DRGs disease group payment data hyperbranched analysis method, system and device and storage medium
By applying the Boston matrix, triple standard deviation and structural change analysis methods in the DRGs disease group system, the in-depth analysis of the medical data was solved, and the problem of difficult to accurately locate the overspin factors in the DRGs system was solved, and effective analysis of medical data and optimization of diagnosis and treatment projects were achieved.
Patent Information
- Application Number
- CN202411883569.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-19
- Publication Date
- 2025-05-16
AI Technical Summary
Under the DRGs disease group system, there is a lack of effective data analysis methods to accurately locate specific factors that lead to overspending, resulting in large differences in costs and difficult to control.
The Boston matrix analysis method, triple standard deviation analysis method and structural change analysis method were used to analyze the medical data of the DRGs disease group, and the factors that caused overspending were gradually located.
It has realized the effective analysis of a large number of medical data, accurately identified the overspending disease group and the diagnosis and treatment projects that caused overspending, providing a scientific basis for the adjustment and optimization of diagnosis and treatment projects.
Smart Images
Figure CN120011712A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of data analysis technology, and specifically relates to a DRGs disease group payment data overspending analysis method, system, equipment and storage medium. Background Art
[0002] While the Diagnosis Related Groups (DRGs) system provides a framework for managing and controlling medical expenses, it currently lacks a comprehensive and effective method for analyzing patient data to pinpoint the specific drivers of overspending. DRGs primarily categorize patients into disease groups based on diagnosis, surgical procedure, age, gender, and other factors, enabling macro-level management and control of medical expenses. However, this grouping approach is often too general and fails to delve into the specific details of medical visits and cost structures.
[0003] In practice, it's often found that even within the same DRGs, costs can vary significantly between different cases. This is often due to a variety of complex factors, such as differences in treatment plans, the use of drugs and consumables, and the complexity of surgical procedures. However, due to the lack of effective data analysis tools and methods, it is difficult to extract valuable information from this complex medical data to further identify the specific factors that lead to overspending. Summary of the Invention
[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a DRGs disease group payment data overspending analysis method, system, device and storage medium to solve the above-mentioned technical problems.
[0005] In a first aspect, the present invention provides a method for analyzing overspending of DRGs disease group payment data, comprising: Obtaining medical treatment data for multiple DRGs disease groups, wherein the medical treatment data includes hospitalization days, medical treatment items, and medical treatment item costs; Boston matrix analysis was used to analyze the medical data of multiple DRGs disease groups, and based on the analysis results, the overspending disease groups with average hospital stay days or average cost per visit higher than the average were identified; The triple standard deviation analysis method was used to confirm the overspending data of the overspending disease group; The structural change analysis method is used to analyze the medical treatment items and medical treatment item costs in the overspending medical treatment data, and the medical treatment items that cause overspending are screened out based on the analysis results.
[0006] In an optional embodiment, obtaining the medical data of multiple DRGs disease groups includes: Obtain medical treatment data within a specified period from the information system; Preprocessing the medical data, including cleaning and processing missing values; The medical data are classified according to the DRGs disease groups to which they belong, and the medical data corresponding to each DRGs disease group are obtained.
[0007] In an optional embodiment, the Boston Matrix analysis method is used to analyze the medical data of multiple DRGs disease groups, and based on the analysis results, overspending disease groups with average length of stay or average cost per visit higher than the average are identified, including: Based on the medical data of each DRGs disease group, the average hospitalization days and average cost per visit of each DRGs disease group were calculated; Calculate the average of the average length of hospital stay and the average of the average cost per visit for all DRGs disease groups; The Boston matrix was constructed with the average length of hospital stay as the horizontal axis and the average cost per visit as the vertical axis; Compare the average length of hospital stay of each DRGs disease group with the overall average to determine its position on the horizontal axis, and compare the average cost per visit of each DRGs disease group with the overall average to determine its position on the vertical axis; Divide the Boston Matrix into four quadrants: In the first quadrant, the average length of hospital stay and the average cost per visit were higher than the average; In the second quadrant, the average cost per visit is higher than the average, but the average length of stay is lower than the average; In the third quadrant, the average length of hospital stay and the average cost per visit were lower than the average; The fourth quadrant: the average length of hospital stay is higher than the average, but the average cost per visit is lower than the average; The DRGs disease group distributed in the first quadrant was marked as the hyperbranchial disease group.
[0008] In an optional embodiment, the overspending medical data of the overspending disease group is confirmed using a triple standard deviation analysis method, including: Calculating the medical expenses of each medical treatment data of the overspending disease group based on the medical treatment data of the overspending disease group, wherein the medical expenses are the accumulated value of the medical treatment item expenses of the medical treatment data; Calculate the standard deviation and mean of the medical expenses of the overspending disease group; The sum of the mean and three times the standard deviation, and the difference between the mean and three times the standard deviation are used as the boundary values of the cost range; Excessive medical consultation data are screened out, where the medical consultation fee of the over-spending medical consultation data exceeds the upper boundary value of the fee range.
[0009] In an optional embodiment, the method further comprises A normal distribution model of the medical expenses of the overspending disease group is constructed based on the cost interval, and the normal distribution model is displayed and output.
[0010] In an optional embodiment, the structural change analysis method is used to analyze the medical treatment items and medical treatment item costs in the overspending medical treatment data, and the medical treatment items that cause overspending are screened out based on the analysis results, including: Based on the medical data of all DRGs disease groups, the average medical expenses of each medical item were calculated; Calculate the structural change value (VSV) of the medical items in the overspending medical treatment data: Structural change value VSV of the i-th medical item i = (Current period expense ratio - Base period expense ratio) / Base period expense ratio × 100% The proportion of current expenses = medical expenses i / the sum of the expenses of multiple medical expenses in the overspending medical data Base period cost ratio = average medical treatment cost i / average medical treatment cost The average medical fee is the sum of the medical expenses of all medical data divided by the number of medical data; Calculate the structural variability DSV of the entire fee structure: DSV=|ΣVSV i |, where i represents diagnosis and treatment item i; Calculate the structural change contribution rate for each expense category: Contribution rate = |VSV i | / DSV×100%; According to the preset weights, the weighted sum of the structural change value and the contribution rate of each medical treatment item is calculated, the maximum value of the weighted sum is screened out, and the medical treatment item to which the maximum value belongs is output as the medical treatment item that causes overspending.
[0011] In an optional embodiment, the method further comprises: Analyze the treatment groups corresponding to the medical data; Count the number and proportion of overspending visits for each treatment group; The medical treatment groups whose number and proportion of overspending medical treatment data exceed the average level are output as medical treatment groups for improvement.
[0012] In a second aspect, the present invention provides a DRGs disease group payment data overspending analysis system, comprising: A data acquisition module is used to obtain the medical data of multiple DRGs disease groups, wherein the medical data includes hospitalization days, medical treatment items and medical treatment item costs; The first analysis module is used to analyze the medical data of multiple DRGs disease groups using the Boston Matrix analysis method, and based on the analysis results, locate the overspending disease groups whose average length of stay or average cost per visit is higher than the average; The second analysis module is used to confirm the overspending medical treatment data of the overspending disease group using a triple standard deviation analysis method; The third analysis module is used to analyze the medical treatment items and medical treatment item costs in the overspending medical treatment data by using a structural change analysis method, and screen out the medical treatment items that cause overspending based on the analysis results.
[0013] According to a third aspect, a device is provided, comprising: A memory device for storing a DRGs disease group payment data overspending analysis program; A processor is used to implement the steps of the DRGs disease group payment data overspending analysis method provided in the first aspect when executing the DRGs disease group payment data overspending analysis program.
[0014] In a fourth aspect, a computer-readable storage medium is provided, on which a DRGs disease group payment data overspending analysis program is stored. When the DRGs disease group payment data overspending analysis program is executed by a processor, the steps of the DRGs disease group payment data overspending analysis method provided in the first aspect are implemented.
[0015] The beneficial effect of the present invention is that the DRGs disease group payment data overspending analysis method, system, equipment and storage medium provided by the present invention use Boston matrix analysis, triple standard deviation analysis method and structural change analysis method in the DRGs system to gradually locate the factors leading to overspending, realize the effective analysis of a large amount of medical data, and provide strong data support for the adjustment and optimization of diagnosis and treatment projects.
[0016] In addition, the present invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.
[0018] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention.
[0019] Figure 2 It is a schematic effect diagram of Boston matrix analysis of the method according to one embodiment of the present invention.
[0020] Figure 3 FIG. 4 is a schematic block diagram of a system according to an embodiment of the present invention.
[0021] Figure 4 A schematic structural diagram of a device provided in an embodiment of the present invention. DETAILED DESCRIPTION
[0022] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.
[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.
[0024] The DRGs disease group payment data overspending analysis method provided in an embodiment of the present invention is executed by a computer device, and accordingly, the DRGs disease group payment data overspending analysis system runs in the computer device.
[0025] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention. Figure 1 The execution entity may be a DRGs disease group payment data overspending analysis system. According to different needs, the order of the steps in the flowchart can be changed, and some steps can be omitted.
[0026] like Figure 1 As shown, the method includes: S1. Obtain medical treatment data of multiple DRGs disease groups, wherein the medical treatment data includes hospitalization days, medical treatment items, and medical treatment item costs.
[0027] Obtaining visit data for multiple DRGs (Diagnosis Related Groups) is the first step in data analysis. This data should be detailed and comprehensive, including but not limited to length of stay, diagnosis and treatment items, and corresponding costs. Length of stay reflects the efficiency of medical services, while diagnosis and treatment items and costs are directly related to cost control. This data provides a solid foundation for subsequent in-depth analysis.
[0028] S2. The Boston Matrix analysis method was used to analyze the medical data of multiple DRGs disease groups, and based on the analysis results, the overspending disease groups with average hospitalization days or average cost per visit higher than the average were identified.
[0029] After obtaining comprehensive patient data, we conducted an in-depth analysis using the Boston Matrix. The Boston Matrix is a classic strategic analysis tool used to assess the market attractiveness and strength of different business units to determine their development direction. Here, we applied it to the analysis of DRGs to identify overspending groups with higher-than-average average length of stay or average cost per visit. This method can intuitively demonstrate which groups are deviating from the normal path in resource consumption, providing clear guidance for subsequent improvement measures.
[0030] S3. Use triple standard deviation analysis method to confirm the overspending data of the overspending disease group.
[0031] Once the overspending groups were identified, triple standard deviation analysis was used to identify the overspending data within them. The triple standard deviation method is a statistical technique used to identify outliers or extreme cases in data. It helps identify unusually high-cost visits, which often reveal the key drivers of overspending.
[0032] S4. Analyze the medical treatment items and medical treatment item costs in the overspending medical treatment data using a structural change analysis method, and screen out the medical treatment items that cause overspending based on the analysis results.
[0033] Structural change analysis is used to conduct an in-depth analysis of the medical procedures and costs within the overspending data. This method reveals changes in the proportion of different components within the overall data, helping to identify the medical procedures that are the primary drivers of cost overspending. Based on this analysis, key medical procedures contributing to overspending can be identified, providing a strong basis for subsequent optimization and adjustment.
[0034] In an embodiment of the present invention, based on step S1, a possible embodiment will be given below to illustrate its specific implementation scheme in a non-limiting manner.
[0035] Acquiring medical visit data from a medical information system within a specified timeframe is the first step in data analysis and mining. This step ensures the timeliness and integrity of the data, providing a reliable foundation for subsequent analysis. The data should cover all patient records during the specified timeframe, including but not limited to diagnosis information, treatment progress, and detailed cost information.
[0036] After acquiring the data, the medical data needs to be preprocessed. Preprocessing is an important step in data analysis, which directly affects the accuracy and reliability of subsequent analysis. In this step, two main tasks are performed: data cleaning and processing missing values. Data cleaning refers to the screening and correction of raw data, removing duplicate, erroneous or irrelevant information, and ensuring the accuracy and consistency of the data. Processing missing values refers to filling or deleting data that has not been recorded or is lost for various reasons to ensure the integrity of the data. Methods for processing missing values include but are not limited to mean filling, regression prediction filling, and filling based on machine learning algorithms. The choice of specific methods needs to be determined based on the characteristics of the data and the purpose of analysis.
[0037] After preprocessing, the next step is to classify the patient data according to their corresponding DRGs (Diagnosis Related Groups). DRGs are an internationally recognized classification method that categorizes cases into disease groups based on diagnosis, surgical procedure, age, gender, and other factors. This provides an important basis for managing and controlling medical costs. By classifying patient data by DRGs, we can obtain the corresponding data for each DRG group, providing a clear data framework for subsequent analysis. This step ensures that subsequent analysis can be conducted on specific disease groups, improving the relevance and accuracy of the analysis.
[0038] Please refer to Figure 2 In one embodiment of the present invention, based on step S2, a possible embodiment will be given below to illustrate its specific implementation scheme in a non-limiting manner.
[0039] Based on the visit data for each DRG (Diagnosis Related Group) disease group, key indicators are first calculated. Specifically, the average length of stay and average cost per visit for each DRG disease group are calculated. These two indicators are key parameters for measuring the efficiency and cost-effectiveness of medical services. The average length of stay reflects the overall management efficiency of the medical institution's patient treatment process, while the average cost per visit is directly related to the economic cost of medical services.
[0040] Next, to establish a baseline for comparison, we calculated the average length of stay and the average cost per visit for all DRGs disease groups. These two overall averages served as the basis for subsequent Boston Matrix construction and DRG disease group classification, helping to identify which disease groups deviated from the norm in terms of length of stay and cost.
[0041] The construction of the Boston Matrix is a key step in data analysis. Using average length of stay as the horizontal axis and average cost per visit as the vertical axis, the average length of stay and average cost per visit for each DRG group are positioned within the matrix. This position of each DRG group within the matrix reflects its relative length of stay and cost relative to the overall average.
[0042] In order to more intuitively understand and analyze the distribution of DRGs disease groups, the Boston Matrix is divided into four quadrants. The first quadrant contains DRGs disease groups with average length of stay and average cost per visit higher than the average. These disease groups have relatively high resource consumption and may need to be managed and optimized. The average cost per visit for disease groups in the second quadrant is higher than the average, but the average length of stay is lower than the average, indicating that these disease groups may face challenges in cost control, but perform well in treatment efficiency. The third quadrant contains DRGs disease groups with average length of stay and average cost per visit lower than the average. These disease groups are more efficient in resource utilization and are ideal medical service models. The average length of stay for disease groups in the fourth quadrant is higher than the average, but the average cost per visit is lower than the average, which may reflect the characteristics of certain specific treatment strategies or patient groups.
[0043] Finally, DRGs in the first quadrant are labeled as overspending groups. These groups, with higher-than-average length of stay and higher-than-average costs, represent key areas of medical resource consumption and require further detailed analysis and improvement measures. This series of analytical steps allows for more precise identification and management of overspending within DRGs groups, providing a scientific basis for optimizing the allocation of medical resources.
[0044] The following is a specific example: S201. Calculate the average Calculate the overall average of the average length of stay and average cost per visit for all DRGs disease groups. These averages will serve as the basis for dividing the four quadrants of the Boston Matrix.
[0045] S202. Constructing the Boston Matrix Horizontal axis (average length of stay): Compare the average length of stay of each DRGs disease group with the overall average to determine its position on the horizontal axis.
[0046] Vertical axis (average cost per visit): Similarly, the average cost per visit of each DRGs disease group is compared with the overall average to determine its position on the vertical axis.
[0047] Quadrant division: Each DRGs disease group is divided into four quadrants according to its position on the horizontal and vertical axes: Quadrant 1: The average length of stay and average cost per visit are higher than average. These disease groups may require additional attention and management because they consume more medical resources.
[0048] Quadrant II: The average cost per visit is higher than the average, but the average length of stay is lower than the average. These disease groups may involve high-value medical services or treatments, and their cost-effectiveness needs to be evaluated.
[0049] Quadrant III: Average length of stay and average cost per visit are both below average. These patient groups are the "cash cows" of hospital operations and should be maintained and optimized.
[0050] Quadrant IV: Average length of stay is higher than average, but average cost per visit is lower than average. These patient groups may need to improve treatment efficiency to reduce hospital stays while maintaining or reducing medical costs.
[0051] S203. Results Analysis The DRGs disease group distributed in the first quadrant was marked as the hyperbranchial disease group.
[0052] In an embodiment of the present invention, based on step S3, a possible embodiment will be given below to illustrate its specific implementation scheme in a non-limiting manner.
[0053] S301. Calculate the medical expenses of each medical treatment data of the overspending disease group based on the medical treatment data of the overspending disease group, where the medical expenses are the accumulated value of the expenses of multiple medical treatment items in the medical treatment data.
[0054] For each medical consultation data, the costs of all medical treatment items included in it are accumulated to obtain the total medical consultation cost of the medical consultation data.
[0055] The total cost of each medical consultation data is stored in a new data table for subsequent analysis.
[0056] S302. Calculate the standard deviation and mean of the medical expenses of the overspending disease group.
[0057] Calculate the average: Use the formula "average = total cost / number of medical records" to calculate the average medical cost of the overspending disease group.
[0058] Calculating the standard deviation: Use the standard deviation formula to calculate the standard deviation of the overspending group based on the difference between each visit expense and the mean. The standard deviation formula is: Standard deviation = √[(Σ(x - μ)^2) / N], where x is the cost per visit, μ is the mean, and N is the number of visits.
[0059] Result recording: Record the calculated mean and standard deviation for use in subsequent steps.
[0060] S303. The sum of the mean and three times the standard deviation, and the difference between the mean and three times the standard deviation are used as the boundary values of the cost range.
[0061] Calculate the upper boundary value: Use the formula "Upper boundary value = mean + 3 * standard deviation" to calculate the upper boundary value of the cost range.
[0062] Calculate the lower bound: Use the formula "Lower bound = Mean - 3 * Standard Deviation" to calculate the lower bound of the cost range. Although the lower bound may not be directly used when subsequently filtering data for overspending, it helps to understand the full range of cost distribution.
[0063] Result recording: Record the calculated upper and lower boundary values for use in subsequent steps.
[0064] S304. Filter out over-spending medical treatment data, where the medical treatment costs of the over-spending medical treatment data exceed the upper boundary value of the cost range.
[0065] Data screening: Traverse all the medical data of the overspending disease group and filter out those records whose medical expenses exceed the upper boundary value.
[0066] Result recording: Record the screened overspending medical visit data in a new data table for subsequent analysis.
[0067] S305. Construct a normal distribution model of the medical expenses of the overspending disease group based on the expense interval, and display and output the normal distribution model.
[0068] All the medical data of the overspending disease group were used as sample data.
[0069] Use statistical software (such as Excel, SPSS, R, etc.) to build a normal distribution model. Usually, these software provide the function of drawing a normal distribution curve.
[0070] When building a model, you need to set the model parameters, such as mean (average), standard deviation, etc. These parameters have been calculated in the previous steps.
[0071] Verify the accuracy of the model by comparing the fit between the actual data and the normal distribution curve. If the fit is poor, you may need to reconsider the validity of the data or the applicability of the model.
[0072] The constructed normal distribution model and its parameters (such as mean, standard deviation, confidence interval, etc.) are output as charts or reports. This helps to intuitively understand the distribution of medical expenses in overspending groups and provides a scientific basis for the subsequent formulation of targeted improvement measures.
[0073] In an embodiment of the present invention, based on step S4, a possible embodiment will be given below to illustrate its specific implementation scheme in a non-limiting manner.
[0074] Based on the medical data of all DRGs disease groups, the average medical expenses of each medical item were calculated; Calculate the structural change value (VSV) of the medical items in the overspending medical treatment data: Structural change value VSV of the i-th medical item i = (Current period expense ratio - Base period expense ratio) / Base period expense ratio × 100% The proportion of current expenses = medical expenses i / the sum of the expenses of multiple medical expenses in the overspending medical data Base period cost ratio = average medical treatment cost i / average medical treatment cost The average medical fee is the sum of the medical expenses of all medical data divided by the number of medical data; Calculate the structural variability DSV of the entire fee structure: DSV=|ΣVSV i |, where i represents diagnosis and treatment item i; Calculate the structural change contribution rate for each expense category: Contribution rate = |VSV i | / DSV×100%; According to the preset weights, the weighted sum of the structural change value and the contribution rate of each medical treatment item is calculated, the maximum value of the weighted sum is screened out, and the medical treatment item to which the maximum value belongs is output as the medical treatment item that causes overspending.
[0075] The following is a specific example: First, outlier cases exceeding the previously set cost range (such as the mean ± 3 times the standard deviation) were screened out.
[0076] Suppose 5 outlier cases are screened out and numbered A, B, C, D, and E.
[0077] For each outlier case screened out, we thoroughly review the medical record homepage to understand the patient's diagnosis, treatment, surgery, medication and other detailed information.
[0078] The purpose of this step is to obtain the complete cost structure of the case, including drug costs, examination fees, treatment fees, surgical fees, etc.
[0079] For each case, its costs were broken down into various cost categories in detail, and the amount of each category was recorded.
[0080] Assume that the cost structure of Case A is: drug fee 4,000 yuan, examination fee 2,000 yuan, treatment fee 4,000 yuan, and surgery fee 10,000 yuan.
[0081] Calculate the structural change value (VSV) of each expense category, which reflects the change in the proportion of the expense category in the total expenses.
[0082] The calculation formula for VSV is: VSV = (current period expense ratio - base period expense ratio) / base period expense ratio × 100%.
[0083] Assuming the average cost structure of all cases is used as the base period, the drug cost VSV of Case A = (4000 / 20000 - drug cost ratio in the base period) / drug cost ratio in the base period × 100%.
[0084] Calculate the Degree of Structural Variation (DSV) of the entire fee structure, which measures the overall degree of change in the fee structure over time.
[0085] The calculation formula of DSV is: DSV = |ΣVSVi|, where i represents each cost category.
[0086] Calculate the structural change contribution rate of each expense category, which reveals the contribution of each expense category to the change in the expense structure.
[0087] The formula for calculating the contribution rate of structural change is: Contribution rate = |VSVi| / DSV × 100%.
[0088] By comparing the structural change values and contribution rates of different expense categories, we can identify which expense categories are the main factors leading to cost overruns.
[0089] Assuming that in Case A, the VSV and contribution rate of surgical fees are significantly higher than those of other cost categories, then it can be considered that surgical fees are the main factor leading to cost overspending in Case A.
[0090] Through the above steps, each identified outlier case can be analyzed in detail, and the Degree of Structural Variation (DSV) analysis method can be used to identify the main factors affecting overspending. This helps hospitals better understand the changes in their cost structure, providing a scientific basis for optimizing cost management and reducing medical costs. Furthermore, based on the identified key factors, hospitals can implement targeted management measures, such as strengthening surgical cost control and optimizing treatment plans, to further reduce medical expenses.
[0091] On the basis of the above embodiments, in order to further improve the depth of data analysis results, in one embodiment, the associated diagnosis and treatment groups are analyzed.
[0092] Analyze the treatment groups corresponding to the medical data; count the number and proportion of overspending medical data in each treatment group; output the treatment groups whose number and proportion of overspending medical data exceed the average level as the treatment groups to be improved.
[0093] In the previous example, we used Degree of Structural Variation (DSV) analysis to identify the main drivers of cost overruns, such as surgical fees. Now, we'll delve deeper into this analysis by linking treatment groups to explore the proportion of overrun cases and the cost structure within each treatment group, aiming to develop more targeted improvement measures.
[0094] Definition of Related Treatment Groups: Treatment groups are typically established based on factors such as hospital departmental divisions, specialized expertise, and disease types, aiming to provide patients with more specialized and systematic medical services. In this example, assume that the hospital has established multiple related treatment groups based on the characteristics of disease RW23, such as the Cardiology Treatment Group, the Surgery Treatment Group, and the Imaging Diagnosis Group.
[0095] Data collation and association: First, the previously screened overspending cases need to be associated with the corresponding diagnosis and treatment groups. This is usually achieved through the hospital's electronic medical record system or information system to ensure that each case is accurately attributed to a diagnosis and treatment group.
[0096] Analysis of the proportion of overspending cases: For each treatment group, the proportion of cases with overspending was calculated as the number of overspending cases divided by the total number of cases in that treatment group. This ratio can intuitively reflect the performance of each treatment group in cost control.
[0097] Cost structure analysis: Next, we conduct an in-depth analysis of the cost structure of overspending cases within each treatment group. This includes a detailed breakdown and comparison of each cost category (such as drug costs, laboratory tests, treatment costs, and surgical costs) to identify which cost categories are the main drivers of overspending.
[0098] Develop improvement measures: Based on the above analysis results, targeted improvement measures were developed for each treatment group. For example, for the surgical treatment group, if surgical costs were the primary factor contributing to overspending, measures such as optimizing surgical plans, improving surgical efficiency, and reducing unnecessary surgical consumables could be considered. For the cardiovascular treatment group, if drug costs accounted for a high proportion, measures such as strengthening drug management, standardizing medication use, and promoting more cost-effective drugs could be considered.
[0099] Implementation and Monitoring: After implementing improvement measures, it is necessary to regularly monitor the cost control status of each diagnosis and treatment group and evaluate the effectiveness of the improvement measures. This can be achieved by setting cost control indicators, regularly analyzing cost data, and conducting internal audits.
[0100] Suppose an in-depth analysis of the surgical treatment group revealed a high proportion of overspending, with surgical fees accounting for a significant portion. Further analysis revealed that some surgeries were subject to excessive use of high-value consumables and prolonged surgery times. To address these issues, the following improvement measures were developed: Optimize surgical plans and reduce unnecessary surgical steps and consumables; Strengthen surgical team building and improve surgical skills and efficiency; Introducing new surgical techniques and equipment to reduce surgical risks and costs; Strengthen communication with patients to ensure the rationality and acceptability of the surgical plan.
[0101] After implementing these improvement measures, regular monitoring of the surgical team's cost control revealed a decrease in the proportion of overspending cases and effective control of surgical costs. This indicates that the improvement measures have achieved initial success, and we will continue to optimize and improve these measures in the future to further improve the efficiency of medical resource utilization.
[0102] In some embodiments, the DRGs disease group payment data overspending analysis system may include multiple functional modules composed of computer program segments. The computer program of each program segment in the DRGs disease group payment data overspending analysis system may be stored in a memory of a computer device and executed by at least one processor to perform (see Figure 1 Description) Function for overspending analysis of DRGs disease group payment data.
[0103] In this embodiment, the DRGs disease group payment data overspending analysis system can be divided into multiple functional modules according to the functions it performs, such as Figure 3 As shown. The functional modules of system 300 may include: a data acquisition module 310, a first analysis module 320, a second analysis module 330, and a third analysis module 340. A module, as referred to in the present invention, refers to a series of computer program segments that can be executed by at least one processor and can perform fixed functions, and is stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.
[0104] A data acquisition module is used to obtain the medical data of multiple DRGs disease groups, wherein the medical data includes hospitalization days, medical treatment items and medical treatment item costs; The first analysis module is used to analyze the medical data of multiple DRGs disease groups using the Boston Matrix analysis method, and based on the analysis results, locate the overspending disease groups whose average length of stay or average cost per visit is higher than the average; The second analysis module is used to confirm the overspending medical treatment data of the overspending disease group using a triple standard deviation analysis method; The third analysis module is used to analyze the medical treatment items and medical treatment item costs in the overspending medical treatment data by using a structural change analysis method, and screen out the medical treatment items that cause overspending based on the analysis results.
[0105] Optionally, as an embodiment of the present invention, obtaining medical data of multiple DRGs disease groups includes: Obtain medical treatment data within a specified period from the information system; Preprocessing the medical data, including cleaning and processing missing values; The medical data are classified according to the DRGs disease groups to which they belong, and the medical data corresponding to each DRGs disease group are obtained.
[0106] Optionally, as an embodiment of the present invention, the Boston matrix analysis method is used to analyze the medical data of multiple DRGs disease groups, and based on the analysis results, the overspending disease groups with average hospitalization days or average cost per visit higher than the average are located, including: Based on the medical data of each DRGs disease group, the average hospitalization days and average cost per visit of each DRGs disease group were calculated; Calculate the average of the average length of hospital stay and the average of the average cost per visit for all DRGs disease groups; The Boston matrix was constructed with the average length of hospital stay as the horizontal axis and the average cost per visit as the vertical axis; Compare the average length of hospital stay of each DRGs disease group with the overall average to determine its position on the horizontal axis, and compare the average cost per visit of each DRGs disease group with the overall average to determine its position on the vertical axis; Divide the Boston Matrix into four quadrants: In the first quadrant, the average length of hospital stay and the average cost per visit were higher than the average; In the second quadrant, the average cost per visit is higher than the average, but the average length of stay is lower than the average; In the third quadrant, the average length of hospital stay and the average cost per visit were lower than the average; The fourth quadrant: the average length of hospital stay is higher than the average, but the average cost per visit is lower than the average; The DRGs disease group distributed in the first quadrant was marked as the hyperbranchial disease group.
[0107] Optionally, as an embodiment of the present invention, the overspending medical data of the overspending disease group is confirmed using a triple standard deviation analysis method, including: Calculating the medical expenses of each medical treatment data of the overspending disease group based on the medical treatment data of the overspending disease group, wherein the medical expenses are the accumulated value of the medical treatment item expenses of the medical treatment data; Calculate the standard deviation and mean of the medical expenses of the overspending disease group; The sum of the mean and three times the standard deviation, and the difference between the mean and three times the standard deviation are used as the boundary values of the cost range; Excessive medical consultation data are screened out, where the medical consultation fee of the over-spending medical consultation data exceeds the upper boundary value of the fee range.
[0108] Optionally, as an embodiment of the present invention, it further includes A normal distribution model of the medical expenses of the overspending disease group is constructed based on the cost interval, and the normal distribution model is displayed and output.
[0109] Optionally, as an embodiment of the present invention, a structural change analysis method is used to analyze the medical treatment items and medical treatment item costs in the overspending medical treatment data, and medical treatment items that cause overspending are screened out based on the analysis results, including: Based on the medical data of all DRGs disease groups, the average medical expenses of each medical item were calculated; Calculate the structural change value (VSV) of the medical items in the overspending medical treatment data: Structural change value VSV of the i-th medical item i = (Current period expense ratio - Base period expense ratio) / Base period expense ratio × 100% The proportion of current expenses = medical expenses i / the sum of the expenses of multiple medical expenses in the overspending medical data Base period cost ratio = average medical treatment cost i / average medical treatment cost The average medical fee is the sum of the medical expenses of all medical data divided by the number of medical data; Calculate the structural variability DSV of the entire fee structure: DSV=|ΣVSV i |, where i represents diagnosis and treatment item i; Calculate the structural change contribution rate for each expense category: Contribution rate = |VSV i | / DSV×100%; According to the preset weights, the weighted sum of the structural change value and the contribution rate of each medical treatment item is calculated, the maximum value of the weighted sum is screened out, and the medical treatment item to which the maximum value belongs is output as the medical treatment item that causes overspending.
[0110] Optionally, as an embodiment of the present invention, the method further includes: Analyze the treatment groups corresponding to the medical data; Count the number and proportion of overspending visits for each treatment group; The medical treatment groups whose number and proportion of overspending medical treatment data exceed the average level are output as medical treatment groups for improvement.
[0111] Figure 4The DRGs disease group payment data overspending analysis method provided for the embodiment of the present application can be applied to a device. Those skilled in the art will understand that the device structure involved in the embodiment of the present invention does not constitute a limitation on the device, and the device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. In the embodiment of the present invention, the device includes but is not limited to a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described and / or required herein.
[0112] The device 400 may include a processor 410, a memory 420, and a communication unit 430. These components communicate via one or more buses. Those skilled in the art will appreciate that the server structure shown in the figure does not limit the present invention. The server structure may be a bus structure or a star structure, and may include more or fewer components than shown, or combine certain components, or arrange the components differently.
[0113] Memory 420 can be used to store execution instructions of processor 410. Memory 420 can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk. When the execution instructions in memory 420 are executed by processor 410, device 400 can perform some or all of the steps in the following method embodiments.
[0114] The processor 410 is the control center of the storage device, which uses various interfaces and lines to connect various parts of the entire electronic device. It runs or executes software programs and / or modules stored in the memory 420, and calls data stored in the memory to perform various functions of the electronic device and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 410 can only include a central processing unit (CPU). In an embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.
[0115] The communication unit 430 is configured to establish a communication channel so that the storage device can communicate with other devices, receive user data sent by other devices, or send user data to other devices.
[0116] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program that, when executed, may include some or all of the steps of each embodiment provided herein. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).
[0117] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software and a necessary general-purpose hardware platform. Based on this understanding, the technical solutions in the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, among other media capable of storing program code, and includes instructions for causing a computer device (which can be a personal computer, a server, or a second device, a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention.
[0118] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiment.
[0119] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or modules, and can be electrical, mechanical or other forms.
[0120] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of the present embodiment according to actual needs.
[0121] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.
[0122] Although the present invention has been described in detail with reference to the accompanying drawings and in conjunction with preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, persons of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and such modifications or substitutions shall be within the scope of the present invention. Any changes or substitutions that can be easily conceived by persons skilled in the art within the technical scope disclosed in the present invention shall be within the scope of protection of the present invention.
Claims
1. A method for analyzing overspending of DRGs disease group payment data, characterized in that: include: Obtaining medical treatment data of multiple DRGs disease groups, wherein the medical treatment data includes hospitalization days, medical treatment items, and medical treatment item costs; Boston matrix analysis was used to analyze the medical data of multiple DRGs disease groups, and based on the analysis results, the overspending disease groups with average hospital stay days or average cost per visit higher than the average were identified; The triple standard deviation analysis method was used to confirm the overspending visit data of the overspending disease group; The structural change analysis method is used to analyze the medical treatment items and medical treatment item costs in the overspending medical treatment data, and the medical treatment items that cause overspending are screened out based on the analysis results.
2. The method according to claim 1, characterized in that Obtain medical data for multiple DRGs disease groups, including: Obtain medical visit data within a specified period from the information system; Preprocessing the medical data, including cleaning and processing missing values; The medical data are classified according to the DRGs disease groups to which they belong, and the medical data corresponding to each DRGs disease group are obtained.
3. The method according to claim 1, characterized in that The Boston Matrix analysis method was used to analyze the medical data of multiple DRGs disease groups, and based on the analysis results, the overspending disease groups with average hospital stays or average costs per visit higher than the average were identified, including: Based on the medical data of each DRGs disease group, the average hospitalization days and average cost per visit of each DRGs disease group were calculated; Calculate the average of the average length of hospital stay and the average cost per visit of all DRGs disease groups; The Boston matrix was constructed with the average length of stay as the horizontal axis and the average cost per visit as the vertical axis; The average length of hospital stay of each DRGs disease group was compared with the overall average to determine its position on the horizontal axis, and the average cost per visit of each DRGs disease group was compared with the overall average to determine its position on the vertical axis; Divide the Boston Matrix into four quadrants: In the first quadrant, the average length of stay and average cost per visit are higher than the average; In the second quadrant, the average cost per visit is higher than the average, but the average length of stay is lower than the average; In the third quadrant, the average length of stay and average cost per visit are lower than the average; The fourth quadrant: the average length of stay is higher than the average, but the average cost per visit is lower than the average; The DRGs disease group distributed in the first quadrant was marked as the hyperbranchial disease group.
4. The method according to claim 1, characterized in that: The triple standard deviation analysis method was used to confirm the overspending data of the overspending disease group, including: Calculate the medical expenses of each medical treatment data of the overspending disease group based on the medical treatment data of the overspending disease group, where the medical treatment expenses are the accumulated values of the expenses of multiple medical treatment items of the medical treatment data; The standard deviation and mean of the visit costs for the overspending disease group were calculated; The sum of the mean and three times the standard deviation, and the difference between the mean and three times the standard deviation are used as the boundary values of the cost range; The over-spending medical consultation data is screened out, wherein the medical consultation fee of the over-spending medical consultation data exceeds the upper boundary value of the fee range.
5. The method according to claim 4, characterized in that The method further comprises A normal distribution model of the medical expenses of the overspending disease group is constructed based on the cost interval, and the normal distribution model is displayed and output.
6. The method according to claim 1, characterized in that The structural change analysis method is used to analyze the medical treatment items and medical treatment item costs of the overspending medical treatment data, and the medical treatment items that cause overspending are screened out based on the analysis results, including: Based on the medical data of all DRGs disease groups, the average medical expenses of each medical item were calculated; Calculate the structural change value VSV of the medical treatment items in the overspending medical treatment data: The structural change value VSV of the i-th diagnosis and treatment item i = (Current period expense ratio - Base period expense ratio) / Base period expense ratio × 100% The proportion of current period expenses = medical treatment item expenses i / the sum of the expenses of multiple medical treatment items in the overspending medical treatment data Base period cost ratio = average medical treatment cost i / average medical treatment cost The average medical fee is the sum of the medical expenses of all medical data divided by the number of medical data; Calculate the structural change DSV of the entire cost structure: DSV=|ΣVSV i |, where i represents diagnosis and treatment item i; Calculate the structural change contribution rate for each expense category: Contribution rate = |VSV i | / DSV×100%; According to the preset weights, the weighted sum of the structural change value and the contribution rate of each medical treatment item is calculated, the maximum value of the weighted sum is screened out, and the medical treatment item to which the maximum value belongs is output as the medical treatment item that causes overspending.
7. The method according to claim 1, characterized in that The method further comprises: Analyze the treatment groups corresponding to the visit data; Count the number and proportion of overspending visits for each treatment group; The treatment groups whose number and proportion of overspending medical visits exceed the average level will be output as the treatment groups for improvement.
8. A DRGs disease group payment data overspending analysis system, characterized in that: include: A data acquisition module, used to acquire the medical data of multiple DRGs disease groups, wherein the medical data includes hospitalization days, medical treatment items and medical treatment item costs; The first analysis module is used to analyze the medical data of multiple DRGs disease groups using the Boston matrix analysis method, and locate the overspending disease groups whose average hospital stay days or average cost per visit are higher than the average based on the analysis results; The second analysis module is used to confirm the overspending medical visit data of the overspending disease group using a triple standard deviation analysis method; The third analysis module is used to analyze the medical treatment items and medical treatment item costs in the overspending medical treatment data by using a structural change analysis method, and screen out the medical treatment items that cause overspending based on the analysis results.
9. A device, characterized in that: include: A memory device for storing a DRGs disease group payment data overspending analysis program; A processor, used to implement the steps of the DRGs disease group payment data overspending analysis method as described in any one of claims 1-7 when executing the DRGs disease group payment data overspending analysis program.
10. A computer-readable storage medium storing a computer program, characterized in that: The readable storage medium stores a DRGs disease group payment data overspending analysis program, which, when executed by a processor, implements the steps of the DRGs disease group payment data overspending analysis method as described in any one of claims 1-7.