CN114493954A - Analysis method for medical service utilization of remote patient seeking medical treatment - Google Patents
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Abstract
The embodiment of the invention relates to an analysis method for medical service utilization of a patient in a different place for hospitalizing, which comprises the following steps: acquiring address information and hospitalizing hospital information of a patient in a different place hospitalizing a target city; determining longitude and latitude coordinates corresponding to the address information; determining the average optimal distance from each patient effluent grade city to the target city; determining distance attenuation coefficients of medical hospitals of different grades, different types and different located functional areas in a target city; determining average radiation distances of medical hospitals of different grades, different types and different located functional areas in the target city; and analyzing the medical service utilization of the hospitalizing of the remote patients based on the hospitalizing amount, the distance attenuation coefficient and the average radiation distance of the remote patients of the hospitalizing hospitals with different grades, different types and different located functional areas in the target city. The technical scheme provided by the invention can accurately analyze the medical service utilization condition of the patient at different places when the patient is hospitalized.
Description
Technical Field
The embodiment of the invention relates to the field of geography and medicine intersection, in particular to an analysis method for medical service utilization of a patient in a different place for seeking medical advice.
Background
In the related art, the ways of studying the hospitalization of the patient at different places generally include questionnaire, statistical analysis, panel data analysis, and measurement and economic regression model analysis, and such study ways may result in study results that do not accurately reflect the difference of the medical service capabilities of different hospitals in the target city (i.e. the patient flows into the city).
Therefore, there is a need for an analysis method for medical service utilization for remote patient hospitalization to solve the above technical problems.
Disclosure of Invention
In order to accurately analyze the medical service utilization condition of the remote patient for hospitalizing, the embodiment of the invention provides an analysis method for the medical service utilization of the remote patient for hospitalizing.
The embodiment of the invention provides an analysis method for medical service utilization of a patient in a different place for hospitalizing, which comprises the following steps:
acquiring address information and hospitalizing hospital information of a patient at a different place hospitalized in a target city; the target city is a local city into which the patient flows, the local city in which the allopatric patient is located is a local city out of which the patient flows, and the information of the hospitalizing hospital comprises the grade and type of the hospitalizing hospital and the functional area in which the hospitalizing hospital is located;
determining longitude and latitude coordinates corresponding to the address information based on the address information;
for each patient effluent grade city, determining the average optimal distance from the current patient effluent grade city to the target city based on the longitude and latitude coordinates of the allopatric patients belonging to the current patient effluent grade city;
determining distance attenuation coefficients of medical hospitals of different grades, different types and different located functional areas in the target city based on the remote patient visit amount of each patient's effluent grade city in the target city and the average optimal distance from each patient's effluent grade city to the target city; wherein the distance attenuation coefficient is used for representing the influence of the distance on the hospitalization of the allopatric patient;
determining average radiation distances of medical hospitals of different grades, different types and different located functional areas in the target city based on the average optimal distance from the different patient outflow grade cities to the target city;
and analyzing the medical service utilization of the hospitalizing of the remote patient based on the hospitalizing amount, the distance attenuation coefficient and the average radiation distance of the remote patient of the hospitalizing hospitals with different grades, different types and different located functional areas in the target city.
In one possible design, the determining longitude and latitude coordinates corresponding to the address information based on the address information includes:
analyzing the address information by using a Geocoder API function of a Baidu map position service to obtain a first hundred-degree coordinate and a first unresolved address;
carrying out doorplate number removal and/or town name removal on the first unresolved address, and resolving the processed first unresolved address by using a Geocoder API function of a Baidu map location service to obtain a second Baidu coordinate and a second unresolved address;
positioning the second unresolved address to the town to obtain a third hundred-degree coordinate and a third unresolved address;
manually positioning the third unresolved address to obtain a fourth hundred-degree coordinate;
and carrying out space correction transformation on the first hundred-degree coordinate, the second hundred-degree coordinate, the third hundred-degree coordinate and the fourth hundred-degree coordinate to obtain a longitude and latitude coordinate corresponding to the address information.
In one possible design, the spatial correction transformation is by way of an affine transformation function, the affine transformation function being:
x'＝Ax+By+C
y'＝Dx+Ey+F
wherein x and y are the hundred degree coordinates before transformation, x 'and y' are the longitude and latitude coordinates after transformation, A, B, C, D, E and F are determined by the position relationship between the source control point and the target control point, and the hundred degree coordinates are corrected by the position relationship between the source control point and the target control point.
In one possible design, the determining an average optimal distance from the current patient effluent grade city to the target city based on longitude and latitude coordinates of off-site patients belonging to the current patient effluent grade city comprises:
processing the longitude and latitude coordinates of each allopatric patient belonging to the current patient outflow grade city to the longitude and latitude coordinates of the hospitalizing hospitals of different grades, different types and different functional areas in the target city by using a route planning API function of the Baidu map to obtain the optimal distance from each allopatric patient to the hospitalizing of the target city;
and averaging the optimal distance from each allopatric patient to the target city for hospitalization to obtain the average optimal distance from the current patient to the target city.
In one possible design, the determining distance attenuation coefficients of medical hospitals of different levels, different types and different located functional areas in the target city based on the off-site patient visit volume of each patient effluent city in the target city and the average optimal distance from each patient effluent city to the target city comprises:
aiming at each medical hospital with different grades, different types and different functional areas in the target city, fitting the remote patient visit amount of each patient in the target city and the average optimal distance from each patient in the target city by using a plurality of preset distance attenuation models to obtain a regression coefficient and a distance attenuation coefficient of each distance attenuation model;
and taking the distance attenuation model with the maximum regression coefficient as a target distance attenuation model, and taking the distance attenuation coefficient of the target distance attenuation model as the distance attenuation coefficient of each medical hospital.
In one possible design, the distance decay model includes an open-square exponential model, an exponential model, a squared exponential model, a Pareto model, and a constant logarithm model.
In one possible design, the mean radiation distance is determined by the following equation:
in the formula, n represents the number of patients in each hospitalization hospital to flow out of the grade city, xiShowing the proportion of the allopatric patients who flow out of the grade city of the ith patient in each hospitalization hospital to all the allopatric patients, diRepresents the average optimal distance from the i-th patient in each hospital to the target city.
The embodiment of the invention provides an analysis method for medical service utilization of hospitalizing of a patient at a different place, which comprises the steps of obtaining address information and hospitalizing hospital information of a patient at a different place hospitalizing in a target city, determining longitude and latitude coordinates corresponding to the address information based on the address information, and further determining the average optimal distance from a grade city of patients to the target city based on the longitude and latitude coordinates of the patient at the different place of the grade city of patients; then, based on the remote patient visit amount of each patient's effluent grade city in the target city and the average optimal distance from each patient's effluent grade city to the target city, determining distance attenuation coefficients of the hospitalization hospitals of different grades, different types and different functional areas in the target city; and the average radiation distance of the hospitalizing hospitals in different grades, different types and different located functional areas in the target city can be determined based on the average optimal distance from the grade city to the target city where different patients flow. Therefore, the medical service utilization of the remote patient for hospitalizing can be analyzed based on the remote patient hospitalizing amount, the distance attenuation coefficient and the average radiation distance of the hospitalizing hospitals with different grades, different types and different located functional areas in the target city, and the medical service utilization condition of the remote patient for hospitalizing can be accurately analyzed.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a method for analyzing medical service utilization for remote patient hospitalization according to an embodiment of the present invention;
fig. 2 is a flowchart of determining longitude and latitude coordinates corresponding to address information according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention, and based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without creative efforts belong to the scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a method for analyzing medical service utilization of a patient at a different location for medical treatment, where the method includes:
step 100: acquiring address information and hospitalizing hospital information of a patient in a different place hospitalizing a target city; the target city is a local city into which the patient flows, the local city in which the patient in the different place is located is a local city out of which the patient flows, and the information of the medical treatment hospital comprises the grade and type of the medical treatment hospital and the functional area in which the medical treatment hospital is located;
step 102: determining longitude and latitude coordinates corresponding to the address information based on the address information;
step 104: aiming at each patient outflow grade city, determining the average optimal distance from the current patient outflow grade city to a target city based on the longitude and latitude coordinates of the allopatric patients belonging to the current patient outflow grade city;
step 106: determining distance attenuation coefficients of medical hospitals of different grades, different types and different functional areas in the target city based on the remote patient visit amount of each patient's effluent grade city in the target city and the average optimal distance from each patient's effluent grade city to the target city; the distance attenuation coefficient is used for representing the influence of the distance on the hospitalization of the allopatric patient;
step 108: determining average radiation distances of medical hospitals of different grades, different types and different located functional areas in the target city based on the average optimal distance from the different patients to the target city;
step 110: and analyzing the medical service utilization of the hospitalization of the patient at the different place based on the hospitalization amount, the distance attenuation coefficient and the average radiation distance of the patient at the hospitalization hospital at different grades, different types and different located functional areas in the target city.
In the embodiment of the invention, by acquiring the address information of the patient at the different place where the patient is hospitalized in the target city and the information of the hospitalizing hospital, the longitude and latitude coordinates corresponding to the address information can be determined based on the address information, and further the average optimal distance from the grade city to the target city where the patient flows out can be determined based on the longitude and latitude coordinates of the patient at the different place where the patient flows out of the grade city; then, based on the remote patient visit amount of each patient's effluent grade city in the target city and the average optimal distance from each patient's effluent grade city to the target city, determining distance attenuation coefficients of the hospitalization hospitals of different grades, different types and different functional areas in the target city; and the average radiation distance of the hospitalizing hospitals in different grades, different types and different located functional areas in the target city can be determined based on the average optimal distance from the grade city to the target city where different patients flow. Therefore, the medical service utilization of the patients in different places can be analyzed based on the different-place patient hospitalization amount, the distance attenuation coefficient and the average radiation distance of the hospitalization hospitals in different grades, different types and different functional areas in the target city, and the hospitalization medical service utilization condition of the patients in different places can be accurately analyzed.
The manner in which the various steps shown in fig. 1 are performed is described below.
With respect to step 100:
taking the target city as an example of Beijing, the prefecture cities except Beijing are called patient-outflowing prefecture cities, and the patients except Beijing are called allopatric patients.
The Beijing city has 16 areas, can be divided into four functional areas, namely an urban core area (east city area and West city area), which is the location of a central government and a financial institution; "urban extension" areas (open-air, lake, mountain, and plateau), which are locations where most higher education institutions and high-tech companies are located; the areas of "new development" (a mountain area, a Tongzhou area, an cis-conducting area, a Chang-Ping area, a Daxing area) and "child care" (a gate head ditch area, a Huairou area, a Pinggu area, a Miyun area, a Yanqing area) are relatively underdeveloped and are often regarded as suburbs in Beijing. The case data is from the health department of Beijing, and mainly comprises the address information and the information of the hospitalization hospitals of the patients at different places, wherein the information of the hospitalization hospitals comprises the grades (including, for example, the second grade and the third grade), the types (including, for example, comprehensive, special and traditional Chinese medicine) and the functional areas (including, for example, the core area, the expansion area, the suburban area and the suburban area).
With respect to step 102:
one of the remarkable features of remote patient hospitalization is that based on the movement of spatial position, the determination of patient's departure grade city is mainly the spatialization processing of the patient's present address based on text description. Because the acquired address information of the patient at different places is original data, the original data may have the problems of fuzzy information, inaccuracy, mismatching of identity information and address information, input errors (such as wrongly written characters), missing filling, inconsistent format (such as correct format: county-township (committee) -village (street)), and the like.
To solve the above problem, referring to fig. 2, in some embodiments, step 102 may include:
analyzing the address information by using a Geocoder API function of a Baidu map position service to obtain a first hundred-degree coordinate and a first unresolved address;
carrying out doorplate number removal and/or town name removal on the first unresolved address, and resolving the processed first unresolved address by using a Geocoder API function of a Baidu map location service to obtain a second Baidu coordinate and a second unresolved address;
positioning the second unresolved address to the town to obtain a third hundred-degree coordinate and a third unresolved address;
manually positioning the third unresolved address to obtain a fourth hundred-degree coordinate;
and carrying out space correction transformation on the first hundred-degree coordinate, the second hundred-degree coordinate, the third hundred-degree coordinate and the fourth hundred-degree coordinate to obtain a longitude and latitude coordinate corresponding to the address information.
In this embodiment, the embodiment of the present invention performs address resolution and accurate matching on address information of different patients by using a Geocoding method in combination with a Geocoder API function of a hundred-degree map location service, and maps the address information described in the text to a unified geographic space, thereby realizing location space accuracy of the source of each patient.
Specifically, an address positioning model is adopted to convert text address information (namely, address information) into hundred-degree coordinates, the model is input into a detailed address text, and the model is output as a hundred-degree coordinate data set. Therefore, the Chinese character processing can be carried out on the address which is in line with or not in line with the address analysis, the address which is not in the Baidu map database and the address which is input by the user and has errors, and finally the Baidu coordinate is obtained. The address location model uses a Geocoder () Function to create an address resolver instance, and uses a getPoint (address: String, callback: Function, city: String) method to resolve the appointed address, so as to obtain the corresponding hundredth coordinate of the address.
Since the Baidu coordinate and the real GPS coordinate (i.e., the longitude and latitude coordinate) have a deviation, the Baidu coordinate needs to be converted into the longitude and latitude coordinate in order to obtain an accurate longitude and latitude coordinate corresponding to the address information.
In some embodiments, the spatial correction transformation is by way of an affine transformation function, the affine transformation function being:
x'＝Ax+By+C
y'＝Dx+Ey+F
wherein x and y are the hundred degree coordinates before transformation, x 'and y' are the longitude and latitude coordinates after transformation, A, B, C, D, E and F are determined by the position relationship between the source control point and the target control point, and the hundred degree coordinates are corrected by the position relationship between the source control point and the target control point.
In this embodiment, scaling, tilting, rotating, and translating data to different degrees can be realized by the coordinate conversion method of the affine transformation function, so that the shifted hundred-degree coordinate can be transformed into a GPS coordinate, input as a hundred-degree coordinate point and a control point having a real coordinate, and output as a corrected GPS coordinate.
With respect to step 104:
because the optimal routes from different patients to the target city are different, for example, the transportation selected by a certain hospital from a baoding (i.e. the patients flow out of the grade city) to Beijing (i.e. the target city) is a high-speed rail + subway, and the optimal route is a running route of the high-speed rail + subway (i.e. the high-speed rail is taken to reach the Beijing high-speed rail station, and then the Beijing high-speed rail station takes the subway to reach the certain hospital); for example, the transportation means selected from the Lhasa (i.e. the patient leaves the prefecture city) to the Beijing hospital is the airplane and the automobile, and the optimal route is the driving route of the airplane and the automobile (i.e. the airplane is taken to arrive at the Beijing airport, and then the automobile is taken to arrive at the hospital from the Beijing airport).
In order to accurately calculate the average optimal distance from the different patient outflow cities to the target city, in some embodiments, step 104 may include:
processing the longitude and latitude coordinates of each allopatric patient belonging to the current patient outflow grade city to the longitude and latitude coordinates of the hospitalizing hospitals of different grades, different types and different functional areas in the target city by using a route planning API function of the Baidu map to obtain the optimal distance from each allopatric patient to the hospitalizing of the target city;
and averaging the optimal distance from each allopatric patient to the target city for hospitalization to obtain the average optimal distance from the current patient to the target city.
In this embodiment, the optimal distance from each different-place patient to the target city for hospitalization can be determined by using the API function for route planning of the Baidu map, and then the most significant distances of all patients who flow out of the grade city are averaged, so as to obtain the average optimal distance from the current patient to the target city.
The averaging process may be an arithmetic average or a weighted average, and is not particularly limited herein.
In addition, the route planning API function of the Baidu map can be referred to the website:
https://lbsyun.baidu.com/index.phptitle＝webapi/direction-api-v2。
for step 106:
in some embodiments, step 106 may comprise:
aiming at each medical hospital with different grades, different types and different functional areas in the target city, fitting the remote patient visit amount of each patient in the target city and the average optimal distance from each patient to the target city by using a plurality of preset distance attenuation models to obtain a regression coefficient and a distance attenuation coefficient of each distance attenuation model;
and taking the distance attenuation model with the maximum regression coefficient as a target distance attenuation model, and taking the distance attenuation coefficient of the target distance attenuation model as the distance attenuation coefficient of each medical hospital.
In this embodiment, the distance attenuation model is used to fit the remote patient visit amount of each patient in the target city and the average optimal distance from each patient in the target city, so as to obtain the regression coefficient of each distance attenuation model, and thus the distance attenuation coefficient of the distance attenuation model with the best fitting effect (i.e. the maximum regression coefficient) can be used as the distance attenuation coefficient of each medical hospital, so as to obtain a more accurate analysis result of medical service utilization.
It should be noted that distance attenuation is a geographic phenomenon describing a decrease with an increase in distance under a spatial interaction, distance attenuation coefficient is a geographic phenomenon describing a strength of an effect of distance on the spatial interaction (in the embodiment of the present invention, the distance attenuation coefficient is used to characterize an effect of distance on hospitalization of an allopatric patient), and a larger distance attenuation coefficient indicates a more significant attenuation effect with an increase in distance.
In some embodiments, the distance decay model includes an open-square exponential model, an exponential model, a squared exponential model, a Pareto model, and a constant logarithm model, which are respectively formulated as:
log I＝a-βd
log I＝a-βd2
log I＝a-βlog d
log I＝a-β(log d)2
in the formula, I represents the amount of remote patient visits from each city-level region unit to beijing, d is the average optimal distance from the local city to the target city, a is a scalar factor (a is calculated by using historical data in advance, namely a is a known amount), and β represents a distance attenuation coefficient. Beta reflects the speed of distance decay, and the larger the value, the larger the influence of the distance on the remote medical volume.
In addition, the OLS (linear regression) method can be used to fit the regression coefficient of the linear model, using the determination coefficient R2And measuring the goodness of fit of the model. R2The dependent variable variation part of the regression model interpretation, i.e.
R2＝ESS/TSS＝1-RSS/TSS
Where TSS is the total square sum and ESS is the explanatory square sum.
For step 108:
in some embodiments, the average radiation distance is determined by the following equation:
in the formula, n represents the number of patients in each hospitalization hospital to flow out of the grade city, xiShowing the proportion of the allopatric patients who flow out of the grade city of the ith patient in each hospitalization hospital to all the allopatric patients, diRepresenting the average optimal distance from the i-th patient in each hospital to the target city.
It should be noted that the above formula is based on the smith center standard distance theory and the formula to calculate the average radiation distance of each kind of medical hospitals in beijing. That is, taking Beijing as the center, every 100km of road mileage is added as a source, and the proportion of the patients in different places in each source is taken as the weight to calculate.
With respect to step 110:
the following description will be made by taking Beijing as an example.
Adopting five distance attenuation models to respectively fit the distribution data of the different-place patient's hospitalization in the Beijing area, and analyzing the different-place patient classification of the hospitals with different hospital grades, different hospital types and different functional areasIn all cases, the results showed that the exponential model fit was the best (R)2Max) (see table 1). The overall distance attenuation coefficient obtained by the fitting was 0.0786. Comparing the distance attenuation coefficients of different hospitals shows that the distance influence of the patient in different places who is seen by the second-level hospital (beta is 0.0797) is larger than that of the patient in the third-level hospital (beta is 0.0786); the distance influence of the patients in different places who visit the special hospital (beta-0812) is larger than that of the general hospital and the traditional Chinese medicine hospital (beta-0.0779) (beta-0.0752), and the influence of the patients in different places who visit the traditional Chinese medicine hospital is the least; the distance of the patient who visits the hospital in the urban extension area (beta-0.0782) is the least affected, and the distance of the patient who visits the hospital in the suburban area (beta-0.0816) is the most affected.
TABLE 1
Differences in medical service utilization for different levels of hospitals:
the cross-regional inpatient probability of the third-level hospital (beta-0.0786) is slower than that of the second-level hospital (beta-0.0797) along with the distance, which indicates that the medical service utilization of the third-level hospital is less affected by the distance. On a scale (data processing omitted), the tertiary hospital has assumed an off-site patient throughput of 95.16% and an average radiation distance of 731.15 km. The secondary hospital has fewer off-site patients, and only bears 4.84% of off-site patient flow, and the average radiation distance is 697.38 km. In contrast, tertiary hospitals have higher medical quality, more advanced medical levels, and more medical resources, and are therefore able to serve cross-regional off-site patients with greater intensity and scope.
Differences in medical service utilization for different types of hospitals:
the probability of inpatients in a traditional Chinese medicine hospital (beta-0.0752) is slower to decline with distance than that in a general hospital (beta-0.0779) and a specialized hospital (beta-0.0812), which indicates that the medical service utilization in the traditional Chinese medicine hospital is influenced by the distance to the minimum. On a scale (data processing omission), the most remote hospitalization patients of the comprehensive hospital bear 66.37% of remote patient flow, and the average radiation distance is 737.87 km. The special hospital bears 30.86% of the flow of the allopatric patients, and the average radiation distance is 705.93km, which is the shortest. The traditional Chinese medical hospital only bears 2.77% of the flow of the patients in different places, and the average radiation distance is 792.16km, which is the longest. The research result shows that the probability of inpatients in the comprehensive medical institution is more rapid than that of the specialized medical institution along with the decay of the distance.
Differences in medical service utilization for different functional area hospitals:
the probability of inpatients in different places in a suburban hospital (beta 0.0816) is more rapidly attenuated with distance than in a core hospital (beta 0.0789), an expansion hospital (beta 0.0782) and a suburban hospital (beta 0.0789), which indicates that the medical service utilization of the suburban hospital is most affected by the distance. From the scale (data processing is omitted), the hospital in the development area has the largest number of patients in different places, which bears 49.72% of patient flow in different places, and the average radiation distance is 751.77 km. The suburban hospitals only undertake 0.71% of the allopatric patient flow, and the average radiation distance is 527.88km, which is the shortest. Therefore, the core area and the development area of Beijing are the areas where the cross-regional patient hospitalization is most concentrated, which is greatly related to the concentrated distribution of high-quality medical resources, about 71% of inflow hospitals analyzed by the embodiment of the invention are distributed in the core area and the development area, the high-quality degree is high, and more than 50% of inflow hospitals are distributed in the eastern area and the western area, taking the distribution of the third hospital as an example.
In addition, Beijing has centralized the best medical resources in China, and has a siphon effect all over the country, and patients all over the country expect to see the disease in Beijing. The patient mobile data set is visualized through a visualization method, a starting point city and a terminal point Beijing of the patient are used as medical supply and demand nodes, and a medical treatment flow graph is formed by connecting lines among the nodes. The Beijing has a wide medical service range, patients come from all over the country, and the status of the Beijing as a national medical service center is reflected.
In summary, the embodiment of the invention analyzes the mode difference of the hospital cross-regional medical service utilization in different grades, different types and different functional areas based on the Beijing city cross-regional inpatient data, determines the optimal distance attenuation function model, and provides a research paradigm for the service area service range of relevant medical institutions, the accessibility research of patient medical resources and the like. Specifically, the distribution of inpatients is fitted by using an evolution index model, an exponential model, a square index model, a Pareto model and a constant logarithm model, mode differences of hospital service utilization of five different places patients are analyzed, and the result shows that an exponential distance attenuation function can optimally express cross-regional patient flow distribution characteristics of hospitals in different levels, different types and different functional areas, wherein the hospital medical service utilization of three levels (beta is 0.0786), traditional Chinese medicine (beta is 0.0752) and an extended area (beta is 0.0782) is minimally influenced by distance, and the model is a model for researching the service mode of the different places hospitalizing hospitals, and is worthy of popularization and further application.
The embodiment of the invention also provides electronic equipment which comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to realize the analysis method for the medical service utilization of the remote patient in the hospitalization of any embodiment of the invention.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, causes the processor to execute an analysis method for medical service utilization of remote patient hospitalization in any embodiment of the present invention.
Specifically, a system or an apparatus equipped with a storage medium on which software program codes that realize the functions of any of the above-described embodiments are stored may be provided, and a computer (or a CPU or MPU) of the system or the apparatus is caused to read out and execute the program codes stored in the storage medium.
In this case, the program code itself read from the storage medium can realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.
Examples of the storage medium for supplying the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (e.g., CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD + RW), a magnetic tape, a nonvolatile memory card, and a ROM. Alternatively, the program code may be downloaded from a server computer via a communications network.
Further, it should be clear that the functions of any one of the above-described embodiments may be implemented not only by executing the program code read out by the computer, but also by causing an operating system or the like operating on the computer to perform a part or all of the actual operations based on instructions of the program code.
Further, it is to be understood that the program code read out from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion module connected to the computer, and then causes a CPU or the like mounted on the expansion board or the expansion module to perform part or all of the actual operations based on instructions of the program code, thereby realizing the functions of any of the above-described embodiments.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an …" does not exclude the presence of other similar elements in a process, method, article, or apparatus that comprises the element.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (7)
1. An analysis method for medical service utilization of a patient in a different place for medical treatment is characterized by comprising the following steps:
acquiring address information and hospitalizing hospital information of a patient in a different place hospitalizing a target city; the target city is a local city into which the patient flows, the local city in which the allopatric patient is located is a local city out of which the patient flows, and the information of the hospitalizing hospital comprises the grade and type of the hospitalizing hospital and the functional area in which the hospitalizing hospital is located;
determining longitude and latitude coordinates corresponding to the address information based on the address information;
for each patient effluent grade city, determining the average optimal distance from the current patient effluent grade city to the target city based on the longitude and latitude coordinates of the allopatric patients belonging to the current patient effluent grade city;
determining distance attenuation coefficients of medical hospitals of different grades, different types and different located functional areas in the target city based on the remote patient visit amount of each patient's effluent grade city in the target city and the average optimal distance from each patient's effluent grade city to the target city; wherein the distance attenuation coefficient is used for representing the influence of the distance on the hospitalization of the allopatric patient;
determining average radiation distances of medical hospitals of different grades, different types and different located functional areas in the target city based on the average optimal distance from the different patient outflow grade cities to the target city;
and analyzing the medical service utilization of the hospitalizing of the remote patient based on the hospitalizing amount, the distance attenuation coefficient and the average radiation distance of the remote patient of the hospitalizing hospitals with different grades, different types and different located functional areas in the target city.
2. The method of claim 1, wherein determining longitude and latitude coordinates corresponding to the address information based on the address information comprises:
analyzing the address information by using a Geocoder API function of a Baidu map position service to obtain a first hundred-degree coordinate and a first unresolved address;
carrying out doorplate number removal and/or town name removal on the first unresolved address, and resolving the processed first unresolved address by using a Geocoder API function of a Baidu map location service to obtain a second Baidu coordinate and a second unresolved address;
positioning the second unresolved address to the town to obtain a third hundred-degree coordinate and a third unresolved address;
manually positioning the third unresolved address to obtain a fourth hundred-degree coordinate;
and carrying out space correction transformation on the first hundred-degree coordinate, the second hundred-degree coordinate, the third hundred-degree coordinate and the fourth hundred-degree coordinate to obtain a longitude and latitude coordinate corresponding to the address information.
3. The method of claim 2, wherein the spatial correction transformation is by way of an affine transformation function, the affine transformation function being:
x'＝Ax+By+C
y'＝Dx+Ey+F
wherein x and y are the hundred degree coordinates before transformation, x 'and y' are the longitude and latitude coordinates after transformation, A, B, C, D, E and F are determined by the position relationship between the source control point and the target control point, and the hundred degree coordinates are corrected by the position relationship between the source control point and the target control point.
4. The method of claim 1, wherein determining an average optimal distance from a current patient's effluent grade to the target city based on longitude and latitude coordinates of off-site patients belonging to the current patient's effluent grade comprises:
processing the longitude and latitude coordinates of each allopatric patient belonging to the current patient outflow grade city to the longitude and latitude coordinates of the hospitalizing hospitals of different grades, different types and different functional areas in the target city by using a route planning API function of the Baidu map to obtain the optimal distance from each allopatric patient to the hospitalizing of the target city;
and averaging the optimal distance from each allopatric patient to the target city for hospitalization to obtain the average optimal distance from the current patient to the target city.
5. The method of any one of claims 1-4, wherein determining distance attenuation coefficients for hospitalizations of different levels, different types, and different located functional areas in the target city based on an off-site patient visit volume of each patient's effluent grade city in the target city and an average optimal distance of each patient's effluent grade city to the target city comprises:
aiming at each medical hospital with different grades, different types and different functional areas in the target city, fitting the remote patient visit amount of each patient in the target city and the average optimal distance from each patient in the target city by using a plurality of preset distance attenuation models to obtain a regression coefficient and a distance attenuation coefficient of each distance attenuation model;
and taking the distance attenuation model with the maximum regression coefficient as a target distance attenuation model, and taking the distance attenuation coefficient of the target distance attenuation model as the distance attenuation coefficient of each medical hospital.
6. The method of claim 5, wherein the distance decay model comprises an open-square exponential model, an exponential model, a squared exponential model, a Pareto model, and a log-constant model.
7. The method according to any one of claims 1-6, wherein the mean radiation distance is determined by the formula:
in the formula, n represents the number of patients in each hospitalization hospital to flow out of the grade city, xiRepresenting the proportion of all the allopatric patients who are present in the grade I patient in each hospitalization hospital, diRepresents the average optimal distance from the i-th patient in each hospital to the target city.
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| CN116501990A (en) * | 2023-04-11 | 2023-07-28 | 北京师范大学-香港浸会大学联合国际学院 | Hospital specialty influence assessment method and device based on outpatient big data |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116501990A (en) * | 2023-04-11 | 2023-07-28 | 北京师范大学-香港浸会大学联合国际学院 | Hospital specialty influence assessment method and device based on outpatient big data |
| CN116501990B (en) * | 2023-04-11 | 2024-01-26 | 北京师范大学-香港浸会大学联合国际学院 | Hospital specialty influence assessment method and device based on outpatient big data |
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