An automated method for balancing the burden of childhood cancer and medical resources

Abstract

The present invention provides an automatic balancing method for burden differences and medical resources for children's tumor diseases, including: obtaining children's disease diagnosis data based on a medical insurance database, and screening the disease diagnosis data to obtain children's tumor disease data; analyzing the children's tumor disease data to determine children's tumor disease frequency indicators and medical resource utilization indicators, and determining the burden differences of children's tumor diseases; formulating a medical resource balancing strategy based on the burden differences of children's tumor diseases, and balancing the medical resources for children's tumor diseases based on the medical resource balancing strategy. Accurate and effective analysis of children's tumor disease frequency indicators and medical resource utilization indicators is achieved, thereby accurately obtaining the burden differences of the disease, and finally effectively balancing medical resources through burden differences, thereby achieving an effective balance between the disease burden and medical resources, and ensuring the utilization effect of medical resources.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data processing, and in particular to an automatic balancing method for burden differences of childhood tumor diseases and medical resources. Background Art

[0002] Currently, no studies have addressed the burden of childhood cancer, and these population-based studies do not consider the burden of health care utilization, an important measure of health access and disease outcomes;

[0003] However, currently, medical resources cannot be automatically balanced with the disease burden in real time, which leads to irrational utilization and allocation of medical resources, thus reducing the treatment effect of tumor diseases in different regions or for children;

[0004] Therefore, the present invention provides a method for automatically balancing the difference in burden of childhood tumor diseases and medical resources. Summary of the invention

[0005] The present invention provides an automatic balancing method for the burden difference of childhood tumor diseases and medical resources, which is used to analyze childhood tumor disease data to achieve accurate and effective analysis of childhood tumor disease frequency indicators and medical resource utilization indicators, thereby accurately obtaining the burden difference of the disease, and finally effectively balancing medical resources through the burden difference, thereby achieving an effective balance between the disease burden and medical resources, and ensuring the utilization effect of medical resources.

[0006] The present invention provides an automatic balancing method for the burden difference of childhood tumor diseases and medical resources, comprising:

[0007] Step 1: Obtaining children's disease diagnosis data based on the medical insurance database, and screening the disease diagnosis data to obtain children's tumor disease data;

[0008] Step 2: Analyze the pediatric tumor disease data to determine the frequency index of the pediatric tumor disease and the medical resource utilization index, and determine the burden difference of the pediatric tumor disease based on the tumor disease frequency index and the medical resource utilization index;

[0009] Step 3: Formulate a medical resource balance strategy based on the differences in the burden of childhood tumor diseases, and balance the medical resources for childhood tumor diseases based on the medical resource balance strategy.

[0010] Preferably, the method for automatically balancing the difference in burden of childhood tumor diseases and medical resources, in step 1, obtaining childhood disease diagnosis data based on the medical insurance database, includes:

[0011] Obtaining a data retrieval request, and parsing the data retrieval request to determine the type of business data corresponding to the data retrieval request;

[0012] Extracting a type identifier of the business data type, accessing the medical insurance database based on the type identifier, and matching it with an index table of the medical insurance database to determine a target data layer where the business data type is located;

[0013] The children's disease diagnosis data corresponding to the business data type is extracted based on the target data layer, and the children's disease diagnosis data is packaged and transmitted to the data access terminal.

[0014] Preferably, the method for automatically balancing the difference in burden of childhood tumor diseases and medical resources, wherein the disease diagnosis data is screened in step 1 to obtain childhood tumor disease data, comprises:

[0015] Acquire the obtained children's disease diagnosis data, and at the same time, acquire historical children's tumor disease data, and extract data features of the historical children's tumor disease data;

[0016] Using the data feature as a target condition and using the target condition as a cluster center to perform clustering processing on the children's disease diagnosis data;

[0017] The Hamming distance between the child disease diagnosis data and the target condition is determined based on the clustering processing result, and the child disease diagnosis data with the Hamming distance less than a preset threshold is determined to be child tumor disease data, thereby completing the screening of the child disease diagnosis data.

[0018] Preferably, the method for automatically balancing the difference in burden of childhood tumor diseases and medical resources completes the screening of childhood disease diagnosis data, including:

[0019] Acquire the obtained children's tumor disease data, and extract basic information of the children from the children's tumor disease data, wherein the basic information includes age, gender and ethnicity;

[0020] Verify the extracted basic information to determine whether there is any missing basic information about the child;

[0021] If the basic information of the child is missing, the child corresponding to the current basic information is determined to be an abnormal individual, and the current basic information is eliminated to obtain standard child tumor disease data;

[0022] Otherwise, it is determined that the basic information corresponding to the child is complete, and the verification of the basic information is completed.

[0023] Preferably, the method for automatically balancing the difference in burden of childhood tumor diseases and medical resources, obtaining standard childhood tumor disease data, includes:

[0024] Obtaining the obtained standard childhood tumor disease data, and determining the age group of the individual corresponding to the childhood tumor disease data based on the basic information of the standard childhood tumor disease data;

[0025] Performing a first classification on the standard childhood tumor disease data based on the age group to obtain a first classification result, and determining the gender of the individual corresponding to the standard childhood tumor disease data in each first classification based on the first classification result;

[0026] Performing a second classification on the first classification result based on the gender to obtain a second classification result, and determining the ethnicity of the individual corresponding to the standard childhood tumor disease data in each second classification based on the second classification result;

[0027] The second classification result is classified into a third category based on the ethnicity to obtain a third classification result, and the privacy data in the basic information of the standard childhood tumor disease data is determined based on the third classification result, and the privacy data is anonymized.

[0028] Preferably, the method for automatically balancing the burden difference of childhood tumor diseases and medical resources, in step 2, analyzing the childhood tumor disease data to determine the childhood tumor disease frequency index and the medical resource utilization index, includes:

[0029] Acquire the obtained pediatric tumor disease data, and determine classification indicators for the pediatric tumor disease data, wherein the classification indicators include age, gender, and ethnicity;

[0030] Classify the children's tumor disease data based on the classification index to obtain a target sub-data set, and determine the number of new children with tumors in the year and the total number of susceptible children insured in the year in each target sub-data in the target sub-data set;

[0031] Determine the incidence rate corresponding to each target sub-data based on the number of new childhood cancer patients in each target sub-data and the total number of susceptible children insured in that year;

[0032] At the same time, the number of pediatric patients diagnosed before the target time period and surviving to the current year and the number of insured persons in the current year in each target sub-data are determined, and the prevalence rate of the target time period corresponding to each target sub-data is determined based on the number of pediatric patients diagnosed before the target time period and surviving to the current year and the number of insured persons in the current year;

[0033] The frequency index of the childhood tumor disease is obtained based on the incidence rate and the prevalence rate in the target time period.

[0034] Preferably, the method for automatically balancing the burden difference of childhood tumor diseases and medical resources, in step 2, analyzing the childhood tumor disease data to determine the childhood tumor disease frequency index and the medical resource utilization index, also includes:

[0035] Acquire the obtained pediatric tumor disease data, and group the pediatric tumor disease data based on gender, age and ethnicity to obtain sub-data groups;

[0036] Generate a treatment cost access request based on the pediatric tumor disease data in each of the sub-data groups, and access a preset patient treatment cost usage database based on the treatment cost access request;

[0037] Based on the preset patient treatment cost usage database, the total medical insurance reimbursement cost of each child patient within one year after diagnosis is obtained, and at the same time, the medical insurance reimbursement ratio is determined, and the treatment cost corresponding to each sub-data group is obtained based on the medical insurance reimbursement ratio and the total medical insurance reimbursement cost;

[0038] Obtaining the consumption index of the current year, and revising the total medical insurance reimbursement cost based on the consumption index to obtain the patient treatment cost corresponding to each sub-data group;

[0039] At the same time, extracting the name of the medical institution visited by each child patient and the total number of medical institutions visited from the pediatric tumor disease data, and determining the name features of the names of the medical institutions visited in turn based on the total number of medical institutions visited, wherein the name of the medical institution visited is at least one;

[0040] Based on the name features, the names of the medical institutions to be visited are deduplicated to obtain a set of target medical institutions to be visited, and each target medical institution name in the set of target medical institutions to be visited is searched in a preset medical institution grade search library to obtain the medical grade of each target medical institution to be visited;

[0041] Based on the total number of medical institutions visited by each child patient, a flow chart of medical institutions visited by each child patient is drawn, and based on the medical grade of each target medical institution visited, each medical institution visited is graded in the flow chart of medical institutions visited;

[0042] Based on the labeling results, the medical institution flow information of each child patient is obtained;

[0043] A medical resource utilization index is obtained based on the patient's treatment costs and the flow information of medical institutions visited.

[0044] Preferably, the method for automatically balancing the burden difference of childhood tumor diseases and medical resources, in step 2, determining the burden difference of childhood tumor diseases based on the tumor disease frequency index and the medical resource utilization index, comprises:

[0045] Obtaining tumor disease frequency indicators and medical resource utilization indicators, and determining the number of children suffering from tumor diseases each year based on the tumor disease frequency indicators;

[0046] At the same time, the treatment cost and the type of medical institution visited by each child patient with tumor disease are determined based on the medical resource utilization index;

[0047] Differences in the burden of childhood cancer were obtained based on the number of children with cancer diseases, treatment costs, and types of medical institutions visited each year.

[0048] Preferably, in the method for automatically balancing the burden difference of childhood tumor diseases and medical resources, in step 3, a medical resource balancing strategy is formulated based on the burden difference of childhood tumor diseases, and medical resources for childhood tumor diseases are balanced based on the medical resource balancing strategy, including:

[0049] Obtain the differences in burden of childhood tumor diseases, and determine the ability of families with childhood tumor diseases to pay for treatment costs and the level of treatment of medical institutions based on the differences in burden;

[0050] Constructing a medical resource balancing strategy based on the payment ability of the treatment expenses and the treatment level of the medical institution visited, and classifying the treatment level of the medical institution visited based on the medical resource balancing strategy;

[0051] Determine the target medical resource set required for the treatment of childhood tumor diseases, and access resources from a preset medical resource allocation center based on the target medical resource set;

[0052] Determine the medical resource storage information of the preset medical resource allocation center based on the resource access, and determine the allocation amount of medical resources to the medical institutions with different treatment levels based on the medical resource storage information;

[0053] Based on the tumor disease frequency index and the medical resource utilization index, the number of children's tumor diseases treated by the medical institution, the utilization rate of medical resources and the material loss coefficient are determined, and based on the number of children's tumor diseases treated, the utilization rate of medical resources and the material loss coefficient, the allocation of medical resources is corrected to obtain the target allocation of medical resources for medical institutions with different treatment levels;

[0054] At the same time, the geographical location characteristics of medical institutions with different treatment levels are determined, and based on the geographical location characteristics, the tiered payment costs of medical resources required by the medical institutions for children with different degrees of tumor diseases are determined;

[0055] The medical resources are balanced based on the target allocation of the medical resources and the tiered payment fees for the medical resources.

[0056] Preferably, the method for automatically balancing the difference in burden of childhood tumor diseases and medical resources completes the balancing process of medical resources, including:

[0057] Obtain the results of the balancing process of medical resources, and based on the balancing process results, conduct real-time monitoring of the medical resource storage information of the preset medical resource allocation center, the treatment level of the medical institution, and the number of children's tumor diseases treated by different medical institutions;

[0058] Based on the monitoring results, when the medical resource storage information of the preset medical resource allocation center, the treatment level of the medical institution, and the number of treatments for childhood tumor diseases in different medical institutions are determined to have changed, the medical resources will be rebalanced.

[0059] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0060] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0062] Figure 1 It is a flow chart of a method for automatically balancing the burden difference of childhood tumor diseases and medical resources in an embodiment of the present invention;

[0063] Figure 2 This is a flowchart of step 1 in the method for automatically balancing the burden difference of childhood tumor diseases and medical resources in an embodiment of the present invention;

[0064] Figure 3 This is a flowchart of step 2 in the method for automatically balancing the burden difference of childhood tumor diseases and medical resources in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0066] Embodiment 1:

[0067] This embodiment provides an automatic balancing method for the burden difference of childhood tumor diseases and medical resources, such as Figure 1 As shown, including:

[0068] Step 1: Obtaining children's disease diagnosis data based on the medical insurance database, and screening the disease diagnosis data to obtain children's tumor disease data;

[0069] Step 2: Analyze the pediatric tumor disease data to determine the frequency index of the pediatric tumor disease and the medical resource utilization index, and determine the burden difference of the pediatric tumor disease based on the tumor disease frequency index and the medical resource utilization index;

[0070] Step 3: Formulate a medical resource balance strategy based on the differences in the burden of childhood tumor diseases, and balance the medical resources for childhood tumor diseases based on the medical resource balance strategy.

[0071] In this embodiment, the medical insurance database is pre-set to store disease diagnosis data, demographic characteristics, ID number, ethnicity, and cost information of children of different genders and age groups.

[0072] In this embodiment, the children's disease diagnosis data refers to the diagnosis data corresponding to various types of diseases that children develop during the medical treatment process.

[0073] In this embodiment, screening the disease diagnosis data refers to determining the type of the acquired child disease diagnosis data, thereby extracting the child tumor disease data from the acquired disease diagnosis data.

[0074] In this embodiment, the frequency index of childhood tumor disease refers to the incidence rate of childhood tumor and the prevalence rate within a certain period of time.

[0075] In this embodiment, the medical resource utilization index refers to the treatment costs of child patients and the flow of hospitals they visit.

[0076] In this embodiment, the difference in the burden of childhood tumor disease refers to the difference in the burden of childhood tumor disease on family treatment expenses, the impact of the incidence and mortality of childhood tumor disease on society, and the severity of childhood tumor disease.

[0077] In this embodiment, the medical resource balancing strategy refers to a method for automatically balancing medical resources according to differences in disease burden.

[0078] The beneficial effect of the above technical solution is: by analyzing the children's tumor disease data, accurate and effective analysis of the children's tumor disease frequency indicators and medical resource utilization indicators can be achieved, so as to accurately obtain the differences in the burden of the disease, and finally effectively balance the medical resources through the burden differences, thereby achieving an effective balance between the disease burden and medical resources, and ensuring the utilization of medical resources.

[0079] Embodiment 2:

[0080] Based on Example 1, this example provides an automatic balancing method for the burden difference of childhood tumor diseases and medical resources, such as Figure 2 As shown, in step 1, children's disease diagnosis data is obtained based on the medical insurance database, including:

[0081] Obtaining a data retrieval request, and parsing the data retrieval request to determine the type of business data corresponding to the data retrieval request;

[0082] Extracting a type identifier of the business data type, accessing the medical insurance database based on the type identifier, and matching it with an index table of the medical insurance database to determine a target data layer where the business data type is located;

[0083] The children's disease diagnosis data corresponding to the business data type is extracted based on the target data layer, and the children's disease diagnosis data is packaged and transmitted to the data access terminal.

[0084] In this embodiment, the data retrieval request is generated by the data access terminal and is used to access the medical insurance database.

[0085] In this embodiment, the business data type refers to the type of data required to be retrieved contained in the data retrieval request, which may specifically be disease diagnosis data or identity information, etc.

[0086] In this embodiment, the type identifier is used to mark the marking tags corresponding to different business data types, so as to facilitate the corresponding data retrieval operation according to the type identifier.

[0087] In this embodiment, the target data layer refers to the data application layer corresponding to the business data type stored in the medical insurance database.

[0088] The beneficial effect of the above technical solution is: by analyzing the data retrieval request, the type of data that needs to be retrieved can be accurately and effectively determined, so that the corresponding data can be retrieved from the medical insurance database, which provides convenience for determining the burden differences of childhood tumor diseases, and also provides convenience for achieving a balance between burden differences and medical resources.

[0089] Embodiment 3:

[0090] Based on Example 1, this example provides an automatic balancing method for the burden difference of childhood tumor diseases and medical resources, such as Figure 3 As shown, in step 1, the disease diagnosis data is screened to obtain children's tumor disease data, including:

[0091] Acquire the obtained children's disease diagnosis data, and at the same time, acquire historical children's tumor disease data, and extract data features of the historical children's tumor disease data;

[0092] Using the data feature as a target condition and using the target condition as a cluster center to perform clustering processing on the children's disease diagnosis data;

[0093] The Hamming distance between the child disease diagnosis data and the target condition is determined based on the clustering processing result, and the child disease diagnosis data with the Hamming distance less than a preset threshold is determined to be child tumor disease data, thereby completing the screening of the child disease diagnosis data.

[0094] In this embodiment, the historical children's tumor disease data is set in advance to provide a reference for screening children's tumor disease data from children's disease diagnosis data.

[0095] In this embodiment, the target condition refers to using the children's tumor disease data as a screening condition for the children's disease diagnosis data, thereby achieving effective screening of the children's disease diagnosis data.

[0096] In this embodiment, data features refer to value features of childhood tumor disease data and correlation features between data.

[0097] In this embodiment, the Hamming distance is used to characterize the distance between the children's disease diagnosis data and the target condition. The smaller the Hamming distance is, the more the children's disease diagnosis data meets the current target condition, that is, it is likely to be children's tumor disease data.

[0098] In this embodiment, the preset threshold is set in advance and is used to measure the maximum range that can meet the target condition, and can be adjusted.

[0099] The beneficial effect of the above technical solution is: by determining the data characteristics of children's tumor diseases, accurate and effective screening of children's disease diagnosis data can be achieved according to the data characteristics, thereby ensuring the reliability of children's tumor disease data selected from children's disease diagnosis data, and providing a guarantee for accurately determining differences in disease burden.

[0100] Embodiment 4:

[0101] Based on Example 3, this example provides an automatic balancing method for the burden difference of childhood tumor diseases and medical resources, and completes the screening of childhood disease diagnosis data, including:

[0102] Acquire the obtained children's tumor disease data, and extract basic information of the children from the children's tumor disease data, wherein the basic information includes age, gender and ethnicity;

[0103] Verify the extracted basic information to determine whether there is any missing basic information about the child;

[0104] If the basic information of the child is missing, the child corresponding to the current basic information is determined to be an abnormal individual, and the current basic information is eliminated to obtain standard child tumor disease data;

[0105] Otherwise, it is determined that the basic information corresponding to the child is complete, and the verification of the basic information is completed.

[0106] In this embodiment, verifying the extracted basic information refers to verifying the age, gender, and nationality contained in the basic information of the child obtained, to verify whether there are children whose basic information is missing.

[0107] In this embodiment, abnormal individuals refer to children whose basic information is missing.

[0108] In this embodiment, the labeled children's tumor disease data refers to the final children's tumor disease data obtained by extracting the data with missing information from the acquired children's tumor disease data.

[0109] The beneficial effect of the above technical solution is: by verifying the acquired children's tumor disease data and eliminating the children's tumor disease data with missing basic information, the accuracy of the children's tumor disease data finally obtained is guaranteed, which provides convenience and guarantee for ensuring the accuracy of the difference in the burden of children's tumor disease.

[0110] Embodiment 5:

[0111] Based on Example 4, this example provides an automatic balancing method for the difference in burden of childhood tumor diseases and medical resources, and obtains standard childhood tumor disease data, including:

[0112] Obtaining the obtained standard childhood tumor disease data, and determining the age group of the individual corresponding to the childhood tumor disease data based on the basic information of the standard childhood tumor disease data;

[0113] Performing a first classification on the standard childhood tumor disease data based on the age group to obtain a first classification result, and determining the gender of the individual corresponding to the standard childhood tumor disease data in each first classification based on the first classification result;

[0114] Performing a second classification on the first classification result based on the gender to obtain a second classification result, and determining the ethnicity of the individual corresponding to the standard childhood tumor disease data in each second classification based on the second classification result;

[0115] The second classification result is classified into a third category based on the ethnicity to obtain a third classification result, and the privacy data in the basic information of the standard childhood tumor disease data is determined based on the third classification result, and the privacy data is anonymized.

[0116] In this embodiment, the first classification refers to classifying the children's tumor disease data according to age groups, specifically, children aged 0-14 years old, and children aged 0-4 years old, 5-9 years old, and 10-14 years old are classified into one category respectively.

[0117] In this embodiment, the second classification refers to classifying the children's tumor disease data of the same gender in each age group into one category according to the first classification result.

[0118] In this embodiment, the third classification refers to classifying the children's tumor disease data of the same gender and the same nationality into one category based on the second classification result.

[0119] In this embodiment, anonymization processing refers to anonymizing the private data in each category according to the final classification result, wherein the private data may specifically be the name of the child, etc.

[0120] The beneficial effects of the above technical solution are: by classifying the acquired standard pediatric tumor disease data according to age groups, genders and ethnicities, and anonymizing the children's privacy data according to the classification results, the protection of children's privacy is ensured, and at the same time, convenience and guarantee are provided for accurately determining the differences in the burden of pediatric tumor diseases.

[0121] Embodiment 6:

[0122] Based on Example 1, this example provides an automatic balancing method for the burden difference of childhood tumor diseases and medical resources. In step 2, the childhood tumor disease data is analyzed to determine the childhood tumor disease frequency index and the medical resource utilization index, including:

[0123] Acquire the obtained pediatric tumor disease data, and determine classification indicators for the pediatric tumor disease data, wherein the classification indicators include age, gender, and ethnicity;

[0124] Classify the children's tumor disease data based on the classification index to obtain a target sub-data set, and determine the number of new children with tumors in the year and the total number of susceptible children insured in the year in each target sub-data in the target sub-data set;

[0125] Determine the incidence rate corresponding to each target sub-data based on the number of new childhood cancer patients in each target sub-data and the total number of susceptible children insured in that year;

[0126] At the same time, the number of pediatric patients diagnosed before the target time period and surviving to the current year and the number of insured persons in the current year in each target sub-data are determined, and the prevalence rate of the target time period corresponding to each target sub-data is determined based on the number of pediatric patients diagnosed before the target time period and surviving to the current year and the number of insured persons in the current year;

[0127] The frequency index of the childhood tumor disease is obtained based on the incidence rate and the prevalence rate in the target time period.

[0128] In this embodiment, the classification index refers to the standard used to classify the children's tumor disease data, thereby achieving effective classification of the children's tumor disease data and providing convenience for accurately determining the children's tumor disease frequency index.

[0129] In this embodiment, the target sub-data set refers to each category obtained after classifying the children's tumor disease data according to the classification index.

[0130] In this embodiment, the total number of susceptible children refers to the total number of all eligible research people insured that year, as childhood cancer is a rare disease with an incidence rate generally below 300 per million people. Therefore, the total number of susceptible children is regarded as the total population.

[0131] In this embodiment, the target time period is set in advance, generally five years.

[0132] In this embodiment, the number of new childhood cancer patients in a preset age group and the total number of susceptible children insured in that year are obtained, and the incidence rate of childhood cancer diseases is calculated based on the number of new childhood cancer patients and the total number of susceptible children insured in that year, and the number of years of life lost by childhood cancer diseases for children in the preset age group is calculated based on the incidence rate. The specific steps include:

[0133] The incidence of childhood tumors was calculated using the following formula:

[0134] ;

[0135] in, Represents the incidence of childhood tumor diseases, and its value range is (0, 1); It indicates the number of children with tumor diseases in the preset age group before the preset time period; Indicates the number of children with tumors in the preset age group after the preset time end, and the value is greater than ; It indicates the total number of susceptible children insured in that year;

[0136] The number of years of life lost due to childhood cancer in children in a preset age group is calculated using the following formula:

[0137] ;

[0138] in, It indicates the number of years of life lost by children with the disease in a preset age group; Represents the error factor, and its value range is (0.02, 0.05); Represents the current number of children with tumor diseases in the preset age group, and its value range is [1, ]; represents the total number of children with neoplastic diseases in the predefined age group; represents the life expectancy of children with neoplastic diseases in a predefined age group; Indicates the first The life span of a child at the time of death; Represents the incidence of childhood tumor diseases, and its value range is (0, 1);

[0139] Comparing the calculated years of life lost with a preset time threshold;

[0140] If the number of years of life lost is less than or equal to the preset time threshold, it is determined that the number of years of life lost by the childhood tumor disease for children in the preset age group is normal;

[0141] Otherwise, it is determined that the number of years of life lost due to the childhood tumor disease in the preset age group of sick children is abnormal, and that there is an impact on the remaining life expectancy of sick children in the preset age group.

[0142] The above life expectancy is the ideal life value determined after many experiments.

[0143] The above-mentioned preset time threshold is set in advance and is used to measure whether the number of years of life lost by children in the preset age group exceeds the normal attenuation range.

[0144] The beneficial effects of the above technical solution are: by accurately and effectively classifying children's tumor disease data according to gender, age and ethnicity, and accurately and effectively analyzing the incidence and prevalence of each type of children's tumor disease based on the classification results, it is ensured that the frequency indicators of children's tumor diseases are accurately and effectively obtained, which provides convenience and guarantee for the accurate determination of the differences in the burden of children's tumor diseases.

[0145] Embodiment 7:

[0146] Based on Example 1, this example provides an automatic balancing method for the burden difference of childhood tumor diseases and medical resources. In step 2, the childhood tumor disease data is analyzed to determine the childhood tumor disease frequency index and the medical resource utilization index, and further includes:

[0147] Acquire the obtained pediatric tumor disease data, and group the pediatric tumor disease data based on gender, age and ethnicity to obtain sub-data groups;

[0148] Generate a treatment cost access request based on the pediatric tumor disease data in each of the sub-data groups, and access a preset patient treatment cost usage database based on the treatment cost access request;

[0149] Based on the preset patient treatment cost usage database, the total medical insurance reimbursement cost of each child patient within one year after diagnosis is obtained, and at the same time, the medical insurance reimbursement ratio is determined, and the treatment cost corresponding to each sub-data group is obtained based on the medical insurance reimbursement ratio and the total medical insurance reimbursement cost;

[0150] Obtaining the consumption index of the current year, and revising the total medical insurance reimbursement cost based on the consumption index to obtain the patient treatment cost corresponding to each sub-data group;

[0151] At the same time, extracting the name of the medical institution visited by each child patient and the total number of medical institutions visited from the pediatric tumor disease data, and determining the name features of the names of the medical institutions visited in turn based on the total number of medical institutions visited, wherein the name of the medical institution visited is at least one;

[0152] Based on the name features, the names of the medical institutions to be visited are deduplicated to obtain a set of target medical institutions to be visited, and each target medical institution name in the set of target medical institutions to be visited is searched in a preset medical institution grade search library to obtain the medical grade of each target medical institution to be visited;

[0153] Based on the total number of medical institutions visited by each child patient, a flow chart of medical institutions visited by each child patient is drawn, and based on the medical grade of each target medical institution visited, each medical institution visited is graded in the flow chart of medical institutions visited;

[0154] Based on the labeling results, the medical institution flow information of each child patient is obtained;

[0155] A medical resource utilization index is obtained based on the patient's treatment costs and the flow information of medical institutions visited.

[0156] In this embodiment, the sub-data group refers to each type of data obtained after the children's tumor disease data is classified according to gender, age and ethnicity.

[0157] In this embodiment, the treatment cost access request is used to access a preset patient treatment cost usage database in order to obtain the medical expenses of children suffering from pediatric tumor diseases.

[0158] In this embodiment, the preset patient treatment cost usage database is set in advance and is used to store the treatment costs of children with different pediatric tumor diseases.

[0159] In this embodiment, the medical insurance reimbursement ratio is used to represent the ratio of self-payment to reimbursement.

[0160] In this embodiment, the consumption index is used to represent the price consumption level of the year, and the purpose is to correct the treatment costs of sick children.

[0161] In this embodiment, the name of the medical institution for treatment refers to the name of the medical institution for treatment.

[0162] In this embodiment, the name feature refers to the characteristics of the names of different medical institutions, including special characters and the like.

[0163] In this embodiment, the target medical institution name set refers to a non-repetitive medical institution name set obtained after removing duplicates from the acquired medical institution names.

[0164] In this embodiment, the preset medical institution level search library is set in advance and is used to store treatment levels of different medical treatment levels, which may be level three or level two, etc.

[0165] In this embodiment, the medical institution flow diagram is used to represent all hospitals visited by the child during the medical treatment process.

[0166] The beneficial effects of the above technical scheme are: by analyzing the data of children's tumor diseases, it is possible to accurately and effectively judge the treatment costs of children's tumor diseases during the medical treatment process; secondly, by analyzing the data of children's tumor diseases, it is possible to effectively obtain the medical institutions where different sick children are treated, and through the treatment costs and the flow of medical institutions, it is possible to accurately and effectively analyze the relevant medical resource utilization indicators of children's tumor diseases, thereby providing a guarantee for accurately analyzing the differences in the burden of children's tumor diseases and ensuring the accuracy of determining the burden differences.

[0167] Embodiment 8:

[0168] Based on Example 1, this example provides an automatic balancing method for the burden difference of childhood tumor diseases and medical resources. In step 2, the burden difference of childhood tumor diseases is determined based on the tumor disease frequency index and the medical resource utilization index, including:

[0169] Obtaining tumor disease frequency indicators and medical resource utilization indicators, and determining the number of children suffering from tumor diseases each year based on the tumor disease frequency indicators;

[0170] At the same time, the treatment cost and the type of medical institution visited by each child patient with tumor disease are determined based on the medical resource utilization index;

[0171] Differences in the burden of childhood cancer were obtained based on the number of children with cancer diseases, treatment costs, and types of medical institutions visited each year.

[0172] The beneficial effects of the above technical solution are: by reliably analyzing the childhood tumor disease data based on the acquired childhood tumor disease frequency indicators and medical resource utilization indicators, it is possible to accurately and effectively grasp the treatment costs and number of patients of childhood tumor diseases, and to accurately and effectively obtain the differences in the burden of childhood tumor diseases through treatment costs and population characteristics, thereby facilitating accurate and effective balancing of medical resources.

[0173] Embodiment 9:

[0174] Based on Example 1, this example provides an automatic balancing method for burden differences and medical resources for children with tumor diseases. In step 3, a medical resource balancing strategy is formulated based on the burden differences of the children with tumor diseases, and medical resources for children with tumor diseases are balanced based on the medical resource balancing strategy, including:

[0175] Obtain the differences in burden of childhood tumor diseases, and determine the ability of families with childhood tumor diseases to pay for treatment and the level of treatment of medical institutions based on the differences in burden;

[0176] Constructing a medical resource balancing strategy based on the payment ability of the treatment expenses and the treatment level of the medical institution visited, and classifying the treatment level of the medical institution visited based on the medical resource balancing strategy;

[0177] Determine the target medical resource set required for the treatment of childhood tumor diseases, and access resources from a preset medical resource allocation center based on the target medical resource set;

[0178] Determine the medical resource storage information of the preset medical resource allocation center based on the resource access, and determine the allocation amount of medical resources to the medical institutions with different treatment levels based on the medical resource storage information;

[0179] Based on the tumor disease frequency index and the medical resource utilization index, the number of children's tumor diseases treated by the medical institution, the utilization rate of medical resources and the material loss coefficient are determined, and based on the number of children's tumor diseases treated, the utilization rate of medical resources and the material loss coefficient, the allocation of medical resources is corrected to obtain the target allocation of medical resources for medical institutions with different treatment levels;

[0180] At the same time, the geographical location characteristics of medical institutions with different treatment levels are determined, and based on the geographical location characteristics, the tiered payment costs of medical resources required by the medical institutions for children with different degrees of tumor diseases are determined;

[0181] The medical resources are balanced based on the target allocation of the medical resources and the tiered payment fees for the medical resources.

[0182] In this embodiment, the medical resource balancing strategy refers to the allocation and charging of medical resources according to the payment ability of different sick children and the treatment level of the medical institution visited.

[0183] In this embodiment, the target medical resource set refers to the medical resources required in the treatment of childhood tumors.

[0184] In this embodiment, the preset medical resource allocation center is set in advance and is used to allocate stored medical resources and store and record existing medical resources.

[0185] In this embodiment, the medical resource storage information refers to the quantity and types of medical resources currently owned by the preset medical resource allocation center.

[0186] In this embodiment, determining the allocation amount of medical resources to medical institutions with different treatment levels based on the medical resource storage information means allocating an appropriate amount of medical resources to medical institutions with higher treatment levels.

[0187] In this embodiment, the material loss coefficient refers to the waste caused by the medical institution in the process of using medical resources.

[0188] In this embodiment, the target allocation amount refers to the medical resources that can be allocated to different medical institutions after the determined allocation amount is corrected.

[0189] In this embodiment, the geographical location feature is suitable for representing the geographical locations of different medical institutions, which may specifically be towns, county towns, provincial capitals, etc.

[0190] In this embodiment, the tiered payment fee means that the payment ability varies with the geographical location characteristics. The charges for medical resources for different degrees of illness are determined according to the payment ability and the location of the medical institution. Specifically, the charges in remote areas are lower, and the charges in well-developed areas are higher.

[0191] The beneficial effects of the above technical scheme are: by accurately and effectively obtaining the payment ability of families with children's tumor diseases and the treatment level of the medical institutions visited according to the burden difference, and accurately and reliably formulating the allocation of medical resources and the charging standards of medical resources according to the ability to pay for treatment costs and the treatment level of the medical institutions visited, thereby achieving an accurate and effective balance between medical resources and the burden differences of children's tumor diseases, thereby providing great convenience for children with different children's tumor diseases during the treatment process and improving the treatment effect.

[0192] Embodiment 10:

[0193] Based on Example 9, this example provides an automatic balancing method for the burden difference of childhood tumor diseases and medical resources, completing the balancing process of medical resources, including:

[0194] Obtain the results of the balancing process of medical resources, and based on the balancing process results, conduct real-time monitoring of the medical resource storage information of the preset medical resource allocation center, the treatment level of the medical institution, and the number of children's tumor diseases treated by different medical institutions;

[0195] Based on the monitoring results, when the medical resource storage information of the preset medical resource allocation center, the treatment level of the medical institution, and the number of treatments for childhood tumor diseases in different medical institutions are determined to have changed, the medical resources will be rebalanced.

[0196] The beneficial effect of the above technical solution is: by real-time monitoring of the parameters that affect the difference in medical resources and disease burden, and balancing the medical resources when the parameters change and when they are re-balanced, the effective balance between the difference in disease burden and medical resources is ensured, providing a treatment basis for sick children and improving the treatment effect.

[0197] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for automatically balancing the difference in burden of childhood tumor diseases and medical resources, characterized in that: include: Step 1: Obtaining children's disease diagnosis data based on the medical insurance database, and screening the disease diagnosis data to obtain children's tumor disease data; Step 2: Analyze the pediatric tumor disease data to determine the frequency index of the pediatric tumor disease and the medical resource utilization index, and determine the burden difference of the pediatric tumor disease based on the tumor disease frequency index and the medical resource utilization index; Step 3: Formulate a medical resource balance strategy based on the difference in burden of the childhood tumor disease, and balance the medical resources for the childhood tumor disease based on the medical resource balance strategy; Wherein, in step 2, determining the burden difference of childhood tumor diseases based on the tumor disease frequency index and the medical resource utilization index includes: Obtaining tumor disease frequency indicators and medical resource utilization indicators, and determining the number of children suffering from tumor diseases each year based on the tumor disease frequency indicators; At the same time, the treatment cost and the type of medical institution visited by each child patient with tumor disease are determined based on the medical resource utilization index; The difference in the burden of childhood cancer was obtained based on the number of children with cancer diseases, treatment costs, and types of medical institutions visited in each year; Wherein, in step 3, a medical resource balance strategy is formulated based on the difference in burden of the childhood tumor disease, and the medical resources for the childhood tumor disease are balanced based on the medical resource balance strategy, including: Obtain the differences in burden of childhood tumor diseases, and determine the ability of families with childhood tumor diseases to pay for treatment and the level of treatment of medical institutions based on the differences in burden; Constructing a medical resource balancing strategy based on the payment ability of the treatment expenses and the treatment level of the medical institution visited, and classifying the treatment level of the medical institution visited based on the medical resource balancing strategy; Determine the target medical resource set required for the treatment of childhood tumor diseases, and access resources from a preset medical resource allocation center based on the target medical resource set; Determine the medical resource storage information of the preset medical resource allocation center based on the resource access, and determine the allocation amount of medical resources to the medical institutions with different treatment levels based on the medical resource storage information; Based on the tumor disease frequency index and the medical resource utilization index, the number of children's tumor diseases treated by the medical institution, the utilization rate of medical resources and the material loss coefficient are determined, and based on the number of children's tumor diseases treated, the utilization rate of medical resources and the material loss coefficient, the allocation of medical resources is corrected to obtain the target allocation of medical resources for medical institutions with different treatment levels; At the same time, the geographical location characteristics of medical institutions with different treatment levels are determined, and based on the geographical location characteristics, the tiered payment costs of medical resources required by the medical institutions for children with different degrees of tumor diseases are determined; The medical resources are balanced based on the target allocation of the medical resources and the tiered payment fees for the medical resources.

2. The method for automatically balancing the burden difference and medical resources of childhood tumor diseases according to claim 1, characterized in that: In step 1, children's disease diagnosis data is obtained based on the medical insurance database, including: Obtaining a data retrieval request, and parsing the data retrieval request to determine the type of business data corresponding to the data retrieval request; Extracting a type identifier of the business data type, accessing the medical insurance database based on the type identifier, and matching it with an index table of the medical insurance database to determine a target data layer where the business data type is located; The children's disease diagnosis data corresponding to the business data type is extracted based on the target data layer, and the children's disease diagnosis data is packaged and transmitted to the data access terminal.

3. The method for automatically balancing the burden difference and medical resources of childhood tumor diseases according to claim 1, characterized in that: In step 1, the disease diagnosis data is screened to obtain childhood tumor disease data, including: Acquire the obtained children's disease diagnosis data, and at the same time, acquire historical children's tumor disease data, and extract data features of the historical children's tumor disease data; Using the data feature as a target condition and using the target condition as a cluster center to perform clustering processing on the children's disease diagnosis data; The Hamming distance between the child disease diagnosis data and the target condition is determined based on the clustering processing result, and the child disease diagnosis data with the Hamming distance less than a preset threshold is determined to be child tumor disease data, thereby completing the screening of the child disease diagnosis data.

4. The method for automatically balancing the burden difference and medical resources of childhood tumor diseases according to claim 3, characterized in that: Complete screening of children's disease diagnosis data, including: Acquire the obtained children's tumor disease data, and extract basic information of the children from the children's tumor disease data, wherein the basic information includes age, gender and ethnicity; Verify the extracted basic information to determine whether there is any missing basic information about the child; If the basic information of the child is missing, the child corresponding to the current basic information is determined to be an abnormal individual, and the current basic information is eliminated to obtain standard child tumor disease data; Otherwise, it is determined that the basic information corresponding to the child is complete, and the verification of the basic information is completed.

5. The method for automatically balancing the burden difference and medical resources of childhood tumor diseases according to claim 4, characterized in that: Get standard childhood oncology disease data, including: Obtaining the obtained standard childhood tumor disease data, and determining the age group of the individual corresponding to the childhood tumor disease data based on the basic information of the standard childhood tumor disease data; Performing a first classification on the standard childhood tumor disease data based on the age group to obtain a first classification result, and determining the gender of the individual corresponding to the standard childhood tumor disease data in each first classification based on the first classification result; Performing a second classification on the first classification result based on the gender to obtain a second classification result, and determining the ethnicity of the individual corresponding to the standard childhood tumor disease data in each second classification based on the second classification result; The second classification result is classified into a third category based on the ethnicity to obtain a third classification result, and the privacy data in the basic information of the standard childhood tumor disease data is determined based on the third classification result, and the privacy data is anonymized.

6. The method for automatically balancing the burden difference and medical resources of childhood tumor diseases according to claim 1, characterized in that: Complete the balance of medical resources, including: Obtain the results of the balancing process of medical resources, and based on the balancing process results, conduct real-time monitoring of the medical resource storage information of the preset medical resource allocation center, the treatment level of the medical institution, and the number of children's tumor diseases treated by different medical institutions; Based on the monitoring results, when the medical resource storage information of the preset medical resource allocation center, the treatment level of the medical institution, and the number of treatments for childhood tumor diseases in different medical institutions are determined to have changed, the medical resources will be rebalanced.

7. The method for automatically balancing the burden difference and medical resources of childhood tumor diseases according to claim 1, characterized in that: In step 2, the pediatric tumor disease data is analyzed to determine the pediatric tumor disease frequency index and the medical resource utilization index, including: Acquire the obtained pediatric tumor disease data, and determine classification indicators for the pediatric tumor disease data, wherein the classification indicators include age, gender, and ethnicity; Classify the children's tumor disease data based on the classification index to obtain a target sub-data set, and determine the number of new children with tumors in the year and the total number of susceptible children insured in the year in each target sub-data in the target sub-data set; Determine the incidence rate corresponding to each target sub-data based on the number of new childhood cancer patients in each target sub-data and the total number of susceptible children insured in that year; At the same time, the number of pediatric patients diagnosed before the target time period and surviving to the current year and the number of insured persons in the current year in each target sub-data are determined, and the prevalence rate of the target time period corresponding to each target sub-data is determined based on the number of pediatric patients diagnosed before the target time period and surviving to the current year and the number of insured persons in the current year; The frequency index of the childhood tumor disease is obtained based on the incidence rate and the prevalence rate in the target time period.

8. The method for automatically balancing the burden difference and medical resources of childhood tumor diseases according to claim 1, characterized in that: In step 2, the pediatric tumor disease data is analyzed to determine the pediatric tumor disease frequency index and the medical resource utilization index, and further includes: Acquire the obtained pediatric tumor disease data, and group the pediatric tumor disease data based on gender, age and ethnicity to obtain sub-data groups; Generate a treatment cost access request based on the pediatric tumor disease data in each sub-data group, and access a preset patient treatment cost usage database based on the treatment cost access request; Based on the preset patient treatment cost usage database, the total medical insurance reimbursement cost of each child patient within one year after diagnosis is obtained, and at the same time, the medical insurance reimbursement ratio is determined, and the treatment cost corresponding to each sub-data group is obtained based on the medical insurance reimbursement ratio and the total medical insurance reimbursement cost; Obtaining the consumption index of the current year, and revising the total medical insurance reimbursement cost based on the consumption index to obtain the patient treatment cost corresponding to each sub-data group; At the same time, extracting the name of the medical institution visited by each child patient and the total number of medical institutions visited from the pediatric tumor disease data, and determining the name features of the names of the medical institutions visited in turn based on the total number of medical institutions visited, wherein the name of the medical institution visited is at least one; Based on the name features, the names of the medical institutions to be visited are deduplicated to obtain a set of target medical institutions to be visited, and each target medical institution name in the set of target medical institutions to be visited is searched in a preset medical institution grade search library to obtain the medical grade of each target medical institution to be visited; Based on the total number of medical institutions visited by each child patient, a flow chart of medical institutions visited by each child patient is drawn, and based on the medical grade of each target medical institution visited, each medical institution visited is graded in the flow chart of medical institutions visited; Based on the labeling results, the medical institution flow information of each child patient is obtained; A medical resource utilization index is obtained based on the patient's treatment costs and the flow information of medical institutions visited.

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