Remote doctor seeing behavior identification method, system and device and storage medium

Abstract

The invention discloses a remote medical treatment behavior identification method, system and device and a storage medium. The method comprises the following steps: acquiring mobile phone signaling data of a plurality of objects and a plurality of hospital AOI data, and determining the position type of each object according to the mobile phone signaling data; according to the mobile phone signaling data and the hospital AOI data, determining a hospital visited object and a hospital visited crowd type corresponding to the hospital visited object; according to the position type, performing medical-seeing behavior identification on the medical-seeing personnel in the hospital visiting crowd type, and determining a medical-seeing crowd type; and according to the medical resource level grade of the city, identifying and classifying the moving purpose of the remote doctor-seeing crowd in the doctor-seeing crowd category. The method is beneficial to improving the precision and accuracy of medical treatment behavior division, and can be widely applied to the technical field of resource layout.

Description

Technical Field

[0001] The present invention relates to the field of resource layout technology, and in particular to a method, system, device and storage medium for identifying remote medical treatment behavior. Background Art

[0002] With the development of big data and mobile communication technologies, mobile phone signaling data, with 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. Existing technical solutions have proposed a number of crowd identification methods based on mobile phone signaling data, such as identifying unemployed people through user mobile phone signaling data, extracting travel chains of urban residents, identifying population migration, and characterizing travelers' work and residence. However, crowd identification is limited to the aforementioned aspects and cannot be applied to more areas. 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 (AOIs, POIs, etc.) that integrate multi-source vector information, limiting their ability to achieve refined identification. Summary of the Invention

[0003] The purpose of the present invention is to provide a sophisticated method, system, device and storage medium for identifying medical treatment behavior in a different place.

[0004] On the one hand, the present application provides a method for identifying out-of-town medical treatment behaviors, the method comprising: obtaining mobile phone signaling data and hospital AOI data of several objects, and determining the location type of each of the objects based on the mobile phone signaling data; determining the hospital visiting objects and the hospital visiting population categories corresponding to the hospital visiting objects based on the mobile phone signaling data and the hospital AOI data; identifying the medical behavior of medical personnel in the hospital visiting population category based on the location type, and determining the medical population category; identifying and classifying the movement purposes of out-of-town medical treatment population in the medical population category based on the city's medical resource level. Based on mobile phone signaling data and hospital AOI data, the present application achieves a fine division of hospital visiting medical population categories, and simultaneously performs a fine identification of medical behavior; it is conducive to improving the fineness and accuracy of medical behavior division.

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

[0006] Based on the travel and residence information in the mobile phone signaling data, determine whether the travel destination is within the hospital AOI and screen hospital visitors;

[0007] The category of the hospital visitor population is determined based on the residency information of the mobile phone signaling data of the hospital visitor in combination with the location type.

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

[0009] If the destination is within the hospital AOI and the location type of the destination is workplace, the person is determined to be a hospital staff member;

[0010] Alternatively, if the destination of the trip is within the hospital AOI and the residence time within the hospital AOI is greater than or equal to the first duration, the person is determined to be seeking medical treatment;

[0011] Alternatively, if the destination of the trip is within the hospital AOI and the residence time within the hospital AOI is less than the first duration, the person is determined to be a visitor.

[0012] Optionally, identifying the medical behavior of medical personnel in the hospital visitor population category according to the location type to determine the medical visitor population category includes:

[0013] Determine whether the medical personnel's visiting place and residence are in the same city, and determine whether the medical personnel are local medical personnel or out-of-town medical personnel;

[0014] The medical behavior characteristics are determined based on the residency information of the out-of-town medical treatment crowd in the hospital AOI.

[0015] Optionally, determining the medical behavior characteristics based on the residency information of the non-local medical treatment group in the hospital AOI includes:

[0016] If the visit time of the medical personnel is within the preset time, and the stay time of the medical personnel in the hospital AOI is less than the second time period, the medical personnel is determined to be an outpatient;

[0017] Alternatively, if the number of consecutive days the medical personnel stays in the hospital AOI is greater than or equal to the first number, and the stay time on any day during the stay period is greater than or equal to the second duration, the medical personnel is determined to be an inpatient.

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

[0019] Determine the level of medical resources in different cities based on the number of hospital beds;

[0020] Alternatively, the medical resource levels of different cities are determined based on the number of doctors in the hospital.

[0021] Optionally, the medical resource levels of the cities are ranked from high to low as first level, second level, third level, and fourth level. Based on the medical resource levels of the cities, the movement purposes of the out-of-town medical treatment population in the medical treatment population category are 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, or the medical resource level of the city of residence is either the third or fourth level, and the medical resource level of the city of medical treatment is either the first or second level, the purpose of movement is determined to be seeking better treatment;

[0023] Alternatively, if the medical resource level of the residential city is the first level and the medical resource level of the medical treatment city is the first level, the purpose of movement is determined to be seeking the best treatment;

[0024] Alternatively, if the medical resource level of the residential city is greater than that of the medical resource level of the medical treatment city, the medical resource level of the medical treatment city is either the third level or the fourth level, the medical resource level of the residential city is either the first level or the second level, and the medical treatment place is the registered place of the medical person, the purpose of movement is determined to be returning home for medical treatment.

[0025] On the other hand, an embodiment of the present invention provides a system for identifying medical treatment behavior in a different place, the system comprising:

[0026] The first module is used to 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;

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

[0028] The third module is used to identify the medical behavior of the medical personnel in the hospital visitor population category according to the location type and determine the medical population category;

[0029] The fourth module is used to identify and classify the movement purposes of the out-of-town medical treatment population in the medical population category according to the city's medical resource level.

[0030] On the other hand, an embodiment of the present invention provides a device for identifying medical treatment behavior in a different place, 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-mentioned method for identifying medical treatment behavior in a different place.

[0034] On the other hand, an embodiment of the present invention provides a storage medium storing a program executable by a processor, wherein the program executable by the processor is used to implement the above-mentioned method for identifying medical treatment behavior in a different place when executed by the processor.

[0035] The method provided by the embodiment of the present invention includes: obtaining mobile phone signaling data and hospital AOI data of several objects, and determining the location type of each of the objects based on the mobile phone signaling data; determining the hospital visit objects and the hospital visitor population category corresponding to the hospital visit objects based on the mobile phone signaling data and the hospital AOI data; identifying the medical behavior of medical personnel in the hospital visitor population category based on the location type to determine the medical population category; identifying and classifying the movement purpose of the out-of-town medical population in the medical population category based on the medical resource level of the city. This application realizes the fine division of the medical population category visiting the hospital based on mobile phone signaling data and hospital AOI data, and at the same time finely identifies the medical behavior; it is conducive to improving the fineness and accuracy of the medical behavior division. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following introduction is made to the drawings of the embodiments of the present invention or the related technical solutions in the prior art. It should be understood that the drawings introduced below are only for the convenience of clearly describing 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 work.

[0037] Figure 1 A flow chart of an embodiment of the method for identifying medical treatment in a different place provided by the present invention;

[0038] Figure 2 A flow chart of another embodiment of the method for identifying medical treatment behavior in a different place provided by the present invention;

[0039] Figure 3 A flow chart of an embodiment of the present invention for classifying and dividing people seeking medical treatment in different places based on their mobility purpose;

[0040] Figure 4 A schematic diagram of the structure of an embodiment of the system for identifying medical treatment behavior in a different place provided by the present invention;

[0041] Figure 5 This is a structural diagram of an embodiment of the device for identifying medical treatment behavior in a different place provided by the present invention. DETAILED DESCRIPTION

[0042] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided for ease of explanation only and do not limit the order of the steps. The order of execution of the steps 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 technology, mobile phone signaling data has gradually become an important data resource for research on human travel and urban mobility due to its advantages of low cost, wide coverage and strong real-time performance. Among the existing technical solutions, some patents have proposed methods for identifying people's travel based on mobile phone signaling data, such as identifying unemployed people through users' mobile phone signaling data, extracting travel chains of urban residents, identifying population migration, and characterizing the work and residence of travelers. However, technical methods for identifying the travel behavior of people seeking medical treatment, especially those seeking medical treatment in other places, have not yet been developed. At the same time, these technical solutions often focus on a single data source or a single application scenario, and lack the comprehensive use of multi-source data, such as large-scale geospatial data sets (AOI, POI, etc.) that integrate multi-source vector information, which limits the ability in refined identification. Specific problems include:

[0044] (1) Existing technologies for crowd travel identification mainly rely on single mobile phone signaling data and fail to fully utilize other data sources such as hospital AOI data, resulting in the inability to fully and accurately identify the travel characteristics of medical groups;

[0045] (2) In terms of identifying people who seek medical treatment outside their hometown, 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), which makes it difficult to directly obtain data on medical treatment outside their hometown at a national scale through the medical insurance system. Therefore, it is impossible to fully describe and analyze the characteristics of medical treatment outside their hometown in my country. On the other hand, some technical solutions use some emerging data sources, such as online consultation platforms such as "Mutual Bao" and "Good Doctor", but these data often only involve some major diseases and cannot comprehensively reflect the situation of medical treatment outside their hometown in my country.

[0046] (3) The existing technical solutions for medical treatment behavior are relatively simple in terms of research objects. On the one hand, the population objects are concentrated in the elderly group or urban medical insurance employees, and fail to cover other resident groups that need medical treatment. On the other hand, the content objects focus on the accessibility of medical travel, the layout of medical resources, etc., and there is a lack of accurate identification and classification of specific types of medical groups, especially the lack of in-depth analysis of the travel purposes of groups seeking medical treatment in other places.

[0047] The present invention aims to make up for the shortcomings of the existing technology. By combining mobile phone signaling data and hospital AOI data, it first provides a technical method for identifying large-scale people seeking medical treatment in other places, making up for the defect that traditional data cannot fully reflect the characteristics of medical treatment in other places in my country due to insufficient accuracy and coverage; secondly, it realizes the accurate identification of local medical treatment and medical treatment in other places, as well as the refined classification of hospital visitor categories. On the basis of further improving the recognition accuracy and timeliness, this method further provides policy guidance and suggestions to the government in the establishment, service and management of the medical care system, and realizes dynamic monitoring of medical treatment in other places at multiple scales with lower time and computing costs. Compared with the existing technology, the present invention provides a more comprehensive, in-depth and scientific and accurate analysis method in terms of the subdivision of hospital visitor categories and the identification of medical behavior by integrating ultra-large-scale multi-source data samples.

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

[0049] Reference Figure 1 In an embodiment of the present invention, a method for identifying medical treatment behavior in a different place is provided, and the method mainly includes the following steps:

[0050] S100: Obtaining mobile phone signaling data of several objects and AOI data of several hospitals, and determining the location type of each object based on the mobile phone signaling data;

[0051] S200: Determine hospital visitor objects and hospital visitor population categories corresponding to the hospital visitor objects based on mobile phone signaling data and hospital AOI data;

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

[0053] S400: Identify and classify the movement purposes of people seeking medical treatment in other places within the category of medical treatment people according to the level of medical resources in the city.

[0054] The objects in this application can be people who hold mobile phones and other terminals. The hospital AOI data can be selected according to the needs of the hospitals in each city, or it can be the AOI data of hospitals of other levels. This application does not limit the type and level of the hospital. The location type in this application can be residence, work place, and visit place. The objects of hospital visits are hospital visitors. The categories of hospital visitors are hospital staff and medical personnel. The categories of medical population are people seeking medical treatment in other places and people seeking medical treatment locally.

[0055] Optionally, based on the mobile phone signaling data and the hospital AOI data, determining the hospital visitor and the hospital visitor population category corresponding to the hospital visitor includes:

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

[0057] The categories of hospital visitors are determined based on the residency information of the hospital visitors' mobile phone signaling data and their location types.

[0058] Optionally, the hospital visitor category is determined based on the residency information of the mobile phone signaling data of the hospital visitor and the location type, including:

[0059] If the destination is within the hospital AOI and the location type of the destination is workplace, the person is determined to be a hospital staff member;

[0060] Alternatively, if the destination of the trip is within the hospital AOI and the residence time within the hospital AOI is greater than or equal to the first duration, the person is determined to be seeking medical treatment;

[0061] Alternatively, if the destination of the trip is within the hospital AOI and the residence time within the hospital AOI is less than the first duration, the person is determined to be a visitor.

[0062] This application considers people whose travel destination is within the hospital's AOI as hospital visitors. Combined with the residency information, each hospital visitor is categorized to obtain the subdivided hospital visitor population categories.

[0063] Optionally, the medical behavior of the medical personnel in the hospital visitor population category is identified according to the location type to determine the medical population category, including:

[0064] Determine whether the place of visit and residence of the medical personnel are in the same city, and determine whether the medical personnel are local medical personnel or those seeking medical treatment in other places;

[0065] The characteristics of medical treatment behavior are determined based on the residence information of people seeking medical treatment in other places in the hospital AOI.

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

[0067] Optionally, based on the residency information of people seeking medical treatment in other places in the hospital AOI, the medical behavior characteristics are determined, including:

[0068] If the patient's visit time is within the preset time, and the patient's stay time in the hospital AOI is less than the second time period, the patient is determined to be an outpatient;

[0069] Alternatively, if the number of consecutive days that the medical person stays in the hospital AOI is greater than or equal to the first number, and the stay time on any day during the stay period is greater than or equal to the second duration, the person is determined to be an inpatient.

[0070] The default time is the hospital's normal business hours, the second time is the total time required for outpatient treatment, and the first time is the minimum time required for inpatient treatment.

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

[0072] Determine the level of medical resources in different cities based on the number of hospital beds;

[0073] Alternatively, the medical resource levels of different cities are determined based on the number of doctors in the hospital.

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

[0075] Optionally, the medical resource levels of the cities are ranked from high to low as first level, second level, third level, and fourth level. Based on the medical resource levels of the cities, the movement purposes of the out-of-town medical treatment population in the medical treatment population category are 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, or the medical resource level of the city of residence is either the third or fourth level, and the medical resource level of the city of medical treatment is either the first or second level, the purpose of movement is determined to be seeking better treatment;

[0077] Alternatively, if the medical resource level of the residential city is the first level and the medical resource level of the medical treatment city is the first level, the purpose of movement is determined to be seeking the best treatment;

[0078] Alternatively, if the medical resource level of the residential city is greater than that of the medical resource level of the medical treatment city, the medical resource level of the medical treatment city is either the third level or the fourth level, the medical resource level of the residential city is either the first level or the second level, and the medical treatment place is the registered place of the medical person, the purpose of movement is determined to be returning home for medical treatment.

[0079] This application can rank the medical levels of several cities and then classify them into levels. For example, the level of urban medical resources can be divided into four levels, which are the first level, the second level, the third level and the fourth level from high to low in order of medical level. Of course, the above are illustrative examples, and this application does not limit the number of level divisions. In this application, the place of medical treatment is the region / city where the corresponding medical hospital of the medical personnel is located. The medical resource level of the city of residence is the medical resource level of the city where the medical personnel have lived for a long time, and the medical resource level of the city of medical treatment is the medical level corresponding to the city where the medical personnel receive medical treatment. It should be noted that in this application, the comma in the above conditional judgment means and.

[0080] The following is a detailed description of the method for identifying medical treatment behavior in a different place provided by this application using a specific embodiment:

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

[0082] This paper uses ultra-large-scale mobile phone signaling data from 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 and characteristics such as residence distribution and travel behavior during mobile phone signaling sampling 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 accurately identifying medical behavior based on mobile phone signaling and hospital AOI data, which includes: pre-processing of mobile phone signaling and hospital AOI data, classification of hospital visitor types, identification of local and remote medical treatment groups, and classification of the movement purpose of remote medical treatment groups (refer to Figure 2 ).

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

[0085] S1. Preprocessing of mobile phone signaling data and hospital AOI data: including extraction of key attributes of mobile phone signaling users and identification of location residency types, crawling and cleaning of hospital AOI data, etc.

[0086] S11. Prioritize obtaining mobile phone signaling data within the study area. Then, obtain the basic attribute information table of the corresponding mobile phone users from the operator, including the user's daily residence, monthly residence, travel behavior, age, number of ownership, etc., and then extract the key core attribute information required for the study (Table 1). Based on the user's monthly residence and travel information, identify each user's location residence type. The identification rules are mainly as follows:

[0087] (1) Residence: Between 9 pm and 8 am the next day, if a user spends the longest time at a certain location, that location is determined as the user's residence;

[0088] (2) Workplace: Between 9:00 a.m. and 5:00 p.m. on weekdays, if a user spends the longest time at a certain location, that location is determined to be the user's work location;

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

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

[0091]

[0092] Table 1

[0093] S2. Classification of hospital visitor types: Identify hospital visitor users based on their travel and residence trajectory information, and further classify hospital visitor types;

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

[0095] S22. Classify the hospital visitor groups. Classify and identify the hospital visitor groups based on the residence time attribute in the mobile phone signaling data and the travel location type identified in S1. Combined with the user location residence type identified in S1, determine the location type corresponding to the user's travel destination, and then filter out the hospital visitor group types. If the travel destination location type is work, it is determined to be a hospital staff member; if it is a visiting type, it is determined to be a medical staff member or other visitor. In order to further accurately distinguish between medical staff and other visitors, judgment is made based on the traveler's residence time characteristics in the hospital AOI. The specific judgment rules are as follows:

[0096] (1) Hospital staff: The trip destination is within the hospital AOI, and the location type of the trip destination is a work location type;

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

[0098] (3) Other visitors: The destination of their trip is within the hospital AOI, but their stay time in the hospital AOI is less than 30 minutes. They generally include deliverymen, temporary visitors, etc.

[0099] S3. Identification of local and out-of-town medical populations: Based on the location type of each user identified in S1, including residence, work, and visited places, the categories and medical behavior characteristics of the medical population are further identified.

[0100] S31. Identification of local and out-of-town medical treatment behavior: Determine whether the place of visit and residence of the medical treatment population are in the same city, thereby identifying whether their medical treatment behavior is local or out-of-town. This includes the following two results:

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

[0102] (2) Non-local medical treatment: The visited location is located in the hospital AOI, and the user's residence and the visited location are in different cities, that is, the user travels from the city of residence to the hospital AOI in a non-residential city to seek medical services;

[0103] S32. Classification of people seeking medical treatment outside their hometown: For the identified people seeking medical treatment outside their hometown, further determine whether they are inpatients or outpatients based on their monthly residence information generated by the hospital's AOI. This includes the following three results:

[0104] (1) Outpatients: Patients often go to the hospital in advance to wait for medical treatment (the present invention sets it to 1 hour in advance). After the consultation, the hospital stay may be extended due to matters such as picking up medicine (the present 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, the present invention makes appropriate adjustments to the patient's visit time during the identification process, and identifies the medical crowd who visit between 7:00 a.m. and 6:00 p.m. (i.e., the preset time in this application) and whose total stay time on that day is less than 11 hours (i.e., the second time length in this application) as outpatients;

[0105] (2) Inpatients: Inpatients tend to stay in the hospital AOI for consecutive days. Therefore, the present invention identifies those who stay in the hospital AOI 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 above two types, some other patients may stay in the hospital AOI for a long time, but this stay is not continuous and does not meet the characteristics of either outpatients or inpatients. Since this type of patient is not within the scope of this study, it is excluded.

[0107] S4. Classification of the purpose of movement of people seeking medical treatment in other places: Classify the types of people seeking medical treatment in other places based on the purpose of movement ( Figure 3 ).

[0108] S41. Measuring the Level of Quality Medical Resources in Cities: The level of medical resources in different cities is measured by counting the total number of hospital beds. 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 behaviors by purpose: By comparing the medical resource level A of the city where the out-of-town medical treatment population resides (i.e., the medical resource level of the city of residence in this application) with the medical resource level B of the destination city (i.e., the medical resource level of the city of medical treatment in this application), the purpose of out-of-town medical treatment behaviors is classified, including the following four results:

[0110] (1) Seeking better treatment and diagnosis: In order to obtain better medical services relative to their city of residence, patients choose to move from their city of residence to a non-residential city with higher levels of medical resources for treatment and diagnosis, that is, A < B, and A∈level 3or4, B∈level 1or 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 or she chooses to go to another non-resident city (B∈level 1) with a high level of medical resources for treatment and diagnosis, that is, A&B∈level 1;

[0112] (3) Migrant workers returning to their hometowns for medical treatment: Different from the above two medical treatment purposes, many people choose to work and live in large or megacities all year round. However, since the social welfare system (including the medical insurance system) is strictly based on household registration, these migrant workers will be excluded. Therefore, they will choose to return to their hometowns for medical treatment during holidays and work breaks. The medical resources in these hometowns are often not as rich as those in their working cities. Therefore, the present invention classifies this special phenomenon as a type and sets its judgment rule as follows: first, compare the medical resource level 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, that is, A>B, and A∈level 1or 2, B∈level3or 4; then further judge whether the patient's city of medical treatment is the city of household registration according to the user's registered city field in Table 1; if the above conditions are met, this type of patient is defined as a migrant worker returning to his hometown for medical treatment.

[0113] (4) Occasional medical treatment in other places: In the rules for identifying the types of medical treatment sought by 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, that is, A>B, but the city of medical treatment does not meet the condition that the patient's registered residence is the city of the patient, then they will be identified as those who seek medical treatment in other places occasionally. This type of people travel to small cities with lower medical resources than their registered residence for business trips, tourism, visiting relatives and friends, etc., and during this period, due to factors such as sudden illness, they seek medical treatment in small cities.

[0114] It can be seen from this that this application combines ultra-large-scale mobile phone signaling data and hospital AOI data to make a detailed division of the hospital visiting population, including medical patients, hospital staff and other visiting populations; it can accurately identify and distinguish between local medical patients and non-local medical patients; combined with the determination of monthly residence time and continuity, the non-local medical population is further subdivided into inpatients and outpatients; by comparing the medical service levels of the cities where non-local medical patients live and the cities where they receive medical treatment, the medical purposes of non-local medical patients are further accurately identified and finely divided.

[0115] At the same time, in terms of identification content, there are already schemes for identifying the mobility of the population through mobile phone signaling data, which mostly identify conventional content such as residence-employment place, permanent population distribution, etc., but there is no mature identification scheme for large-scale medical population and behavior. This scheme is innovative and scientific in terms of identification content. In terms of data usage, the present invention combines ultra-large-scale mobile phone signaling data and hospital AOI vector data, gives full play to the big data advantages of high-frequency updates and timestamp integrity of mobile phone signaling data, precise geographic boundaries of AOI data, and designs a set of rules for identifying out-of-town medical behavior. This application identifies hospital visitors through the fusion of travel trajectories and geographic spatial information, and then accurately identifies and finely divides the categories of hospital visitors and medical behavior. The present invention identifies and divides three categories of hospital visitors, and combines the actual medical behavior rules, comprehensively considers information such as user residence location type, residence time and city affiliation in mobile phone signaling data, and further finely divides the categories and purposes of medical population.

[0116] This invention leverages the advantages of large-scale national mobile phone signaling data and hospital AOI data to accurately identify specific types of hospital visitors. By combining the cities of residence and medical treatment, it accurately identifies the population and spatial pattern of people seeking medical treatment outside their hometowns at a national level, and further accurately identifies and differentiates between inpatients and outpatients within this population. By comparing the level of high-quality medical resources in the cities of residence and medical treatment, it accurately compares and quantifies the four types of medical purposes of people seeking medical treatment outside their hometowns.

[0117] Secondly, refer to the attached Figure 4 A system for identifying medical treatment behavior in a different place proposed according to an embodiment of the present invention is described. The system specifically includes:

[0118] The first module 310 is used to 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;

[0119] The second module 320 is used to determine the hospital visitor and the hospital visitor group category corresponding to the hospital visitor based on the mobile phone signaling data and the hospital AOI data;

[0120] The third module 330 is used to identify the medical behavior of the medical personnel in the hospital visitor population category according to the location type and determine the medical population category;

[0121] The fourth module 340 is used to identify and classify the movement purposes of people seeking medical treatment in other places among the medical population categories according to the level of medical resources in the city.

[0122] It can be seen that the contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments 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.

[0123] Reference Figure 5 The embodiment of the present invention provides a device for identifying medical treatment behavior in a different place, the device comprising:

[0124] at least one processor 410;

[0125] at least one memory 420, for storing 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 identifying out-of-town medical treatment behavior.

[0127] Similarly, the contents of the above method embodiments are applicable to the present device embodiments. The functions specifically implemented by the present device embodiments 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] An embodiment of the present invention further provides a computer-readable storage medium, which stores a program executable by a processor. When the program executable by the processor is executed by the processor, it is used to execute the above-mentioned method for identifying medical treatment behavior in a different place.

[0129] Similarly, the contents of the above method embodiments are applicable to the present storage medium embodiment. The functions specifically implemented by the present storage medium 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.

[0130] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operation and logic flow presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0131] In addition, although the present invention is 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 separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the ordinary skill of an engineer. Therefore, a person skilled in the art will be able to implement the present invention set forth in the claims using ordinary skill without undue experimentation. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0132] If the functions are implemented in the form of 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 the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several programs for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

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

[0134] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0135] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable program execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0136] In the above description of this specification, reference to the terms "one embodiment / example," "another embodiment / example," or "certain embodiments / examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0137] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0138] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A method for identifying out-of-town medical treatment behavior, characterized in that: The method comprises the following steps: Obtaining mobile phone signaling data of a plurality of objects and a plurality of hospital AOI data, and determining the location type of each of the objects based on the mobile phone signaling data; Determine, based on the mobile phone signaling data and the hospital AOI data, hospital visitor objects and hospital visitor population categories corresponding to the hospital visitor objects; Identifying the medical behavior of medical personnel in the hospital visitor population category according to the location type to determine the medical population category; According to the medical resource level of the city, the movement purpose of the people seeking medical treatment in other places in the above-mentioned medical population category is identified and classified.

2. The method for identifying medical treatment in a different place according to claim 1, characterized in that: Determining hospital visitor objects and hospital visitor population categories corresponding to the hospital visitor objects based on the mobile phone signaling data and the hospital AOI data includes: Based on the travel and residence information in the mobile phone signaling data, determine whether the travel destination is within the hospital AOI and screen hospital visitors; The category of the hospital visitor population is determined based on the residency information of the mobile phone signaling data of the hospital visitor in combination with the location type.

3. The method for identifying medical treatment behavior in a different place according to claim 2, characterized in that: Determine the category of hospital visitors based on the residency information of the mobile phone signaling data of the hospital visitors and the location type, including: If the destination is within the hospital AOI and the location type of the destination is workplace, the person is determined to be a hospital staff member; Alternatively, if the destination of the trip is within the hospital AOI and the residence time within the hospital AOI is greater than or equal to the first duration, the person is determined to be seeking medical treatment; Alternatively, if the destination of the trip is within the hospital AOI and the residence time within the hospital AOI is less than the first duration, the person is determined to be a visitor.

4. The method for identifying medical treatment in a different place according to claim 1, characterized in that: Identifying the medical behavior of medical personnel in the hospital visitor population category according to the location type to determine the medical population category includes: Determine whether the medical personnel's visiting place and residence are in the same city, and determine whether the medical personnel are local medical personnel or out-of-town medical personnel; The medical behavior characteristics are determined based on the residency information of the out-of-town medical treatment crowd in the hospital AOI.

5. The method for identifying medical treatment in a different place according to claim 4, characterized in that: Determining the medical behavior characteristics based on the residency information of the non-local medical treatment group in the hospital AOI includes: If the visit time of the medical personnel is within the preset time, and the stay time of the medical personnel in the hospital AOI is less than the second time period, the medical personnel is determined to be an outpatient; Alternatively, if the number of consecutive days the medical personnel stays in the hospital AOI is greater than or equal to the first number, and the stay time on any day during the stay period is greater than or equal to the second duration, the medical personnel is determined to be an inpatient.

6. The method for identifying medical treatment in a different place according to claim 1, characterized in that: The method further comprises: Determine the level of medical resources in different cities based on the number of hospital beds; Alternatively, the medical resource levels of different cities are determined based on the number of doctors in the hospital.

7. The method for identifying medical treatment in a different place according to claim 1, characterized in that: The medical resource levels of cities are ranked from high to low as first, second, third, and fourth levels. Based on the medical resource levels of the cities, the mobility purposes of the medical population seeking medical treatment in other places are identified and classified, including: If the medical resource level of the city of residence is lower than that of the city of medical treatment, or the medical resource level of the city of residence is either the third or fourth level, and the medical resource level of the city of medical treatment is either the first or second level, the purpose of movement is determined to be seeking better treatment; Alternatively, if the medical resource level of the residential city is the first level and the medical resource level of the medical treatment city is the first level, the purpose of movement is determined to be seeking the best treatment; Alternatively, if the medical resource level of the residential city is greater than that of the medical resource level of the medical treatment city, the medical resource level of the medical treatment city is either the third level or the fourth level, the medical resource level of the residential city is either the first level or the second level, and the medical treatment place is the registered place of the medical person, the purpose of movement is determined to be returning home for medical treatment.

8. A system for identifying medical treatment behavior in a different place, characterized by: The system comprises: The first module is used to 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; The second module is used to determine the hospital visitor and the hospital visitor group category corresponding to the hospital visitor based on the mobile phone signaling data and the hospital AOI data; The third module is used to identify the medical behavior of the medical personnel in the hospital visitor population category according to the location type and determine the medical population category; The fourth module is used to identify and classify the movement purposes of the out-of-town medical treatment population in the medical population category according to the city's medical resource level.

9. A device for identifying medical treatment behavior in a different place, characterized in that: The device comprises: 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 identifying out-of-town medical treatment behavior as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement the method for identifying medical treatment behavior in a different place as described in any one of claims 1 to 7 when executed by the processor.

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