A method and system for analyzing medical treatment strategies based on waiting time estimation

Abstract

The present invention relates to the field of data processing, and specifically to a medical treatment strategy analysis method and system based on waiting time estimation, which parses the user's symptom description to obtain the target department where the user needs to make an appointment. At the same time, the time required for the user to go to each hospital is analyzed based on the user's required time period, while taking into account the travel time and waiting time in the hospital. This application understands the congestion level and average waiting time of each hospital in a specific time period, and can guide patients to choose a more relaxed time period for appointments or registration, thereby shortening the waiting time before the actual medical treatment and improving medical efficiency. For patients with chronic diseases who need frequent medical treatment or who need to reach the hospital quickly in an emergency, this type of analysis can serve as an important decision-making reference to help choose the most suitable medical institution for the current situation.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method and system for analyzing medical consultation strategies based on waiting time estimation. Background Art

[0002] With the popularity of online appointment booking, more and more people can make appointments online quickly and conveniently. For those who want to see a doctor quickly, they generally choose to book an appointment at a nearby hospital. However, different hospitals have different levels of congestion at different times of the day. Therefore, unless the case qualifies as an emergency, users may still face a long wait before receiving medical treatment after arriving at the hospital. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a method and system for analyzing medical consultation strategies based on waiting time estimation to solve the problems in the background technology.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] The present invention provides a method for analyzing a medical consultation strategy based on waiting time estimation, comprising the steps of:

[0006] Obtaining the current user's medical consultation demand information and the user's current location information, wherein the medical consultation demand information includes a consultation time period and a description of symptoms;

[0007] Extracting symptom entities from the symptom description, and determining the target department with the highest probability for the current user based on the symptom entities and a symptom-department relationship data table, wherein the symptom-department relationship data table represents the corresponding probabilities between multiple symptom types and departments;

[0008] Filter candidate hospitals based on the target department and the current location information;

[0009] Sampling the consultation time period to obtain multiple candidate time points; using the multiple candidate time points as departure time points, estimating the estimated arrival time of the current user at the multiple candidate hospitals, and determining the estimated waiting time for the target department of the multiple candidate hospitals based on the estimated arrival time and a pre-constructed waiting time relationship curve for the multiple hospitals;

[0010] A medical consultation decision is made based on the multiple candidate time points, the estimated arrival times of the multiple candidate hospitals, and the estimated waiting times of the target departments of the multiple candidate hospitals.

[0011] In one embodiment of the present application, the method for constructing the symptom-department relationship data table includes:

[0012] Access to triage records;

[0013] Extracting a plurality of triage sample data from the triage record, wherein the triage sample data includes a symptom description sample and a triage result;

[0014] Extracting a symptom entity sample from the symptom description sample, wherein the symptom entity sample includes a body part entity and a disease entity;

[0015] Vectorizing the symptom entity sample to obtain a symptom entity sample vector; and mixing the symptom entity sample vector into a sample vector with a label for clustering to obtain multiple clusters, wherein the label is used to represent the symptom type;

[0016] Using the labels of the sample vectors in the same cluster as the labels of the symptom entity sample vectors to obtain a plurality of labeled triage sample data;

[0017] Divide the labeled multiple triage sample data based on the labels to obtain multiple data units;

[0018] Calculate the triage results for each data unit Probability , ,in, For triage results The number of corresponding triage sample data, is the total number of triage sample data for the data unit, is the serial number of the triage result;

[0019] A symptom-department relationship data table is constructed based on multiple labels and the probabilities of triage results corresponding to the multiple labels.

[0020] In one embodiment of the present application, determining the target department with the highest probability for the current user based on the symptom entity and the symptom-department relationship data table includes:

[0021] Marking the symptom entity to obtain a label of the symptom entity;

[0022] Match the label of the symptom entity with the symptom-department relationship data table to obtain the probability of multiple triage results ;

[0023] When there is only one symptom entity, the department in the triage result with the highest probability is extracted as the target department; when there are multiple symptom entities, the total probability of each triage result is calculated, and the department in the triage result with the highest total probability is taken as the target department.

[0024] In one embodiment of the present application, a method for constructing a waiting time relationship curve includes:

[0025] Obtain multiple historical medical records from multiple departments of multiple hospitals, where the historical medical records include the registration time point and time of visit ;

[0026] Based on the registration time of each historical medical record and time of visit Calculate the waiting time for each consultation ;

[0027] Calculate the average waiting time of multiple historical medical records within each natural day ;

[0028] Based on the first average waiting time Cluster multiple historical medical records based on the registration time point to obtain multiple sample clusters;

[0029] The sample cluster with the number of records in the cluster greater than the preset number threshold is taken as the target sample cluster, and the regular time period corresponding to the target sample cluster is extracted;

[0030] Divide each natural day into multiple sub-time periods, and divide the historical medical records in the target sample cluster into multiple sub-time periods based on the registration time point to obtain multiple record units;

[0031] Calculate the second average waiting time of multiple historical medical records in each record unit and standard deviation ;

[0032] Based on the second average waiting time and standard deviation Construct a reference range of waiting time for the corresponding sub-time period;

[0033] A waiting time relationship curve of a regular time period is constructed based on the waiting time reference ranges of multiple sub-time periods.

[0034] In one embodiment of the present application, the waiting time relationship curve includes a minimum reference curve, a typical reference curve, and a maximum reference curve. The waiting time relationship curve of a regular time period is constructed based on the waiting time reference ranges of multiple sub-time periods, including:

[0035] Mapping the waiting time reference ranges of the multiple sub-time periods into a two-dimensional coordinate system to obtain a maximum waiting time line segment, an average waiting time line segment, and a minimum waiting time line segment for each sub-time period, wherein the horizontal axis of the two-dimensional coordinate system is the time axis and the vertical axis of the two-dimensional coordinate system is the waiting time axis;

[0036] Take the midpoint of the maximum waiting time segment, the midpoint of the average waiting time segment, and the midpoint of the minimum waiting time segment respectively to obtain the minimum value point, typical value point, and maximum value point of each sub-time period;

[0037] The minimum value points, typical value points and maximum value points corresponding to multiple sub-time periods are fitted respectively to obtain a minimum reference curve, a typical reference curve and a maximum reference curve.

[0038] In one embodiment of the present application, the estimated waiting time for a target department in multiple candidate hospitals is determined based on the estimated arrival time and a pre-built waiting time relationship curve for multiple hospitals, including:

[0039] Determine the waiting time relationship curve of the target department in each hospital in regular time periods based on the estimated arrival time;

[0040] Map the estimated arrival time to the two-dimensional coordinate system to obtain the intersection of the estimated arrival time with the minimum reference curve, the typical reference curve, and the maximum reference curve to obtain the minimum reference duration. , Typical reference duration and maximum reference duration .

[0041] In one embodiment of the present application, a medical consultation decision is made based on the multiple candidate time points, the estimated arrival times of the multiple candidate hospitals, and the estimated waiting times of the target departments of the multiple candidate hospitals, including:

[0042] Construct candidate plans by starting from any candidate time point and reaching the target department of any candidate hospital;

[0043] Calculate the time cost of each candidate solution and risk costs ;

[0044] Based on the time cost and the risk cost Calculate the evaluation value of each candidate solution ,in, , is the serial number of the candidate solution;

[0045] The multiple candidate solutions with the highest evaluation values ​​are used as target solutions, and the target solutions are returned to the current user.

[0046] In one embodiment of the present application, the time cost of each candidate solution is calculated. and risk costs ,include:

[0047] Calculate the total reference duration for each candidate solution, which is the estimated arrival time and the typical reference time. The estimated arrival time is the difference between the estimated arrival time and the candidate time point;

[0048] Calculate the waiting time fluctuation ratio for each candidate solution , ;

[0049] Normalize the total reference duration of multiple candidate solutions to obtain the time cost of multiple candidate solutions ; Floating ratio of waiting time for multiple candidate solutions Perform normalization to obtain the risk costs of multiple candidate solutions .

[0050] In one embodiment of the present application, the multiple candidate time points are used as departure time points, and the estimated arrival time of the current user at the multiple candidate hospitals is estimated, including:

[0051] Call the external navigation API interface, use multiple candidate time points as departure time points, the user's current location information as the starting point, and multiple candidate hospitals as the end point for navigation planning, and obtain the estimated arrival time of multiple travel modes for multiple options, where each option includes a candidate time point and a candidate hospital;

[0052] When the travel mode selection information is received from the current user, the estimated arrival time of the current user at multiple candidate hospitals when the current user departs at multiple candidate time points is obtained.

[0053] This application also provides a medical consultation strategy analysis system based on waiting time estimation, including:

[0054] An acquisition module is used to obtain the current user's medical treatment demand information and the user's current location information, wherein the medical treatment demand information includes a medical treatment time period and a symptom description;

[0055] a department determination module, configured to extract symptom entities from the symptom description and determine the target department with the highest probability for the current user based on the symptom entities and a symptom-department relationship data table, wherein the symptom-department relationship data table represents the corresponding probabilities between multiple symptom types and departments;

[0056] A preliminary screening module, configured to screen candidate hospitals based on the target department and the current location information;

[0057] A duration estimation module is configured to sample the visit time period to obtain multiple candidate time points; use the multiple candidate time points as departure time points, estimate the estimated arrival time of the current user at multiple candidate hospitals, and determine the estimated waiting time for the target department of the multiple candidate hospitals based on the estimated arrival time and a pre-constructed waiting time relationship curve for the multiple hospitals;

[0058] The decision-making module is used to make a medical treatment decision based on the multiple candidate time points, the estimated arrival time of the multiple candidate hospitals, and the estimated waiting time of the target departments of the multiple candidate hospitals.

[0059] The beneficial effects of the present invention are as follows: a medical treatment strategy analysis method and system based on waiting time estimation of the present invention parses the user's symptom description to obtain the target department that the user needs to make an appointment with. At the same time, the time required for the user to go to each hospital is analyzed according to the user's required time period, while taking into account the travel time and waiting time in the hospital. This application understands the congestion level and average waiting time of each hospital in a specific time period, and can guide patients to choose a more relaxed time period to make an appointment or register, thereby shortening the waiting time before the actual medical treatment and improving the efficiency of medical treatment. For patients with chronic diseases who need frequent medical treatment or who need to reach the hospital quickly in an emergency, this type of analysis can serve as an important decision-making reference to help choose the most suitable medical institution for the current situation. In addition, from a macro perspective, if a large number of users can make more reasonable medical choices based on this type of analysis, the resource utilization efficiency of the entire medical system will be improved, such as reducing the pressure on popular hospitals and balancing the patient flow between different medical institutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0061] Figure 1 This is a flow chart of a method for analyzing a medical consultation strategy based on waiting time estimation, shown in one embodiment of the present application;

[0062] Figure 2 This is an application scenario diagram of a method for analyzing a medical consultation strategy based on waiting time estimation, shown in one embodiment of the present application;

[0063] Figure 3 Schematic diagram of a waiting time relationship curve in an embodiment of the present application;

[0064] Figure 4 This is a schematic diagram of the predicted waiting time in one embodiment of the present application;

[0065] Figure 5 This is a structural diagram of a medical consultation strategy analysis system based on waiting time estimation shown in one embodiment of the present application. DETAILED DESCRIPTION

[0066] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0067] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention. Therefore, the drawings only show the layers related to the present invention and are not drawn according to the number, shape and size of the layers in actual implementation. In actual implementation, the type, quantity and proportion of each layer can be changed arbitrarily, and the layer layout type may also be more complicated.

[0068] In the following description, numerous details are set forth to provide a more thorough explanation of the embodiments of the present invention; however, it is apparent to one skilled in the art that the embodiments of the present invention may be practiced without these specific details.

[0069] The relevant information involved in this application has been obtained with full consent and authorization, and the collection, use and processing of relevant information must comply with the relevant laws, regulations and standards of relevant countries and regions.

[0070] Figure 1 This is a flow chart of a method for analyzing a medical consultation strategy based on waiting time estimation, as shown in one embodiment of the present application. Figure 1 As shown, a method for analyzing a medical consultation strategy based on waiting time estimation in this embodiment may include the following steps:

[0071] S110, obtaining the current user's medical consultation demand information and the user's current location information, wherein the medical consultation demand information includes a consultation time period and a symptom description;

[0072] Figure 2 This is an application scenario diagram of a method for analyzing a medical consultation strategy based on waiting time estimation shown in an embodiment of the present application, such as Figure 2 As shown, in this embodiment, a user accesses the server via a terminal device 220, such as a mobile phone, PC, or tablet computer, and uploads current user information, current user medical needs, and current location information to server 210. Server 210 automatically makes appointment recommendations and travel plan decisions based on the user's medical needs and current location information, thereby helping users make quick medical decisions.

[0073] S120. Extract symptom entities from the symptom description, and determine the target department with the highest probability for the current user based on the symptom entities and the symptom - department relationship data table, where the symptom - department relationship data table represents the corresponding probabilities between multiple symptom types and departments.

[0074] In this application, the matching method is used to extract symptom entities. First, a dictionary containing various possible symptom descriptions needs to be constructed. For example, symptoms of common diseases (such as headache, fever), medical terms (such as chest pain, difficulty breathing), and common expressions used by patients (such as "headache", "out of breath"). Then, necessary pre - processing is performed on the input free text to improve the accuracy of matching. This includes: Word segmentation: splitting the text into words or phrases. Stop - word removal: removing common words that do not provide useful information during the matching process (such as "of", "is"). Word form reduction: converting words to their basic forms (for example, "headache" and "head pain" are uniformly processed). Finally, the constructed symptom dictionary is used to match the pre - processed text, for example, exact matching or fuzzy matching, to extract symptom entities.

[0075] In this application, big data analysis is also performed on triage records to obtain the symptom - department relationship data table, thereby determining the triage results (such as the target department) corresponding to one or more extracted symptom entities.

[0076] The construction process of the symptom - department relationship data table includes:

[0077] (1) Obtain triage records; triage records are usually generated by the emergency department of a hospital or a dedicated triage system and stored in the hospital's information management system, such as the Hospital Information System (HIS) or the Electronic Health Record (EHR) system. After obtaining authorization, the triage records can be extracted from the Hospital Information System (HIS) or the Electronic Health Record (EHR) system.

[0078] In this embodiment, when extracting triage records, it is also necessary to remove privacy data to avoid the leakage of patients' privacy data.

[0079] (2) Extract multiple triage sample data from the triage records, where the triage sample data includes symptom description samples and triage results;

[0080] When extracting, it is necessary to clean the errors, missing values, and other inconsistencies in the original data. In addition, standardization of the format is required, such as unifying the date format, symptom description, etc.

[0081] (3) Extract symptom entity samples from the symptom description samples, where the symptom entity samples include body part entities and disease entities.

[0082] The extraction of symptom entities can be referred to the previous article and will not be repeated here.

[0083] (4) vectorizing the symptom entity sample to obtain a symptom entity sample vector; and clustering the symptom entity sample vector into a sample vector with a label to obtain multiple clusters, wherein the label is used to represent the symptom type;

[0084] In this application, word2vec word embedding technology is used to vectorize symptom entity samples. The vectorized symptom entity samples can be quickly labeled.

[0085] In this application, clustering is used to achieve rapid labeling. By introducing labeled sample vectors, clustering algorithms can be guided to more accurately identify similarity patterns, which is particularly effective when dealing with complex or fuzzy-boundary datasets. At the same time, using existing label information to assist clustering can quickly classify a large number of unlabeled symptom entities without the need for complete manual labeling, thereby significantly improving labeling efficiency.

[0086] Specifically, this application uses symptom types as labels to categorize a wide variety of symptoms, such as pain, fatigue, fever, digestive system symptoms, etc. Combined with body parts, the computer can quickly identify the symptom type corresponding to the current user's symptom description.

[0087] (5) Using the labels of the sample vectors in the same cluster as the labels of the symptom entity sample vectors to obtain multiple labeled triage sample data;

[0088] Since similar symptom entity vectors are clustered together during clustering, the labels within the same cluster can be directly used as labels for the symptom entity sample vectors, thereby achieving fast clustering.

[0089] (6) Divide the labeled multiple triage sample data based on the labels to obtain multiple data units;

[0090] The divided data units contain multiple triage results corresponding to each symptom type.

[0091] For example, consider the symptom type "headache." If the headache is sudden and severe, you might need to consider a neurology department or emergency department, especially if it's accompanied by symptoms like altered consciousness or blurred vision. Therefore, the triage includes neurology or the emergency department. If the headache is accompanied by nasal congestion or a runny nose, it could be caused by a cold or allergies, and an ENT department is recommended. Therefore, the triage includes an ENT department. If the headache is frequent and related to stress or emotions, you might need to consult a psychologist or mental health department. Therefore, the triage includes a psychologist or mental health department.

[0092] (7) Calculate the number of triage results for each data unit Probability , ,in, For triage results The number of corresponding triage sample data, is the total number of triage sample data for the data unit, is the serial number of the triage result;

[0093] In this embodiment, since the same symptom type may correspond to multiple triage results, this application uses the department as the triage result to calculate the probability of the recommended department corresponding to the symptom type, that is, the probability .

[0094] (8) Construct a symptom-department relationship data table based on multiple labels and the probabilities of triage results corresponding to the multiple labels.

[0095] The symptom-department relationship data table contains various symptom labels and the probabilities of recommended departments for each symptom label. Based on this table, the process of making department appointment recommendations is as follows:

[0096] S121, labeling the symptom entity to obtain a label of the symptom entity;

[0097] Symptom entities can also be mixed into labeled sample vectors for clustering, thereby achieving rapid labeling.

[0098] S122, matching the label of the symptom entity with the symptom-department relationship data table to obtain the probability of multiple triage results ;

[0099] S123, when there is only one symptom entity, extract the department in the triage result with the highest probability as the target department; when there are multiple symptom entities, calculate the total probability of each triage result, and use the department in the triage result with the highest total probability as the target department.

[0100] Since a symptom description may contain one or more symptom entities, there are multiple probability values ​​for different triage results. For example, "headache" and "fatigue" may appear in the symptom description at the same time;

[0101] The triage results for “headache” included: neurology: 15%, emergency department: 16%, otolaryngology: 21%;

[0102] The triage results corresponding to "fatigue" include: emergency department: 13%, internal medicine: 17%, cardiovascular department: 5%;

[0103] The corresponding results are: Neurology: 15%, Emergency Department: 29%, Otolaryngology: 21%, Internal Medicine: 17%, and Cardiology: 5%.

[0104] The final recommendation was "Emergency Department".

[0105] S130, screening candidate hospitals based on the target department and the current location information;

[0106] The candidate hospitals are hospitals within a certain distance range from the current user. Since the purpose of this embodiment is to improve the speed of medical treatment, hospitals within 10-20 kilometers of the current location are selected as candidate hospitals for screening.

[0107] S140: Sampling the visit time period to obtain multiple candidate time points; using the multiple candidate time points as departure time points, estimating the estimated arrival time of the current user at the multiple candidate hospitals, and determining the estimated waiting time for the target department of the multiple candidate hospitals based on the estimated arrival time and a pre-constructed waiting time relationship curve for the multiple hospitals;

[0108] Since the corresponding travel time and waiting time may be different when using different departure times, in order to find the best travel time, the medical treatment time points are first sampled. Specifically, sampling is performed every 30 minutes to obtain multiple candidate time points.

[0109] Then this application estimates the arrival time by calling external third-party API interfaces, including:

[0110] Call the external navigation API interface, use multiple candidate time points as departure time points, the user's current location information as the starting point, and multiple candidate hospitals as the end point for navigation planning, and obtain the estimated arrival time of multiple travel modes for multiple options, where each option includes a candidate time point and a candidate hospital;

[0111] After obtaining the estimated arrival times for various travel options, in order to determine which travel mode the user will use (e.g., driving, taxi, public transportation, walking, etc.), the various options need to be sent to the current user for confirmation. For example, the message could read: "Please select your travel mode. Hospital A: driving, taxi, public transportation, walking; Hospital B: driving, taxi, public transportation, walking..."

[0112] When the travel mode selection information is received from the current user, the estimated arrival time of the current user at multiple candidate hospitals when the current user departs at multiple candidate time points is obtained.

[0113] Finally, the transportation method used to reach each hospital is determined based on the user's selection, and the estimated arrival time of the current user at multiple candidate hospitals is obtained when the current user departs at multiple candidate time points.

[0114] Since the waiting time may be different when arriving at the target department of the hospital at different times, the application determines the possible waiting time by pre-constructing the waiting time relationship curves of multiple hospitals for different estimated arrival times.

[0115] The process of constructing the waiting time relationship curve includes:

[0116] (1) Obtain multiple historical medical records from multiple departments of multiple hospitals, where the historical medical records include the registration time point and time of visit ;

[0117] Among them, historical medical records can also be extracted from the hospital information system (HIS) or electronic health record (EHR) system. The historical medical records record the various time points after the patient registers online, registers at the hospital, and then enters the clinic for treatment. Since this application needs to extract the waiting time, it mainly extracts the registration time point and time of visit .

[0118] (2) Based on the registration time of each historical medical record and time of visit Calculate the waiting time for each consultation ;

[0119]

[0120] (3) Calculate the average waiting time of multiple historical medical records within each natural day ;

[0121] The waiting time for consultation varies with the time of day and the time of year. For example, the waiting time in the morning is shorter, while that at other times is longer. Flu season and summer can cause the waiting time in some departments to be significantly longer. Therefore, in this application, we first calculate the first average waiting time of multiple historical consultation records in each natural day. , to find the changes in waiting time brought by different seasons.

[0122] (4) Based on the first average waiting time Cluster multiple historical medical records based on the registration time point to obtain multiple sample clusters;

[0123] (5) The sample cluster with the number of records greater than the preset threshold is taken as the target sample cluster, and the regular time period corresponding to the target sample cluster is extracted;

[0124] Through clustering, historical medical records with similar average waiting time and registration time points can be grouped together.

[0125] This application sets a smaller maximum distance within a cluster, so there may be some sample clusters with smaller data volumes. These sample clusters may be caused by unexpected collective injuries and other phenomena. In order to find the patterns under normal circumstances, this application removes these clusters with smaller data volumes and only retains sample clusters with a number of records within the cluster greater than the preset threshold as target sample clusters.

[0126] Each target sample cluster represents a regular time period, and the average waiting time in the regular time period is similar.

[0127] (6) Divide each natural day into multiple sub-time periods, and divide the historical medical records in the target sample cluster into multiple sub-time periods based on the registration time point to obtain multiple record units;

[0128] After finding the changes in the annual cycle, the changes in the corresponding natural daily cycle within each target sample cluster are analyzed.

[0129] First, the working time is divided into multiple sub-time periods. In this embodiment, since the sampling at the time point is performed every 30 minutes, 30 minutes is also used as the duration of the sub-time period here.

[0130] Then, the historical medical records of the target sample cluster are divided into corresponding sub-time periods to complete the second division.

[0131] (7) Calculate the second average waiting time of multiple historical medical records in each record unit and standard deviation ;

[0132] (8) Based on the second average waiting time and standard deviation Construct a reference range of waiting time for the corresponding sub-time period;

[0133] Since the average daily waiting time and registration time within the same target sample cluster are similar during clustering, the daily fluctuations of the consultation records within the target sample cluster are also roughly similar. The characteristics of gradually increasing waiting time from the morning and gradually decreasing before the end of the working day are met. Therefore, by calculating the second average waiting time of multiple historical consultation records in each record unit and standard deviation A reference range of three times the standard deviation was constructed.

[0134] (9) Construct a waiting time relationship curve for regular time periods based on the waiting time reference ranges of multiple sub-time periods.

[0135] After obtaining the reference range that reflects the regular changes, a fluctuation curve is constructed based on multiple reference ranges, including:

[0136] (9-1) Mapping the waiting time reference ranges of the multiple sub-time periods into a two-dimensional coordinate system to obtain a maximum waiting time line segment, an average waiting time line segment, and a minimum waiting time line segment for each sub-time period, wherein the horizontal axis of the two-dimensional coordinate system is the time axis, and the vertical axis of the two-dimensional coordinate system is the waiting time axis;

[0137] (9-2) Take the midpoint of the maximum waiting time segment, the midpoint of the average waiting time segment, and the midpoint of the minimum waiting time segment respectively to obtain the minimum value point, typical value point, and maximum value point of each sub-time period;

[0138] (9-3) The minimum value points, typical value points and maximum value points corresponding to multiple sub-time periods are fitted respectively to obtain the minimum reference curve, typical reference curve and maximum reference curve.

[0139] Figure 3 This is a schematic diagram of a waiting time relationship curve in an embodiment of the present application. The waiting time relationship curve finally constructed is as follows: Figure 3 shown.

[0140] Figure 4 This is a schematic diagram of the predicted waiting time in an embodiment of the present application. Figure 4 As shown in the figure, after obtaining the curve, we can use the estimated arrival time of the current user to predict the approximate waiting time, including:

[0141] S141, determining a waiting time relationship curve for a target department in each hospital in a regular time period based on the estimated arrival time;

[0142] S142, mapping the estimated arrival time to the two-dimensional coordinate system, obtaining the intersection of the estimated arrival time with the minimum reference curve, the typical reference curve, and the maximum reference curve, and obtaining the minimum reference duration , Typical reference duration and maximum reference duration .

[0143] When the estimated arrival time is mapped to the two-dimensional coordinate system, a vertical straight line can be generated, and the intersection of this straight line with the minimum reference curve, the typical reference curve and the maximum reference curve can be used as the prediction point. , Typical reference duration and maximum reference duration .

[0144] S150 , making a medical consultation decision based on the multiple candidate time points, the estimated arrival times of the multiple candidate hospitals, and the estimated waiting times of the target departments of the multiple candidate hospitals.

[0145] After obtaining multiple candidate departure times, travel times to multiple hospitals, and estimated waiting times, this application makes decisions based on time cost and risk cost, specifically including:

[0146] S151, constructing a candidate plan by starting from any candidate time point and reaching the target department of any candidate hospital;

[0147] For example, candidate plan 1 departs at 7:40 and is expected to arrive at the hospital in 1 hour. The estimated minimum reference waiting time is 10 minutes, the estimated typical reference waiting time is 13 minutes, and the estimated maximum reference waiting time is 18 minutes.

[0148] S152, calculate the time cost of each candidate solution and risk costs ;

[0149] Risk cost and maximum reference time Related, if there is a large maximum reference duration , it means that the user may have to wait for a long time, so the risk cost is high.

[0150] Time cost in this application and risk costs The calculation is done as follows:

[0151] S1521, calculate the reference total duration of each candidate solution, the reference total duration is the estimated arrival time and the typical reference time The estimated arrival time is the difference between the estimated arrival time and the candidate time point;

[0152] S1522, calculate the floating ratio of the waiting time of each candidate solution , ;

[0153] From the above formula, we can see that the risk cost and typical reference time in this application are and maximum reference duration Relevant and typical reference duration and maximum reference duration The greater the deviation, the higher the risk cost. The time cost is related to the total time predicted in the previous article.

[0154] S1523, normalize the reference total duration of multiple candidate solutions to obtain the time cost of multiple candidate solutions ; Floating ratio of waiting time for multiple candidate solutions Perform normalization to obtain the risk costs of multiple candidate solutions .

[0155] Since we only need to compare different solutions, we use normalization to compare the advantages and disadvantages of different solutions.

[0156] Normalization is performed using the min-max normalization algorithm.

[0157] S153, based on the time cost and the risk cost Calculate the evaluation value of each candidate solution ,in, , is the serial number of the candidate solution;

[0158] In general, the importance of time cost is greater than risk cost. In this embodiment, , .

[0159] S154: Taking the multiple candidate solutions with the highest evaluation values ​​as target solutions, and returning the target solutions to the current user.

[0160] Finally, the 3-5 most highly rated solutions are selected and returned to the target user for reference. If the target user selects one of the target solutions as the implementation solution, they can directly refer to the implementation solution to make an online appointment, travel, and wait. This application can greatly facilitate users' medical treatment and travel, saving users a lot of time by minimizing waiting time.

[0161] The present invention provides a medical strategy analysis method based on waiting time estimation, which parses the user's symptom description to obtain the target department that the user needs to make an appointment with. At the same time, the time required for the user to go to each hospital is analyzed according to the user's required time period, while taking into account the travel time and waiting time in the hospital. This application understands the congestion level and average waiting time of each hospital in a specific time period, and can guide patients to choose a more relaxed time period to make an appointment or register, thereby shortening the waiting time before the actual medical treatment and improving the efficiency of medical treatment. For patients with chronic diseases who need frequent medical treatment or who need to reach the hospital quickly in an emergency, this type of analysis can serve as an important decision-making reference to help choose the most suitable medical institution for the current situation. In addition, from a macro perspective, if a large number of users can make more reasonable medical choices based on this type of analysis, the resource utilization efficiency of the entire medical system will be improved, such as reducing the pressure on popular hospitals and balancing the patient flow between different medical institutions.

[0162] like Figure 5 As shown, the present application also provides a medical consultation strategy analysis system based on waiting time estimation, including:

[0163] An acquisition module is used to obtain the current user's medical treatment demand information and the user's current location information, wherein the medical treatment demand information includes a medical treatment time period and a symptom description;

[0164] a department determination module, configured to extract symptom entities from the symptom description and determine the target department with the highest probability for the current user based on the symptom entities and a symptom-department relationship data table, wherein the symptom-department relationship data table represents the corresponding probabilities between multiple symptom types and departments;

[0165] A preliminary screening module, configured to screen candidate hospitals based on the target department and the current location information;

[0166] A duration estimation module is configured to sample the visit time period to obtain multiple candidate time points; use the multiple candidate time points as departure time points, estimate the estimated arrival time of the current user at multiple candidate hospitals, and determine the estimated waiting time for the target department of the multiple candidate hospitals based on the estimated arrival time and a pre-constructed waiting time relationship curve for the multiple hospitals;

[0167] The decision-making module is used to make a medical treatment decision based on the multiple candidate time points, the estimated arrival time of the multiple candidate hospitals, and the estimated waiting time of the target departments of the multiple candidate hospitals.

[0168] The present invention provides a medical treatment strategy analysis method and system based on waiting time estimation, which parses the user's symptom description to obtain the target department that the user needs to make an appointment with. At the same time, the time required for the user to go to each hospital is analyzed according to the user's required time period, while taking into account the travel time and waiting time in the hospital. This application understands the congestion level and average waiting time of each hospital in a specific time period, and can guide patients to choose a more relaxed time period to make an appointment or register, thereby shortening the waiting time before the actual medical treatment and improving the efficiency of medical treatment. For patients with chronic diseases who need frequent medical treatment or who need to reach the hospital quickly in an emergency, this type of analysis can serve as an important decision-making reference to help choose the most suitable medical institution for the current situation. In addition, from a macro perspective, if a large number of users can make more reasonable medical treatment choices based on this type of analysis, the resource utilization efficiency of the entire medical system will be improved, such as reducing the pressure on popular hospitals and balancing the patient flow between different medical institutions.

[0169] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0170] The memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory, so that the terminal executes any one of the methods in this embodiment.

[0171] Regarding the computer-readable storage medium in this embodiment, those skilled in the art will appreciate that all or part of the steps in the aforementioned method embodiments can be implemented using hardware associated with the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps in the aforementioned method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0172] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used for communication, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes the various steps of the above method.

[0173] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage.

[0174] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0175] In the above embodiments, although the present invention has been described in conjunction with specific embodiments of the present invention, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. The embodiments of the present invention are intended to cover all such replacements, modifications and variations that fall within the broad scope of the appended claims.

[0176] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical principles disclosed herein are intended to be covered by the claims of the present invention.

Claims

1. A method for analyzing medical consultation strategies based on waiting time estimation, characterized in that: Including steps: Obtaining the current user's medical consultation demand information and the user's current location information, wherein the medical consultation demand information includes a consultation time period and a description of symptoms; Extracting symptom entities from the symptom description, and determining the target department with the highest probability for the current user based on the symptom entities and a symptom-department relationship data table, wherein the symptom-department relationship data table represents the corresponding probabilities between multiple symptom types and departments; Filter candidate hospitals based on the target department and the current location information; The medical consultation time period is sampled to obtain multiple candidate time points; the multiple candidate time points are used as departure time points to estimate the estimated arrival time of the current user at multiple candidate hospitals, and the estimated waiting time of the target departments of multiple candidate hospitals is determined based on the estimated arrival time and the pre-constructed waiting time relationship curve of multiple hospitals; the method for constructing the waiting time relationship curve includes: obtaining multiple historical medical consultation records of multiple departments of multiple hospitals, wherein the historical medical consultation records include the registration time point and time of visit ; Based on the registration time of each historical medical record and time of visit Calculate the waiting time for each consultation ; Calculate the first average waiting time of multiple historical medical records within each natural day ; Based on the first average waiting time Cluster multiple historical medical records based on the registration time point to obtain multiple sample clusters; select the sample cluster with a number of records greater than a preset threshold as the target sample cluster, and extract the regular time period corresponding to the target sample cluster; divide each natural day into multiple sub-time periods, and divide the historical medical records in the target sample cluster into multiple sub-time periods based on the registration time point to obtain multiple record units; calculate the second average waiting time of multiple historical medical records in each record unit and standard deviation Based on the second average waiting time and standard deviation Constructing a waiting time reference range for a corresponding sub-time period; constructing a waiting time relationship curve for a regular time period based on the waiting time reference ranges of multiple sub-time periods; the waiting time relationship curve includes a minimum reference curve, a typical reference curve and a maximum reference curve, and constructing a waiting time relationship curve for a regular time period based on the waiting time reference ranges of multiple sub-time periods, including: mapping the waiting time reference ranges of multiple sub-time periods into a two-dimensional coordinate system to obtain a maximum waiting time segment, an average waiting time segment and a minimum waiting time segment for each sub-time period, wherein the horizontal axis of the two-dimensional coordinate system is the time axis and the vertical axis of the two-dimensional coordinate system is the waiting time axis; taking the midpoint of the maximum waiting time segment, the midpoint of the average waiting time segment and the midpoint of the minimum waiting time segment respectively to obtain the minimum value point, typical value point and maximum value point of each sub-time period; fitting the minimum value points, typical value points and maximum value points corresponding to multiple sub-time periods respectively to obtain the minimum reference curve, the typical reference curve and the maximum reference curve; A medical consultation decision is made based on the multiple candidate time points, the estimated arrival times of the multiple candidate hospitals, and the estimated waiting times of the target departments of the multiple candidate hospitals.

2. A method for analyzing medical consultation strategies based on waiting time estimation according to claim 1, characterized in that: The method for constructing the symptom-department relationship data table includes: Access to triage records; Extracting a plurality of triage sample data from the triage record, wherein the triage sample data includes a symptom description sample and a triage result; Extracting a symptom entity sample from the symptom description sample, wherein the symptom entity sample includes a body part entity and a disease entity; Vectorizing the symptom entity sample to obtain a symptom entity sample vector; and mixing the symptom entity sample vector into a sample vector with a label for clustering to obtain multiple clusters, wherein the label is used to represent the symptom type; Using the labels of the sample vectors in the same cluster as the labels of the symptom entity sample vectors to obtain a plurality of labeled triage sample data; Divide the labeled multiple triage sample data based on the labels to obtain multiple data units; Calculate the triage results for each data unit Probability , ,in, For triage results The number of corresponding triage sample data, is the total number of triage sample data for the data unit, is the serial number of the triage result; A symptom-department relationship data table is constructed based on multiple labels and the probabilities of triage results corresponding to the multiple labels.

3. A method for analyzing medical consultation strategies based on waiting time estimation according to claim 2, characterized in that: Determining the target department with the highest probability for the current user based on the symptom entity and the symptom-department relationship data table includes: Marking the symptom entity to obtain a label of the symptom entity; Match the label of the symptom entity with the symptom-department relationship data table to obtain the probability of multiple triage results ; When there is only one symptom entity, the department in the triage result with the highest probability is extracted as the target department; when there are multiple symptom entities, the total probability of each triage result is calculated, and the department in the triage result with the highest total probability is taken as the target department.

4. The method for analyzing medical treatment strategies based on waiting time estimation according to claim 1, characterized in that: Determine the estimated waiting time for the target department at multiple candidate hospitals based on the estimated arrival time and pre-built waiting time relationship curves for multiple hospitals, including: Determine the waiting time relationship curve of the target department in each hospital in regular time periods based on the estimated arrival time; Map the estimated arrival time to the two-dimensional coordinate system to obtain the intersection of the estimated arrival time with the minimum reference curve, the typical reference curve, and the maximum reference curve to obtain the minimum reference duration. , Typical reference duration and maximum reference duration .

5. The method for analyzing medical treatment strategies based on waiting time estimation according to claim 4, characterized in that: Making a medical consultation decision based on the multiple candidate time points, the estimated arrival times of the multiple candidate hospitals, and the estimated waiting times of the target departments of the multiple candidate hospitals includes: Construct candidate plans by starting from any candidate time point and reaching the target department of any candidate hospital; Calculate the time cost of each candidate solution and risk costs ; Based on the time cost and the risk cost Calculate the evaluation value of each candidate solution ,in, , is the serial number of the candidate solution; The multiple candidate solutions with the highest evaluation values ​​are used as target solutions, and the target solutions are returned to the current user.

6. A method for analyzing medical consultation strategies based on waiting time estimation according to claim 5, characterized in that: Calculate the time cost of each candidate solution and risk costs ,include: Calculate the total reference duration for each candidate solution, which is the estimated arrival time and the typical reference time. The estimated arrival time is the difference between the estimated arrival time and the candidate time point; Calculate the waiting time fluctuation ratio for each candidate solution , ; Normalize the total reference duration of multiple candidate solutions to obtain the time cost of multiple candidate solutions ; Floating ratio of waiting time for multiple candidate solutions Perform normalization to obtain the risk costs of multiple candidate solutions .

7. The method for analyzing medical consultation strategies based on waiting time estimation according to claim 1, characterized in that: Using the multiple candidate time points as departure time points, estimating the estimated arrival time of the current user at the multiple candidate hospitals, including: Call the external navigation API interface, use multiple candidate time points as departure time points, the user's current location information as the starting point, and multiple candidate hospitals as the end point for navigation planning, and obtain the estimated arrival time of multiple travel modes for multiple options, where each option includes a candidate time point and a candidate hospital; When the travel mode selection information is received from the current user, the estimated arrival time of the current user at multiple candidate hospitals when the current user departs at multiple candidate time points is obtained.

8. A medical consultation strategy analysis system based on waiting time estimation, characterized in that: include: An acquisition module is used to obtain the current user's medical treatment demand information and the user's current location information, wherein the medical treatment demand information includes a medical treatment time period and a symptom description; a department determination module, configured to extract symptom entities from the symptom description and determine the target department with the highest probability for the current user based on the symptom entities and a symptom-department relationship data table, wherein the symptom-department relationship data table represents the corresponding probabilities between multiple symptom types and departments; A preliminary screening module, configured to screen candidate hospitals based on the target department and the current location information; The duration estimation module is used to sample the consultation time period to obtain multiple candidate time points; using the multiple candidate time points as departure time points, estimating the estimated arrival time of the current user at multiple candidate hospitals, and determining the estimated waiting time of the target departments of multiple candidate hospitals based on the estimated arrival time and the pre-constructed waiting time relationship curves of multiple hospitals; the construction method of the waiting time relationship curve includes: obtaining multiple historical consultation records of multiple departments of multiple hospitals, wherein the historical consultation records include the registration time point and time of visit ; Based on the registration time of each historical medical record and time of visit Calculate the waiting time for each consultation ; Calculate the first average waiting time of multiple historical medical records within each natural day ; Based on the first average waiting time Cluster multiple historical medical records based on the registration time point to obtain multiple sample clusters; select the sample cluster with a number of records greater than a preset threshold as the target sample cluster, and extract the regular time period corresponding to the target sample cluster; divide each natural day into multiple sub-time periods, and divide the historical medical records in the target sample cluster into multiple sub-time periods based on the registration time point to obtain multiple record units; calculate the second average waiting time of multiple historical medical records in each record unit and standard deviation Based on the second average waiting time and standard deviation Constructing a waiting time reference range for a corresponding sub-time period; constructing a waiting time relationship curve for a regular time period based on the waiting time reference ranges of multiple sub-time periods; the waiting time relationship curve includes a minimum reference curve, a typical reference curve and a maximum reference curve, and constructing a waiting time relationship curve for a regular time period based on the waiting time reference ranges of multiple sub-time periods, including: mapping the waiting time reference ranges of multiple sub-time periods into a two-dimensional coordinate system to obtain a maximum waiting time segment, an average waiting time segment and a minimum waiting time segment for each sub-time period, wherein the horizontal axis of the two-dimensional coordinate system is the time axis and the vertical axis of the two-dimensional coordinate system is the waiting time axis; taking the midpoint of the maximum waiting time segment, the midpoint of the average waiting time segment and the midpoint of the minimum waiting time segment respectively to obtain the minimum value point, typical value point and maximum value point of each sub-time period; fitting the minimum value points, typical value points and maximum value points corresponding to multiple sub-time periods respectively to obtain the minimum reference curve, the typical reference curve and the maximum reference curve; The decision-making module is used to make a medical treatment decision based on the multiple candidate time points, the estimated arrival time of the multiple candidate hospitals, and the estimated waiting time of the target departments of the multiple candidate hospitals.

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