and method based on medical resource perception is designed. Summary of the invention

[0005] In order to overcome the above-mentioned shortcomings, the present invention provides an artificial intelligence chronic disease management system and method based on medical resource perception.

[0006] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0007] An artificial intelligence chronic disease management system based on medical resource perception, including

[0008] Patient information collection module, used to collect patient information, appointment time and disease severity;

[0009] The bed information collection module is used to collect bed resources, including bed type, current bed status, and department where the bed is located;

[0010] A priority calculation module, used for calculating the patient priority based on the data from the patient information collection module;

[0011] A bed matching module is used to match beds to patients based on the data from the bed information collection module and the priority calculation module;

[0012] The scheduling execution module is used to notify patients and confirm their admission, as well as record hospitalization information.

[0013] Preferably, the priority algorithm in the priority calculation module is as follows:

[0014] P=ω 1 S+ω 2 T

[0015] Among them, P is the priority, S is the patient's condition, T is the appointment time, ω 1 and ω 2 is the weight coefficient used to adjust the impact of illness severity and appointment time on priority.

[0016] Preferably, the steps of bed matching in the bed matching module are as follows:

[0017] S11. Collect data from the bed information collection module. The bed type is T b , the current state of the bed is S b , the beds are regarded as a bed set B, where each bed b∈B has a corresponding bed type T b and the current status of the bed S b , the current status of the bed is idle S b =1, the current state of the bed occupies S b =0;

[0018] S12, collect data from the priority calculation module, the priority P of the nth patient n , the patient's priority is a priority set Q1, where P n ∈Q1,

[0019] S13, perform priority screening, when P n >ω 3 T b S b And S b =1, then P n As a matching set Q2;

[0020] S14, perform queue matching, and match P in Q2 n Sort by P n The largest match first.

[0021] Preferably, the scheduling execution module comprises the following steps:

[0022] S21, sending a matching SMS to the patient or the patient's family;

[0023] S22, waiting for confirmation from the patient or the patient's family;

[0024] S23, record bed information;

[0025] S24. Issue patient information and condition registration form;

[0026] S25. Update patient information in real time and prioritize n and the current status of the bed S b renew;

[0027] S26, create a set to be matched Q3, when the patient priority P n <P min When P n Put into the to-be-matched set Q3, P min To meet the priority of freeing up hospital beds.

[0028] Preferably, the specific steps of step S13 are as follows:

[0029] S131, bed information inventory calculation, S in set B b Add all together to get M;

[0030] S132, when M ≥ 1, priority screening is performed, that is, when P n >ω 3 T b S b And S b =1, then P n As a matching set Q2;

[0031] S133, when M=0, call the to-be-matched set Q3, if P is satisfied later n >P max , then the patient replaces the patient in the to-be-matched set Q3.

[0032] Preferably, in step S25, the patient information is uploaded and updated in real time via a wireless communication module.

[0033] An artificial intelligence chronic disease management method based on medical resource perception as described above comprises the following steps:

[0034] Step 1: Patient information collection: collect patient information through the patient information collection module, and collect the patient information of the occupied beds in real time;

[0035] Step 2: Bed information collection: collect bed information in real time through the bed information collection module;

[0036] Step 3: Patient priority calculation: the priority of patient medical resource allocation is calculated through the priority calculation module;

[0037] Step 4: Match patients to beds according to their priorities, and use the bed matching module to match patients to beds;

[0038] Step 5: Scheduling execution, scheduling patients and collecting information feedback through the bed matching module.

[0039] The beneficial effects of the present invention are: in the artificial intelligence chronic disease management system and method based on medical resource perception,

[0040] 1. The priority of medical resource allocation for patients is calculated through the priority calculation module, and then the bed matching module is used to match the patient with a bed, so as to achieve the maximum arrangement of medical resources for patients with chronic diseases;

[0041] 2. Create the to-be-matched set Q3, which can reallocate the beds in use and improve the utilization rate of medical resources;

[0042] 3. The scheduling execution module can schedule patients and collect real-time information at the same time, improving the reliability of management. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present invention will now be described by way of example with reference to the accompanying drawings, in which:

[0044] Figure 1 It is a diagram of the method steps of the present invention;

[0045] Figure 2 is a step diagram of the bed matching module of the present invention;

[0046] Figure 3 It is a step diagram of the scheduling execution module of the present invention. DETAILED DESCRIPTION

[0047] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0048] An artificial intelligence chronic disease management system based on medical resource perception, including

[0049] Patient information collection module, used to collect patient information, appointment time and disease severity;

[0050] The bed information collection module is used to collect bed resources, including bed type, current bed status, and department where the bed is located;

[0051] A priority calculation module, used for calculating the patient priority based on the data from the patient information collection module;

[0052] A bed matching module is used to match beds to patients based on the data from the bed information collection module and the priority calculation module;

[0053] The scheduling execution module is used to notify patients and confirm their admission, as well as record hospitalization information.

[0054] Preferably, the priority algorithm in the priority calculation module is as follows:

[0055] P=ω 1 S+ω 2 T

[0056] Among them, P is the priority, S is the patient's condition, T is the appointment time, ω 1 and ω 2 is the weight coefficient used to adjust the impact of illness severity and appointment time on priority.

[0057] like Figure 1 As shown, as a specific embodiment, an artificial intelligence chronic disease management method based on medical resource perception as described above includes the following steps:

[0058] Step 1: Patient information collection: collect patient information through the patient information collection module, and collect the patient information of the occupied beds in real time;

[0059] Step 2: Bed information collection: collect bed information in real time through the bed information collection module;

[0060] Step 3: Patient priority calculation: the priority of patient medical resource allocation is calculated through the priority calculation module;

[0061] Step 4: Match patients to beds according to their priorities, and use the bed matching module to match patients to beds;

[0062] Step 5: Scheduling execution, scheduling patients and collecting information feedback through the bed matching module. Figure 2 As shown in the figure, as a specific embodiment, the steps of bed matching in the bed matching module are as follows:

[0063] S11. Collect data from the bed information collection module. The bed type is T b , the current state of the bed is S b , the beds are regarded as a bed set B, where each bed b∈B has a corresponding bed type T b and the current status of the bed S b , the current status of the bed is idle S b =1, the current state of the bed occupies S b =0;

[0064] S12, collect data from the priority calculation module, the priority P of the nth patient n , the patient's priority is a priority set Q1, where Pn ∈Q1,

[0065] S13, perform priority screening, when P n >ω 3 T b S b And S b =1, then P n As a matching set Q2;

[0066] S14, perform queue matching, and match P in Q2 n Sort by P n The largest match first.

[0067] The specific steps of step S13 are as follows:

[0068] S131, bed information inventory calculation, S in set B b Add all together to get M;

[0069] S132, when M ≥ 1, priority screening is performed, that is, when P n >ω 3 T b S b And S b =1, then P n As a matching set Q2;

[0070] S133, when M=0, call the to-be-matched set Q3, if P is satisfied later n >P max , then the patient replaces the patient in the to-be-matched set Q3.

[0071] like Figure 3 As shown, as a specific embodiment, the scheduling execution module includes the following steps:

[0072] S21, sending a matching SMS to the patient or the patient's family;

[0073] S22, waiting for confirmation from the patient or the patient's family;

[0074] S23, record bed information;

[0075] S24. Issue patient information and condition registration form;

[0076] S25. Update patient information in real time and prioritize n and the current status of the bed S b Update: real-time upload and update of patients through wireless communication module;

[0077] S26, create a set to be matched Q3, when the patient priority P n<P min When P n Put into the to-be-matched set Q3, P min To meet the priority of freeing up hospital beds.

[0078] The above is based on the present invention as an inspiration. Through the above description, relevant staff can make various changes and modifications without departing from the technical idea of ​​this invention. The technical scope of this invention is not limited to the content in the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. An artificial intelligence chronic disease management system based on medical resource perception, characterized by: include Patient information collection module, used to collect patient information, appointment time and disease severity; The bed information collection module is used to collect bed resources, including bed type, current bed status, and department where the bed is located; A priority calculation module, used for calculating the patient priority based on the data from the patient information collection module; A bed matching module is used to match beds to patients based on the data from the bed information collection module and the priority calculation module; The scheduling execution module is used to notify patients and confirm their admission, as well as record hospitalization information.

2. According to claim 1, an artificial intelligence chronic disease management system based on medical resource perception is characterized by: The priority algorithm in the priority calculation module is as follows: P=ω1S+ω2T Among them, P is the priority, S is the severity of the patient's condition, T is the appointment time, and ω1 and ω2 are weight coefficients used to adjust the impact of the severity of the condition and the appointment time on the priority.

3. According to claim 2, an artificial intelligence chronic disease management system based on medical resource perception is characterized by: The steps of bed matching in the bed matching module are as follows: S11. Collect data from the bed information collection module. The bed type is T b , the current state of the bed is S b , the beds are regarded as a bed set B, where each bed b∈B has a corresponding bed type T b and the current status of the bed S b , the current status of the bed is idle S b =1, the current state of the bed occupies S b =0; S12, collect data from the priority calculation module, the priority P of the nth patient n , the patient's priority is a priority set Q1, where P n ∈Q1, S13, perform priority screening, when P n >ω3T b S b And S b =1, then P n As a matching set Q2; S14, perform queue matching, and match P in Q2 n Sort by P n The largest match first.

4. According to claim 3, an artificial intelligence chronic disease management system based on medical resource perception is characterized by: The scheduling execution module includes the following steps: S21, sending a matching SMS to the patient or the patient's family; S22, waiting for confirmation from the patient or the patient's family; S23, record bed information; S24. Issue patient information and condition registration form; S25. Update patient information in real time and prioritize n and the current status of the bed S b renew; S26, create a set to be matched Q3, when the patient priority P n <P min When P n Put into the to-be-matched set Q3, P min To meet the priority of freeing up hospital beds.

5. According to claim 4, an artificial intelligence chronic disease management system based on medical resource perception is characterized by: The specific steps of step S13 are as follows: S131, bed information inventory calculation, S in set B b Add all together to get M; S132, when M ≥ 1, priority screening is performed, that is, when P n >ω3T b S b And S b =1, then P n As a matching set Q2; S133, when M=0, call the to-be-matched set Q3, if P is satisfied later n >P max , then the patient replaces the patient in the to-be-matched set Q3.

6. According to claim 4, an artificial intelligence chronic disease management system based on medical resource perception is characterized by: In step S25, the patient's information is uploaded and updated in real time through the wireless communication module.

7. An artificial intelligence chronic disease management method based on medical resource perception according to any one of claims 1 to 6, characterized in that: The following steps are involved: Step 1: Patient information collection: collect patient information through the patient information collection module, and collect the patient information of the occupied beds in real time; Step 2: Bed information collection: collect bed information in real time through the bed information collection module; Step 3: Patient priority calculation: the priority of patient medical resource allocation is calculated through the priority calculation module; Step 4: Match patients to beds according to their priorities, and use the bed matching module to match patients to beds; Step 5: Scheduling execution, scheduling patients and collecting information feedback through the bed matching module.

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