A medical treatment behavior identification method, system and device in a different place and a storage medium

Abstract

This invention discloses a method, system, device, and storage medium for identifying out-of-town medical treatment behavior. The method includes: acquiring mobile phone signaling data of several objects and hospital AOI data of several objects; determining the location type of each object based on the mobile phone signaling data; determining hospital visitors and their corresponding hospital visitor categories based on the mobile phone signaling data and the hospital AOI data; identifying the medical treatment behavior of individuals within the hospital visitor categories based on the location type to determine the medical visitor category; and identifying and classifying the travel purpose of out-of-town medical treatment individuals within the medical visitor categories based on the level of medical resources in the city. This application helps improve the precision and accuracy of medical treatment behavior classification and can be widely applied in the field of resource allocation technology.

Description

Technical Field

[0001] This invention relates to the field of resource allocation technology, and in particular to a method, system, device, and storage medium for identifying out-of-town medical treatment behavior. Background Technology

[0002] With the development of big data and mobile communication technologies, mobile signaling data has gradually become an important data resource for research on human travel and urban mobility due to its advantages such as low cost, wide coverage, and strong real-time performance. Existing technical solutions propose some methods for identifying population movement based on mobile signaling data, such as identifying the unemployed, extracting urban residents' travel chains, identifying population migration, and characterizing travelers' work-residence locations. However, these methods are limited to the aforementioned aspects and cannot achieve population identification in more fields. Furthermore, these technical solutions often focus on a single data source or a single application scenario, lacking the comprehensive utilization of multi-source data, such as large-scale geospatial datasets integrating multi-source vector information (AOI, POI, etc.), which limits the ability to perform refined identification. Summary of the Invention

[0003] The purpose of this invention is to provide a sophisticated method, system, device, and storage medium for identifying out-of-town medical treatment behavior.

[0004] On the one hand, this application provides a method for identifying out-of-town medical treatment behavior. The method includes: acquiring mobile phone signaling data of several objects and hospital AOI data of several objects, and determining the location type of each object based on the mobile phone signaling data; determining hospital visitors and their corresponding hospital visitor categories based on the mobile phone signaling data and the hospital AOI data; identifying the medical treatment behavior of patients within the hospital visitor categories based on the location type to determine the patient category; and identifying and classifying the travel purpose of out-of-town medical treatment patients within the patient category based on the city's medical resource level. This application, based on mobile phone signaling data and hospital AOI data, achieves a fine-grained classification of hospital visitor categories and performs fine-grained identification of medical treatment behavior; this is beneficial for improving the precision and accuracy of medical treatment behavior classification.

[0005] Optionally, determining the hospital visitor and the corresponding hospital visitor category based on the mobile phone signaling data and the hospital AOI data includes:

[0006] Based on the travel and stay information in the mobile phone signaling data, determine whether the travel destination is located within the hospital AOI and filter hospital visitors.

[0007] Based on the mobile phone signaling data of the people visiting the hospital, and in conjunction with the location type, the category of the people visiting the hospital is determined.

[0008] Optionally, determining the category of hospital visitors based on the residency information of the mobile phone signaling data of the hospital visitors, combined with the location type, includes:

[0009] If the destination is located within the hospital's AOI (Area of ​​Interest), and the location type of the destination is "workplace," then the person is identified as a hospital employee.

[0010] Alternatively, if the destination is within a hospital AOI and the stay time within the hospital AOI is greater than or equal to the first duration, the person is identified as a patient seeking medical treatment.

[0011] Alternatively, if the destination is within a hospital AOI and the stay time within the hospital AOI is less than the first duration, the person is identified as a visitor.

[0012] Optionally, the step of identifying the medical visitor behavior of individuals within the hospital visitor category based on the location type to determine the medical visitor category includes:

[0013] Determine whether the place of visit and place of residence of the medical patient are in the same city, and determine whether the medical patient is a local medical patient or a medical patient from another place.

[0014] Based on the residency information of the out-of-town medical treatment population in the hospital's AOI (Automated Access Center), the characteristics of their medical treatment behavior are determined.

[0015] Optionally, determining the medical treatment behavior characteristics based on the residency information of the out-of-town medical treatment population in the hospital's AOI includes:

[0016] If the patient's arrival time is within a preset time and the patient's stay time at the hospital's AOI is less than the second duration, the patient is identified as an outpatient.

[0017] Alternatively, if the number of consecutive days a patient stays at the AOI (Area of ​​Inpatient Services) of the hospital is greater than or equal to the number of days on the first day, and the length of stay on any day during the stay is greater than or equal to the length of the second day, the patient is identified as an inpatient.

[0018] Optionally, the identification method of this application further includes:

[0019] The level of medical resources in different cities is determined based on the number of hospital beds.

[0020] Alternatively, the level of medical resources in different cities can be determined based on the number of doctors in the hospital.

[0021] Optionally, the city's medical resources are ranked from highest to lowest as Level 1, Level 2, Level 3, and Level 4. Based on the city's medical resource level, the movement purpose of out-of-town medical patients within the aforementioned patient population categories is identified and classified, including:

[0022] If the medical resource level of the city of residence is lower than that of the city of medical treatment, the medical resource level of the city of residence is either level three or level four, and the medical resource level of the city of medical treatment is either level one or level two, the purpose of the movement is determined to be to seek better treatment.

[0023] Alternatively, if the medical resources level of the city of residence is the first level and the medical resources level of the city of medical treatment is the first level, the purpose of the movement is determined to be to seek the best treatment;

[0024] Alternatively, if the medical resource level of the city of residence is higher than that of the city of medical treatment, the medical resource level of the city of medical treatment is either level three or level four, the medical resource level of the city of residence is either level one or level two, and the place of medical treatment is the place of household registration of the person seeking medical treatment, then the purpose of the movement is determined to be returning to one's hometown for medical treatment.

[0025] On the other hand, embodiments of the present invention propose a system for recognizing out-of-town medical treatment behavior, the system comprising:

[0026] The first module is used to acquire mobile phone signaling data of several objects and several hospital AOI data, and determine the location type of each object based on the mobile phone signaling data;

[0027] The second module is used to determine the hospital visitor and the corresponding hospital visitor population category based on the mobile phone signaling data and the hospital AOI data.

[0028] The third module is used to identify the medical visitor behavior of the medical visitor category based on the location type, and to determine the medical visitor category.

[0029] The fourth module is used to identify and classify the travel purpose of people seeking medical treatment in other places within the category of patients, based on the level of medical resources in the city.

[0030] On the other hand, embodiments of the present invention provide a device for identifying out-of-town medical treatment behavior, the device comprising:

[0031] At least one processor;

[0032] At least one memory for storing at least one program;

[0033] When the at least one program is executed by the at least one processor, the at least one processor implements the above-described method for identifying out-of-town medical treatment behavior.

[0034] On the other hand, embodiments of the present invention provide a storage medium storing a processor-executable program, which, when executed by a processor, is used to implement the above-described method for identifying out-of-town medical treatment behavior.

[0035] The method provided in this invention includes: acquiring mobile phone signaling data of several objects and hospital AOI data of several objects; determining the location type of each object based on the mobile phone signaling data; determining hospital visitors and their corresponding hospital visitor categories based on the mobile phone signaling data and the hospital AOI data; identifying the medical behavior of patients in the hospital visitor categories based on the location type to determine the medical visitor category; and identifying and classifying the travel purpose of out-of-town medical visitors in the medical visitor categories based on the city's medical resource level. This application, based on mobile phone signaling data and hospital AOI data, achieves a fine-grained classification of hospital visitor categories and a fine-grained identification of medical behavior; this is beneficial for improving the precision and accuracy of medical behavior classification. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following description is provided with accompanying drawings of the relevant technical solutions in the embodiments of the present invention or the prior art. It should be understood that the accompanying drawings described below are only for the purpose of clearly illustrating some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0037] Figure 1 A flowchart illustrating one embodiment of the method for recognizing out-of-town medical treatment behavior provided by the present invention;

[0038] Figure 2 A flowchart illustrating another embodiment of the method for recognizing out-of-town medical treatment behavior provided by the present invention;

[0039] Figure 3 A flowchart illustrating an embodiment of the classification of people seeking medical treatment in different locations based on their mobility purpose provided by the present invention.

[0040] Figure 4 A schematic diagram of the structure of an embodiment of the cross-regional medical treatment behavior recognition system provided by the present invention;

[0041] Figure 5 This is a schematic diagram of one embodiment of the cross-regional medical treatment behavior recognition device provided by the present invention. Detailed Implementation

[0042] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0043] With the development of big data and mobile communication technologies, mobile signaling data, due to its advantages of low cost, wide coverage, and strong real-time performance, has gradually become an important data resource for research on human travel and urban mobility. Among existing technical solutions, some patents have proposed methods for identifying population movement based on mobile signaling data, such as identifying unemployed individuals, extracting urban residents' travel chains, identifying population migration, and characterizing the work-residence locations of travelers. However, technical methods for identifying the travel behavior of people seeking medical treatment, especially those seeking treatment in other locations, have not yet been developed. Furthermore, these technical solutions often focus on a single data source or a single application scenario, lacking the comprehensive utilization of multi-source data, such as large-scale geospatial datasets (AOI, POI, etc.) integrating multi-source vector information, thus limiting the ability for refined identification. Specific problems include:

[0044] (1) Existing technologies for identifying the travel of people mainly rely on single mobile phone signaling data and fail to make full use of other data sources such as hospital AOI data, resulting in the inability to comprehensively and accurately identify the travel characteristics of the medical group.

[0045] (2) In terms of identifying people seeking medical treatment in other places, existing technical solutions mainly rely on medical insurance data. However, there are serious administrative barriers between administrative units at all levels in my country (such as between cities and provinces), making it difficult to obtain national-level data on medical treatment in other places directly through the medical insurance system. Therefore, it is impossible to comprehensively describe and analyze the characteristics of medical treatment in other places in my country. On the other hand, some technical solutions use some emerging data sources, such as online consultation platforms like "Mutual Aid" and "Good Doctor". However, these data often only involve some major diseases and cannot comprehensively reflect the situation of medical treatment in other places in my country.

[0046] (3) Existing technical solutions for medical treatment behavior are relatively singular in terms of research subjects. On the one hand, the target population is concentrated on the elderly or urban medical insurance employees, failing to cover other residents who need medical treatment. On the other hand, the content focuses on the accessibility of medical treatment travel and the layout of medical resources, lacking precise identification and classification of specific types of medical treatment groups, especially lacking in-depth analysis of the travel purpose of out-of-town medical treatment groups.

[0047] This invention aims to overcome the shortcomings of existing technologies. By combining mobile signaling data and hospital AOI data, it first provides a technical method for identifying large-scale cross-regional medical treatment populations, thus overcoming the deficiency of traditional data in terms of accuracy and coverage, which fails to comprehensively reflect the characteristics of cross-regional medical treatment in my country. Secondly, it achieves accurate identification of local and cross-regional medical treatment behaviors, as well as refined classification of hospital visitor groups. This method, while further improving identification accuracy and timeliness, also provides policy guidance and suggestions for the government in the establishment, service, and management of the healthcare system, enabling dynamic monitoring of cross-regional medical treatment at multiple scales with lower time and computational costs. Compared with existing technologies, this invention, by integrating ultra-large-scale multi-source data samples, provides a more comprehensive, in-depth, and scientifically accurate analytical method for the segmentation of hospital visitor categories and the identification of medical treatment behaviors.

[0048] The following describes in detail the method and system for identifying out-of-town medical treatment behavior according to embodiments of the present invention. First, the method for identifying out-of-town medical treatment behavior according to embodiments of the present invention will be described with reference to the accompanying drawings.

[0049] Reference Figure 1 This invention provides a method for identifying cross-regional medical treatment behavior, which mainly includes the following steps:

[0050] S100: Obtain mobile phone signaling data of several objects and several hospital AOI data, and determine the location type of each object based on the mobile phone signaling data;

[0051] S200: Based on mobile signaling data and hospital AOI data, determine the hospital visitors and the corresponding hospital visit population category;

[0052] S300: Identify the medical visitor behavior of people in the hospital visitor category based on location type to determine the medical visitor category;

[0053] S400: Based on the level of medical resources in the city, identify and classify the purpose of movement of people seeking medical treatment in other places within the category of medical patients.

[0054] The target audience in this application can be individuals holding mobile phones or other terminals. Hospital AOI data can be selected from hospitals in various cities as needed, or it can be AOI data from hospitals of other levels; this application does not limit the type or level of the hospital. The location type in this application can be residence, workplace, or place of visit. Hospital visitors are hospital visitors. Hospital visitor categories include hospital staff and patients. Patient categories include those seeking medical treatment in other locations and those seeking medical treatment locally.

[0055] Optionally, based on mobile signaling data and hospital AOI data, the hospital visitors and their corresponding hospital visitor categories are determined, including:

[0056] Based on travel and stay information in mobile phone signaling data, determine whether the travel destination is located within the hospital's AOI and filter hospital visitors;

[0057] Based on the mobile phone signaling data of those visiting the hospital, and combined with the location type, the category of the people visiting the hospital is determined.

[0058] Optionally, based on the mobile phone signaling data of hospital visitors and their location type, the categories of hospital visitors can be determined, including:

[0059] If the destination is located within the hospital's AOI (Area of ​​Interest), and the location type of the destination is "workplace," then the person is identified as a hospital employee.

[0060] Alternatively, if the destination is within the hospital's AOI (Automated Area Inspection) and the stay time within the hospital's AOI is greater than or equal to the first duration, the person is identified as a patient seeking medical treatment.

[0061] Alternatively, if the destination is within the hospital's AOI (Area of ​​Interest), and the stay time within the hospital's AOI is less than the first duration, the person is identified as a visitor.

[0062] This application considers individuals whose final destination is within the hospital's Area of ​​Interest (AOI) as hospital visitors. Based on residency information, each hospital visitor is categorized to obtain further subcategories of hospital visitor populations.

[0063] Optionally, based on location type, the medical visitor categories are identified to determine the medical visitor behavior, including:

[0064] Determine whether the place of visit and place of residence of the medical patient are in the same city, and determine whether the medical patient is a local medical patient or a medical patient from another place.

[0065] Based on the residency information of out-of-town patients in the hospital's AOI (Automated Access Center), the characteristics of their medical treatment behavior are determined.

[0066] In some possible implementations, if the location of the hospital visited is in the same city as the patient's place of residence, it is considered local medical treatment; if the location of the hospital visited is not in the same city as the patient's place of residence, it is considered out-of-town medical treatment.

[0067] Optionally, based on the residency information of out-of-town patients in the hospital's AOI (Area of ​​Interest), medical behavior characteristics can be determined, including:

[0068] If the patient's arrival time falls within the preset time and the patient's stay time at the hospital's AOI is less than the second duration, the patient is identified as an outpatient.

[0069] Alternatively, if a patient's continuous stay at the AOI (Area of ​​Inpatient Services) of the hospital is greater than or equal to the first day's number of days, and the stay time on any day during the stay period is greater than or equal to the second day's duration, the patient is identified as an inpatient.

[0070] The preset time is the hospital's normal operating hours. The second duration is the total time required for outpatient treatment. The first day's duration is the minimum time required for inpatient treatment.

[0071] Optionally, the identification method of this application further includes:

[0072] The level of medical resources in different cities is determined based on the number of hospital beds.

[0073] Alternatively, the level of medical resources in different cities can be determined based on the number of doctors in the hospital.

[0074] In this application, the medical resource level can represent the city's medical level and is determined based on the total number of beds in the city's hospitals. Alternatively, the medical resource level can be determined by a weighted sum based on the number of doctors in the hospital, or the weight of the number of doctors and their corresponding doctor levels.

[0075] Optionally, the city's medical resources are ranked from highest to lowest as Level 1, Level 2, Level 3, and Level 4. Based on the city's medical resource level, the movement purpose of out-of-town medical patients within the aforementioned patient population categories is identified and classified, including:

[0076] If the medical resource level of the city of residence is lower than that of the city of medical treatment, the medical resource level of the city of residence is either level three or level four, and the medical resource level of the city of medical treatment is either level one or level two, the purpose of the movement is determined to be to seek better treatment.

[0077] Alternatively, if the medical resources level of the city of residence is the first level and the medical resources level of the city of medical treatment is the first level, the purpose of the movement is determined to be to seek the best treatment;

[0078] Alternatively, if the medical resource level of the city of residence is higher than that of the city of medical treatment, the medical resource level of the city of medical treatment is either level three or level four, the medical resource level of the city of residence is either level one or level two, and the place of medical treatment is the place of household registration of the person seeking medical treatment, then the purpose of the movement is determined to be returning to one's hometown for medical treatment.

[0079] This application can rank the medical levels of several cities and then classify them into levels. For example, the medical resource levels of cities can be divided into four levels, ranked from highest to lowest: Level 1, Level 2, Level 3, and Level 4. Of course, the above is merely an example, and this application does not limit the number of levels. In this application, the place of medical treatment refers to the region / city where the hospital corresponding to the patient is located. The medical resource level of the city of residence refers to the medical resource level of the city where the patient resides long-term, and the medical resource level of the city of treatment refers to the medical level corresponding to the city where the patient receives treatment. It should be noted that in this application, the comma in the above conditional judgments indicates "and".

[0080] The following is a detailed description of the method for identifying out-of-town medical treatment behavior provided in this application, using a specific embodiment:

[0081] The purpose of this invention is to provide a method for accurately identifying cross-regional medical treatment behavior based on mobile phone signaling and hospital AOI data.

[0082] This invention uses ultra-large-scale mobile signaling data across the entire region at a national scale, and comprehensively applies methods such as data cleaning, topology correction, and geographic information science. It combines hospital AOI data with features such as dwell distribution and travel behavior during the mobile signaling sampling process to accurately identify the specific population categories and medical behavior of hospital visitors.

[0083] To achieve the above objectives, the present invention proposes a method for accurate identification of medical visitor behavior based on mobile phone signaling and hospital AOI data, which includes: preprocessing of mobile phone signaling and hospital AOI data, classification of hospital visitor types, identification of local and out-of-town medical visitors, and classification of the travel purpose of out-of-town medical visitors (see reference). Figure 2 ).

[0084] The basic steps of this invention are as follows:

[0085] S1. Preprocessing of mobile signaling data and hospital AOI data: including extraction of key user attributes and identification of location and residence type in mobile signaling, crawling and cleaning of hospital AOI data, etc.

[0086] S11. Prioritize acquiring mobile signaling data within the study area, then obtain the basic attribute information table of the corresponding mobile phone users from the operators, including daily dwell time, monthly dwell time, travel behavior, age, and number location. Extract the key core attribute information required for the research (Table 1), and identify the location dwell type of each user based on their monthly dwell time and travel information. The identification rules are mainly as follows:

[0087] (1) Residence: Between 9 pm and 8 am the next day, if a user stays at a certain location for the longest time, then that location is determined to be the user's residence.

[0088] (2) Work location: Between 9:00 AM and 5:00 PM on weekdays, if a user stays at a certain location for the longest time, that location will be determined as the user's work location;

[0089] (3) Visited locations: In addition to the above location types, all locations where users stay are judged as visited locations.

[0090] S12. First, based on the list of hospitals published by relevant departments, use web crawling tools to collect AOI data of all hospitals within the study area from Baidu Maps; then collect and count the number of beds in each hospital from official information platforms such as hospital websites and government websites, and finally form a hospital AOI dataset containing hospital name, AOI spatial geographic information and the number of beds.

[0091]

[0092] Table 1

[0093] S2. Hospital Visitor Segmentation: Identify hospital visitors based on their travel and stay information, and further categorize hospital visitors.

[0094] S21. Filter hospital visitors. Traverse the dataset and, based on mobile users' travel and stay information, count whether the user's travel destination is within the hospital's AOI, thereby filtering out hospital visitors;

[0095] S22. Categorize hospital visitors. Based on the dwell time attributes in mobile signaling data and the travel location type identified in S1, classify and identify hospital visitors. Combining the user location dwell type identified in S1, determine the location type corresponding to the user's travel destination, and then filter out the hospital visitor types. If the travel destination location type is "work," then it is determined as hospital staff; if it is a "visit" type, then it is determined as a patient or other visitor. To further accurately distinguish between patient and other visitors, the determination is based on the dwell time characteristics of the traveler at the hospital's AOI. The specific determination rules are as follows:

[0096] (1) Hospital staff: The destination is located within the hospital's AOI, and the location type of the destination is the work area type;

[0097] (2) Medical patients: The destination of the trip is located in the AOI of the hospital, and the stay time in the AOI of the hospital is greater than or equal to 30 minutes (i.e. the first duration);

[0098] (3) Other visitors: The destination is within the AOI of the hospital, but the stay time in the AOI is less than 30 minutes. This generally includes food delivery workers, temporary visitors, etc.

[0099] S3. Identification of Local-Out-of-Town Medical Treatment Population: Based on the location type of each user identified in S1, including place of residence, place of work, and place of visit, further identify the category and medical treatment behavior characteristics of the medical treatment population.

[0100] S31. Local-to-Out-of-Town Medical Treatment Behavior Identification: This involves determining whether the patient's destination and place of residence are in the same city, thus identifying whether their medical treatment is local or out-of-town. It includes the following two results:

[0101] (1) Local medical treatment: The place of visit is located in the AOI of the hospital, and the user's place of residence and the place of visit are in the same city;

[0102] (2) Seeking medical treatment in a different location: The destination is located in the AOI of the hospital, and the user's place of residence and the destination are in different cities. That is, the user travels from the city of residence to the AOI of the hospital in a city other than the city of residence to seek medical services.

[0103] S32. Classification of Out-of-Town Medical Treatment Population: For identified out-of-town medical treatment populations, their monthly residency information generated by the hospital's AOI is used to further determine whether they are inpatients or outpatients. This includes the following three results:

[0104] (1) Outpatients: Patients often arrive at the hospital in advance to wait (this invention sets it to 1 hour in advance). After the consultation, the stay may be extended due to matters such as picking up medication (this invention sets it to be extended by 0.5 hours). The normal working hours of the hospital are generally 8:00-12:00 in the morning and 1:30-5:30 in the afternoon. Therefore, this invention makes appropriate adjustments to the patient's arrival time during the identification process, and identifies patients who arrive between 7:00 in the morning and 6:00 in the afternoon (i.e., the preset time in this application) and whose total stay time on the same day is less than 11 hours (i.e., the second duration in this application) as outpatients.

[0105] (2) Inpatients: Inpatients often stay in the AOI of the hospital for consecutive days. Therefore, the present invention identifies patients who stay in the AOI of the hospital for three consecutive days (i.e., the first day in this application) or more, and whose total stay time per day is not less than 11 hours as inpatients.

[0106] (3) Other types: In addition to the two situations mentioned above, there are other groups of patients who, although they stay in the AOI (Area of ​​Inpatient) of the hospital for a relatively long time, do not stay continuously and do not meet the characteristics of either outpatients or inpatients. Since this group is not within the scope of this invention, it has been excluded.

[0107] S4. Classification of the purpose of travel for out-of-town medical treatment: Classifying the types of out-of-town medical treatment population based on the purpose of their travel. Figure 3 ).

[0108] S41. Measuring the level of quality medical resources in cities: The level of medical resources in different cities is measured by statistically analyzing the total number of hospital beds, and the results are divided into four levels using the natural breakpoint method: highest (level 1, i.e., the first level in this application), high (level 2, i.e., the second level in this application), low (level 3, i.e., the third level in this application), and lowest (level 4, i.e., the fourth level in this application).

[0109] S42. Classification of Out-of-Town Medical Treatment Behavior for Different Purposes: By comparing the level of medical resources A (i.e., the level of medical resources of the city of residence in this application) and the level of medical resources B (i.e., the level of medical resources of the city of treatment in this application) of the out-of-town medical treatment population, the purposes of out-of-town medical treatment behavior are classified, including the following four results:

[0110] (1) Seeking better treatment and diagnosis: In order to obtain better medical services than their city of residence, patients choose to go from their city of residence to a non-resident city with higher medical resources for treatment and diagnosis, i.e., A < B, and A ∈ level 3 or 4, B ∈ level 1 or 2.

[0111] (2) Seeking the best treatment and diagnosis: The patient’s city of residence has a high level of medical resources (A∈level 1), but in order to obtain the best medical services, he chooses to go to another non-resident city with a high level of medical resources (B∈level 1) for treatment and diagnosis, i.e. A&B∈level 1;

[0112] (3) Migrant workers returning home for medical treatment: Unlike the two medical treatment purposes mentioned above, many people choose to work and live in large or mega-cities year-round. However, due to the strict household registration system (including medical insurance system), these migrant workers are excluded. Therefore, they choose to return to their hometown cities for medical treatment during holidays and rest periods. However, the medical resources in these hometown cities are often not as abundant as those in their work cities. Therefore, this invention classifies this special phenomenon as a separate type and sets its judgment rules as follows: First, compare the medical resource levels of the patient's city of residence and the city of medical treatment. If the medical resource level of the city of residence is significantly higher than that of the city of medical treatment, i.e., A>B, and A∈level 1 or 2, B∈level 3 or 4; then, based on the user's registered residence city field in Table 1, determine whether the patient's city of medical treatment is the same as their registered residence city; if the above conditions are met, then this type of patient is defined as a migrant worker returning home for medical treatment.

[0113] (4) Occasional Out-of-Town Medical Treatment: In the rules for identifying the types of medical treatment for migrant workers returning to their hometowns, if the level of medical resources in the city of residence is significantly higher than that in the city of medical treatment (i.e., A > B), but the city of medical treatment is not the city where the patient's household registration is located, then they are identified as the group of people who seek medical treatment in occasional out-of-town medical treatment. This group of people travels to small cities with lower levels of medical resources than their place of household registration for purposes such as business trips, tourism, or visiting relatives and friends, and seek medical treatment in small cities during this period due to factors such as sudden illness.

[0114] Therefore, this application combines ultra-large-scale mobile signaling data and hospital AOI data to finely segment hospital visitors, including patients, hospital staff, and other visitors; it can accurately identify and distinguish local patients from out-of-town patients; based on monthly stay time and continuity determination, out-of-town patients are further subdivided into inpatients and outpatients; and by comparing the medical service levels of the cities where out-of-town patients reside and the cities where they receive treatment, the purpose of their medical treatment is further accurately identified and finely segmented.

[0115] Meanwhile, regarding the content identified, existing solutions for identifying population mobility using mobile signaling data primarily identify conventional information such as residence-employment location and permanent resident population distribution. However, there is no mature identification solution specifically for large-scale medical visit populations and behaviors. This solution is innovative and scientific in its identification content. In terms of data usage, this invention combines ultra-large-scale mobile signaling data and hospital AOI vector data, fully leveraging the advantages of big data such as the high-frequency updates and timestamp integrity of mobile signaling data and the precise geographical boundaries of AOI data to design a set of rules for identifying cross-regional medical visit behavior. This application identifies hospital visitors by fusing travel trajectories and geospatial information, thereby accurately identifying and finely classifying hospital visit population categories and medical visit behaviors. This invention identifies and classifies three categories of hospital visitors and, combined with real-world medical visit behavior patterns, comprehensively considers information such as user location type, stay time, and city affiliation in mobile signaling data to further refine the classification of medical visit population categories and purposes.

[0116] This invention fully leverages the advantages of large-scale mobile signaling data and hospital AOI data across the country to accurately identify the specific types of people visiting hospitals. By combining the city of residence and the city of treatment, it accurately identifies the population and spatial distribution of people seeking medical treatment in other locations across the country, and further precisely identifies and distinguishes between inpatients and outpatients within this population. By comparing the level of high-quality medical resources in the cities of residence and treatment, it precisely compares and quantifies four types of medical purposes for people seeking medical treatment in other locations.

[0117] Secondly, refer to the appendix Figure 4 A system for recognizing out-of-town medical treatment behavior according to an embodiment of the present invention is described, the system specifically comprising:

[0118] The first module 310 is used to acquire mobile phone signaling data of several objects and several hospital AOI data, and determine the location type of each object based on the mobile phone signaling data;

[0119] The second module 320 is used to determine the hospital visitors and the corresponding hospital visit population category based on mobile phone signaling data and hospital AOI data.

[0120] The third module 330 is used to identify the medical behavior of patients in the category of hospital visitors based on location type, and to determine the category of medical visitors.

[0121] The fourth module 340 is used to identify and classify the travel purpose of people seeking medical treatment in other places within the category of medical patients, based on the level of medical resources in the city.

[0122] It is evident that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0123] Reference Figure 5 This invention provides a device for recognizing out-of-town medical treatment behavior, the device comprising:

[0124] At least one processor 410;

[0125] At least one memory 420 is used to store at least one program;

[0126] When the at least one program is executed by the at least one processor 410, the at least one processor 410 implements the method for recognizing out-of-town medical treatment behavior.

[0127] Similarly, the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0128] This invention also provides a computer-readable storage medium storing a processor-executable program, which, when executed by a processor, is used to perform the aforementioned method for identifying out-of-town medical treatment behavior.

[0129] Similarly, the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0130] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is altered and sub-operations described as part of a larger operation are executed independently.

[0131] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0132] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several programs to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0133] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable programs for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, a program execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can retrieve and execute a program from or in conjunction with such a program execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit a program for use by or in conjunction with a program execution system, apparatus, or device.

[0134] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0135] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable program execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0136] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0137] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0138] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A method for identifying cross-regional medical treatment behavior, characterized in that, The method includes the following steps: Acquire mobile phone signaling data of several objects and AOI data of several hospitals, and determine the location type of each object based on the mobile phone signaling data; Based on the mobile phone signaling data and the hospital AOI data, the hospital visitors and the corresponding hospital visit population categories are determined. Based on the location type, the medical visitor behavior of the patients in the hospital visitor category is identified to determine the medical visitor category; Based on the level of medical resources in the city, the purpose of movement of people seeking medical treatment in other places within the aforementioned patient population category is identified and classified. Based on the mobile phone signaling data and the hospital AOI data, determine the hospital visitors and the corresponding hospital visitor categories, including: Based on the travel and stay information in the mobile phone signaling data, determine whether the travel destination is located within the hospital AOI and filter hospital visitors. Based on the mobile phone signaling data of the hospital visitors and the location type, the category of the hospital visitors is determined. The city's medical resources are ranked from highest to lowest as Level 1, Level 2, Level 3, and Level 4. Based on the city's medical resource level, the movement purpose of out-of-town medical patients within the aforementioned patient population categories is identified and classified, including: If the medical resource level of the city of residence is lower than that of the city of medical treatment, the medical resource level of the city of residence is either level three or level four, and the medical resource level of the city of medical treatment is either level one or level two, the purpose of the movement is determined to be to seek better treatment. Alternatively, if the medical resources level of the city of residence is the first level and the medical resources level of the city of medical treatment is the first level, the purpose of the movement is determined to be to seek the best treatment; Alternatively, if the medical resource level of the city of residence is higher than that of the city of medical treatment, the medical resource level of the city of medical treatment is either level three or level four, the medical resource level of the city of residence is either level one or level two, and the place of medical treatment is the place of household registration of the person seeking medical treatment, then the purpose of the movement is determined to be returning to one's hometown for medical treatment.

2. The method for identifying cross-regional medical treatment behavior according to claim 1, characterized in that, Based on the mobile phone signaling data of the hospital visitors and the location type, the categories of hospital visitors are determined, including: If the destination is located within the hospital's AOI (Area of ​​Interest), and the location type of the destination is "workplace," then the person is identified as a hospital employee. Alternatively, if the destination is within a hospital AOI and the stay time within the hospital AOI is greater than or equal to the first duration, the person is identified as a patient seeking medical treatment. Alternatively, if the destination is within a hospital AOI and the stay time within the hospital AOI is less than the first duration, the person is identified as a visitor.

3. The method for identifying cross-regional medical treatment behavior according to claim 1, characterized in that, Based on the location type, the medical visitor categories are identified to determine the medical visitor behavior and the medical visitor categories, including: Determine whether the place of visit and place of residence of the medical patient are in the same city, and determine whether the medical patient is a local medical patient or a medical patient from another place. Based on the residency information of the out-of-town medical treatment population in the hospital's AOI (Automated Access Center), the characteristics of their medical treatment behavior are determined.

4. The method for identifying cross-regional medical treatment behavior according to claim 3, characterized in that, The step of determining medical treatment behavior characteristics based on the residency information of the out-of-town medical treatment population in the hospital's AOI includes: If the patient's arrival time is within a preset time and the patient's stay time at the hospital's AOI is less than the second duration, the patient is identified as an outpatient. Alternatively, if the number of consecutive days a patient stays at the AOI (Area of ​​Inpatient Services) of the hospital is greater than or equal to the number of days on the first day, and the length of stay on any day during the stay is greater than or equal to the length of the second day, the patient is identified as an inpatient.

5. The method for identifying cross-regional medical treatment behavior according to claim 1, characterized in that, The method further includes: The level of medical resources in different cities is determined based on the number of hospital beds. Alternatively, the level of medical resources in different cities can be determined based on the number of doctors in the hospital.

6. A system for recognizing out-of-town medical treatment behavior, characterized in that, The system includes: The first module is used to acquire mobile phone signaling data of several objects and several hospital AOI data, and determine the location type of each object based on the mobile phone signaling data; The second module is used to determine the hospital visitor and the corresponding hospital visitor population category based on the mobile phone signaling data and the hospital AOI data. The third module is used to identify the medical visitor behavior of the medical visitor category based on the location type, and to determine the medical visitor category. The fourth module is used to identify and classify the travel purpose of people seeking medical treatment in other places within the category of patients, based on the level of medical resources in the city. Based on the mobile phone signaling data and the hospital AOI data, determine the hospital visitors and the corresponding hospital visitor categories, including: Based on the travel and stay information in the mobile phone signaling data, determine whether the travel destination is located within the hospital AOI and filter hospital visitors. Based on the mobile phone signaling data of the hospital visitors and the location type, the category of the hospital visitors is determined. The city's medical resources are ranked from highest to lowest as Level 1, Level 2, Level 3, and Level 4. Based on the city's medical resource level, the movement purpose of out-of-town medical patients within the aforementioned patient population categories is identified and classified, including: If the medical resource level of the city of residence is lower than that of the city of medical treatment, the medical resource level of the city of residence is either level three or level four, and the medical resource level of the city of medical treatment is either level one or level two, the purpose of the movement is determined to be to seek better treatment. Alternatively, if the medical resources level of the city of residence is the first level and the medical resources level of the city of medical treatment is the first level, the purpose of the movement is determined to be to seek the best treatment; Alternatively, if the medical resource level of the city of residence is higher than that of the city of medical treatment, the medical resource level of the city of medical treatment is either level three or level four, the medical resource level of the city of residence is either level one or level two, and the place of medical treatment is the place of household registration of the person seeking medical treatment, then the purpose of the movement is determined to be returning to one's hometown for medical treatment.

7. A device for recognizing out-of-town medical treatment behavior, characterized in that, The device includes: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method for recognizing out-of-town medical treatment behavior as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a processor-executable program, characterized in that, The processor-executable program, when executed by the processor, is used to implement the method for recognizing out-of-town medical treatment behavior as described in any one of claims 1 to 5.

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