Intelligent bed reservation method from outpatient service to hospitalization

Abstract

The invention discloses an intelligent bed reservation method from outpatient service to hospitalization, and the method comprises the following steps: S1, visiting a doctor in an outpatient service: a patient visits the doctor in an outpatient department through a reservation or registration system, and a doctor carries out the diagnosis of the patient after the inquiry. The intelligent bed appointment management platform is combined with the HIS system and the bed management system, bed resources of a hospital can be automatically matched, inquired and managed, real-time and intelligent bed allocation is achieved, low efficiency and errors of manual management are avoided, meanwhile, AI prediction is combined, and the management efficiency is improved. According to the method, the bed use condition can be monitored and dynamically predicted in real time, bed cleaning and hospital discharge time can be arranged in advance, the maximum bed use rate is ensured, the resource waste phenomenon caused by improper bed management is reduced, a patient can easily inquire the bed state and the predicted hospital admission time through an App or a self-service terminal and know the bed distribution progress at any time, and the patient experience is improved. And anxiety is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of bed reservation, and in particular to an intelligent bed reservation method from outpatient to inpatient. Background Art

[0002] Traditionally, when patients need to be hospitalized after outpatient treatment, they need to manually coordinate beds according to their hospitalization needs. The manual coordination of beds has the following problems: 1. The allocation of beds mainly depends on the bed manager or nurse of the inpatient department to manually query the bed usage. After the patient's outpatient visit, the doctor needs to contact the bed management department to verify whether the bed is vacant. This process is prone to information asymmetry and communication errors, which increases the time cost of hospitalization arrangements; 2. There is no unified standard for the allocation and cleaning time of beds, and there is a lack of automated prediction and scheduling methods. The time for releasing or cleaning beds is not fixed, and the ward manager cannot accurately predict when the bed will be vacant. , resulting in low bed utilization rate; 3. Patients with urgent conditions cannot be given priority in bed allocation, and hospitalization applications basically follow the "first come, first served" or manual judgment method. Emergency patients may delay hospitalization due to bed shortage, and the rapid admission of emergency patients cannot be guaranteed; 4. Since bed allocation relies on manual coordination, patients cannot know the specific admission time in advance. Hospitalization arrangements are usually made in manual order, and there is no waiting queue management or real-time bed prediction mechanism. The patient's waiting time becomes uncontrollable, and they may stay in the hospital for a long time or even go home to wait for notification; Based on the above situation, this application proposes an intelligent bed reservation method from outpatient to inpatient. Summary of the invention

[0003] Based on the technical problems existing in the background technology, the present invention proposes an intelligent bed reservation method from outpatient to inpatient.

[0004] The present invention proposes a smart bed reservation method from outpatient to inpatient, comprising the following steps:

[0005] S1: Outpatient treatment: Patients visit the outpatient department through the appointment or registration system. The doctor diagnoses the patient after the consultation. When the doctor determines that the patient needs hospitalization, the hospitalization application process is initiated. The HIS system (hospital management information system) automatically records the hospitalization needs and pushes the records to the ward's smart bed reservation management platform;

[0006] S2: Patient information collection and hospitalization application: The HIS system establishes a connection with the intelligent bed reservation management platform through the API interface. The intelligent bed reservation management platform extracts the patient's medical history, diagnosis information and doctor's orders from the HIS system and formats them into a data format that meets the HIS system standards. The HIS system cleans and standardizes the data, integrates the processed data, and generates a detailed hospitalization application form based on hospitalization needs;

[0007] S3: Bed resource query: The HIS system connects with the hospital's bed management system through the API interface to obtain real-time bed status information. The intelligent bed reservation management platform calls the bed management system through the API interface to obtain the bed status of each ward in real time, and synchronizes the bed information in real time through the event trigger mechanism to ensure that the bed status update is consistent with the actual situation. At the same time, the intelligent bed reservation management platform selects the corresponding bed information according to the department where the patient is located and integrates it. The integrated information uses charts and dashboards to visualize the bed usage to bed managers and patients;

[0008] S4: Bed occupancy rate analysis: The intelligent bed reservation management platform builds a prediction model based on the current bed occupancy rate, historical bed occupancy rate, patient hospitalization time, bed cleaning time, and discharge plan information. The prediction model uses AI algorithms to predict future bed occupancy rates and idle time based on historical bed turnover rates, bed cleaning frequency, and expected discharge time of hospitalized patients. The intelligent bed reservation management platform uses the prediction results to optimize the allocation of bed resources and presents them to bed managers through visual charts. At the same time, the intelligent bed reservation management platform reminds doctors and managers whether they need to adjust hospitalization arrangements based on the prediction results;

[0009] S5: Intelligent waiting time prediction: The intelligent bed reservation management platform combines the current bed usage status, historical bed turnover data, cleaning time, patient hospitalization duration and discharge plan information, and calculates the expected bed free time through AI algorithm. At the same time, the intelligent bed reservation management platform uses a sorting algorithm to adjust the predicted waiting time according to the severity of the patient's condition and emergency hospitalization needs. When the patient's condition is serious, a bed can be reserved for him in advance or given priority hospitalization arrangements. The predicted bed vacancy time and the patient's expected admission time are transmitted to the patient's mobile app and self-service inquiry terminal, and the patient can query the latest waiting time prediction information at any time;

[0010] S6: Bed matching and queuing: When a patient submits an application for hospitalization, the intelligent bed reservation management platform will query and match eligible vacant beds in the bed management system based on the patient's condition, hospitalization department, and bed demand (including ward type, gender requirements, special nursing needs, etc.). If there are no eligible vacant beds, the intelligent bed reservation management platform will automatically add the patient to the waiting queue and arrange the waiting order for the patient based on the patient's condition and hospitalization needs. When a bed is vacant, the intelligent bed reservation management platform will automatically allocate a bed to the patient in the waiting queue. At the same time, the intelligent bed reservation management platform will provide patients with a visual display of the waiting queue. Patients can view their queue position and estimated bed vacancy time in real time through mobile phone apps and self-service terminals;

[0011] S7: Bed binding and confirmation: When a bed is vacant, the intelligent bed reservation management platform will immediately match and generate bed binding information based on the rule-based matching algorithm. At this time, the bed status will change from "free" to "reserved" to ensure that the bed is not occupied by other patients. The generated bed binding information will be notified to the patient and relevant departments via SMS and App. The patient can confirm the bed online and arrange a suitable admission time according to the bed reservation time;

[0012] S8: Admission notification and navigation: After the bed binding is confirmed, the intelligent bed reservation management platform will send an admission notification to the patient, including the bed number, inpatient department, and relevant admission process information. It also provides inpatient navigation services to help patients find relevant inpatient departments and beds through the hospital's indoor positioning system (such as Bluetooth, WiFi, etc.).

[0013] Preferably, in S1, the recorded hospitalization requirements include department, treatment type and gender.

[0014] Preferably, in said S2, the HIS system stores the patient's medical history, current diagnosis, laboratory results, imaging data and medical information of the doctor's orders;

[0015] The data format that complies with the HIS system standard is HL7.

[0016] Preferably, in S3, the real-time bed status information includes detailed data of free beds, occupied beds, bed types, and expected discharge time;

[0017] The bed status of each ward includes fields such as bed ID, bed status (free, occupied, reserved) and bed type, and the field format is one of JSON and XML formats;

[0018] The intelligent bed reservation management platform will provide a detailed display of the beds in each department through the bed visualization module. The displayed information includes the specific status of each bed (free, occupied, waiting to be cleaned), expected discharge time, expected cleaning time, etc.

[0019] Preferably, in S4, when the intelligent bed reservation management platform predicts that there will be a large number of vacant beds in a certain time period in the future, the intelligent bed reservation management platform can automatically schedule related tasks, including arranging cleaning staff to clean the beds in advance and notifying patients to prepare for admission.

[0020] Preferably, in S5, the intelligent bed reservation management platform can construct a weighted model based on multiple factors, assign different weights to different factors, including the severity of the disease, bed cleaning time and historical turnover rate, and comprehensively calculate the optimal waiting time through the weighted model.

[0021] Preferably, in S7, in order to ensure that the patient can be admitted to the hospital smoothly, the intelligent bed reservation management platform will reserve the matched bed for the patient for a period of time (such as 24 hours), during which the bed will not be used by other patients. At the same time, the intelligent bed reservation management platform will remind the patient of the specific time arrangement for admission through SMS or App, and provide detailed steps for handling hospitalization procedures, including hospitalization fees, the place for handling procedures and the contact information of relevant departments;

[0022] In addition, the intelligent bed reservation management platform will monitor the bed reservation time through scheduled tasks. When the reservation time expires and the patient is not admitted to the hospital, the bed will be automatically released, its status will be restored to "free" and re-added to the bed allocation pool.

[0023] Preferably, in S6, when the intelligent bed reservation management platform provides patients with a visual display of the waiting queue, the displayed content includes the waiting ranking, expected admission time, and department bed occupancy rate, to help patients understand their own queuing situation. At the same time, the intelligent bed reservation management platform dynamically updates the patient's waiting ranking through real-time docking with the bed management system, ensuring that patients can understand their admission order in a timely manner.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] By combining the intelligent bed reservation management platform with the HIS system and the bed management system, the present invention can automatically match, query and manage the bed resources of the hospital, realize the real-time and intelligent allocation of beds, avoid the inefficiency and errors of manual management, and at the same time, combined with AI prediction, it can monitor and dynamically predict the use of beds in real time, arrange bed cleaning and discharge time in advance, ensure the maximum use rate of beds, and reduce the waste of resources caused by improper bed management. Patients can easily query the bed status and expected admission time through App or self-service terminals, and understand the progress of bed allocation at any time, reducing anxiety. Based on the urgency of the patient's condition, the waiting queue can be dynamically adjusted to ensure that patients in urgent need of hospitalization can be given priority, thereby improving the admission efficiency and survival chance of emergency patients. In addition, information such as bed utilization rate, waiting queue and expected hospitalization time is publicly displayed to patients through a visualization platform, which improves the transparency of hospital operations, enables patients to clearly understand their queuing status and enhances their trust in hospital management. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 The present invention is a flowchart of a smart bed reservation method from outpatient to inpatient. DETAILED DESCRIPTION

[0027] The present invention will be further explained below in conjunction with specific embodiments.

[0028] Example

[0029] Reference Figure 1 , this embodiment proposes a smart bed reservation method from outpatient to inpatient, including the following steps:

[0030] S1: Outpatient treatment: Patients visit the outpatient department through the appointment or registration system. The doctor diagnoses the patient after the consultation. When the doctor determines that the patient needs hospitalization, the hospitalization application process is initiated. The HIS system (hospital management information system) automatically records the hospitalization needs and pushes the records to the ward's smart bed reservation management platform, where the recorded hospitalization needs include department, treatment type and gender;

[0031] S2: Patient information collection and hospitalization application: The HIS system establishes a connection with the intelligent bed reservation management platform through the API interface. The intelligent bed reservation management platform extracts the patient's medical history, diagnosis information and doctor's orders from the HIS system and formats them into a data format that meets the HIS system standards. The HIS system cleans and standardizes the data, integrates the processed data, and generates a detailed hospitalization application form based on hospitalization needs;

[0032] The HIS system stores medical information such as the patient's medical history, current diagnosis, laboratory results, imaging data, and doctor's orders;

[0033] The data format that complies with HIS system standards is HL7;

[0034] In addition, the hospitalization application form includes the patient's basic information (name, ID number, medical record number, etc.), current diagnosis information (disease name, code, acute or chronic), doctor's instructions and hospitalization recommendations (such as observation treatment, surgical plan, etc.), and necessary medical examination reports (imaging data, laboratory results, etc.);

[0035] S3: Bed resource query: The HIS system connects with the hospital's bed management system through the API interface to obtain real-time bed status information, including detailed data such as vacant beds, occupied beds, bed types, and expected discharge time;

[0036] The intelligent bed reservation management platform calls the bed management system through the API interface to obtain the bed status of each ward in real time, including fields such as bed ID, bed status (free, occupied, reserved) and bed type. The field format is one of JSON and XML formats. To ensure high availability, the intelligent bed reservation management platform can use a cache layer (such as Redis) to optimize query performance and reduce frequent interactions with the bed management system;

[0037] The bed information is synchronized in real time through the event trigger mechanism to ensure that the bed status update is consistent with the actual situation. At the same time, the intelligent bed reservation management platform selects and integrates the corresponding bed information according to the department where the patient is located. The integrated information uses charts and dashboards to visualize the bed usage to the bed manager and patients. The intelligent bed reservation management platform will display the beds of each department in detail through the bed visualization module. The displayed information includes the specific status of each bed (free, occupied, to be cleaned), expected discharge time, expected cleaning time, etc.

[0038] S4: Bed occupancy rate analysis: The intelligent bed reservation management platform builds a prediction model based on the current bed occupancy rate, historical bed occupancy rate, patient hospitalization time, bed cleaning time, and discharge plan information. The prediction model uses AI algorithms to predict future bed occupancy rates and idle time based on historical bed turnover rates, bed cleaning frequency, and expected discharge time of hospitalized patients. The intelligent bed reservation management platform uses the prediction results to optimize the allocation of bed resources and presents them to bed managers through visual charts. At the same time, the intelligent bed reservation management platform reminds doctors and managers whether they need to adjust hospitalization arrangements based on the prediction results;

[0039] The features used in building the prediction model include: current bed occupancy rate, patient length of stay, average bed cleaning time in historical data, discharge time, and seasonal changes (e.g., flu season affects respiratory department bed occupancy rate);

[0040] In addition, when the intelligent bed reservation management platform predicts that there will be a large number of vacant beds in a certain period of time in the future, the intelligent bed reservation management platform can automatically schedule related tasks, including arranging cleaning staff to clean the beds in advance and notifying patients waiting to be admitted to the hospital;

[0041] For example, the smart bed reservation management platform analyzed the bed usage data of the respiratory department in the past month and found that the average length of stay for each bed was 5 days and the average bed cleaning time was 3 hours. The smart bed reservation management platform predicted that in the next 48 hours, the bed occupancy rate of the department would remain at around 85%, and 5 beds would be vacated in the next 24-48 hours. Hospital managers can arrange the hospitalization time of new patients and adjust the cleaning tasks of cleaning staff accordingly.

[0042] S5: Intelligent waiting time prediction: The intelligent bed reservation management platform combines the current bed usage status, historical bed turnover data, cleaning time, patient hospitalization time and discharge plan information to calculate the expected bed idle time through AI algorithm. For example, Mr. Wang needs to be hospitalized due to acute nephritis, but the current internal medicine ward is full. The intelligent bed reservation management platform analyzes the usage data of internal medicine beds and finds that the average hospitalization time for internal medicine beds is 5 days, and the ward cleaning time is 2 hours. It is expected that the next batch of wards will be vacant within 48 hours, and Mr. Wang can arrange to be admitted to the hospital afterwards;

[0043] The model used by the AI ​​algorithm is a time series prediction model, which includes the ARIMA model and the autoregressive integrated moving average model. The ARIMA model is used to analyze historical bed turnover data to predict future bed demand and usage. The regression model is combined to predict bed idle time. The input features of the time series prediction model include the current bed occupancy status and usage time, the patient's expected discharge time, bed cleaning time, and historical bed turnover data and cleaning efficiency. The output prediction results are the expected idle time, cleaning time, and possible admission time of each bed. The prediction results are displayed in the form of charts and text on the intelligent bed reservation management platform.

[0044] At the same time, the intelligent bed reservation management platform uses a sorting algorithm to adjust the predicted waiting time according to the severity of the patient's condition and the need for emergency hospitalization. When the patient's condition is serious, a bed can be reserved for him in advance or given priority hospitalization. The predicted bed vacancy time and the patient's estimated admission time are transmitted to the patient's mobile app and self-service inquiry terminal. Patients can query the latest waiting time prediction information at any time. For example: Mr. Wang's acute nephritis is a medium-urgent case. The intelligent bed reservation management platform will adjust the prediction priority according to the severity of the disease. For patients with serious conditions, the system will try to give priority to them in the beds that will be vacated soon, while patients with milder conditions may be arranged in empty beds later.

[0045] In addition, the intelligent bed reservation management platform can build a weighted model based on multiple factors, assign different weights to different factors, including the severity of the disease, bed cleaning time and historical turnover rate, and comprehensively calculate the optimal waiting time through the weighted model;

[0046] S6: Bed matching and queuing: When a patient submits an application for hospitalization, the intelligent bed reservation management platform will query and match eligible vacant beds in the bed management system based on the patient's condition, hospitalization department, and bed demand (including ward type, gender requirements, special nursing needs, etc.). If there are no eligible vacant beds, the intelligent bed reservation management platform will automatically add the patient to the waiting queue and arrange the waiting order for the patient based on the patient's condition and hospitalization needs. When a bed is vacant, the intelligent bed reservation management platform will automatically allocate a bed to the patient in the waiting queue. At the same time, the intelligent bed reservation management platform will provide patients with a visual display of the waiting queue. Patients can view their queue position and estimated bed vacancy time in real time through mobile phone apps and self-service terminals;

[0047] The intelligent bed reservation management platform provides patients with a visual display of the waiting queue, including the waiting list ranking, expected admission time, and department bed utilization rate, to help patients understand their own queue status. At the same time, the intelligent bed reservation management platform dynamically updates the patient's waiting list ranking through real-time connection with the bed management system, ensuring that patients can understand their admission order in a timely manner.

[0048] S7: Bed binding and confirmation: When a bed is vacant, the intelligent bed reservation management platform will immediately match and generate bed binding information based on the rule-based matching algorithm. At this time, the bed status will change from "free" to "reserved" to ensure that the bed is not occupied by other patients. The generated bed binding information will be notified to the patient and relevant departments via SMS and App. The patient can confirm the bed online and arrange a suitable admission time according to the bed reservation time;

[0049] In addition, in order to ensure that patients can be admitted to the hospital smoothly, the smart bed reservation management platform will reserve the matched bed for the patient for a period of time (such as 24 hours). During this period, the bed will not be used by other patients. At the same time, the smart bed reservation management platform will remind the patient of the specific time arrangement for admission through SMS or App, and provide detailed steps for handling hospitalization procedures, including hospitalization fees, the place of handling procedures and the contact information of relevant departments;

[0050] In addition, the intelligent bed reservation management platform will monitor the bed reservation time through scheduled tasks. When the reservation time expires and the patient is not admitted, the bed will be automatically released, its status will be restored to "free" and re-added to the bed allocation pool;

[0051] S8: Admission notification and navigation: After the bed binding is confirmed, the intelligent bed reservation management platform will send the patient an admission notification, including the bed number, hospitalization department, and related admission process information. It also provides hospitalization navigation services to help patients find relevant hospitalization departments and beds through the hospital's indoor positioning system (such as Bluetooth, WiFi, etc.);

[0052] This embodiment can automatically match, query and manage the hospital's bed resources through the intelligent bed reservation management platform, realize real-time and intelligent bed allocation, avoid the inefficiency and errors of manual management, and combine with AI prediction to monitor and dynamically predict bed usage in real time, arrange bed cleaning and discharge time in advance, ensure maximum bed utilization, and reduce resource waste caused by improper bed management. Patients can easily query bed status and expected admission time through App or self-service terminals, and understand the progress of bed allocation at any time, reducing anxiety. Based on the urgency of the patient's condition, the waiting queue can be dynamically adjusted to ensure that patients in urgent need of hospitalization can be given priority, thereby improving the admission efficiency and survival chances of emergency patients. In addition, information such as bed utilization rate, waiting queue and expected hospitalization time is publicly displayed to patients through a visualization platform, which improves the transparency of hospital operations, enables patients to clearly understand their queuing status, and enhances their trust in hospital management.

[0053] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A smart bed reservation method from outpatient to inpatient, characterized in that: The following steps are involved: S1: Outpatient treatment: Patients visit the outpatient department through the appointment or registration system. The doctor diagnoses the patient after the consultation. When the doctor determines that the patient needs hospitalization, the hospitalization application process is initiated. The HIS system automatically records the hospitalization needs and pushes the records to the ward's smart bed reservation management platform; S2: Patient information collection and hospitalization application: The HIS system establishes a connection with the intelligent bed reservation management platform through the API interface. The intelligent bed reservation management platform extracts the patient's medical history, diagnosis information and doctor's orders from the HIS system and formats them into a data format that meets the HIS system standards. The HIS system cleans and standardizes the data, integrates the processed data, and generates a detailed hospitalization application form based on hospitalization needs; S3: Bed resource query: The HIS system connects with the hospital's bed management system through the API interface to obtain real-time bed status information. The intelligent bed reservation management platform calls the bed management system through the API interface to obtain the bed status of each ward in real time, and synchronizes the bed information in real time through the event trigger mechanism to ensure that the bed status update is consistent with the actual situation. At the same time, the intelligent bed reservation management platform selects the corresponding bed information according to the department where the patient is located and integrates it. The integrated information uses charts and dashboards to visualize the bed usage to bed managers and patients; S4: Bed occupancy rate analysis: The intelligent bed reservation management platform builds a prediction model based on the current bed occupancy rate, historical bed occupancy rate, patient hospitalization time, bed cleaning time, and discharge plan information. The prediction model uses AI algorithms to predict future bed occupancy rates and idle time based on historical bed turnover rates, bed cleaning frequency, and expected discharge time of hospitalized patients. The intelligent bed reservation management platform uses the prediction results to optimize the allocation of bed resources and presents them to bed managers through visual charts. At the same time, the intelligent bed reservation management platform reminds doctors and managers whether they need to adjust hospitalization arrangements based on the prediction results; S5: Intelligent waiting time prediction: The intelligent bed reservation management platform combines the current bed usage status, historical bed turnover data, cleaning time, patient hospitalization duration and discharge plan information, and calculates the expected bed free time through AI algorithm. At the same time, the intelligent bed reservation management platform uses a sorting algorithm to adjust the predicted waiting time according to the severity of the patient's condition and emergency hospitalization needs. When the patient's condition is serious, a bed can be reserved for him in advance or given priority hospitalization arrangements. The predicted bed vacancy time and the patient's expected admission time are transmitted to the patient's mobile app and self-service inquiry terminal, and the patient can query the latest waiting time prediction information at any time; S6: Bed matching and queuing: When a patient submits an application for hospitalization, the intelligent bed reservation management platform will query and match eligible vacant beds in the bed management system based on the patient's condition, hospitalization department, and bed demand. If there are no eligible vacant beds, the intelligent bed reservation management platform will automatically add the patient to the waiting queue and arrange the waiting order for the patient based on the patient's condition and hospitalization demand. When a bed is vacant, the intelligent bed reservation management platform will automatically allocate a bed to the patient in the waiting queue. At the same time, the intelligent bed reservation management platform will provide patients with a visual display of the waiting queue. Patients can view their queue position and estimated bed vacancy time in real time through mobile phone apps and self-service terminals; S7: Bed binding and confirmation: When a bed is vacant, the intelligent bed reservation management platform will immediately match and generate bed binding information based on the rule-based matching algorithm. At this time, the bed status will change from "free" to "reserved" to ensure that the bed is not occupied by other patients. The generated bed binding information will be notified to the patient and relevant departments via SMS and App. The patient can confirm the bed online and arrange a suitable admission time according to the bed reservation time; S8: Admission notification and navigation: After the bed binding is confirmed, the intelligent bed reservation management platform will send an admission notification to the patient, including information on the bed number, inpatient department, and related admission procedures. It also provides inpatient navigation services to help patients find relevant inpatient departments and beds through the hospital's indoor positioning system.

2. According to claim 1, a smart bed reservation method from outpatient to inpatient, characterized in that: In S1, the recorded hospitalization needs include department, treatment type and gender.

3. According to claim 1, a smart bed reservation method from outpatient to inpatient, characterized in that: In said S2, the HIS system stores the patient's medical history, current diagnosis, laboratory results, imaging data and medical information of the doctor's orders; The data format that complies with the HIS system standard is HL7.

4. According to claim 1, a smart bed reservation method from outpatient to inpatient is characterized in that: In S3, the real-time bed status information includes detailed data of free beds, occupied beds, bed types, and expected discharge time; The bed status of each ward includes fields for bed ID, bed status, and bed type, and the field format is one of JSON and XML formats; The intelligent bed reservation management platform will provide a detailed display of the beds in each department through the bed visualization module. The displayed information includes the specific status of each bed, expected discharge time, and expected cleaning time.

5. According to claim 1, a smart bed reservation method from outpatient to inpatient, characterized in that: In S4, when the smart bed reservation management platform predicts that there will be a large number of vacant beds in a certain period of time in the future, the smart bed reservation management platform can automatically schedule related tasks, including arranging cleaning staff to clean the beds in advance and notifying patients to prepare for admission.

6. The intelligent bed reservation method from outpatient to inpatient according to claim 1, characterized in that: In S5, the intelligent bed reservation management platform can construct a weighted model based on multiple factors, assign different weights to different factors, including the severity of the disease, bed cleaning time and historical turnover rate, and comprehensively calculate the optimal waiting time through the weighted model.

7. The intelligent bed reservation method from outpatient to inpatient according to claim 1, characterized in that: In S7, in order to ensure that the patient can be admitted to the hospital smoothly, the smart bed reservation management platform will reserve the matched bed for the patient for a period of time. During this period, the bed will not be used by other patients. At the same time, the smart bed reservation management platform will remind the patient of the specific time arrangement for admission through SMS or App, and provide detailed steps for handling hospitalization procedures, including hospitalization fees, the place for handling procedures and the contact information of relevant departments; In addition, the intelligent bed reservation management platform will monitor the bed reservation time through scheduled tasks. When the reservation time expires and the patient is not admitted to the hospital, the bed will be automatically released, its status will be restored to "free" and re-added to the bed allocation pool.

8. The intelligent bed reservation method from outpatient to inpatient according to claim 1, characterized in that: In S6, when the intelligent bed reservation management platform provides patients with a visual display of the waiting queue, the displayed content includes the waiting ranking, expected admission time, and department bed occupancy rate, to help patients understand their own queuing status. At the same time, the intelligent bed reservation management platform dynamically updates the patient's waiting ranking through real-time connection with the bed management system, ensuring that patients can understand their admission order in a timely manner.

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