CN114493953A - Method for analyzing influence factors of hospitalizing of remote patient - Google Patents
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Abstract
The embodiment of the invention relates to a method for analyzing influence factors of hospitalizing of a patient at different places, which comprises the following steps: acquiring case data, geographic element data and socioeconomic data of a patient in a different place who needs a doctor in a target city; determining a plurality of first influence factors for the patient in different places to seek medical advice based on the case data, the geographic element data and the socioeconomic data; screening various first influence factors by using a multiple regression model to obtain various second influence factors; the diagnosis rate of the patient from the grade city to the target city is a dependent variable, the multiple second influence factors are independent variables, and the dependent variable and the independent variables are subjected to fitting analysis by utilizing an OLS (on-line analytical system) regression model, a GWR (global warming potential) regression model and an MGWR (minor groove wave projection) regression model to determine a target regression model with the highest fitting degree; and determining the target influence factors from the second influence factors based on the significance test variables of the target regression model so as to analyze the target influence factors. The technical scheme provided by the invention can accurately analyze the influence factor condition of the patient in different places when the patient is hospitalized.
Description
Technical Field
The embodiment of the invention relates to the cross field of geography, health statistics, health management and mathematics, in particular to an analysis method of influence factors of hospitalization of a patient at a different place.
Background
The medical treatment of the patient at different places is a ubiquitous medical phenomenon, and reflects the conditions of national and regional medical reform, legislative change and medical health care system development. In china, the remote patient hospitalization usually refers to a hospitalization behavior that the hospitalization place of the medical insurance ginseng insurance person is separated from the ginseng insurance region, wherein the patient usually leaves the province and flows to big cities such as beijing, shanghai and the like with better medical resources.
In the related art, the way of analyzing the influence factors of the hospitalization of the allopatric patient is usually based on the Ordinary Least Squares (OLS) expansion, which implies the assumption of spatial homogeneity and considers the regression coefficients of each sample point to be equal. However, since some regions have spatial aggregation characteristics, which may cause spatial non-stationarity in the relationship between independent variables and dependent variables at each sample point, a global regression model represented by an OLS model cannot measure the differential effect of independent variables in different regions on the dependent variables.
Therefore, there is a need for an analysis method of influence factors for remote patient hospitalization to solve the above technical problems.
Disclosure of Invention
In order to accurately analyze the condition of the influence factors for the hospitalization of the patient at the different place, the embodiment of the invention provides an analysis method of the influence factors for the hospitalization of the patient at the different place.
The embodiment of the invention provides a method for analyzing influence factors of hospitalizing of a patient in different places, which comprises the following steps:
acquiring case data, geographic element data and socioeconomic data of a patient in a different place who needs a doctor in a target city; wherein the target city is a grade city of inflow of the patient, and the grade city of the allopatric patient is a grade city of outflow of the patient;
determining a plurality of first influence factors for the remote patient to seek medical advice based on the case data, the geographic element data and the socioeconomic data;
screening multiple first influence factors by using a multiple regression model to obtain multiple second influence factors;
taking the diagnosis rate of the patient from the grade city to the target city as a dependent variable and taking a plurality of second influence factors as independent variables, and respectively performing fitting analysis on the dependent variable and the independent variables by utilizing an OLS (on-line analytical system) regression model, a GWR (global warming curve) regression model and an MGWR (small particle size distribution) regression model to determine a target regression model with the highest fitting degree;
and determining a target influence factor from the plurality of second influence factors based on the significance test variable of the target regression model.
In one possible design, the case data includes patient class market, type of medical insurance payment, age, and disease type;
the geographic element data comprises time data and distance data of patients flowing out of a grade city to the target city by means of automobiles, trains and planes, distance data to other medical centers, distance data to provincial cities and administrative division data; wherein the other medical centers are obtained by performing spatial autocorrelation analysis on the distribution of the visit volume of the patient from the grade city to the target city;
the socioeconomic data includes medical statistics, demographic data, and regional production total data, and the medical statistics includes the number of medical institutions, number of actual beds, number of medical practitioners, and number of tertiary hospitals when patients are out of grade.
In one possible design, the spatial autocorrelation analysis is to analyze the spatial correlation and the difference of the distribution of the visit volume of the patient from the grade city to the target city by using a global Moran's index and a local Moran's index;
the global Moran' sI index is calculated by the following formula:
wherein n is the total number of the analyzed region units; y isiAnd yjRespectively the medical volume of the space object from the unit of the ith and jth areas to Beijing,
is the average value of y; w is aijIs a weight matrix, wijThe method is used for representing the link relation of the space object between the ith area unit and the jth area unit; i is>0 represents a spatial positive autocorrelation; i is<0 represents a spatial negative autocorrelation; i-0 means that there is no spatial autocorrelation;
the local Moran' sI index is calculated by the following formula:
in the formula, s2Is yiThe discrete variance of (a); n is the total number of area units analyzed; y isiAnd yjRespectively the medical volume of the space object from the unit of the ith and jth areas to Beijing,
is the average value of y; w is aijIs a weight matrix, wijThe method is used for characterizing the link relation of the space object between the ith area unit and the jth area unit; i is>0 represents a spatial positive autocorrelation; i is<0 represents a spatial negative autocorrelation; i-0 means that there is no spatial autocorrelation.
In one possible design, the first influencing factors include regional basic medical supply level, number of third-level hospitals, patient affordability, accessibility of traffic from patient export to the target city, attractiveness of other medical centers, regional population density, average GDP of patient export to their provincial cities, distance of patient export to their provincial cities, and patient age average;
the determining a plurality of first influencing factors for the remote patient to seek medical advice based on the case data, the geographic element data and the socioeconomic data comprises:
weighting and summing the number of medical institutions, the number of actual beds and the number of medical practitioners of the patient class city to obtain the regional basic medical supply level;
determining the patient affordability based on a medical insurance payment proportion of a patient's effluent grade market;
carrying out weighted summation on time data from the patient class city to the target city in a mode of a car, a train and an airplane to obtain the traffic accessibility from the patient class city to the target city;
determining an attraction of the other medical centers based on a distance from the patient grade city to the target city and a distance from the patient grade city to the other medical center closest thereto;
determining the regional population density based on demographic data and land area of the patient shed class market;
determining the average GDP of a patient's class of outflowing city based on demographic data and regional production total data for the patient.
In one possible design, the second influencing factors include regional basic medical supply level, number of third-level hospitals, accessibility of traffic to the target city by the patient's export city, attractiveness of other medical centers, average GDP by the patient's export city, distance from the patient's export city to their provincial city, and patient age average.
In one possible design, the target regression model is a MGWR regression model;
the target influence factors comprise the number of third-level hospitals, the traffic accessibility of the patient from the grade city to the target city, the attractiveness of other medical centers, the per-capita GDP of the patient from the grade city and the distance from the patient from the grade city to the provincial city;
the visit rate from the patient-out grade city to the target city is respectively in positive correlation with the traffic accessibility from the patient-out grade city to the target city, the per-capita GDP from the patient-out grade city and the distance from the patient-out grade city to the provincial city;
the rates of patients visiting the target city out of grade II are significantly inversely related to the number of grade II hospitals and the attractiveness of other medical centers, respectively.
In one possible design, the distance from the patient to the target city is less than a preset distance, and the preset distance is obtained by analyzing the amount of remote visits at different distances from the target city.
In one possible design, further comprising:
aiming at each different-place patient with different age groups and different medical insurance payment types in the target city, fitting the treatment amount of each different-place patient in the target city and the distance from the 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 allopatric patient.
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 target distance attenuation model is a Pareto model.
The embodiment of the invention provides an analysis method of influence factors for hospitalizing of a patient at a different place, which is characterized in that a plurality of first influence factors for hospitalizing of the patient at the different place can be determined based on case data, geographic element data and socioeconomic data by acquiring the case data, the geographic element data and the socioeconomic data of the patient at the different place hospitalized in a target city, and a multivariate regression model can be further utilized to screen the plurality of first influence factors to obtain a plurality of second influence factors; then, taking the diagnosis rate of the patient from the grade city to the target city as a dependent variable and taking a plurality of second influence factors as independent variables, and performing fitting analysis on the dependent variable and the independent variables by respectively using an OLS (on-line analytical system) regression model, a GWR (glow-wire regression) model and an MGWR (minor groove-wave regression) regression model to determine a target regression model with the highest fitting degree; therefore, the target influence factors can be determined from the multiple second influence factors based on the significance test variables of the target regression model so as to analyze the target influence factors, and the influence factor condition of the remote patient for medical treatment can be accurately analyzed.
Drawings
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 flowchart of an analysis method of influence factors for a patient in a different location to seek medical advice 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 influence factors of a patient in an offsite medical service, where the method includes:
step 100: acquiring case data, geographic element data and socioeconomic data of a patient in a different place who needs a doctor in a target city; wherein, the target city is the grade city of the inflow of the patient, and the grade city of the off-site patient is the grade city of the outflow of the patient;
step 102: determining a plurality of first influence factors for the patient in different places to seek medical advice based on the case data, the geographic element data and the socioeconomic data;
step 104: screening various first influence factors by using a multiple regression model to obtain various second influence factors;
step 106: taking the diagnosis rate of the patient from the grade city to the target city as a dependent variable and taking a plurality of second influence factors as independent variables, and performing fitting analysis on the dependent variable and the independent variables by respectively utilizing an OLS (on-line analytical system) regression model, a GWR (global warming response) regression model and an MGWR (media gateway reactor) regression model to determine a target regression model with the highest fitting degree;
step 108: and determining the target influence factors from the multiple second influence factors based on the significance test variables of the target regression model so as to analyze the target influence factors.
In the embodiment of the invention, by acquiring case data, geographic element data and socioeconomic data of a patient in a different place hospitalized in a target city, various first influence factors of the patient in the different place hospitalized can be determined based on the case data, the geographic element data and the socioeconomic data, and the various first influence factors can be further screened by utilizing a multiple regression model to obtain various second influence factors; then, the diagnosis rate of the patient from the grade city to the target city is used as a dependent variable and multiple second influence factors are used as independent variables, and the dependent variable and the independent variables are subjected to fitting analysis by respectively utilizing an OLS (on-line analytical system) regression model, a GWR (global warming curve) regression model and an MGWR (media-weighted curve) regression model to determine a target regression model with the highest fitting degree; therefore, the target influence factors can be determined from the multiple second influence factors based on the significance test variables of the target regression model so as to analyze the target influence factors, and the influence factor condition of the remote patient for medical treatment 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 a target city as an example of Beijing, namely, the prefectural cities except Beijing are called patient outflow prefectural cities, and the patients except Beijing are called allopatric patients, case data selected by the embodiment of the invention are allopatric to Beijing inpatient case data of 154 hospitals in the Beijing City 2015, and 59.94 pieces of effective data of 341 prefectural patients to Beijing medical treatment are included after the data are analyzed and processed.
In some embodiments, the case data includes patient export grade, medical insurance payment type, age, and disease type;
the geographic element data comprises time data and distance data of patients flowing out of a grade city to a target city by means of automobiles, trains and planes, distance data to other medical centers, distance data to provincial cities and administrative division data; wherein, the other medical centers are obtained by carrying out spatial autocorrelation analysis on the distribution of the amount of treatment of patients flowing out of the grade city to the target city;
the socioeconomic data includes medical statistics, demographic data, and regional production total data, and the medical statistics include the number of medical institutions, number of actual beds, number of medical practitioners, and number of tertiary hospitals where patients are out of grade.
See table 1 for specific description and sources:
TABLE 1 data description and sources
It should be noted that the spatial autocorrelation analysis is an analysis method for studying whether the spatial unit observation value is correlated with the observation value of its neighboring unit, and is used to measure the aggregation degree of the spatial unit observation values.
In some embodiments, the spatial autocorrelation analysis is an analysis of spatial correlation and variability of the distribution of the amount of visits by the patient from the grade city to the target city using a global Moran's index and a local Moran's index;
the global Moran' sI index is calculated by the following formula:
wherein n is the total number of the analyzed region units; y isiAnd yjRespectively the medical volume of the space object from the unit of the ith and jth areas to Beijing,
is the average value of y; w is aijIs a weight matrix, wijThe method is used for representing the link relation of the space object between the ith area unit and the jth area unit; i is>0 represents a spatial positive autocorrelation; i is<0 represents a spatial negative autocorrelation; i-0 means that there is no spatial autocorrelation;
the local Moran' sI index is calculated by the following formula:
in the formula, s2Is yiThe discrete variance of (a); n is the total number of area units analyzed; y isiAnd yjRespectively the medical volume of the space object from the unit of the ith and jth areas to Beijing,
is the average value of y; w is aijIs a weight matrix, wijThe method is used for representing the link relation of the space object between the ith area unit and the jth area unit; i is>0 represents a spatial positive autocorrelation; i is<0 represents a spatial negative autocorrelation; i-0 means that there is no spatial autocorrelation.
The local Moran's i index subdivides the spatial association pattern into 4 types, corresponding to 4 quadrants in the Moran scattergram, respectively, including high-high aggregation areas (high offsite location surrounded by high offsite location), high-low aggregation areas (high offsite location surrounded by low offsite location), low-high aggregation areas (low offsite location surrounded by high offsite location), and low-low aggregation areas (low offsite location surrounded by low offsite location).
Global and local Moran's i indices were calculated to detect spatial autocorrelation between various municipal regions to identify spatial differences in the flow of hospitalized patients to beijing. The results (calculation results are omitted) show that the number of patients treated between the local regions (I is 0.57, p is less than 0.01) has positive spatial autocorrelation, and a certain spatial clustering pattern is presented.
The result of the local spatial autocorrelation analysis (the calculation result is abbreviated) appears as: a high-high aggregation area appears in the local area surrounding the periphery of beijing, and is mainly concentrated in parts of north China, inner Mongolia and northeast China, and comprises 22 local area units. Comparing and analyzing medical resources in high-high gathering areas, finding that the number of third-level hospitals per million population in the province of Hebei and the part of inner Mongolia is lower than the average level of the whole country, and the number of medical practitioners per thousand population and the number of beds per thousand population are also at lower level, which indicates that the 22 high-quality medical resources in the grade of the city are deficient (for example, the number of large medical institutions is relatively small). In contrast, low-oligomeric aggregation regions were distributed mainly in parts of the southwest, south China, east China, coastal areas, and northwest, including 104 metro-level regional units. Comparing and analyzing medical resources of the low-low aggregation areas, and finding that medical resources of Guangxi province, Hainan province and autonomous Tibet district with low aggregation degree are relatively deficient, but have fewer patients seeking medical services to Jing due to the fact that the medical resources are far away from the Jing; meanwhile, the number of the third-level hospitals of each million population and the number of the sanitary technicians of each thousand population in Sichuan province and Guangdong province are higher than the average level in China, which indicates that although the local medical resources are rich, the number of the patients seeking medical service to Jing is less, and the distance from Jing and the situation of the local medical resources can influence the number of the patients seeking medical service to Jing together.
With respect to step 102:
in some embodiments, the first influencing factor comprises regional basic medical supply level, number of third-level hospitals, patient affordability, accessibility to traffic from patient export to target cities, attractiveness of other medical centers, regional population density, per-capita GDP of patient export to city of provincial societies, distance from patient export to city of provincial societies, and patient age average;
determining a plurality of first influence factors for the remote patient to seek medical advice based on the case data, the geographic element data and the socioeconomic data, wherein the first influence factors comprise:
weighting and summing the number of medical institutions, the number of actual beds and the number of medical practitioners of the grade city where the patient flows out to obtain the regional basic medical supply level;
determining patient affordability based on a medical insurance payment proportion of a patient out of grade;
carrying out weighted summation on time data from the patient class city to a target city in a mode of a car, a train and an airplane to obtain the accessibility of the patient class city to the target city;
determining the attractiveness of other medical centers based on the distance from the patient grade city to the target city and the distance from the patient grade city to other medical centers closest to the patient grade city;
determining a regional population density based on demographic data and land area of the patient shed class market;
determining the average GDP of the patient out of grade based on the demographic data and the total regional production value data of the patient out of grade.
With respect to step 104:
in some embodiments, the second influencing factors include regional base medical supply level, number of tertiary hospitals, traffic accessibility of patient out of grade city to target city, attractiveness of other medical centers, average GDP of patient out of grade city, distance of patient out of grade city to their provincial city, and average age of patient.
In this embodiment, the remote diagnosis rate from the local city region to Beijing is used as a dependent variable of the study to characterize the utilization degree of the medical service of Beijing city by the remote patients. In order to accurately find the optimal influence variable, a multivariate regression model is adopted to obtain the overall difference characteristics of the influence factors, and screening is carried out at a significance level (alpha is 0.05). There are 7 variables (i.e., the second contributing factor) that pass the test (p <0.001), the per-person GDP, the number of local third-level hospitals, the regional basic medical supply level, the accessibility to traffic to beijing, the distance to congress, the average age of patients, and the attractiveness of other medical centers, respectively. Wherein the dependent variables and independent variables that are finally determined are shown in table 2:
TABLE 2
For step 106:
the results of regression analysis of the three models OLS, GWR and MGWR are shown in Table 3. The results show that the MGWR model has better performance than OLS and common GWR models, and reflects the change of 82.7% of the diagnosis rate of different places to Beijing. Compared with an OLS model (AICc: 793.204; SRS: 193.589) and a common GWR model (AICc: 608.351; SRS: 79.098), the MGWR model generates a smaller corrected hematid pool information amount criterion value (AICc:556.80) and a smaller sum of squared residuals (SRS: 58.888), which shows that the MGWR model has better fitting degree and accuracy.
TABLE 3
Note: indicates significance at 1%, 5%, 10% levels, respectively.
The results show that there are 5 variables that satisfy the significance test
The remote diagnosis rate to Beijing is obviously positively and positively related to the GDP of everyone, the accessibility of traffic to Beijing and the distance to provincial meeting cities, and is obviously negatively related to the number of local third-level hospitals and the attraction of other medical centers.
That is, the target regression model is the MGWR regression model;
the target influence factors comprise the number of third-level hospitals, the traffic accessibility of patients from the grade city to the target city, the attractiveness of other medical centers, the per-capita GDP of patients from the grade city and the distance from the patients from the grade city to the provincial city;
the visit rate of the patient from the grade city to the target city is respectively in positive correlation with the traffic accessibility of the patient from the grade city to the target city, the per-capita GDP of the patient from the grade city and the distance from the patient from the grade city to the provincial city;
the rates of patients visiting the target city out of grade II are significantly inversely related to the number of grade II hospitals and the appeal of other medical centers, respectively.
In addition, by analyzing the remote medical treatment amounts from different distances to the target city, the remote medical treatment amounts from the local city level region to Beijing show a trend of gradient decreasing along with the increase of the distance from the Beijing on the whole, and more than 80 percent of patients from the remote location to Beijing are from the region of the local city less than 1300 km away from the Beijing, which shows that the distance attenuation effect exists when the patients flow to the Beijing in a remote location.
Thus, to reduce analysis errors, in some embodiments, the distance from the patient's effluent grade city to the target city is less than a preset distance (e.g., 1300 km), which is obtained by analyzing the amount of off-site visits at different distances from the target city.
In some embodiments, the above method further comprises:
aiming at each different-place patient with different age groups and different medical insurance payment types in the target city, fitting the amount of each different-place patient in the target city and the distance from the 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 different patient.
And (4) calculating distance attenuation by taking the mass centers of various urban areas as the outflow points of the patients and taking 100 kilometers as step length. In the calculation process, the accumulated proportion of the treatment amount beyond 3000 kilometers is only 0.48 percent, and the distance is limited to 3000 kilometers in order to avoid the adverse effect of long tail effect on the calculation result. The distance attenuation is measured by a cumulative distribution method, the results of five distance attenuation fitting functions are shown in table 4, and the results show that the Pareto function fitting effect is optimal, so that the Pareto function is selected as the fitting function of the distance attenuation effect in the embodiment of the invention. That is, the target distance attenuation model is a Pareto model.
TABLE 4
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 remote patient visit amount of each city-level region unit going to beijing, d is the 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.
The difference of different patient groups in different places in medical treatment behaviors is analyzed, and the medical resource allocation fairness is favorably understood. Embodiments of the present invention analyze flow patterns of different types of patient populations and compare populations of different ages and different types of medical payments. Analysis of the populations at different ages revealed (table 5) that the distance attenuation effect exhibited a general trend of decreasing first and increasing second with increasing patient age, with different populations being affected by increasing distance to different extents. The old (more than or equal to 75 years old) with Beijing has the biggest influence of distance increase on remote hospitalization (beta-0.633), and the middle-aged population (45-59 years old) has the smallest influence of distance increase (beta-0.739). This result indicates that mobility of elderly (more than or equal to 75 years old) going to Beijing doctor from other places is most susceptible to distance, which is consistent with the current situation that elderly are not convenient to move from a long distance due to their age and body.
TABLE 5
Population analysis for different medical payment types found (table 6) that patient populations with different medical payment types had different distance attenuation coefficients, with hospitalization flows being most affected by distance for the population of patients paid for the full fee (β -0.541), less affected by distance for poverty relief (β -0.778) and other patient populations paid for (β -0.783). Comparing town employee basic medical treatment (β ═ 0.688), town resident basic medical treatment (β ═ 0.707) and novel rural cooperative medical treatment (β ═ 0.624), it was found that rural patients were more affected by distance than town patients. The result shows that the mobility of the patient group with the payment of the whole public fee to the Beijing doctor in other places is most easily influenced by the distance, which is greatly related to the policy that the public fee medical treatment in the medical system of China generally needs to be seen in a fixed-point hospital. Patients in rural areas are more affected by distance than in urban areas, possibly because of higher income, better traffic accessibility and greater medical awareness of patients in urban areas. The difference between the urban and rural areas of the patient flow pattern reflects the difference of the accessibility of medical services and the geographical difference of medical resource distribution among urban and rural areas in China.
TABLE 6
For step 108:
table 7 is a scale of the differential effect of MGWR regression models on each variable:
TABLE 7
Based on the significance test result of the independent variable, the embodiment of the invention determines 5 independent variables such as the per capita GDP, the traffic accessibility to Beijing, the distance to provincial meeting cities, the number of local third-level hospitals, the attraction of other medical centers and the like as important influence factors for researching the Beijing remote diagnosis rate. In order to further explore the spatial pattern of the influence degree of each influence factor on the dependent variable, the embodiment of the invention further applies a natural breakpoint method to analyze the variation conditions of the dependent variable (the different-place diagnosis rate from each city-level region to Beijing) and the 5 independent variables in different regions and different bandwidths, thereby quantitatively researching the spatial diversity influenced by each influence factor.
(1) Aiming at the influence analysis of the GDP on the diagnosis rate of arriving at Jing from different places
The results of the MGWR model show that the GDP of the average person is positively correlated with the remote diagnosis rate to Beijing, namely, the higher the GDP of the average person is, the higher the remote diagnosis rate to Beijing is. The human-average GDP is used as a measurement index of regional abundance, and reflects the economic payment capacity of a regional patient group. Patients in affluent areas have higher economic capability and are more prone to seeking medical services from other places to big cities such as Beijing and the like with better medical conditions across areas; patients in areas with lower income are more likely to choose to visit local primary care facilities and are limited in their ability to pay, so that the low-income population is less likely to visit the doctor remotely.
The action scale of the homo-GDP is 183, which shows that the influence on the remote diagnosis rate of the patient from the West to the Jing is exerted on the medium scale, the positive influence is gradually reduced from the West to the east, the influence high-value areas are concentrated in the southwest areas of Qinghai, Gansu, Ningxia and inner Mongolia, and the influence on the coastal areas of Shandong, Jiangsu, Shanghai, Fujian, Guangdong and the like is lower. Further analyzing and discovering the influence high-value areas, wherein the remote-place diagnosis rate is influenced by the per-capita GDP in areas where the economy of China is relatively laggard, particularly the average remote-place diagnosis rate of the high-value areas in the inner Mongolia autonomous area and Gansu province is up to 33.29 and 10.20, which indicates that the areas have higher requirements on high-quality medical resources in Beijing city on one hand; on the other hand, the situation that more potential patient groups which cannot meet the medical needs of different places exist in the areas is also shown, and the contradiction between the payment capacity and the medical needs of the patients is more obvious. Following the reform direction from 'some diseases are treated' to 'healthy China' in the medical system of China, if the patients in the areas are recommended to select local medical treatment preferentially for common and common diseases, policy guidance is made by related medical institutions, the types and the proportion of the common and common diseases are clarified, and the related medical service level of basic diseases is improved; aiming at other serious diseases which cannot be solved by local medical treatment, the medical subsidy of the patients in different places is increased, so that the option of the patients in the areas in different places for medical treatment is increased.
(2) Analysis of influence of traffic accessibility to Beijing on remote Beijing visit rate
The traffic accessibility to Beijing has a positive influence on the remote diagnosis rate to Beijing, i.e., the higher the traffic accessibility to Beijing, the higher the remote diagnosis rate to Beijing. The higher accessibility to traffic increases to some extent the possibility of the patient to seek medical advice off-site. In areas with better traffic conditions, patients have more opportunities to obtain high-quality medical resources, and tend to go to more distant and better medical institutions for treatment.
The influence scale of the traffic accessibility to Beijing is small, and the action scale is 59, which shows that the influence scale has influence on the remote diagnosis rate of patients from Beijing. The forward influence shows a trend of annular outward gradient decreasing with Beijing as a center, and the influence high-value areas are mainly concentrated in 28 cities around the Beijing and mainly located in Hebei province, inner Mongolia autonomous region, Liaoning province and other areas, namely the allopatric hospitalization effect generated by the high traffic accessibility only exists in a small area range. However, when the analysis of the low-value region having a low influence on the remote diagnosis rate is performed, it is found that there are a small number of regions having low traffic accessibility but a high rate of patients hospitalized at remote locations, and the regions are mainly distributed in 16 places in total, such as the regions of black longjiang boundary (the dazzling region, the black river, the crane, the double-duck mountain, the seven-tai river, the chicken, the Yanbian, and the white mountain), the intra-mongolian alashang france, the tibetan lasza, the Xinjiang Changji, and the regions of the south of Gansu province (the silver city, the fixed city, the Qingyang city, the plain city, and the Longnan city). Since the patients seeking medical treatment in different places in these areas need to spend more travel cost for seeking high-quality medical treatment, better traffic conditions need to be created for the patients, the cooperation of the remote medical treatment with a large-scale hospital is strengthened, the on-line medical treatment in different places is realized, and more convenient medical service is provided for the patient groups seeking medical treatment in different places.
With the development of Chinese traffic, particularly the promotion of the construction of a seven-shot nine-longitudinal-eighteen-transverse expressway network and an eight-longitudinal-eighty-transverse expressway network, the flow of elements such as remote medical treatment, tourism and the like is greatly promoted, and the influence scale of the traffic accessibility on the remote medical treatment effect of patients is further expanded.
(3) Aiming at the influence analysis of the distance from provincial meeting city on the diagnosis rate from different places to Beijing
The distance to provincial meeting city has positive influence on the remote diagnosis rate to Beijing, i.e. the farther the distance to provincial meeting city is, the higher the remote diagnosis rate to Beijing is. The action scale is 206, the coefficient is stable in space, and the influence on the patient's diagnosis rate from Beijing to other places is close to the global scale. The most excellent hospitals such as the third-level special hospitals, the third-level first hospitals and the like are mainly distributed in the provincial meeting center city nationwide, if the patient is located in the provincial meeting center city or the peripheral city, the availability of high-quality medical resources is high, and the possibility of selecting different places to seek medical advice is low. However, the patient far away from the provincial meeting center city has to go to the local province or the neighborhood of the provincial meeting center city for medical treatment in order to obtain high-quality medical service, and in this case (equivalent or higher travel cost), the more patients go to Beijing for medical treatment in high-quality medical treatment crossing the local province, i.e. the higher the tendency of the patient to seek medical treatment.
The regression coefficient of distance from provincial cities presents a trend of descending in an annular outward gradient mode by taking the Shanxi and the Shaanxi as centers, the positive influence is weaker on the whole, high-value areas are mainly concentrated in the Shanxi and most prefectures in the Shaanxi, the middle part of inner Mongolia and other areas, 27 prefecture-level areas are in total, the relation between the distance from provincial cities to provincial cities and the diagnosis rate at different places is analyzed and found, compared with provincial cities of other provinces (such as Shijiazhuang city, Taiyuan city and Yinchuan city), the provincial cities of the inner Mongolia autonomous area have higher high medical dependence degree on Beijing on local patients, and the medical resources of the provincial cities cannot effectively attract the patients of the provincial cities. For the great call, on one hand, the radiation capability of high-quality medical resources of the patients needs to be enhanced, and the utilization efficiency of the patients in the jurisdictions on the sanitary services of the patients needs to be improved; on the other hand, the construction of correct medical concept is further enhanced, the classified treatment is reasonably planned, and the flow direction of the treatment is guided.
(4) Aiming at the influence analysis of the number of local third-level hospitals on the different-place Jing visit rate
The number of local third-level hospitals has negative influence on the remote diagnosis rate, namely the remote diagnosis rate to Beijing is higher in the area with less local third-level hospitals. The number of local third-level hospitals reflects regional high-quality medical resources, and the model result shows that compared with the supply level of basic medical resources, the patient seeking medical treatment in different places is greatly influenced by the current situation of local high-quality medical resource allocation.
The action scale of the number of local third-level hospitals is 150, which shows that the local third-level hospitals have influence on the remote diagnosis rate of patients from Beijing on a medium scale, the negative influence of the local third-level hospitals presents a decreasing trend from north to south, the influence on high-value distribution in 32 local-level areas in provinces such as Hebei, Shanxi, inner Mongolia and Liaoning around the Beijing city, and 44.88 percent of the remote-location patients from the high-value areas indicate that the Beijing city bears a great deal of medical requirements of the patients for the peripheral areas. Further comparing the local three-level hospitals in the areas with the remote hospitalization rates, the local patients with a greater proportion go to Beijing to seek medical advice in spite of relatively more local three-level hospitals in Bitou city (9) and Henhaite city (9); and the Fuxin City (3 families), the white city (0 family), the Kogyang City (2 families), the Luliang City (2 families), and the Fenuguadao City (1 family), the local three-level hospitals are few, and patients going to Beijing to seek medical treatment are small. Aiming at the special areas, when in health planning and medical center construction, the actual medical needs of the patients in the remote medical flow are fully considered, the establishment of a third-level hospital is promoted, the policy guidance is strengthened, and more patients are advocated to have medical treatment nearby in the provincial region.
(5) Aiming at the influence analysis of the attraction of other medical centers on the diagnosis rate of different places to Beijing
The attraction of other medical centers has negative influence on the remote diagnosis rate to Beijing, namely, the stronger the attraction of other medical centers is, the lower the remote diagnosis rate to Beijing is. According to the radiation capability of the urban hospitals, the prefectural urban area, the Nanjing city, Shanghai city, Jiangsu province and Guangzhou city, which mainly flow into the patients at different places, are other medical centers except Beijing, and the siphon effect of the core high-quality medical resources of the Beijing city can be obviously weakened due to the existence of the other medical centers, because the medical centers have better radiation effect and medical treatment attraction capability due to the high-quality medical resources in the region. Also known as medical centers, patients tend to visit their doctor closer than to Beijing when they are closer to other medical centers, rather than crossing further distances to visit Beijing.
The action scale of the attraction force of other medical centers is 339, the coefficient is stable in space, which shows that the overall scale influences the doctor's rate of visiting the Jing different places, the space heterogeneity hardly exists, and the doctor's rate of visiting the Jing different places in various market-level areas is basically influenced by the attraction force of other medical centers. The medical center collected by the patients in different places can provide important reference for the construction of the national medical center and the national regional medical center, so that the patients with the requirements of seeking medical treatment in different places can be effectively treated in a certain region, the patients can be guided to seek medical treatment reasonably, and the improvement of the medical service level of the whole and each region of China is facilitated.
In summary, the embodiment of the invention analyzes the spatial pattern of the flow of the patients from different places to Beijing hospitalization of each regional unit in the local cities in China based on the data of the transregional inpatients in the Beijing city, explores the spatial clustering characteristics and the distance attenuation effect in detail, and utilizes the MGWR model to explore the influence factors of the different-place hospitalization rate from each regional unit in the local cities to the Beijing.
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 of the influence factors of the remote patient hospitalizing in any embodiment of the invention.
The embodiment of the invention also provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the processor is caused to execute the method for analyzing the influence factors of the remote patient hospitalizing in any embodiment of the 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 (10)
1. An analytical method of influence factors for remotely hospitalizing patients is characterized by comprising the following steps:
acquiring case data, geographic element data and socioeconomic data of a patient in a different place who needs a doctor in a target city; wherein the target city is a grade city of inflow of the patient, and the grade city of the allopatric patient is a grade city of outflow of the patient;
determining a plurality of first influence factors for the remote patient to seek medical advice based on the case data, the geographic element data and the socioeconomic data;
screening multiple first influence factors by using a multiple regression model to obtain multiple second influence factors;
taking the diagnosis rate of the patient from the grade city to the target city as a dependent variable and taking a plurality of second influence factors as independent variables, and respectively performing fitting analysis on the dependent variable and the independent variables by utilizing an OLS (on-line analytical system) regression model, a GWR (global warming curve) regression model and an MGWR (small particle size distribution) regression model to determine a target regression model with the highest fitting degree;
and determining a target influence factor from the plurality of second influence factors based on the significance test variable of the target regression model so as to analyze the target influence factor.
2. The method of claim 1, wherein the case data includes patient class of origin, type of medical insurance payment, age, and disease type;
the geographic element data comprises time data and distance data of patients flowing out of a grade city to the target city by means of automobiles, trains and planes, distance data to other medical centers, distance data to provincial cities and administrative division data; wherein the other medical centers are obtained by performing spatial autocorrelation analysis on the distribution of the visit volume of the patient from the grade city to the target city;
the socioeconomic data includes medical statistics, demographic data, and regional production total data, and the medical statistics includes the number of medical institutions, number of actual beds, number of medical practitioners, and number of tertiary hospitals when patients are out of grade.
3. The method according to claim 2, wherein the spatial autocorrelation analysis is an analysis of spatial correlation and variability of the distribution of the visit volume of the patient out of the grade city to the target city using a global Moran's i index and a local Moran's i index;
the global Moran' sI index is calculated by the following formula:
wherein n is the total number of the analyzed region units; y isiAnd yjRespectively the medical volume of the space object from the unit of the ith and jth areas to Beijing,
is the average value of y; w is aijIs a weight matrix, wijThe method is used for representing the link relation of the space object between the ith area unit and the jth area unit; i is>0 represents a spatial positive autocorrelation; i is<0 represents a spatial negative autocorrelation; i-0 means that there is no spatial autocorrelation;
the local Moran' sI index is calculated by the following formula:
in the formula, s2Is yiThe discrete variance of (a); n is the total number of area units analyzed; y isiAnd yjRespectively the medical volume of the space object from the unit of the ith and jth areas to Beijing,
is the average value of y; w is aijIs a weight matrix, wijThe method is used for representing the link relation of the space object between the ith area unit and the jth area unit; i is>0 represents a spatial positive autocorrelation; i is<0 represents a spatial negative autocorrelation; i-0 means that there is no spatial autocorrelation.
4. The method of claim 2, wherein the first influencing factors include regional basal medical supply level, number of third-level hospitals, patient affordability, traffic accessibility of patient outflow city to the target city, attractiveness of other medical centers, regional population density, average GDP of patient outflow city, distance of patient outflow city to their provincial city, and patient age average;
the determining a plurality of first influencing factors for the remote patient to seek medical advice based on the case data, the geographic element data and the socioeconomic data comprises:
weighting and summing the number of medical institutions, the number of actual beds and the number of medical practitioners of the patient class city to obtain the regional basic medical supply level;
determining the patient affordability based on a medical insurance payment proportion for a patient out of grade;
carrying out weighted summation on time data from the patient class city to the target city in a mode of a car, a train and an airplane to obtain the traffic accessibility from the patient class city to the target city;
determining an attraction of the other medical centers based on a distance from the patient grade city to the target city and a distance from the patient grade city to the other medical center closest thereto;
determining the regional population density based on demographic data and land area of the patient shed class market;
determining the average GDP of a patient's class of outflowing city based on demographic data and regional production total data for the patient.
5. The method of claim 4, wherein the second influencing factors include regional basal medical supply level, number of third-level hospitals, accessibility of traffic to the target city by patient export city class, attractiveness of other medical centers, average-of-population GDP by patient export city class, distance of patient export city class to their provincial city, and average age of patients.
6. The method of claim 5, wherein the target regression model is a MGWR regression model;
the target influence factors comprise the number of third-level hospitals, the traffic accessibility of the patient from the grade city to the target city, the attractiveness of other medical centers, the per-capita GDP of the patient from the grade city and the distance from the patient from the grade city to the provincial city;
the visit rate from the patient-out grade city to the target city is respectively in positive correlation with the traffic accessibility from the patient-out grade city to the target city, the per-capita GDP from the patient-out grade city and the distance from the patient-out grade city to the provincial city;
the rates of patients visiting the target city out of grade II are significantly inversely related to the number of grade II hospitals and the attractiveness of other medical centers, respectively.
7. The method of any one of claims 2-6, wherein the distance from the patient's effluent grade city to the target city is less than a preset distance obtained by off-site visit volume analysis for different distances from the target city.
8. The method according to any one of claims 2-7, further comprising:
aiming at each different-place patient with different age groups and different medical insurance payment types in the target city, fitting the treatment amount of each different-place patient in the target city and the distance from the 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 allopatric patient.
9. The method of claim 8, 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.
10. The method of claim 8, wherein the target distance attenuation model is a Pareto model.
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