Automatic consulting room distribution method based on medical reception service

Abstract

The invention relates to the technical field of automatic allocation, in particular to an automatic consulting room allocation method based on medical reception service, which comprises the following steps: acquiring multi-dimensional data such as medical requirements of patients, specialities of doctors, use states of consulting rooms, equipment configuration and the like in real time, and realizing optimal allocation of consulting room resources by utilizing a multi-dimensional matching algorithm and a situation simulation technology. The system can dynamically respond to special conditions and continuously optimize the distribution strategy through adaptive learning. Through intelligent analysis and real-time adjustment, the utilization rate of a consulting room is remarkably improved, the waiting time of a patient is shortened, and meanwhile the overall quality and efficiency of medical services are improved. According to the invention, the consulting room distribution strategy is continuously optimized through historical data and feedback information, and the distribution process is more intelligent and accurate by identifying key influence factors and automatically adjusting model parameters, so that the overall quality of medical services and the satisfaction degree of patients are remarkably improved; therefore, the method has outstanding application value in modern medical resource management.

Description

Technical Field

[0001] The present invention relates to the field of automatic allocation technology, and in particular to a method for automatically allocating consulting rooms based on medical reception services. Background Art

[0002] As the demand for medical services continues to grow, hospitals and clinics are facing increasingly complex resource management challenges, especially in terms of clinic allocation. Effective management of clinic allocation not only affects the patient's experience, but is also directly related to the efficiency of hospital resource utilization and the work arrangements of medical staff. With the development of information technology, medical institutions have gradually adopted information technology to manage clinic resources, including real-time collection of clinic usage, doctor work status and patient demand information.

[0003] However, the inventors have discovered that this technical solution still has at least the following defects:

[0004] First, most existing clinic allocation methods rely on manual or rule-driven systems, which are often inadequate in the face of complex and changing medical environments. Especially during peak periods or public health emergencies, the lag in resource allocation may further increase the pressure on the medical system.

[0005] Second, although the existing automated clinic management systems can collect information about clinic usage, equipment status, and patient information, most of them lack intelligent analysis and dynamic adjustment capabilities. They are usually unable to make effective predictions and optimizations based on real-time data, historical usage patterns, and special circumstances. When faced with complex clinical needs, the allocation of clinic resources is often difficult to achieve optimality, resulting in waste of resources or irrational allocation. Summary of the invention

[0006] Based on the above objectives, the present invention provides a method for automatically allocating consulting rooms based on medical reception services.

[0007] The method for automatically allocating consulting rooms based on medical reception services comprises the following steps:

[0008] S1, patient information and movement trajectory collection: obtain the patient's basic information and medical needs information through medical reception, including the patient's symptom description, medical history, appointment department, and doctor's specialty matching, and use the positioning in the hospital to collect the patient's movement trajectory data in real time;

[0009] S2, Clinic resource information collection and analysis: Real-time acquisition and analysis of the status information of available clinics in hospitals or clinics. The status information includes the current usage of the clinic, equipment configuration, department category, doctor's workload and expertise, combined with the patient's movement trajectory data, to comprehensively analyze the availability of clinic resources and the estimated time for patients to arrive at the clinic;

[0010] S3, Patient path prediction and clinic optimization: Based on the patient's movement trajectory data and the distribution of departments in the hospital, the patient's treatment path is predicted. Combined with the clinic resource information and patient demand information, a multi-dimensional matching algorithm is used to calculate the matching degree between each clinic and the patient's needs, and the clinic is optimally allocated according to the matching priority principle;

[0011] S4, intelligent clinic scheduling and dynamic adjustment: After completing the initial clinic allocation, monitor the usage of clinic resources in real time, predict and deal with clinic conflicts or resource shortages, and dynamically adjust the clinic allocation plan based on real-time data through intelligent scheduling algorithms;

[0012] S5, Feedback and real-time update of clinic allocation results: The final clinic allocation results are fed back to patients and relevant medical staff in a timely manner, and the real-time update function of the clinic usage status is provided, allowing medical staff and patients to make timely adjustments according to actual conditions;

[0013] S6, Clinic use evaluation and data storage: After the patient completes the consultation, the clinic use effect is evaluated through automatic collection, and the evaluation results and clinic use data are stored in the database;

[0014] S7, self-learning and allocation strategy optimization: Based on historical clinic allocation data, patient movement trajectory data and usage evaluation data, the clinic allocation strategy is continuously optimized to analyze and respond to various special situations, including seasonal changes, holiday peaks and public health emergencies.

[0015] Optionally, S1 includes:

[0016] S11, Collection of basic patient information: After arriving at a medical institution, the patient enters basic information of the patient through the self-service terminal and the front desk receptionist. The basic information includes the patient's name, age, gender and medical record number, which is received in real time by the medical receptionist and stored in the central database;

[0017] S12, entry of medical needs information: After entering basic information, patients fill in or update their current medical needs information in detail through self-service terminals or with the assistance of reception staff, including description of their symptoms, medical history, appointment department, and matching of needs with doctors’ expertise, which are collected and stored in the central database;

[0018] S13, positioning activation and movement trajectory collection: After the patient enters the hospital, the positioning in the hospital is automatically activated, and the patient's location information is collected in real time through the patient's wearable device. The positioning transmits the patient's movement trajectory data to the central database and associates it with the patient's basic information and medical needs information.

[0019] Optionally, S2 includes:

[0020] S21, Clinic status information collection: Through the hospital's internal clinic management, the current usage of each clinic is obtained in real time, including whether the clinic is occupied, the expected idle time, and the appointment schedule. The configuration status and functional information of the equipment in the clinic are collected, including the availability of medical equipment, maintenance status, and distribution of special equipment;

[0021] S22, Department and doctor information acquisition: Obtain the current department distribution information, the department category to which the clinic belongs, and the workload of doctors in each department from the hospital's human resources management, including the current work status of doctors, patient appointment status, and doctors' professional fields and expertise;

[0022] S23, Clinic availability analysis: Combined with the collected clinic status information, department and doctor information, the data analysis module is used to comprehensively analyze the availability of each clinic and generate a real-time list of available clinics. The analysis content includes the clinic's idle time prediction, equipment adaptation, and the matching degree between doctor expertise and patient needs;

[0023] S24, patient path and clinic accessibility assessment: calculate the estimated time for patients to arrive at each available clinic, evaluate the convenience of the patient's treatment path, and optimize the allocation plan of the clinic based on the estimated time for patients to arrive at the clinic.

[0024] Optionally, S3 includes:

[0025] S31, Department distribution and path mapping: Using the department distribution data in the hospital and the patient's current location, a set of possible paths for the patient to reach each department is generated. The path mapping is modeled according to the department's geographical location, floor distribution, and existing paths to form a path optimization matrix;

[0026] S32, path time estimation and path optimization: Based on the patient's real-time movement trajectory data, calculate the estimated time T for the patient to arrive at different clinics i , where T i is the estimated time for the patient to arrive at clinic i, according to T i value, and the clinic with the shortest time and smoothest path is preferred;

[0027] S33, multi-dimensional matching degree calculation: the room resource information R i and patient demand information p Input the multi-dimensional matching algorithm to calculate the matching degree M between each clinic i and patient p ip ;

[0028] S34, clinic allocation optimization: According to the matching priority principle, the matching degree M of each clinic i is ip Sort and combine the path time estimate T i , select the clinic with the best path for allocation.

[0029] Optionally, S4 includes:

[0030] S41, Real-time monitoring of clinic resources: Through clinic management, real-time monitoring of resource usage of each clinic, including current occupancy status, diagnosis and treatment progress, equipment usage and doctor workload, continuous acquisition and update of data;

[0031] S42, Prediction of clinic conflict and resource shortage: Based on real-time monitoring data, analyze the clinic conflict or resource shortage problems that occur, and use the prediction algorithm to calculate the demand for clinics in the future S i (t) and available resources R i (t) Determine whether there are resource shortages and conflicts;

[0032] S43, Intelligent Scheduling and Dynamic Adjustment: Through intelligent scheduling algorithms, based on real-time monitoring data and prediction results, the clinic allocation plan is dynamically adjusted. The scheduling algorithm is based on the resource adequacy C i (t) and patient need priority P p (t) Optimize calculations and adjust the allocation to give priority to emergency needs;

[0033] S44, allocation plan update and execution: according to the calculated scheduling priority S i (t) Re-optimize the clinic allocation plan, give priority to clinics with sufficient resources and able to meet patient needs, and implement the plan immediately after the update.

[0034] Optionally, the S42 includes:

[0035] S421, clinic demand forecast: Use real-time monitoring data to analyze the current usage of each clinic and the appointment status in the future, and calculate the predicted demand D of clinic i at time t i (t);

[0036] S422, calculation of available resources in the clinic: Based on real-time monitoring data, calculate the available resources R of clinic i at time t. i (t), available resources R i (t) including the availability of consulting rooms, the number of available medical equipment, and the available working hours of doctors;

[0037] S423, resource adequacy assessment: the demand D i (t) and available resources R i (t) and calculate the resource adequacy C of clinic i at time t i (t), predicting that the clinic will face resource shortage or conflict in the future;

[0038] S424, Conflict and Insufficiency Warning: When resource sufficiency Ci When (t) is lower than the threshold value θ, the early warning mechanism is automatically triggered to indicate conflicts and shortages in clinic resources, which serves as the basis for subsequent dynamic adjustments and resource scheduling.

[0039] Optionally, S5 includes:

[0040] S51, generating and sorting out the results of the clinic allocation: after completing the clinic allocation, automatically generating the final clinic allocation results, including the clinic number, consultation time, and doctor information assigned to the patient, sorting out and storing the information in the central database;

[0041] S52, feedback on allocation results: timely feedback the sorted clinic allocation results to patients and relevant medical staff through relevant channels, including text messages, mobile phone applications, and hospital self-service terminals, and prepare in advance as needed;

[0042] S53, real-time update of clinic usage status: continuously monitor the usage status of each clinic, update the occupancy of the clinic and the progress of patients' visits in real time, and automatically push the updated information to relevant medical staff. At the same time, a dynamically updated clinic status view is provided for medical staff to view and make adjustment decisions at any time;

[0043] S54, real-time adjustment and feedback: Medical staff or patients find the need to adjust the appointment time or change the clinic based on the real-time updated clinic status information, and initiate an adjustment request. The feasibility of the adjustment request is automatically evaluated based on the current clinic resource situation, the adjustment operation is quickly executed, and the updated clinic arrangement is fed back to the relevant personnel.

[0044] Optionally, S6 includes:

[0045] S61, automatic collection of clinic usage data: After the patient completes the consultation, the clinic usage data is automatically collected through sensors and information integrated in the clinic, including the actual usage time of the clinic, the patient's consultation time, the use of medical equipment and the doctor's work efficiency;

[0046] S62, evaluation of the use effect of the clinic: based on the collected data, the use effect of the clinic is automatically evaluated. The evaluation criteria include the utilization rate of the clinic, patient satisfaction and the efficiency of the use of medical resources. The use effect score of the clinic is comprehensively calculated, and a detailed evaluation report is generated for each clinic;

[0047] S63, Data storage and management: The evaluation results of the clinic’s use effect and all the collected raw data are stored in the hospital’s central database for long-term tracking of the clinic’s use;

[0048] S64, Historical data analysis and strategy optimization: Regularly analyze the stored clinic usage data to identify patterns and trends in usage, and based on this, propose optimization strategies for clinic allocation and management to improve clinic allocation methods.

[0049] Optionally, S7 includes:

[0050] S71, Feature extraction and model training: Extract key features from the preprocessed data through feature extraction algorithms, including clinic usage frequency, patient movement patterns, and physician workload, train models on the features, and generate a prediction model for optimizing clinic allocation strategies;

[0051] S72, Special Situation Analysis and Adaptability Enhancement: Apply the model to actual data and analyze various special situations in combination with historical data, including seasonal changes, holiday peaks, and public health emergencies. Through scenario simulation and sensitivity analysis, calculate the fluctuations in clinic demand under various special situations and adjust the allocation strategy accordingly.

[0052] S73, Strategy optimization and adaptive learning: Regularly evaluate the optimized allocation strategy, use feedback data to continuously adjust and optimize the machine learning model, and conduct adaptive learning after each model update to gradually improve the stability and adaptability of the clinic allocation strategy;

[0053] S74, application and feedback of model results: Apply the optimized clinic allocation strategy to the actual clinic scheduling, monitor its execution effect in real time, and fine-tune the model parameters based on the feedback data from actual operation.

[0054] Optionally, the S72 includes:

[0055] S721, Scenario simulation and data preparation: Organize and pre-process the clinic usage history data, patient movement trajectory data, and clinic usage effect evaluation data, and establish corresponding scenario models based on seasonal changes, holiday visit peaks, and public health emergencies data;

[0056] S722, Sensitivity analysis: Using the Sobol sensitivity analysis method, calculate the impact of each input characteristic variable on the fluctuation of clinic demand;

[0057] S723, Influencing Factor Analysis and Strategy Adjustment: Based on the results of Sobol sensitivity analysis, identify the factors that have the greatest impact on the fluctuation of clinic demand, combine the changes and characteristic changes of key factors with the clinic allocation strategy, and dynamically adjust the allocation strategy;

[0058] S724, Model update and strategy implementation: Based on the matching of sensitivity analysis results and actual conditions, update the clinic allocation model and optimize the weight coefficient α i , βi and γ i , automatically apply new strategies and optimize the allocation of clinic resources.

[0059] Beneficial effects of the present invention:

[0060] The present invention significantly improves the efficiency and flexibility of allocation of clinic resources by introducing intelligent algorithms and dynamic adjustment mechanisms. Its innovation lies in the use of real-time data collection and multi-dimensional matching algorithms, which can comprehensively analyze the patient's needs, the doctor's expertise and the equipment configuration of the clinic to achieve optimal clinic allocation.

[0061] The present invention continuously optimizes the clinic allocation strategy through historical data and feedback information, and can also automatically adjust the model parameters by identifying key influencing factors, making the allocation process more intelligent and accurate, thereby significantly improving the overall quality of medical services and patient satisfaction, making the present invention have outstanding application value in modern medical resource management. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0063] Figure 1 Schematic diagram of a method for automatically allocating consulting rooms based on medical reception services according to an embodiment of the present invention;

[0064] Figure 2 Schematic diagram of intelligent clinic scheduling and dynamic adjustment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0065] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0066] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0067] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0068] like Figure 1-Figure 2 As shown, the method for automatically allocating consulting rooms based on medical reception services includes the following steps:

[0069] S1, patient information and movement trajectory collection: obtain the patient's basic information and medical needs information through medical reception, including the patient's symptom description, medical history, appointment department, and doctor's specialty matching, and use the positioning in the hospital to collect the patient's movement trajectory data in real time;

[0070] S2, Clinic resource information collection and analysis: Real-time acquisition and analysis of the status information of available clinics in hospitals or clinics. The status information includes the current usage of the clinic, equipment configuration, department category, doctor's workload and expertise, combined with the patient's movement trajectory data, to comprehensively analyze the availability of clinic resources and the estimated time for patients to arrive at the clinic;

[0071] S3, Patient path prediction and clinic optimization: Based on the patient's movement trajectory data and the distribution of departments in the hospital, the patient's treatment path is predicted. Combined with the clinic resource information and patient demand information, a multi-dimensional matching algorithm is used to calculate the matching degree between each clinic and the patient's needs, and the clinic is optimally allocated according to the matching priority principle;

[0072] S4, intelligent clinic scheduling and dynamic adjustment: After completing the initial clinic allocation, monitor the use of clinic resources in real time, predict and deal with clinic conflicts or resource shortages, and dynamically adjust the clinic allocation plan based on real-time data through intelligent scheduling algorithms to ensure optimal use of clinic resources;

[0073] S5, Feedback and real-time update of clinic allocation results: The final clinic allocation results are fed back to patients and relevant medical staff in a timely manner, and the real-time update function of the clinic usage status is provided, allowing medical staff and patients to make timely adjustments according to actual conditions;

[0074] S6, Clinic use evaluation and data storage: After the patient completes the consultation, the clinic use effect is evaluated through automatic collection, and the evaluation results and clinic use data are stored in the database;

[0075] S7, self-learning and allocation strategy optimization: Based on historical clinic allocation data, patient movement trajectory data and usage evaluation data, the clinic allocation strategy is continuously optimized to analyze and respond to various special situations, including seasonal changes, holiday peaks and public health emergencies, to enhance adaptability and stability.

[0076] S1 includes:

[0077] S11, Collection of basic patient information: After arriving at a medical institution, the patient enters basic information of the patient through the self-service terminal and the front desk receptionist. The basic information includes the patient's name, age, gender and medical record number, which is received in real time by the medical receptionist and stored in the central database;

[0078] S12, entry of medical needs information: After entering basic information, patients fill in or update their current medical needs information in detail through self-service terminals or with the assistance of reception staff, including description of their symptoms, medical history, appointment department, and matching of needs with doctors’ expertise, which are collected and stored in the central database;

[0079] S13, positioning activation and movement trajectory collection: After the patient enters the hospital, the positioning in the hospital is automatically activated, and the patient's location information is collected in real time through the patient's wearable device. The positioning transmits the patient's movement trajectory data to the central database and associates it with the patient's basic information and medical needs information.

[0080] S2 includes:

[0081] S21, Clinic status information collection: Through the hospital's internal clinic management, the current usage of each clinic is obtained in real time, including whether the clinic is occupied, the expected idle time, and the appointment schedule. The configuration status and functional information of the equipment in the clinic are collected, including the availability of medical equipment, maintenance status, and distribution of special equipment;

[0082] S22, Department and doctor information acquisition: Obtain the current department distribution information, the department category to which the clinic belongs, and the workload of doctors in each department from the hospital's human resources management, including the current work status of doctors, patient appointment status, and doctors' professional fields and expertise;

[0083] S23, Clinic availability analysis: Combined with the collected clinic status information, department and doctor information, the data analysis module is used to comprehensively analyze the availability of each clinic and generate a real-time list of available clinics. The analysis content includes the clinic's idle time prediction, equipment adaptation, and the matching degree between doctor expertise and patient needs;

[0084] S24, patient path and clinic accessibility assessment: calculate the estimated time for patients to arrive at each available clinic, evaluate the convenience of the patient's treatment path, and optimize the allocation plan of the clinic based on the estimated time for patients to arrive at the clinic.

[0085] By collecting the clinic status, department and doctor information in real time and combining data analysis to evaluate the availability of the clinic and the patient path, the efficient allocation of clinic resources can be ensured. It can accurately predict the idle time and equipment adaptation of the clinic, optimize the patient's treatment path, improve the utilization rate of the clinic and the work efficiency of the doctor, reduce the waiting time of patients, and improve the overall quality of medical services and patient satisfaction.

[0086] S3 includes:

[0087] S31, Department distribution and path mapping: Using the department distribution data in the hospital and the patient's current location, a set of possible paths for the patient to reach each department is generated. The path mapping is modeled according to the department's geographical location, floor distribution, and existing paths to form a path optimization matrix;

[0088] S32, path time estimation and path optimization: Based on the patient's real-time movement trajectory data, calculate the estimated time T for the patient to arrive at different clinics i , where T i is the estimated time for the patient to arrive at clinic i, expressed as:

[0089]

[0090] Among them, d i is the distance from the patient's current location to clinic i, v p is the average moving speed of the patient, t j is the waiting time in the path, which includes the elevator waiting time and congestion time. n is the number of waiting nodes in the path. According to T i value, and the clinic with the shortest time and smoothest path is preferred;

[0091] S33, multi-dimensional matching degree calculation: the room resource information R i and patient demand information p Input the multi-dimensional matching algorithm to calculate the matching degree M between each clinic i and patient p ip , expressed as:

[0092] M ip =w1·C si +w2·D ip +w3·E ip ;

[0093] Among them, C si is the matching degree between the department of clinic i and the department required by the patient, Dip The matching degree between doctor's expertise and patient's needs, E ip is the matching degree between the clinic equipment and the patient's needs, w1, w2, w3 are the weight factors for the matching calculation;

[0094] S34, clinic allocation optimization: According to the matching priority principle, the matching degree M of each clinic i is ip Sort and combine the path time estimate T i , select the clinic with the best path for allocation, and ensure that patients can quickly and accurately enter the appropriate clinic for treatment.

[0095] By combining path time estimation and multi-dimensional matching algorithms, mathematical formulas are used to calculate the optimal clinic allocation plan, which improves the accuracy and efficiency of clinic allocation, reduces patient waiting time, optimizes hospital resource utilization, and ensures that required medical services can be obtained quickly and accurately. It is suitable for complex scenarios in large-scale hospitals and has significantly improved the quality of medical services and patient satisfaction.

[0096] S4 includes:

[0097] S41, Real-time monitoring of clinic resources: Through clinic management, real-time monitoring of the resource usage of each clinic, including current occupancy status, diagnosis and treatment progress, equipment usage and doctor workload, continuous acquisition and update of data to ensure the timeliness and accuracy of clinic status information;

[0098] S42, Prediction of clinic conflict and resource shortage: Based on real-time monitoring data, analyze the clinic conflict or resource shortage problems that occur, and use the prediction algorithm to calculate the demand for clinics in the future. i (t) and available resources R i (t), determine whether there is insufficient resources and conflict, expressed as:

[0099] C i (t) = R i (t)-D i (t);

[0100] Among them, C i (t) is the resource adequacy of clinic i at time t. i When (t) is less than a certain threshold, it is predicted to be a resource shortage or conflict situation;

[0101] S43, Intelligent Scheduling and Dynamic Adjustment: Through intelligent scheduling algorithms, based on real-time monitoring data and prediction results, the clinic allocation plan is dynamically adjusted. The scheduling algorithm is based on the resource adequacy C i (t) and patient need priority P p(t) performs optimization calculations, and the adjusted allocation gives priority to emergency needs and ensures optimal utilization of clinic resources, which is expressed as:

[0102] S i (t) = α·P p (t)+β·C i (t);

[0103] Among them, S i (t) is the scheduling priority of clinic i, α and β are scheduling weight coefficients, and the weights are dynamically adjusted to cope with sudden emergencies or resource shortages;

[0104] S44, allocation plan update and execution: according to the calculated scheduling priority S i (t) Re-optimize the clinic allocation plan, give priority to clinics with sufficient resources and able to meet patients’ needs, and execute the plan immediately after it is updated to ensure the dynamic and real-time nature of the allocation plan and improve diagnosis and treatment efficiency.

[0105] S42 includes:

[0106] S421, clinic demand forecast: Use real-time monitoring data to analyze the current usage of each clinic and the appointment status in the future, and calculate the predicted demand D of clinic i at time t i (t), demand D i (t) including the number of patients scheduled, the estimated time of consultation and treatment, and the need for possible emergencies (such as emergency treatment);

[0107] S422, calculation of available resources in the clinic: Based on real-time monitoring data, calculate the available resources R of clinic i at time t. i (t), available resources R i (t) including the availability of consulting rooms, the number of available medical equipment, and the available working hours of doctors;

[0108] S423, resource adequacy assessment: the demand D i (t) and available resources R i (t) and calculate the resource adequacy C of clinic i at time t i (t), expressed as:

[0109] C i (t) = R i (t)-D i (t);

[0110] Among them, C i (t) is the resource adequacy of clinic i at time t. i When the value of (t) is less than a preset threshold value θ, it is predicted that the clinic will have insufficient resources or conflict in the future time period;

[0111] S424, Conflict and Insufficiency Warning: When resource sufficiency C i When (t) is lower than the threshold value θ, the early warning mechanism is automatically triggered to indicate conflicts and shortages in clinic resources, which serves as the basis for subsequent dynamic adjustments and resource scheduling.

[0112] S5 includes:

[0113] S51, generation and arrangement of clinic allocation results: after completing the clinic allocation, the final clinic allocation result is automatically generated, including the clinic number, consultation time, and doctor information assigned to the patient, and the information is arranged and stored in the central database to ensure the integrity and traceability of the data;

[0114] S52, Allocation result feedback: The sorted clinic allocation results are fed back to patients and relevant medical staff in a timely manner through relevant channels, including text messages, mobile phone applications, and hospital self-service terminals, to ensure that they can quickly obtain the latest clinic arrangement information and prepare in advance as needed;

[0115] S53, real-time update of clinic usage status: continuously monitor the usage status of each clinic, update the occupancy of the clinic and the progress of patients' visits in real time, and automatically push the updated information to relevant medical staff. At the same time, a dynamically updated clinic status view is provided for medical staff to view and make adjustment decisions at any time;

[0116] S54, real-time adjustment and feedback: Medical staff or patients find the need to adjust the appointment time or change the clinic based on the real-time updated clinic status information, and initiate an adjustment request. The feasibility of the adjustment request is automatically evaluated based on the current clinic resource situation, and the adjustment operation is quickly executed. The updated clinic arrangement is fed back to relevant personnel to ensure the smooth progress of the diagnosis and treatment process.

[0117] By automatically generating and organizing the results of clinic allocation, providing timely feedback to patients and medical staff, updating the clinic usage status in real time, and flexibly handling adjustment requests, the diagnosis and treatment process is ensured to be efficient and smooth. It can dynamically monitor and optimize resource allocation, ensure the timeliness and accuracy of information, improve the utilization rate of clinic resources and patient satisfaction, reduce potential scheduling conflicts, and optimize the efficiency and quality of overall medical services.

[0118] S6 includes:

[0119] S61, automatic collection of clinic usage data: After the patient completes the consultation, the clinic usage data is automatically collected through sensors and information integrated in the clinic, including the actual usage time of the clinic, the patient's consultation time, the use of medical equipment and the doctor's work efficiency;

[0120] S62, evaluation of the use effect of the clinic: based on the collected data, the use effect of the clinic is automatically evaluated. The evaluation criteria include the utilization rate of the clinic, patient satisfaction and the efficiency of the use of medical resources. The use effect score of the clinic is comprehensively calculated, and a detailed evaluation report is generated for each clinic;

[0121] S63, Data storage and management: The evaluation results of the clinic use effect and all the collected raw data are stored in the hospital's central database for long-term tracking of the clinic use and to provide data support for subsequent clinic allocation optimization and improvement of medical service quality;

[0122] S64, Historical Data Analysis and Strategy Optimization: Regularly analyze the stored clinic usage data to identify usage patterns and trends, and based on this, propose optimization strategies for clinic allocation and management, improve clinic allocation methods, and improve the overall utilization of clinic resources.

[0123] S7 includes:

[0124] S71, Feature extraction and model training: Extract key features from the preprocessed data through feature extraction algorithms, including clinic usage frequency, patient movement patterns, and physician workload, train models on the features, and generate a prediction model for optimizing clinic allocation strategies;

[0125] S72, Special Situation Analysis and Adaptability Enhancement: The model is applied to actual data and combined with historical data to analyze various special situations, including seasonal changes, holiday peaks, and public health emergencies. Through scenario simulation and sensitivity analysis, the fluctuations in clinic demand under various special situations are calculated, and the allocation strategy is adjusted accordingly, expressed as:

[0126]

[0127] Among them, P i (t) is the priority assigned to the prediction of clinic i at time t, X j (t) Assign the jth feature in the historical data to the clinic, Y j (t) is the jth feature in the patient's movement trajectory data, Z j (t) is the jth feature in the clinic usage effect evaluation data, α j , β j and γ j is the weight coefficient of the corresponding feature;

[0128] S73, Strategy optimization and adaptive learning: Regularly evaluate the optimized allocation strategy, use feedback data to continuously adjust and optimize the machine learning model, and conduct adaptive learning after each model update to gradually improve the stability and adaptability of the clinic allocation strategy to ensure efficient operation under various complex conditions;

[0129] S74, application and feedback of model results: Apply the optimized clinic allocation strategy to the actual clinic scheduling, monitor its execution effect in real time, and fine-tune the model parameters according to the feedback data from actual operation to ensure the efficiency and accuracy of the model.

[0130] Dynamic optimization of the clinic allocation strategy is achieved through feature extraction and model training, special situation analysis and adaptability enhancement, strategy optimization and adaptive learning, as well as model result application and feedback. This method can accurately identify key influencing factors, flexibly respond to various complex situations, and continuously improve the stability and accuracy of the model through adaptive learning, ensuring efficient clinic resource allocation under complex conditions, improving diagnosis and treatment efficiency and patient satisfaction.

[0131] S72 includes:

[0132] S721, Scenario simulation and data preparation: Organize and pre-process the clinic usage history data, patient movement trajectory data, and clinic usage effect evaluation data, and establish corresponding scenario models based on seasonal changes, holiday visit peaks, and public health emergencies data;

[0133] S722, Sensitivity analysis: Using the Sobol sensitivity analysis method, calculate the impact of each input characteristic variable on the fluctuation of clinic demand, expressed as:

[0134]

[0135] Among them, X i represents the input variable (specific features in the clinic allocation history data, patient movement trajectory data, or clinic use effect evaluation data), and Y is the output variable (the predicted allocation priority P of the clinic). i (t)), S total (X i ) is the total impact index of the i-th input variable, indicating that X i Contribution to the total variation of the output variable Y;

[0136] S723, Influencing Factor Analysis and Strategy Adjustment: Based on the results of Sobol sensitivity analysis, identify the factors that have the greatest impact on the fluctuation of clinic demand, combine the changes and characteristic changes of key factors with the clinic allocation strategy, and dynamically adjust the allocation strategy to adapt to different special situations;

[0137] S724, Model update and strategy implementation: Based on the matching of sensitivity analysis results and actual conditions, update the clinic allocation model and optimize the weight coefficient α i , β i and γ i, automatically apply new strategies, optimize the allocation of clinic resources, and ensure efficient use of clinic resources in various special situations.

[0138] By accurately calculating the impact of various input variables, including clinic allocation history data, patient movement trajectory data, and clinic use effect evaluation data, on clinic demand fluctuations, we can identify the key factors that have the greatest impact on resource allocation, so that in the face of special circumstances such as seasonal changes, holiday peaks, and public health emergencies, we can dynamically adjust the allocation strategy to ensure efficient use of clinic resources. The adaptive update of the model further improves the accuracy and adaptability of the prediction, ultimately improving the efficiency of diagnosis and treatment services and patient satisfaction.

[0139] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0140] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for automatically allocating consulting rooms based on medical reception services, characterized in that: The following steps are involved: S1, patient information and movement trajectory collection: obtain the patient's basic information and medical needs information through medical reception, including the patient's symptom description, medical history, appointment department, and doctor's specialty matching, and use the positioning in the hospital to collect the patient's movement trajectory data in real time; S2, Clinic resource information collection and analysis: Real-time acquisition and analysis of the status information of available clinics in hospitals or clinics. The status information includes the current usage of the clinic, equipment configuration, department category, doctor's workload and expertise, combined with the patient's movement trajectory data, to comprehensively analyze the availability of clinic resources and the estimated time for patients to arrive at the clinic; S3, Patient path prediction and clinic optimization: Based on the patient's movement trajectory data and the distribution of departments in the hospital, the patient's treatment path is predicted. Combined with the clinic resource information and patient demand information, a multi-dimensional matching algorithm is used to calculate the matching degree between each clinic and the patient's needs, and the clinic is optimally allocated according to the matching priority principle; S4, intelligent clinic scheduling and dynamic adjustment: After completing the initial clinic allocation, monitor the usage of clinic resources in real time, predict and deal with clinic conflicts or resource shortages, and dynamically adjust the clinic allocation plan based on real-time data through intelligent scheduling algorithms; S5, Feedback and real-time update of clinic allocation results: The final clinic allocation results are fed back to patients and relevant medical staff in a timely manner, and the real-time update function of the clinic usage status is provided, allowing medical staff and patients to make timely adjustments according to actual conditions; S6, Clinic use evaluation and data storage: After the patient completes the consultation, the clinic use effect is evaluated through automatic collection, and the evaluation results and clinic use data are stored in the database; S7, self-learning and allocation strategy optimization: Based on historical clinic allocation data, patient movement trajectory data and usage evaluation data, the clinic allocation strategy is continuously optimized to analyze and respond to various special situations, including seasonal changes, holiday peaks and public health emergencies.

2. The method for automatically allocating consulting rooms based on medical reception services according to claim 1, characterized in that: The S1 includes: S11, Collection of basic patient information: After arriving at a medical institution, the patient enters basic information of the patient through the self-service terminal and the front desk receptionist. The basic information includes the patient's name, age, gender and medical record number, which is received in real time by the medical receptionist and stored in the central database; S12, entry of medical needs information: After entering basic information, patients fill in or update their current medical needs information in detail through self-service terminals or with the assistance of reception staff, including description of their symptoms, medical history, appointment department, and matching of needs with doctors’ expertise, which are collected and stored in the central database; S13, positioning activation and movement trajectory collection: After the patient enters the hospital, the positioning in the hospital is automatically activated, and the patient's location information is collected in real time through the patient's wearable device. The positioning transmits the patient's movement trajectory data to the central database and associates it with the patient's basic information and medical needs information.

3. The method for automatically allocating consulting rooms based on medical reception services according to claim 1, characterized in that: The S2 includes: S21, Clinic status information collection: Through the hospital's internal clinic management, the current usage of each clinic is obtained in real time, including whether the clinic is occupied, the expected idle time, and the appointment schedule. The configuration status and functional information of the equipment in the clinic are collected, including the availability of medical equipment, maintenance status, and distribution of special equipment; S22, Department and doctor information acquisition: Obtain the current department distribution information, the department category to which the clinic belongs, and the workload of doctors in each department from the hospital's human resources management, including the current work status of doctors, patient appointment status, and doctors' professional fields and expertise; S23, Clinic availability analysis: Combined with the collected clinic status information, department and doctor information, the data analysis module is used to comprehensively analyze the availability of each clinic and generate a real-time list of available clinics. The analysis content includes the clinic's idle time prediction, equipment adaptation, and the matching degree between doctor expertise and patient needs; S24, patient path and clinic accessibility assessment: calculate the estimated time for patients to arrive at each available clinic, evaluate the convenience of the patient's treatment path, and optimize the allocation plan of the clinic based on the estimated time for patients to arrive at the clinic.

4. The method for automatically allocating consulting rooms based on medical reception services according to claim 1, characterized in that: The S3 includes: S31, Department distribution and path mapping: Using the department distribution data in the hospital and the patient's current location, a set of possible paths for the patient to reach each department is generated. The path mapping is modeled according to the department's geographical location, floor distribution, and existing paths to form a path optimization matrix; S32, path time estimation and path optimization: Based on the patient's real-time movement trajectory data, calculate the estimated time T for the patient to arrive at different clinics i , where T i is the estimated time for the patient to arrive at clinic i, according to T i value, and the clinic with the shortest time and smoothest path is preferred; S33, multi-dimensional matching degree calculation: the room resource information R i and patient demand information p Input the multi-dimensional matching algorithm to calculate the matching degree M between each clinic i and patient p ip ; S34, clinic allocation optimization: According to the matching priority principle, the matching degree M of each clinic i is ip Sort and combine the path time estimate T i , select the clinic with the best path for allocation.

5. The method for automatically allocating consulting rooms based on medical reception services according to claim 1, characterized in that: The S4 includes: S41, Real-time monitoring of clinic resources: Through clinic management, real-time monitoring of resource usage of each clinic, including current occupancy status, diagnosis and treatment progress, equipment usage and doctor workload, continuous acquisition and update of data; S42, Prediction of clinic conflict and resource shortage: Based on real-time monitoring data, analyze the clinic conflict or resource shortage problems that occur, and use the prediction algorithm to calculate the demand for clinics in the future. i (t) and available resources R i (t) Determine whether there are resource shortages and conflicts; S43, Intelligent Scheduling and Dynamic Adjustment: Through intelligent scheduling algorithms, based on real-time monitoring data and prediction results, the clinic allocation plan is dynamically adjusted. The scheduling algorithm is based on the resource adequacy C i (t) and patient need priority P p (t) Optimize calculations and adjust the allocation to give priority to emergency needs; S44, allocation plan update and execution: according to the calculated scheduling priority S i (t) Re-optimize the clinic allocation plan, give priority to clinics with sufficient resources and able to meet patient needs, and implement the plan immediately after the update.

6. The method for automatically allocating consulting rooms based on medical reception services according to claim 5, characterized in that: The S42 includes: S421, clinic demand forecast: Use real-time monitoring data to analyze the current usage of each clinic and the appointment status in the future, and calculate the predicted demand D of clinic i at time t i (t); S422, calculation of available resources in the clinic: Based on real-time monitoring data, calculate the available resources R of clinic i at time t. i (t), available resources R i (t) including the availability of consulting rooms, the number of available medical equipment, and the available working hours of doctors; S423, Resource Adequacy Assessment: Demand D i (t) and available resources R i (t) and calculate the resource adequacy C of clinic i at time t i (t), predicting that the clinic will face resource shortage or conflict in the future; S424, Conflict and Insufficiency Warning: When resource sufficiency C i When (t) is lower than the threshold value θ, the early warning mechanism is automatically triggered to indicate conflicts and shortages in clinic resources, which serves as the basis for subsequent dynamic adjustments and resource scheduling.

7. The method for automatically allocating consulting rooms based on medical reception services according to claim 1, characterized in that: The S5 includes: S51, generating and sorting out the results of the clinic allocation: after completing the clinic allocation, automatically generating the final clinic allocation results, including the clinic number, consultation time, and doctor information assigned to the patient, sorting out and storing the information in the central database; S52, feedback on allocation results: timely feedback the sorted clinic allocation results to patients and relevant medical staff through relevant channels, including text messages, mobile phone applications, and hospital self-service terminals, and prepare in advance as needed; S53, real-time update of clinic usage status: continuously monitor the usage status of each clinic, update the occupancy of the clinic and the progress of patients' visits in real time, and automatically push the updated information to relevant medical staff. At the same time, a dynamically updated clinic status view is provided for medical staff to view and make adjustment decisions at any time; S54, real-time adjustment and feedback: Medical staff or patients find the need to adjust the appointment time or change the clinic based on the real-time updated clinic status information, and initiate an adjustment request. The feasibility of the adjustment request is automatically evaluated based on the current clinic resource situation, the adjustment operation is quickly executed, and the updated clinic arrangement is fed back to the relevant personnel.

8. The method for automatically allocating consulting rooms based on medical reception services according to claim 1, characterized in that: The S6 includes: S61, automatic collection of clinic usage data: After the patient completes the consultation, the clinic usage data is automatically collected through sensors and information integrated in the clinic, including the actual usage time of the clinic, the patient's consultation time, the use of medical equipment and the doctor's work efficiency; S62, evaluation of the use effect of the clinic: based on the collected data, the use effect of the clinic is automatically evaluated. The evaluation criteria include the utilization rate of the clinic, patient satisfaction and the efficiency of the use of medical resources. The use effect score of the clinic is comprehensively calculated, and a detailed evaluation report is generated for each clinic; S63, Data storage and management: The evaluation results of the clinic’s use effect and all the collected raw data are stored in the hospital’s central database for long-term tracking of the clinic’s use; S64, Historical data analysis and strategy optimization: Regularly analyze the stored clinic usage data to identify patterns and trends in usage, and based on this, propose optimization strategies for clinic allocation and management to improve clinic allocation methods.

9. The method for automatically allocating consulting rooms based on medical reception services according to claim 1, characterized in that: The S7 includes: S71, Feature extraction and model training: Extract key features from the preprocessed data through feature extraction algorithms, including clinic usage frequency, patient movement patterns, and physician workload, train models on the features, and generate a prediction model for optimizing clinic allocation strategies; S72, Special Situation Analysis and Adaptability Enhancement: Apply the model to actual data and analyze various special situations in combination with historical data, including seasonal changes, holiday peaks, and public health emergencies. Through scenario simulation and sensitivity analysis, calculate the fluctuations in clinic demand under various special situations and adjust the allocation strategy accordingly. S73, Strategy optimization and adaptive learning: Regularly evaluate the optimized allocation strategy, use feedback data to continuously adjust and optimize the machine learning model, and conduct adaptive learning after each model update to gradually improve the stability and adaptability of the clinic allocation strategy; S74, application and feedback of model results: Apply the optimized clinic allocation strategy to the actual clinic scheduling, monitor its execution effect in real time, and fine-tune the model parameters based on the feedback data from actual operation.

10. The method for automatically allocating consulting rooms based on medical reception services according to claim 9, characterized in that: The S72 includes: S721, Scenario simulation and data preparation: Organize and pre-process the clinic usage history data, patient movement trajectory data, and clinic usage effect evaluation data, and establish corresponding scenario models based on seasonal changes, holiday visit peaks, and public health emergencies data; S722, Sensitivity analysis: Using the Sobol sensitivity analysis method, calculate the impact of each input characteristic variable on the fluctuation of clinic demand; S723, Influencing Factor Analysis and Strategy Adjustment: Based on the results of Sobol sensitivity analysis, identify the factors that have the greatest impact on the fluctuation of clinic demand, combine the changes and characteristic changes of key factors with the clinic allocation strategy, and dynamically adjust the allocation strategy; S724, Model update and strategy implementation: Based on the matching of sensitivity analysis results and actual conditions, update the clinic allocation model and optimize the weight coefficient α i , β i and γ i , automatically apply new strategies and optimize the allocation of clinic resources.

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