Intelligent management system for queuing based on multi-source data analysis

Abstract

The application belongs to the technical field of waiting management, and particularly relates to a waiting sorting intelligent management system based on multi-source data analysis, which comprises a waiting data fusion preprocessing module, a disease emergency quantification analysis module, a multi-dimensional waiting priority decision module and an intelligent sorting result visualization module; the disease emergency quantification analysis module quantitatively calculates the disease emergency degree from three dimensions of symptom emergency degree, sign abnormality degree and basic disease risk, so as to ensure that the medical resources are reasonably tilted to patients with high emergency degree; the factor correction module takes the historical waiting performance of patients into consideration, so as to avoid that the medical resources are excessively occupied by patients who frequently cancel or delay medical treatment; the multi-dimensional waiting priority decision module calculates the waiting priority coefficient through multi-dimensional analysis and sorting; and the intelligent sorting result visualization module displays the waiting queue, so as to effectively balance the medical safety, waiting fairness and treatment efficiency, and realize accurate and reasonable sorting of waiting patients.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of queuing management, and in particular to a queuing intelligent management system based on multi-source data analysis. BACKGROUND

[0002] In the field of medical service management, a queuing system is a core support for optimizing diagnosis and treatment processes and improving service efficiency, and the scientificity and rationality directly affect the utilization rate of medical resources and the medical experience of patients. With the advancement of medical informatization, various intelligent queuing and calling technologies have emerged.

[0003] At present, queuing technologies mainly focus on the prediction of queuing time or the simple adjustment of queues. For example, a method and device for calculating outpatient queuing time are disclosed in Chinese Patent No. CN106372391A. The core of the technical solution is to obtain the registration time of a patient and the historical diagnosis efficiency data of a department and a doctor, calculate and predict the queuing waiting time of the patient, and reduce the invalid waiting of the patient through time prediction.

[0004] However, the queuing logic of the above-mentioned invention only focuses on the registration time sequence and historical diagnosis efficiency, and cannot integrate the multi-dimensional factors of disease urgency, queuing time fairness, patient special needs and historical queuing performance, so it is difficult to realize accurate and reasonable queuing of queuing patients, resulting in low efficiency of medical resource allocation, delayed treatment of emergency patients and poor patient medical experience, and the balance between medical safety, queuing fairness and diagnosis efficiency cannot be effectively achieved. Therefore, a solution is proposed. SUMMARY

[0005] The purpose of the present application is to provide a queuing intelligent management system based on multi-source data analysis to solve the technical defects proposed in the background art.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solution: a queuing intelligent management system based on multi-source data analysis, comprising a queuing data fusion preprocessing module, a disease emergency quantification analysis module, a multi-dimensional queuing priority decision module and an intelligent queuing result visualization module.

[0007] The queuing data fusion preprocessing module collects multi-source heterogeneous data of queuing patients and pre-processes and fuses the data, and outputs the fused standardized data set in real time.

[0008] The disease emergency quantification analysis module quantifies the disease emergency degree of the patient from three dimensions of symptom emergency degree, sign abnormality degree and basic disease risk, and sends the disease emergency degree of the patient to the multi-dimensional queuing priority decision module.

[0009] The multi-dimensional queuing priority decision module obtains a queuing priority coefficient of the corresponding queuing patient through multi-dimensional queuing priority analysis calculation, sorts all queuing patients of the corresponding department according to the values of the queuing priority coefficients from large to small, generates a department queuing queue, and sends the department queuing queue to the intelligent queuing result visualization module. The intelligent queuing result visualization module displays the department queuing queue.

[0010] Further, the queuing data fusion preprocessing module connects the hospital HIS system, LIS system and electronic medical record system through an API interface, collects patient basic information, registration information and inspection data, obtains real-time patient current sign data through Internet of Things connection of sign monitoring equipment, and collects special demand declaration and illness supplement description through a patient terminal applet;

[0011] The collected data is cleaned. For missing values, a multiple imputation method based on similar patient medical records is used. For abnormal values, the quartile range method is used to identify and eliminate invalid data. For format differences, all data is converted to JSON format, and indexed according to "patient ID-data type-collection time". The data is fused to generate a standardized data set containing patient unique identification, basic information, real-time signs, chief complaint symptoms, registration duration and special needs, and output.

[0012] Further, the operation analysis process of the illness emergency quantification analysis module includes:

[0013] Data extraction: receiving the patient standardized data set transmitted by the queuing data fusion preprocessing module, extracting the key fields related to the illness emergency degree, including chief complaint symptoms, real-time signs and basic disease history;

[0014] Calculating the symptom emergency degree S: mapping the patient's chief complaint symptoms to the initial emergency degree Sy by comparing the set symptom emergency degree classification table, and the symptom emergency degree S = max(Sy1, Sy2, …, Syg), where g is the number of chief complaint symptoms of the queuing patient;

[0015] Calculating the sign abnormality degree T: obtaining the abnormal deviation degree of each sign index by analysis, and calculating the mean value of the abnormal deviation degrees of all sign indexes to obtain the sign abnormality degree T;

[0016] Calculating the basic disease risk value B: according to the relevance value of the basic disease and the current symptoms, obtaining the basic disease risk value B of the corresponding queuing patient;

[0017] Determine the weights a, b, c of S, T, B by the analytic hierarchy process, and calculate the illness emergency degree E by the formula E = a x S + b x T + c x B. The E value and the S, T, B parameters in the calculation process are transmitted to the multi-dimensional queuing priority decision module in real time.

[0018] Further, the analysis method of the abnormal deviation degree of the physical indicators is as follows:

[0019] After all the physical indicators are obtained and the measured values of the corresponding physical indicators are collected, the abnormal deviation degree of the corresponding physical indicators is calculated through the physical abnormality degree calculation formula.

[0020] Further, the multi-dimensional queuing priority decision module is communicatively connected to the correction factor calculation module. The correction factor calculation module analyzes the queuing records of the corresponding queuing patient in the historical stage, and obtains the target correction factor e of the corresponding queuing patient accordingly. The target correction factor e of the corresponding queuing patient is sent to the multi-dimensional queuing priority decision module in real time.

[0021] Further, the analysis method of the target correction factor e is as follows:

[0022] After the total number of queuing times and the number of queuing no-shows of the corresponding queuing patient in the historical stage are obtained, the number of queuing no-shows is calculated by the ratio of the total number of queuing times to obtain the queuing abnormal value. When the queuing is not no-show, the time difference between the queuing call time and the treatment time is calculated to obtain the treatment delay value, and the average value of all treatment delay values of the corresponding queuing patient in the historical stage is calculated to obtain the treatment delay value. The queuing traceability coefficient is calculated by weighted summation of the queuing abnormal value and the treatment delay value.

[0023] A plurality of preset queuing traceability coefficient ranges are set in advance, and each preset queuing traceability coefficient range corresponds to a decision correction factor. The queuing traceability coefficient of the corresponding queuing patient is compared with all preset queuing traceability coefficient ranges one by one, and the preset queuing traceability coefficient range containing the corresponding queuing traceability coefficient is marked as the target range, and the decision correction factor corresponding to the target range is marked as the target correction factor e.

[0024] Further, the running analysis process of the multi-dimensional queuing priority decision module is as follows:

[0025] After the registration time of the corresponding queuing patient is collected, the time difference between the current time and the registration time is calculated to obtain the queuing time length D. The queuing time length influence value D is obtained by analyzing the formula D = min {(Ds / 120) x 100, 100} with the maximum reasonable queuing time length of 120 minutes as the threshold. If Ds>120, then D=100.

[0026] And determine the special demand coefficient K; wherein, the elderly patients over 65 years old K=1.15, the children patients under 5 years old K=1.2, the disabled patients K=1.25, and the ordinary patients K=1.0;

[0027] The illness emergency E and the target correction factor e of the corresponding patient are called from the illness emergency quantification analysis module and the correction factor calculation module respectively, and the corresponding patient's queuing priority coefficient P is calculated through a multi-dimensional queuing priority calculation formula.

[0028] Further, the intelligent sorting result visualization module is in communication connection with the background management and control terminal, the intelligent sorting result visualization module and the background management and control terminal are in bidirectional communication, the intelligent sorting result visualization module transmits the received information to the background management and control terminal, and the background management and control terminal controls the intelligent sorting result visualization module by sending a control instruction to it.

[0029] Further, the background management and control terminal is in communication connection with the visualization performance evaluation module, the visualization performance evaluation module evaluates and analyzes the running performance of the intelligent sorting result visualization module, judges whether to generate a visualization abnormal signal through analysis, and sends the visualization abnormal signal to the background management and control terminal when the visualization abnormal signal is generated, and the background management and control terminal sends a corresponding early warning when receiving the visualization abnormal signal.

[0030] Further, the specific analysis process of the visualization performance evaluation module is as follows:

[0031] The number of times that the intelligent sorting result visualization module cannot display within a unit time is obtained and marked as a queuing display abnormal frequency value, and the total duration that the intelligent sorting result visualization module cannot display within a unit time is marked as a queuing black screen situation value, the queuing display abnormal frequency value and the queuing black screen situation value are compared with the preset queuing display abnormal frequency threshold and the preset queuing black screen situation threshold respectively, and if the queuing display abnormal frequency value or the queuing black screen situation value exceeds the corresponding preset threshold, a visualization abnormal signal is generated;

[0032] If the queuing display abnormal frequency value and the queuing black screen situation value do not exceed the corresponding preset threshold, the time at which the intelligent sorting result visualization module receives the department queuing queue is obtained and marked as a first time, and the time at which the intelligent sorting result visualization module updates the queuing queue is marked as a second time, the first time and the second time are calculated to obtain a queuing update delay value, and all queuing update delay values within a unit time are calculated to obtain a queuing update coefficient;

[0033] and the display brightness of the intelligent sorting result visualization module is obtained, the display brightness and the median value of the preset appropriate brightness range are calculated by difference and the absolute value is taken to obtain a queuing display brightness value, and the queuing display brightness value within a unit time is calculated to obtain a queuing display brightness characteristic value; and when the queuing display brightness value exceeds the preset queuing display brightness threshold, it is judged that the intelligent sorting result visualization module is in a high brightness deviation state, the total duration that the intelligent sorting result visualization module is in the high brightness deviation state within a unit time is marked as a queuing brightness high deviation value.

[0034] The visual performance evaluation value is calculated by weighting and summing the queuing delay coefficient, the queuing highlight characteristic value and the queuing brightness high bias value, the visual performance evaluation value is compared with a preset visual performance evaluation threshold value, if the visual performance evaluation value exceeds the preset visual performance evaluation threshold value, a visual abnormal signal is generated.

[0035] Compared with the prior art, the beneficial effects of the present application are:

[0036] 1、In the present application, the disease emergency is quantitatively analyzed and calculated by the disease emergency quantitative analysis module, so as to ensure that the medical resources are reasonably tilted to patients with high emergency, the patient's historical queuing performance is taken into account by the correction factor calculation module, so as to avoid that the medical resources are excessively occupied by patients who frequently cancel or delay medical treatment, and the queuing priority coefficient is calculated through multi-dimensional analysis, the queuing priority coefficient is used for queuing sorting and displaying the queuing queue, so as to effectively balance the medical safety, queuing fairness and diagnosis and treatment efficiency, and realize accurate and reasonable sorting of queuing patients.

[0037] 2、In the present application, the running performance of the intelligent sorting result visualization module is evaluated and analyzed by the visual performance evaluation module, so as to realize real-time monitoring of the running state of the intelligent sorting result visualization module, timely find and warn display abnormalities, update delay, brightness inadaptation and other problems, avoid that the medical staff misjudge the queuing order or the patients are confused due to information display problems, ensure the stability, timeliness and accuracy of the queuing queue display, and maintain the smooth operation of the whole queuing management process. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to facilitate the understanding of those skilled in the art, the present application will be further described below in conjunction with the drawings;

[0039] Figure 1 The system block diagram of example one in the present application is shown in the figure;

[0040] Figure 2 The system block diagram of example two in the present application is shown in the figure. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings of the embodiments of the present application, obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0042] Example one: as Figure 1As shown, the intelligent queuing management system for queuing based on multi-source data analysis comprises a queuing data fusion preprocessing module, a disease emergency quantification analysis module, a correction factor calculation module, a multi-dimensional queuing priority decision module, and an intelligent queuing result visualization module.

[0043] The queuing data fusion preprocessing module collects multi-source heterogeneous data of queuing patients and pre-processes and fuses the data, and outputs the fused standardized data set in real time, effectively solving the problems of scattered data sources, chaotic formats, and uneven quality in the traditional data collection process, providing high-quality and consistent data support for subsequent disease emergency quantification analysis, multi-dimensional queuing priority decision, and other modules, avoiding deviations in subsequent analysis results due to data problems, and ensuring the reliability of system queuing decision from the source.

[0044] Specifically, the queuing data fusion preprocessing module interfaces with the hospital HIS system, LIS system, and electronic medical record system through API, collects patient basic information, registration information, and inspection data, real-time acquires current patient vital sign data through Internet of Things interfacing with vital sign monitoring equipment, and collects special demand declaration and disease supplement description through the patient end applet.

[0045] The collected data is cleaned, the multiple imputation method based on similar patient medical records is used for missing values, the quartile range method is used to identify and eliminate invalid data for abnormal values, all data is converted to JSON format for format differences, and an index is established according to "patient ID-data type-collection time"; the data is fused to generate a standardized data set containing patient unique identification, basic information, real-time vital signs, chief complaint symptoms, registration duration, and special needs and output.

[0046] The disease emergency quantification analysis module quantifies the patient's disease emergency degree from three dimensions of symptom emergency degree, vital sign abnormality degree, and basic disease risk, and sends the patient's disease emergency degree to the multi-dimensional queuing priority decision module, breaking through the limitations of traditional methods that rely only on single symptoms or subjective judgment to evaluate the disease emergency degree, realizing the quantification analysis of disease emergency degree from three dimensions of symptoms, vital signs, and basic diseases, and accurately identifying patients who really need emergency treatment, avoiding delays in emergency treatment or ordinary patients occupying emergency medical resources, and significantly improving the scientificity of medical judgment and the safety of medical treatment; the operation analysis process of the disease emergency quantification analysis module comprises:

[0047] Data extraction: receiving the patient standardized data set transmitted by the queuing data fusion preprocessing module, extracting the key fields related to the disease emergency degree, mainly including chief complaint symptoms (such as chest pain, dyspnea, high fever, etc.), real-time vital signs (such as HR, SpO2, etc.), and basic disease history (such as coronary heart disease, hypertension, diabetes, etc.);

[0048] Calculate the symptom urgency S (S value range 0-100): According to the set symptom urgency grading table, preferably according to the ICD-11 symptom urgency grading table; map the patient's complaint symptoms to the initial urgency Sy (such as acute chest pain Sy = 90, mild headache Sy = 15), symptom urgency S = max{Sy1, Sy2, …, Syg}, that is, when there are multiple symptoms, take the maximum value as S; Where, g is the number of complaint symptoms of the waiting patient;

[0049] Calculate the sign abnormality T (T value range 0-100): By analyzing the abnormal deviation of each sign index, and calculating the mean of the abnormal deviation of all sign indexes to obtain the sign abnormality T; The analysis method of the abnormal deviation of the sign index is as follows:

[0050] Get all sign indexes, collect the measured values of the corresponding sign indexes, and calculate the abnormal deviation of the corresponding sign indexes by the sign abnormality calculation formula, the sign abnormality calculation formula is as follows:

[0051]

[0052] Wherein, Ti represents the abnormal deviation of the i-th sign index (0-100, the higher the value, the more serious the abnormality);

[0053] Xi represents the measured value of the i-th sign index of the corresponding waiting patient;

[0054] Xn represents the clinical normal reference value of the i-th sign index;

[0055] Xmax represents the clinical upper limit value of the i-th sign index (such as heart rate Xmax = 150 times / min);

[0056] Xmin represents the clinical lower limit value of the i-th sign index (such as heart rate Xmin = 40 times / min);

[0057] Calculate the basic disease risk value B (B value range 0-100): According to the relevance of the basic disease and the current symptoms, such as patients with coronary heart disease and complaint of chest pain B = 95, patients with diabetes but complaint of cold B = 30, patients without basic disease B = 10, according to the basic disease risk value B of the corresponding waiting patient is obtained;

[0058] The weights a, b, and c of S, T, and B are determined by the analytic hierarchy process (AHP) combined with the opinions of more than thirty clinical experts, preferably a = 0.45, b = 0.35, and c = 0.2; the illness emergency degree E is calculated by the formula E = a × S + b × T + c × B, and the E value and the S, T, and B parameters in the calculation process are transmitted in real time to the multi-dimensional waiting priority decision module. Preferably, E ≥ 80 is urgent, 60 ≤ E < 80 is relatively urgent, and E < 60 is ordinary.

[0059] The correction factor calculation module analyzes the historical stage waiting records of the corresponding waiting patient, thereby obtaining the target correction factor e of the corresponding waiting patient, and sending the target correction factor e of the corresponding waiting patient to the multi-dimensional waiting priority decision module in real time. The patient waiting priority is appropriately adjusted by the correction factor to avoid patients frequently occupying medical resources but not timely visiting, while ensuring the fair waiting rights and interests of patients with good historical performance, further optimizing the allocation efficiency of medical resources, and maintaining the fairness of the waiting order; the analysis and acquisition method of e is as follows:

[0060] The waiting abnormal value is calculated by the ratio of the waiting no-show times to the total waiting times of the corresponding waiting patient in the historical stage; and when the waiting is not no-show, the treatment delay value is calculated by the time difference between the waiting call time and the treatment time, and the treatment delay value of the corresponding waiting patient in the historical stage is calculated by the mean value.

[0061] The waiting traceability coefficient is calculated by the weighted sum of the waiting abnormal value and the treatment delay value, that is, the corresponding preset weight coefficients are respectively assigned to the waiting abnormal value and the treatment delay value, and the sum of the two groups of product results is marked as the waiting traceability coefficient; it should be noted that when the value of the waiting traceability coefficient is large, it indicates that the historical waiting performance of the corresponding waiting patient is poor, and the waiting is relatively lazy, and the priority can be appropriately reduced;

[0062] A plurality of groups of preset waiting traceability coefficient ranges are set in advance, and each group of preset waiting traceability coefficient ranges corresponds to a group of decision correction factors with a value greater than zero; and the larger the value of the corresponding preset waiting traceability coefficient range, the larger the value of the decision correction factor matched therewith; the waiting traceability coefficient of the corresponding waiting patient is compared with all the preset waiting traceability coefficient ranges one by one, the preset waiting traceability coefficient range containing the corresponding waiting traceability coefficient is marked as the target range, and the decision correction factor corresponding to the target range is marked as the target correction factor e.

[0063] The multi-dimensional queuing priority decision module calculates the queuing priority coefficient of the corresponding queuing patient through multi-dimensional queuing priority analysis, sorts all queuing patients of the corresponding department according to the queuing priority coefficient from large to small, generates a department queuing queue, and sends it to the intelligent queuing result visualization module. The intelligent queuing result visualization module displays the department queuing queue, allowing queuing patients to understand their queuing progress, reducing anxiety and disputes caused by information opacity, and effectively improving the use experience and communication efficiency of both parties.

[0064] The multi-dimensional queuing priority decision module integrates multiple factors such as disease emergency, queuing time fairness, patient special needs, and historical queuing performance, ensuring that emergency patients are given priority for treatment, while avoiding patient dissatisfaction due to long queuing times. At the same time, the special needs coefficient K reflects the humanistic care for vulnerable patient groups, effectively balancing medical safety, queuing fairness, and treatment efficiency, significantly improving the rationality and humanization level of overall queuing management. The operation analysis process of the multi-dimensional queuing priority decision module is as follows:

[0065] The queuing time of the corresponding queuing patient is collected, the time difference between the current time and the queuing time is calculated to obtain the queuing time Ds, and the maximum reasonable queuing time is 120 minutes. The queuing time influence value D is obtained by analyzing the formula D = min {(Ds / 120) x 100, 100}, that is, the queuing time Ds is converted to a value between 0 and 100 by the formula. The larger Ds is, the larger D is, and if Ds > 120, then D = 100.

[0066] The special needs coefficient K is determined, which can reflect humanistic care. For example, patients over 65 years old K = 1.15, children under 5 years old K = 1.2, disabled patients K = 1.25, and ordinary patients K = 1.0.

[0067] The disease emergency E and the target correction factor e of the corresponding queuing patient are retrieved from the disease emergency quantification analysis module and the correction factor calculation module, respectively. The queuing priority coefficient P of the corresponding queuing patient is calculated by the multi-dimensional queuing priority calculation formula, and the multi-dimensional queuing priority calculation formula is as follows:

[0068] P = K x (w1 x E + w2 x D) - e; where w1 and w2 are preset weight coefficients, and w1 + w2 = 1. It should be noted that the larger the value of the queuing priority coefficient P is, the higher the queuing priority of the corresponding queuing patient is, and the higher the queuing order is.

[0069] Embodiment two: as Figure 2As shown, the difference between the embodiment and Embodiment One is that the intelligent sorting result visualization module is communicatively connected to the background management and control terminal, the intelligent sorting result visualization module and the background management and control terminal perform bidirectional communication, the intelligent sorting result visualization module transmits the received information to the background management and control terminal, and the background management and control terminal controls the intelligent sorting result visualization module by issuing a control instruction to it.

[0070] Further, the background management and control terminal is communicatively connected to the visualization performance evaluation module, the visualization performance evaluation module evaluates and analyzes the performance of the intelligent sorting result visualization module, determines whether to generate a visualization abnormal signal through analysis, and sends the visualization abnormal signal to the background management and control terminal when the visualization abnormal signal is generated; the background management and control terminal issues a corresponding early warning when the visualization abnormal signal is received.

[0071] The visualization performance evaluation module monitors the running state of the intelligent sorting result visualization module in real time, discovers problems such as display abnormalities, update delays, and inappropriate brightness in a timely manner, and processes them in a timely manner through the background management and control terminal, so as to avoid misjudgment of the waiting order by medical staff or confusion of patients due to display problems, and to ensure the stability, timeliness and accuracy of the waiting queue display, and to maintain the smooth operation of the entire waiting management process; the specific analysis process of the visualization performance evaluation module is as follows:

[0072] The number of times that the intelligent sorting result visualization module fails to display within a unit time is obtained and marked as a waiting display abnormal frequency value, and the total duration of the intelligent sorting result visualization module failing to display within a unit time is marked as a waiting black screen situation value; the waiting display abnormal frequency value and the waiting black screen situation value are compared with the preset waiting display abnormal frequency threshold value and the preset waiting black screen situation threshold value, respectively; if the waiting display abnormal frequency value or the waiting black screen situation value exceeds the corresponding preset threshold value, it indicates that the intelligent sorting result visualization module is difficult to display stably and reliably within a unit time, and a visualization abnormal signal is generated;

[0073] If the waiting display abnormal frequency value and the waiting black screen situation value do not exceed the corresponding preset threshold value, the time at which the intelligent sorting result visualization module receives the department waiting queue is obtained and marked as a first time, and the time at which the intelligent sorting result visualization module updates the waiting queue is marked as a second time; the first time and the second time are compared to obtain a waiting update delay value, and all waiting update delay values within a unit time are averaged to obtain a waiting update coefficient.

[0074] and the display brightness is obtained, a difference value between the display brightness and a median value of a preset suitable brightness range is calculated and an absolute value is taken to obtain a waiting display brightness value, and a mean value of the waiting display brightness value in a unit time is calculated to obtain a waiting display brightness characteristic value, and when the waiting display brightness value exceeds a preset waiting display brightness threshold value, it is judged that the intelligent sorting result visualization module is in a high brightness deviation state, and a total time length of the intelligent sorting result visualization module in the high brightness deviation state in a unit time is marked as a waiting brightness high deviation value;

[0075] The visualization performance evaluation value is obtained by weighted summation calculation of the waiting delay coefficient, the waiting display brightness characteristic value and the waiting brightness high deviation value, that is, the waiting delay coefficient, the waiting display brightness characteristic value and the waiting brightness high deviation value are respectively given corresponding preset weight coefficients, and the waiting delay coefficient, the waiting display brightness characteristic value and the waiting brightness high deviation value are respectively multiplied by the corresponding preset weight coefficients, and a sum value of the three groups of product results is marked as the visualization performance evaluation value.

[0076] It should be noted that the larger the value of the visualization performance evaluation value is, the more abnormal the running state of the intelligent sorting result visualization module in a unit time is, and the worse the visualization performance is; the visualization performance evaluation value is compared with a preset visualization performance evaluation threshold value, if the visualization performance evaluation value exceeds the preset visualization performance evaluation threshold value, it indicates that the running state of the intelligent sorting result visualization module in a unit time is relatively abnormal, and the visualization performance is poor, and a visualization abnormal signal is generated.

[0077] The working principle of the application is: when in use, the multi-source heterogeneous data is cleaned and standardized by the waiting data fusion preprocessing module, a unified data set containing key information of patients is generated, the illness emergency quantification analysis module quantitatively calculates the illness emergency from three dimensions of symptom emergency degree, sign abnormality degree and basic disease risk, accurately identifies patients such as acute chest pain and high fever with basic diseases that need to be treated preferentially, effectively avoids the medical safety hidden danger of delayed treatment of emergency illness, ensures that the medical resources are reasonably tilted to patients with high emergency degree, the patient historical waiting performance is considered in the priority consideration by the correction factor calculation module, the medical resources are prevented from being excessively occupied by patients who frequently cancel or delay medical treatment, the fairness of the waiting order is further maintained, the multi-dimensional waiting priority decision module integrates the illness emergency, the waiting time influence value, the special demand coefficient and the target correction factor, calculates the waiting priority coefficient through multi-dimensional analysis and sorts, effectively balances the medical safety, the waiting fairness and the diagnosis and treatment efficiency, reduces the dissatisfaction of patients, and the intelligent sorting result visualization module displays the waiting queue, reduces the anxiety and disputes caused by information opacity.

[0078] The threshold or the preset value, the preset range and the like in the technical scheme of the present application are set for result comparison and analysis, so as to determine whether it is good or not, and the size of the value is set according to the large model analysis of sample data and the combination of artificial experience, and is also stored, and can be appropriately adjusted through seasonal or reasonable influence conditions. The preset weight coefficient and the influence factor are set according to the influence of each parameter on the result, and the specific value is allocated to finally reflect the influence on the result, and the large model analysis of sample data and the combination of artificial experience are also used to set the storage, and appropriate adjustment can be made through seasonal or reasonable influence conditions.

[0079] The preferred embodiments of the present application disclosed above are only used to help explain the present application, and the preferred embodiments do not describe all the details and limit the present application to the specific embodiments. Obviously, many modifications and changes can be made according to the content of the present application. The present application selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present application, so that the skilled in the art can well understand and utilize the present application. The present application is limited by the claims and the entire scope and equivalents thereof.

Claims

1. A queuing intelligent management system based on multi-source data analysis, characterized in that, The system comprises a waiting data fusion preprocessing module, a disease emergency quantification analysis module, a multi-dimensional waiting priority decision module, and an intelligent sorting result visualization module. The waiting data fusion preprocessing module collects and preprocesses and fuses multi-source heterogeneous data of the waiting patients, and outputs the fused standardized data set in real time. The disease emergency quantification analysis module quantifies the emergency degree of the patient from three dimensions of symptom emergency degree, sign abnormality degree, and basic disease risk, and sends the emergency degree of the patient to the multi-dimensional waiting priority decision module. The multi-dimensional waiting priority decision module calculates the waiting priority coefficient of the corresponding waiting patient through multi-dimensional waiting priority analysis, sorts all the waiting patients in the corresponding department according to the values of the waiting priority coefficients from large to small, generates a department waiting queue, and sends it to the intelligent sorting result visualization module, which displays the department waiting queue. The operation analysis process of the disease emergency quantification analysis module comprises: Receiving the patient standardized data set transmitted by the waiting data fusion preprocessing module, extracting the key fields related to the emergency degree of the disease, including the chief complaint, real-time signs, and history of basic diseases; mapping the patient's chief complaint to the initial emergency degree Sy by referring to the set symptom emergency degree classification table, and calculating the symptom emergency degree S = max (Sy1, Sy2, …, Syg), wherein g is the number of chief complaints of the waiting patient. By analyzing the abnormal deviation degree of each sign index, the mean value of the abnormal deviation degrees of all sign indexes is calculated to obtain the sign abnormality degree T; the basic disease risk value B of the corresponding waiting patient is obtained according to the correlation value between the basic disease and the current symptoms; the weights a, b, and c of S, T, and B are determined by the analytic hierarchy process, and the disease emergency degree E is calculated by the formula E = a × S + b × T + c × B. The abnormal deviation degree of the sign index is analyzed and obtained by: obtaining all the sign indexes, collecting the measured values of the corresponding sign indexes, and calculating the abnormal deviation degree of the corresponding sign index by the sign abnormality degree calculation formula. 2.The intelligent management system for queuing based on multi-source data analysis according to claim 1, wherein, The waiting data fusion preprocessing module connects the hospital HIS system, LIS system, and electronic medical record system through the API interface, collects the patient's basic information, registration information, and inspection data, obtains the patient's current sign data in real time through the Internet of Things, and collects special demand declaration and disease supplement description through the patient terminal applet. The collected data is cleaned, the multiple imputation method based on similar patient medical records is used for missing values, the quartile range method is used to identify and eliminate invalid data for abnormal values, and all data is converted to JSON format for format differences; the data is fused to generate a standardized data set and output. 3.The intelligent management system for queuing based on multi-source data analysis according to claim 1, wherein, The multi-dimensional waiting priority decision module is communicatively connected to the correction factor calculation module, which analyzes the waiting records of the corresponding waiting patient in the historical stage to obtain the target correction factor e of the corresponding waiting patient, and sends the target correction factor e of the corresponding waiting patient to the multi-dimensional waiting priority decision module in real time. 4.The intelligent management system for queuing based on multi-source data analysis according to claim 3, characterized in that, The analysis acquisition method of the target correction factor e is specifically as follows: The waiting patient tracing coefficient is calculated by weighted summation of the waiting abnormal value and the treatment delay table value, the waiting patient tracing coefficient of the corresponding waiting patient is compared with all preset waiting patient tracing coefficient ranges one by one, the preset waiting patient tracing coefficient range containing the corresponding waiting patient tracing coefficient is marked as a target range, and the decision correction factor corresponding to the target range is marked as a target correction factor e. 5.The intelligent management system for queuing based on multi-source data analysis according to claim 1, wherein, The running analysis process of the multi-dimensional waiting priority decision module is specifically as follows: The waiting time Ds is calculated by time difference between the current time and the registration time, the maximum reasonable waiting time is 120 minutes, the waiting time influence value D is obtained by analysis through the formula D = min{ (Ds / 120) x 100, 100}; And determine the special demand coefficient K, retrieve the emergency degree E and the target correction factor e of the corresponding waiting patient, and calculate the waiting priority coefficient P of the corresponding waiting patient through the multi-dimensional waiting priority calculation formula. 6.The intelligent management system for queuing based on multi-source data analysis according to claim 1, wherein, The intelligent sorting result visualization module is connected with the background control terminal, the intelligent sorting result visualization module transmits the received information to the background control terminal, and the background control terminal controls the intelligent sorting result visualization module by sending control instructions to it. 7.The intelligent management system for queuing based on multi-source data analysis according to claim 6, wherein, The background control terminal is connected with the visualization performance evaluation module, the visualization performance evaluation module evaluates and analyzes the running performance of the intelligent sorting result visualization module, judges whether to generate a visualization abnormal signal through analysis, and sends the visualization abnormal signal to the background control terminal when the visualization abnormal signal is generated. 8.The intelligent management system for queuing based on multi-source data analysis according to claim 7, wherein, The specific analysis process of the visualization performance evaluation module is as follows: if the waiting display abnormal frequency value or the waiting black screen situation value exceeds the corresponding preset threshold value, a visualization abnormal signal is generated; if the waiting display abnormal frequency value and the waiting black screen situation value do not exceed the corresponding preset threshold value, the visualization performance evaluation value is calculated by weighted summation of the waiting more delay coefficient, the waiting display bright characteristic value and the waiting brightness high bias value, and if the visualization performance evaluation value exceeds the preset visualization performance evaluation threshold value, a visualization abnormal signal is generated.

Images