Disclosure of Invention
In order to accurately analyze the space mode condition of the patient visit rate of the different places, the embodiment of the invention provides an analysis method of the space mode of the patient visit rate of the different places.
The embodiment of the invention provides a method for analyzing a spatial mode of a treatment rate of a patient in a different place, which comprises the following steps:
acquiring case data, geographic element data and socioeconomic data of a patient in a different place where the patient is seeking medical attention in a target city, wherein the target city is a place where the patient flows into the place, and the place where the patient is located is a place where the patient flows out;
determining a plurality of first influencing factors for the medical treatment of the off-site patient based on the case data, the geographic element data and the socioeconomic data;
Screening the first influence factors by utilizing a multiple regression model to obtain second influence factors;
Taking the visit rate of the patient flowing out of the ground city to the target city as a dependent variable and taking a plurality of second influence factors as independent variables, and carrying out fitting analysis on the dependent variable and the independent variables by using an OLS regression model, a GWR regression model and a MGWR regression model respectively to determine a target regression model with highest fitting degree;
and determining a target influence factor from a 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 outflow to the local market, type of medical insurance payment, age, and type of disease;
The geographic element data comprise time data and distance data of a patient flowing out of a ground city to the target city through automobiles, trains and planes, distance data of other medical centers, distance data of a provincial city and administrative division data, wherein the other medical centers are obtained by performing space autocorrelation analysis on the visit volume distribution of the patient flowing out of the ground city to the target city;
The socioeconomic data includes medical statistics including the number of medical institutions that the patient is out of the ground market, the number of actual beds, the number of medical practitioners, and the number of tertiary hospitals, demographic data, and regional production summary data.
In one possible design, the spatial autocorrelation analysis is to analyze the spatial correlation and variability of the patient's volume distribution of visits from the ground city to the target city using a global Moran ' sI index and a local Moran ' sI index;
The global Moran' sI index is calculated by the following formula:
In the formula,For the total number of area units analyzed; AndThe medical treatment amount of the space object from the ith and jth regional units to the target city is respectively,Is thatAverage value of (2); As a matrix of weights, the weight matrix,For characterizing the linking relation of the space object between the ith and jth region units; representing a spatial positive autocorrelation; representing a spatial negative autocorrelation; indicating that there is no spatial autocorrelation;
the local Moran' sI index is calculated by the following formula:
In the formula,Is thatDiscrete variance of (a); For the total number of area units analyzed; AndThe medical treatment amount of the space object from the ith and jth regional units to the target city is respectively,Is thatAverage value of (2); As a matrix of weights, the weight matrix,For characterizing the linking relation of the space object between the ith and jth region units; representing a spatial positive autocorrelation; Representing a spatial negative autocorrelation; indicating that there is no spatial autocorrelation.
In one possible design, the first influencing factors include regional basal medical supply level, tertiary hospital count, patient affordability, patient outflow ground city to the target city traffic accessibility, appeal to other medical centers, regional population density, patient outflow ground city average GDP, patient outflow ground city to its provincial city distance, and patient age average;
The determining a plurality of first influencing factors of the offsite patient to seek medical advice based on the case data, the geographic element data and the socioeconomic data comprises:
the number of medical institutions, the number of actual beds and the number of medical practitioners of the market of the area are weighted and summed to obtain the basic medical supply level of the area;
determining the patient affordability based on a medical insurance payment duty cycle of the patient flowing out of the ground market;
The time data of the patient flowing out of the ground city to the target city through the modes of automobiles, trains and planes is weighted and summed to obtain the traffic accessibility of the patient flowing out of the ground city to the target city;
Determining the appeal of the other medical center based on the distance from the patient's outflow city to the target city and the distance from the patient's outflow city to the other medical center closest thereto;
determining the regional population density based on demographic data of patients flowing out of the ground city and the land area;
the patient outflow city average GDP is determined based on demographic data and regional production summary data for the patient outflow city.
In one possible design, the second influencing factors include regional basal medical supply level, tertiary hospital count, patient outflow city to the target city traffic accessibility, appeal to other medical centers, patient outflow city average GDP, patient outflow city to its provincial city distance, and patient age average.
In one possible design, the target regression model is a MGWR regression model;
The target influencing factors comprise three-level hospital numbers, the traffic accessibility of patients flowing out of the ground city to the target city, the attractions of other medical centers, the people-average GDP of patients flowing out of the ground city and the distance of patients flowing out of the ground city to the provincial city;
The treatment rate of the patient flowing out of the ground city to the target city is obviously and positively correlated with the traffic accessibility of the patient flowing out of the ground city to the target city, the people-average GDP of the patient flowing out of the ground city and the distance of the patient flowing out of the ground city to the provincial city;
The rate of visits by patients flowing out of the ground city to the target city is significantly inversely related to the number of tertiary hospitals and the appeal of other medical centers, respectively.
In one possible design, the distance from the patient's outflow to the target city is less than a preset distance, which is obtained by analyzing the out-of-place visits at different distances from the target city.
In one possible design, the method further comprises:
fitting the visit amount of each different-place patient in the target city and the distance from the patient outflow ground city to the target city by using a plurality of preset distance attenuation models aiming at each different-place patient in different age groups and different medical insurance payment types 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-place patient.
In one possible design, the distance decay model includes an evolution index model, an index model, a square index model, a Pareto model, and a constant logarithm model.
In one possible design, the target distance decay model is a Pareto model.
The embodiment of the invention provides a space mode analysis method for the visit rate of a patient in a different place, which can determine a plurality of first influence factors of the patient in the different place based on case data, geographic element data and socioeconomic data by acquiring the case data, geographic element data and socioeconomic data of the patient in the different place, further can screen the plurality of first influence factors by utilizing a multiple regression model to obtain a plurality of second influence factors, then takes the visit rate of the patient flowing out of the place to the target city as a dependent variable and takes the plurality of second influence factors as independent variables, respectively utilizes an OLS regression model, a GWR regression model and a MGWR regression model to carry out fitting analysis on the dependent variable and the independent variable to determine a target regression model with the highest fitting degree, so that the target influence factors can be determined from the plurality of second influence factors based on the significance test variable of the target regression model to analyze the target influence factors, and the space mode condition of the visit rate of the patient in the different place can be accurately analyzed.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by persons of ordinary skill in the art without making any inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a method for analyzing a spatial mode of a patient visit rate in a different place, the method comprising:
Step 100, acquiring case data, geographic element data and socioeconomic data of a patient in a different place where the patient is seeking medical attention in a target city, wherein the target city is a place where the patient flows into the place, and the place where the patient in the different place is located is a place where the patient flows out;
step 102, determining a plurality of first influence factors for medical treatment of patients in different places based on case data, geographic element data and socioeconomic data;
104, screening the plurality of first influence factors by utilizing a multiple regression model to obtain a plurality of second influence factors;
Step 106, taking the visit rate of the patient flowing out of the ground city to the target city as a dependent variable and taking a plurality of second influencing factors as independent variables, and carrying out fitting analysis on the dependent variable and the independent variable by using an OLS regression model, a GWR regression model and a MGWR regression model respectively to determine a target regression model with highest fitting degree;
And step 108, determining a target influence factor from a plurality of second influence factors based on the significance test variable of the target regression model so as to analyze the target influence factor.
According to the embodiment of the invention, by acquiring case data, geographic element data and socioeconomic data of a patient in a different place where a patient is hospitalized in a target city, a plurality of first influence factors of the patient in the different place where the patient is hospitalized can be determined based on the case data, the geographic element data and the socioeconomic data, the plurality of first influence factors can be further screened by utilizing a multiple regression model to obtain a plurality of second influence factors, then the diagnosis rate of the patient flowing out of the place to the target city is taken as a dependent variable, the plurality of second influence factors are taken as independent variables, the dependent variable and the independent variable are subjected to fitting analysis by utilizing an OLS regression model, a GWR regression model and a MGWR regression model respectively to determine a target regression model with the highest fitting degree, and therefore, the target influence factors can be determined from the plurality of second influence factors based on the significance test variable of the target regression model, so that the space mode situation of the diagnosis rate of the patient in the different place can be accurately analyzed.
The manner in which the individual steps shown in fig. 1 are performed is described below.
For step 100:
taking the target city as an example of the A city, namely that all the ground cities except the A city are called patient outflow ground cities, and all the patients except the A city are called different-place patients, the case data selected by the embodiment of the invention are different-place to A city hospitalization case data of 154 hospitals in the A city in 2015, and 59.94 ten thousands of effective data of 341 ground-level patients to A city hospitalization are included after the data are analyzed and processed.
In some embodiments, the case data includes patient outflow to the local market, type of medical insurance payment, age, and type of disease;
The geographic element data comprise time data and distance data of the patient flowing out of the ground city to a target city through automobiles, trains and planes, distance data of other medical centers, distance data of the provincial city and administrative division data, wherein the other medical centers are obtained by performing space autocorrelation analysis on the visit amount distribution of the patient flowing out of the ground city to the target city;
The socioeconomic data includes medical statistics including the number of medical institutions that the patient has discharged from the district, the number of actual beds, the number of medical practitioners, and the number of tertiary hospitals, demographic data, and regional production summary data.
See table 1 for specific description and sources:
TABLE 1 description of data and sources
It should be noted that, the spatial autocorrelation analysis is an analysis method for researching whether the observed value of the spatial unit has correlation with the observed value of the adjacent unit, so as to measure the aggregation degree of the observed value of the spatial unit.
In some embodiments, the spatial autocorrelation analysis is an analysis of spatial correlation and variability of the patient's distribution of visits from the ground city to the target city using a global Moran ' sI index and a local Moran ' sI index;
the global Moran' sI index is calculated by the following formula:
In the formula,For the total number of area units analyzed; AndThe medical volumes of the space object from the ith and jth regional units to the market A are respectively,Is thatAverage value of (2); As a matrix of weights, the weight matrix,For characterizing the linking relation of the space object between the ith and jth region units; representing a spatial positive autocorrelation; representing a spatial negative autocorrelation; indicating that there is no spatial autocorrelation;
the local Moran' sI index is calculated by the following formula:
In the formula,Is thatDiscrete variance of (a); For the total number of area units analyzed; AndThe medical volumes of the space object from the ith and jth regional units to the market A are respectively,Is thatAverage value of (2); As a matrix of weights, the weight matrix,For characterizing the linking relation of the space object between the ith and jth region units; representing a spatial positive autocorrelation; representing a spatial negative autocorrelation; indicating that there is no spatial autocorrelation.
The local Moran' sI index subdivides the spatial association pattern into 4 types, corresponding to the 4 quadrants in the Moran scatter plot, respectively, including high-high aggregation areas (high-off site areas surrounded by surrounding high-off site areas), high-low aggregation areas (high-off site areas surrounded by surrounding low-off site areas), low-high aggregation areas (low-off site areas surrounded by surrounding high-off site areas), low-low aggregation areas (low-off site areas surrounded by surrounding low-off site areas).
Global and local Moran' sI indices are calculated to detect spatial autocorrelation between the urban areas to identify spatial differences in the flow of hospitalized patients to market a. The results (calculation results are abbreviated) show that the number of patient visits between the urban areas (i=0.57, p < 0.01) has positive spatial autocorrelation and presents a certain spatial aggregation mode.
The results of the local spatial autocorrelation analysis (calculation result is abbreviated) show that a high-high aggregation region appears around the ground city level region around the periphery of the a city. In contrast, the low-low aggregation areas are mainly distributed in the parts of southwest, south China, eastern coast and northwest, including 104 units of urban area. The distance from market A and the local medical resource condition affect the number of patients seeking medical attention in market A together.
For step 102:
in some embodiments, the first influencing factors include regional basal medical supply level, tertiary hospital count, patient affordability, patient outflow ground city to target city traffic accessibility, appeal to other medical centers, regional population density, patient outflow ground city average GDP, patient outflow ground city to its provincial city distance, and patient age average;
Determining a plurality of first influencing factors for medical treatment of the off-site patient based on the case data, the geographic element data and the socioeconomic data, comprising:
the number of medical institutions, the number of actual beds and the number of medical practitioners of the market of the local area are weighted and summed to obtain the basic medical supply level of the area;
determining patient affordability based on a medical insurance payment duty cycle of the patient flowing out of the ground market;
The time data of the patient flowing out of the ground city to the target city in the modes of an automobile, a train and an airplane are weighted and summed to obtain the traffic accessibility of the patient flowing out of the ground city to the target city;
Determining the attractiveness of other medical centers based on the distance from the patient's outflow city to the target city and the distance from the patient's outflow city to the other medical center closest thereto;
determining regional population density based on demographic data of patients flowing out of the ground city and the land area;
The patient outflow city average GDP is determined based on demographic data of the patient outflow city and regional production summary data.
For step 104:
In some embodiments, the second influencing factors include regional basal medical supply level, tertiary hospital count, patient outflow ground city to target city traffic accessibility, appeal of other medical centers, patient outflow ground city average GDP, patient outflow ground city to its provincial city distance, and patient age average.
In this embodiment, the degree of use of medical services in the city of a by patients in different places is described using the consultation rates from the region of each city to the different places in the city of a as a dependent variable for the study. In order to accurately find the optimal influencing variable, a multiple regression model is adopted to obtain the overall difference characteristics of the influencing factors, and screening is carried out under the significance level (alpha=0.05). There are a total of 7 variables (i.e., the second influencing factor) that pass the test (p < 0.001), respectively, are average human GDP, local tertiary hospital count, regional basic medical supply level, traffic accessibility to market a, distance to meeting, average age of patient, and appeal of other medical centers. Wherein the final dependent and independent variables are as shown in table 2:
TABLE 2
For step 106:
Regression analysis results of the three models of OLS, GWR and MGWR are shown in table 3. The results show that MGWR model performance is superior to OLS and common GWR models, reflecting 82.7% change in off-site to a market visit rate. The MGWR model generated a smaller corrected red pool information quantity criterion value (AICc: 556.80) and sum of squares residual (SRS: 58.888) compared to the OLS model (AICc:793.204; SRS: 193.589) and the normal GWR model (AICc:608.351; SRS: 79.098), which illustrates that the MGWR model has better fit and accuracy.
TABLE 3 Table 3
Note that:, and:, represent significant at 1%, 5% and 10% levels, respectively.
The results show that there are 5 variables satisfying the significance testNot less than 1.96), wherein the foreign visit rate to the A city is significantly positively correlated with the average GDP of people, the accessibility of traffic to the A city, and the distance to the provincial city, and significantly negatively correlated with the number of local tertiary hospitals and the attractiveness of other medical centers.
That is, the target regression model is MGWR regression model;
The target influencing factors include tertiary hospital number, traffic accessibility of the patient from the ground city to the target city, attractions of other medical centers, average person GDP of the patient from the ground city, and distance of the patient from the ground city to the provincial city;
the treatment rate from the patient outflow city to the target city is obviously and positively related to the traffic accessibility from the patient outflow city to the target city, the people-average GDP from the patient outflow city and the distance from the patient outflow city to the provincial city respectively;
The rate of patient visits to the target city from the local market is significantly inversely related to the number of tertiary hospitals and the appeal of other medical centers, respectively.
In addition, through analysis of the different-place visit amount of different distances from the target city, the trend that the different-place visit amount from the district city level area to the A city is gradually decreased along with the increase of the distance from the district city level area to the A city is found, and more than 80% of patients in the different-place visit to the A city come from the district city area with the distance from the A city less than 1300 km, so that the obvious distance attenuation effect exists when the different-place flow to the A city visit.
Thus, to reduce analysis errors, in some embodiments, the distance from the patient's outflow city to the target city is less than a preset distance (e.g., 1300 km) that is obtained by analyzing the out-of-place visits at different distances from the target city.
In some embodiments, the above method further comprises:
Fitting the visit quantity of each different-place patient in the target city and the distance from the patient flowing out of the ground city to the target city by using a plurality of preset distance attenuation models aiming at each different-place patient in different age groups and different medical insurance payment types 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-place patient.
And taking the mass center of each city area as a patient outflow point, and taking 100 km as a step length to perform distance attenuation calculation. In the calculation process, the proportion accumulation of the treatment amount beyond 3000 km is only 0.48%, so that the distance is limited to be within 3000 km in order to avoid adverse effects of long tail effect on the calculation result. The cumulative distribution method is used for measuring the distance attenuation, the results of the five distance attenuation fitting functions are shown in table 4, and the results show that the fitting effect of the Pareto function 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 Table 4
In some embodiments, the distance decay model includes an evolution index model, an index model, a square index model, a Pareto model, and a constant logarithm model, the formulas of which are respectively:
Wherein I represents the visit amount of the patients in different places of the city A, d is the distance from the outflow city of the patients to the target city,Is scalar factor [ (]Is calculated in advance by using historical data, i.eIs a known quantity) and beta characterizes the distance decay coefficient. Beta reflects the speed of the distance decay, with a larger value indicating a greater effect of distance on its medical attention from different places.
In addition, the regression coefficients of the linear model may be fitted using the OLS (i.e., linear regression) method, with the determination coefficient R2 being used to measure the goodness of fit of the model. R2 is the variable part of the regression model interpretation, i.e
R2=ESS/TSS=1−RSS/TSS
Where TSS is the sum of the total squares and ESS is the sum of the interpreted squares.
And analyzing the variability of the different patient groups in different places to seek medical attention, so as to be helpful for understanding the fairness of medical resource allocation. The embodiment of the invention analyzes the flow patterns of different types of patient groups and compares the groups with different ages and different medical payment types. Analysis of different age groups revealed (table 5) that the distance attenuation effect showed a general trend of decreasing first and then increasing as the patient age increased, with different groups being affected to different extents by distance increase. Elderly people (. Gtoreq.75 years) attend A market in the different places with the greatest influence of distance increase (beta= -0.633), while middle-aged population (45-59 years) is least influenced by distance increase (beta= -0.739). This result shows that the mobility of elderly people (. Gtoreq.75 years) in different places to market A is most susceptible to distance, which is consistent with the current situation that elderly people are inconvenient to move remotely due to age and body reasons.
TABLE 5
Group analysis of different medical payment types shows (table 6) that the urban and rural differences of the patient flow patterns also reflect the differences of medical service accessibility and the geographic differences of medical resource allocation between urban and rural areas of China.
TABLE 6
For step 108:
Table 7 shows the differential effect scale of MGWR regression models on each variable:
TABLE 7
Based on the significance test results of the independent variables, the embodiment of the invention determines 5 independent variables such as the average person GDP, the traffic accessibility to the A market, the distance to the provincial city, the number of local tertiary hospitals, the attractive force of other medical centers and the like as important influencing factors for the investigation of the foreign visit rate of the A market. In order to deeply explore the space 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 change condition of the dependent variable (the foreign visit rate from each city level area to the A city) and the 5 independent variables in different areas and different bandwidths, so that the space dissimilarity of the influence of each influence factor is quantitatively researched.
(1) Analysis of impact of GDP on the frequency of visits to market A from different places on average
The MGWR model results show that the average human GDP is positively correlated with the off-site visit rate to A market, i.e. the higher the average human GDP, the higher the off-site visit rate to A market. The average person GDP is used as a measure of regional abundance and reflects the economic payment capability of a regional patient population.
The scale of action of human-average GDP was 183, indicating that it has an effect on the patients' rate of out-of-site visits to A city on the mesoscale. The medical system of China is recommended to take care of the medical system from 'sick and congratulation' to 'healthy China', if patients are normal and common diseases, local medical treatment is preferably selected, the relevant medical institutions conduct policy guidance, the types of the normal and common diseases and the specific gravity of the patients are clear, the relevant medical service level of basic diseases is improved, and medical subsidies of patients who take medical care in different places are recommended to be added aiming at important diseases which cannot be solved by other local medical treatment, so that the selection rights of the patients who take medical care in different places in the areas are increased.
(2) Analysis of impact of traffic reachability to market A on foreign to market A visit rate
The accessibility of traffic to city a has a positive impact on the frequency of visits to city a from different places, i.e. the higher the accessibility of traffic to city a, the higher the frequency of visits to city a from different places. The higher traffic accessibility increases the likelihood of the patient seeking medical attention from a different place to some extent.
The effect scale of the traffic accessibility to the A market is smaller, and the effect scale is 59, which shows that the effect scale has an effect on the foreign visit rate of the patient to the A market on a smaller scale. Because the patients seeking medical treatment in different places need to spend more travel cost for seeking high-quality medical treatment, better traffic conditions need to be created for the patients, and meanwhile, cooperation with remote medical treatment of a large-scale hospital is enhanced, so that medical treatment on different places is realized, and more convenient medical services are provided for the patient groups seeking medical treatment in different places.
Along with the development of Chinese traffic, in particular to the promotion of the construction of the expressway network with seven-jet nine-longitudinal-eighteen-transverse and the high-speed railway network with eight-longitudinal-eight-transverse, the flow of elements such as medical treatment in different places, travel and the like is greatly promoted, and the influence scale of the traffic accessibility on the medical treatment effect of patients in different places is further enlarged.
(3) Analysis of influence of distance to provincial city on foreign to A city visit rate
The distance to the provincial city has a positive influence on the foreign-area-to-A-city visit rate, i.e. the further the distance to the provincial city is, the higher the foreign-area visit rate to the A city is. The action scale is 206, the coefficient is stable in space, and the frequency of the patient to the different places of A market is affected on the global scale.
The regression coefficients from provincial city distance show a decreasing trend of annular outward gradient, which has a weaker overall positive effect.
(4) Analysis of influence of local tertiary hospital quantity on visit rate from different places to A market
The number of local tertiary hospitals has a negative effect on the off-site visit rate to the A market, i.e. the lower the number of local tertiary hospitals, the higher the off-site visit rate to the A market. The number of local tertiary hospitals reflects regional high-quality medical resources, and model results show that compared with the basic medical resource supply level, the local high-quality medical resource allocation current situation of patients with medical treatment in different places is greatly influenced.
The action scale of the local tertiary hospital number is 150, which shows that the local tertiary hospital number has an influence on the visit rate of patients to the different places of A market on the mesoscale. When the health planning and the medical center are constructed, the actual medical requirements of patients in the off-site medical treatment flow are fully considered, the three-stage hospital creation is promoted, the policy guidance is strengthened, and more patients are advocated to be treated nearby in the province.
(5) Analysis of impact of attractiveness to other medical centers on off-site to A-market visit rates
Other medical center attractions have a negative impact on the offsite to a market visit rate, i.e., the more attractive the other medical center is in the area, the lower the offsite visit rate to a market. As with the medical center, the closer the patient is to the other medical center, the more likely it is to visit the doctor nearby, rather than going to market a for a further distance across.
The attractive force of other medical centers has a dimension of 339, and the coefficient is stable in space, which indicates that the different-place visit rate of the patient to the A city is influenced on the global dimension, the spatial heterogeneity is almost not existed, and the different-place visit rate of the different-place city region to the A city is influenced by the attractive force of other medical centers basically the same. The medical centers collected by the patients in different places can provide important references for the construction of the national medical centers and the national regional medical centers, so that the patients with the medical requirements in different places can obtain effective treatment in a certain region, the patients can be guided to medical reasonably, and the medical service level of the whole body and each region in China can be improved.
In summary, the embodiment of the invention analyzes the space mode of the medical visit flow from the different places to the A market of the patients in the regional units of each city in China based on the data of the inpatients in the cross-regional A market, explores the space clustering characteristics and the distance attenuation effect in detail, and discusses the influence factors of the different places from the regional units of each city to the A market by utilizing a MGWR model.
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 realizes the analysis method of the spatial mode of the remote patient visit rate in any embodiment of the invention when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program when executed by a processor causes the processor to execute the method for analyzing the spatial mode of the treatment rate of the off-site patient in any embodiment of the invention.
Specifically, a system or apparatus provided with a storage medium on which a software program code realizing the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or apparatus may be caused to read out and execute the program code stored in the storage medium.
In this case, the program code itself read from the storage medium may realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code form part of the present invention.
Examples of storage media for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (e.g., CD-ROMs, CD-R, CD-RWs, DVD-ROMs, DVD-RAMs, DVD-RWs, DVD+RWs), magnetic tapes, nonvolatile memory cards, and ROMs. Alternatively, the program code may be downloaded from a server computer by a communication network.
Further, it should be apparent that the functions of any 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 part or all of the actual operations based on the instructions of the program code.
Further, it is understood that the program code read out by the storage medium is written into a memory provided in an expansion board inserted into a computer or into a memory provided in an expansion module connected to the computer, and then a CPU or the like mounted on the expansion board or the expansion module is caused to perform part and all of actual operations based on instructions of the program code, thereby realizing the functions of any of the above embodiments.
It is noted that relational terms such as first and second, and the like, are 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. Moreover, 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 one.," does not exclude that an additional identical element is present in a process, method, article, or apparatus that comprises the element.
It will be appreciated by those of ordinary skill in the art that implementing all or part of the steps of the above method embodiments may be accomplished by hardware associated with program instructions, and that the above program may be stored in a computer readable storage medium which, when executed, performs the steps comprising the above method embodiments, where the above storage medium includes various media that may store program code, such as ROM, RAM, magnetic or optical disks.
It should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention, and not for limiting the same, and although the present invention has been described in detail with reference to the above-mentioned embodiments, it should be understood by those skilled in the art that the technical solution described in the above-mentioned embodiments may be modified or some technical features may be equivalently replaced, and these modifications or substitutions do not make the essence of the corresponding technical solution deviate from the spirit and scope of the technical solution of the embodiments of the present invention.