Hospital operation management data analysis method and device, electronic equipment and storage medium

Abstract

The invention provides a hospital operation management data analysis method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring to-be-evaluated operation index data of each department in a to-be-analyzed hospital; analyzing the data volume and type of the to-be-evaluated operation index data corresponding to each department, and determining a data processing model corresponding to each to-be-evaluated operation index data; according to the data processing model corresponding to each piece of to-be-evaluated operation index data and each piece of to-be-evaluated operation index data, obtaining a classification result of each piece of to-be-evaluated operation index data; and obtaining an operation management data analysis result of each department in the to-be-analyzed hospital according to the classification result of each to-be-evaluated operation index data. According to the method, the corresponding data processing model can be automatically matched according to the data characteristics of the departments, the analysis efficiency is improved, any form of operation management data analysis results of the departments can be obtained as required, and the accuracy and the efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a hospital operation management data analysis method, device, electronic equipment and storage medium. Background Art

[0002] In order to evaluate the operational management data of hospital departments, it is a regular task for hospitals to analyze the various indicators of departments, such as the completion of cost indicators. Traditional analysis methods generally use quantitative analysis, qualitative analysis, analogy, etc. and budget (plan) targets for comprehensive evaluation. However, these methods are mostly analyzed from a single perspective, and are often scattered, forming multiple or even hundreds of analysis reports. It is very cumbersome for managers to query these indicator analysis reports. Although there are visual analysis systems based on single or several indicators on the market, it is difficult to systematically and intuitively evaluate the comprehensive situation of the completion of department cost indicators, and it is difficult to form the overall evaluation results of the department and the position of the department in different indicators. It is difficult for hospital managers to intuitively grasp the operation of the department in the hospital. For example, there is no comparability between a cost reduction rate of 10% and a cost reduction of 100,000. Moreover, when it is necessary to evaluate or benchmark the cost indicators of the department from different perspectives, it not only consumes a lot of manpower and time to compare indicators one by one, but also has problems such as easy errors, high repetitiveness, and scattered analysis results. It is even more difficult to form a systematic analysis document, making the data analysis process cumbersome and inefficient. Traditional methods can no longer meet the needs of modern hospital operations and management. Summary of the invention

[0003] The embodiments of the present invention provide a hospital operation management data analysis method, device, electronic device and storage medium to solve the problem that traditional methods are difficult to meet the needs of modern hospital operation management.

[0004] In a first aspect, an embodiment of the present invention provides a hospital operation management data analysis method, comprising:

[0005] Obtain the operational indicator data to be evaluated for each department in the hospital to be analyzed;

[0006] Analyze the data volume and type of the operational indicator data to be evaluated for each department, and determine the data processing model corresponding to each operational indicator data to be evaluated;

[0007] According to the data processing model corresponding to each of the operation indicator data to be evaluated and each of the operation indicator data to be evaluated, a classification result of each of the operation indicator data to be evaluated is obtained;

[0008] According to the classification results of each operational indicator data to be evaluated, the analysis results of the operational management data of each department in the hospital to be analyzed are obtained.

[0009] In a possible implementation, the data volume and type of the operational indicator data to be evaluated corresponding to each department are analyzed to determine the data processing model corresponding to each operational indicator data to be evaluated, including:

[0010] For any operation indicator data to be evaluated corresponding to any department, if any operation indicator data to be evaluated corresponding to the department is a percentage, it is determined that the data processing model corresponding to the operation indicator data to be evaluated is a first-type data processing model;

[0011] If any of the to-be-evaluated operational indicator data corresponding to the department is not a percentage, then the evaluation level of the to-be-evaluated operational indicator data corresponding to each department is determined according to the data volume of the to-be-evaluated operational indicator data corresponding to each department; and the second type of data processing model corresponding to the to-be-evaluated operational indicator data is determined according to the evaluation level;

[0012] If any of the to-be-evaluated operational indicator data corresponding to the department is not a percentage and is of quantifiable data type, then the evaluation level of the to-be-evaluated operational indicator data corresponding to each department is determined according to the data volume of the to-be-evaluated operational indicator data corresponding to each department, and the third type of data processing model corresponding to the to-be-evaluated operational indicator data is determined according to the evaluation level;

[0013] Alternatively, if any of the to-be-evaluated operational indicator data corresponding to the department is not a percentage, then the data processing model corresponding to each to-be-evaluated operational indicator data is determined to be a fourth type of data processing model.

[0014] In a possible implementation, the first type of data processing model is a proportional evaluation model;

[0015] The second type of data processing model is the star-level global evaluation model;

[0016] The third type of data processing model is the star-rated regional evaluation model;

[0017] The fourth type of data processing model is the star rating evaluation model.

[0018] In a possible implementation, according to the data processing model corresponding to each of the operation indicator data to be evaluated and each of the operation indicator data to be evaluated, a classification result of each of the operation indicator data to be evaluated is obtained, including:

[0019] Input each to-be-evaluated operational indicator data into a data processing model corresponding to each to-be-evaluated operational indicator data to obtain an evaluation value of each to-be-evaluated operational indicator data;

[0020] According to the evaluation value of each operation indicator data to be evaluated, the classification result of each operation indicator data to be evaluated is obtained;

[0021] Among them, for any operation indicator data to be evaluated, if the data processing model corresponding to the operation indicator data to be evaluated is a first-type data processing model, the following relationship is satisfied between the operation indicator data to be evaluated and the evaluation value:

[0022] Z1=((Nx)+1)

[0023] Wherein, N is the total amount corresponding to the operation indicator data to be evaluated; x is the ranking of the operation indicator data to be evaluated;

[0024] If the data processing model corresponding to the to-be-evaluated operating indicator data is a second-type data processing model, the to-be-evaluated operating indicator data and the evaluation value satisfy the following relationship:

[0025] Z2=(Q-INT(x / (N / Q)-T))*(N / Q)

[0026] Among them, Q is the evaluation level; T is the integer adjustment value; x is the ranking of the operating indicator data to be evaluated; N is the total amount corresponding to the operating indicator data to be evaluated;

[0027] If the data processing model corresponding to the to-be-evaluated operating indicator data is a third-category data processing model, the to-be-evaluated operating indicator data and the evaluation value satisfy the following relationship:

[0028] Z3=(Q-INT(x / (N / Q)-T))-1)*(N*(ND) / (Q-1))+N*D

[0029] Among them, D is the minimum evaluation rate;

[0030] If the data processing model corresponding to the to-be-evaluated operating indicator data is the fourth type of data processing model, the to-be-evaluated operating indicator data and the evaluation value satisfy the following relationship:

[0031] DJ=INT(x / (N / Qn)-T)+1

[0032] Z4=SUBSTR(Fn,(DJ-1)*3+1,2)

[0033] Among them, DJ represents the number of stars after quantification; Qn represents the preset number of stars; and Fn represents the number of stars corresponding to each value in the operational indicator data to be evaluated.

[0034] In a possible implementation, according to the evaluation value of each operation indicator data to be evaluated, a classification result of each operation indicator data to be evaluated is obtained, including:

[0035] The evaluation value of each operational indicator data to be evaluated is multiplied by the preset weight to obtain the operational management effect analysis data of each department; wherein the operational management effect analysis data of each department includes the indicator score of each operational indicator data to be evaluated in each department and the comprehensive score of each department; the indicator score of each operational indicator data to be evaluated in each department is obtained based on the multiplication of the evaluation value of each operational indicator data to be evaluated by the preset weight; the comprehensive score of each department is the sum of the indicator scores of each operational indicator data to be evaluated in the department;

[0036] According to the preset order, each department is sorted according to its corresponding operation management effect analysis data, and based on the sorting results, each department is divided into multiple categories to obtain the classification results of the operation management effect of each department in the hospital to be analyzed.

[0037] In a possible implementation, after obtaining the analysis results of the operation management data of each department in the hospital to be analyzed according to the classification results of each operation indicator data to be evaluated, the following steps are included:

[0038] The analysis results of the operational management data of each department in the hospital to be analyzed are visualized.

[0039] In a possible implementation, the data volume and type of the operational indicator data to be evaluated corresponding to each department are analyzed to determine the data processing model corresponding to each operational indicator data to be evaluated, including:

[0040] According to the types of the operation indicator data to be evaluated corresponding to each department, the operation indicator data to be evaluated corresponding to each department are classified to obtain the first type of operation indicator data to be evaluated corresponding to each department;

[0041] For any first-category operational indicator data to be evaluated, based on its corresponding data volume, determine the data processing model corresponding to the first-category operational indicator to be evaluated, so as to determine the data processing model corresponding to each operational indicator data to be evaluated; wherein the data processing model is a neural network model.

[0042] In a second aspect, an embodiment of the present invention provides a hospital operation management data analysis device, including:

[0043] The acquisition module is used to obtain the operational indicator data to be evaluated of each department in the hospital to be analyzed;

[0044] A determination module is used to analyze the data volume and type of the operational indicator data to be evaluated corresponding to each department, and determine the data processing model corresponding to each operational indicator data to be evaluated;

[0045] A classification module, used to obtain classification results of each operation indicator data to be evaluated according to the data processing model corresponding to each operation indicator data to be evaluated and each operation indicator data to be evaluated;

[0046] The analysis module is used to obtain the analysis results of the operation management data of each department in the hospital to be analyzed based on the evaluation values ​​of each operation indicator data to be evaluated.

[0047] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the processor implements the steps of the method described in the first aspect or any possible implementation manner of the first aspect.

[0048] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation method of the first aspect are implemented.

[0049] The embodiment of the present invention provides a hospital operation management data analysis method, device, electronic device and storage medium. Compared with the traditional method that needs to analyze each department separately, it is not only inefficient, but also the data between different departments are different, and it is impossible to intuitively compare the management effects of each department. The embodiment of the present invention can automatically match the corresponding data processing model according to the data characteristics of each department, including the amount of data and the type of data therein, to improve the analysis efficiency. The data processing model obtained by matching various types of data can calculate the evaluation value of each operation indicator data to be evaluated, so as to quantify all data, so that the evaluation value can intuitively reflect the management effect of each department, and then the evaluation value of each operation indicator data to be evaluated is processed centrally and uniformly through the evaluation value, and the analysis results of the operation management data of each department in any form can be obtained as needed. Compared with the comparison of indicators one by one in the traditional method, it has the advantages of improving accuracy and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0051] Figure 1 is a flowchart of the implementation of the hospital operation management data analysis method provided by an embodiment of the present invention;

[0052] Figure 2 This is a visualization effect display diagram of the hospital operation management data analysis method provided by an embodiment of the present invention;

[0053] Figure 3is a flowchart of a hospital operation management data analysis method provided by another embodiment of the present invention;

[0054] Figure 4 is a schematic diagram of the structure of a hospital operation management data analysis device provided by an embodiment of the present invention;

[0055] Figure 5 is a schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0057] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0058] Figure 1 FIG. 1 is a flowchart of the implementation of the hospital operation management data analysis method provided by the embodiment of the present invention. Figure 1 As shown, the method may include:

[0059] Step 110: Obtain the operational indicator data to be evaluated for each department in the hospital to be analyzed.

[0060] Step 120: Analyze the data volume and type of the operational indicator data to be evaluated corresponding to each department, and determine the data processing model corresponding to each operational indicator data to be evaluated.

[0061] Step 130: According to the data processing model corresponding to each of the operation indicator data to be evaluated and each of the operation indicator data to be evaluated, a classification result of each of the operation indicator data to be evaluated is obtained.

[0062] Step 140: According to the classification results of each operational indicator data to be evaluated, the analysis results of the operational management data of each department in the hospital to be analyzed are obtained.

[0063] In this embodiment, the operational indicator data to be evaluated of each department in the hospital to be analyzed may include asset utilization efficiency, cost-to-expense ratio, per capita revenue, patient satisfaction, etc. Different departments have different natures and numbers of medical staff, so the amount of data for each indicator in each department is different.

[0064] In this embodiment, in order to accurately take into account the different data volumes and data types of each operation indicator data to be evaluated, it is difficult to analyze them through a unified model. Therefore, this embodiment automatically matches a corresponding data processing model for each operation indicator data to be evaluated according to the data volume and data type of each operation indicator data to be evaluated, thereby improving data processing efficiency and making the data processing results more reasonable.

[0065] In this embodiment, each operational indicator data to be evaluated can be input into its corresponding data processing model to obtain the evaluation value corresponding to each operational indicator data to be evaluated in each department. Then, according to the type of each operational indicator data to be evaluated, its corresponding evaluation threshold is determined, and its corresponding evaluation value and evaluation threshold are compared to obtain multiple categories; wherein each category is used to characterize the level of hospital operation management data analysis. Exemplarily, three levels can be set, namely excellent, good, and poor; each level sets a different evaluation threshold according to the type of each operational indicator data to be evaluated, and then determines the evaluation value range of each category, and then compares the evaluation value corresponding to each operational indicator data to be evaluated with the evaluation value range to determine the category corresponding to each operational indicator data to be evaluated, and then determines the classification result of each operational indicator data to be evaluated in each department.

[0066] In this embodiment, after determining the classification results of each operation indicator data to be evaluated in each department, each department can be sorted according to the corresponding preset order of each operation indicator data to be evaluated. Among them, the preset order can be determined according to the nature of each operation indicator data to be evaluated. For example, if the operation indicator data to be evaluated is the cost-to-expense ratio, then for this indicator, the smaller the value, the better, so when sorting, each type of data can be arranged in ascending order; if the operation indicator data to be evaluated is the per capita revenue, then for this indicator, the larger the value, the better, so when sorting, each type of data can be arranged in descending order.

[0067] Finally, a ladder diagram and a text analysis report are obtained based on the classification results of the operational indicator data to be evaluated in each department after arrangement, and then the ladder diagram is visualized; in the ladder diagram, Figure 2 As shown, it may include the comprehensive evaluation ranking of each department based on the data of each operational indicator to be evaluated included in each department, and may also include the indicator ranking of each department determined according to the number of operational indicators to be evaluated. When displaying, the ranking of each department in each indicator can be displayed according to the operation of the staff, such as displayed by the dark background. In addition, the ladder chart can also display the corresponding actual data, calculated evaluation value and corresponding ranking when it is selected.

[0068] From the above, it can be seen that compared with the traditional method, which requires separate analysis for each department, the embodiment of the present invention is not only inefficient, but also has different data or data types between different departments, and it is impossible to intuitively compare the management effects of each department. The embodiment of the present invention can automatically match the corresponding data processing model according to the data characteristics of each department, including the amount of data and the type of data, to improve the analysis efficiency. The data processing model obtained by matching various types of data can calculate the evaluation value of each operational indicator data to be evaluated, so as to quantify all data so that the evaluation value can intuitively reflect the management effect of each department, and then the evaluation value of each operational indicator data to be evaluated is processed in a centralized and unified manner through the evaluation value, and the analysis results of the operational management data of each department in any form can be obtained as needed. Compared with the comparison of indicators one by one in the traditional method, it has the advantages of improving accuracy and efficiency.

[0069] The following are some optional embodiments. Figure 1 The relevant steps in the provided method are described:

[0070] In an optional embodiment, in step 120, analyzing the data volume and type of the operation indicator data to be evaluated corresponding to each department and determining the data processing model corresponding to each operation indicator data to be evaluated may include:

[0071] For any operation indicator data to be evaluated corresponding to any department, if any operation indicator data to be evaluated corresponding to the department is a percentage, then the data processing model corresponding to the operation indicator data to be evaluated is determined to be a first-type data processing model; wherein, the first-type data processing model may be a proportional evaluation model.

[0072] If any of the to-be-evaluated operational indicator data corresponding to the department is not a percentage, the evaluation level of the to-be-evaluated operational indicator data corresponding to each department is determined according to the data volume of the to-be-evaluated operational indicator data corresponding to each department; and the second type of data processing model corresponding to the to-be-evaluated operational indicator data is determined according to the evaluation level. Among them, the second type of data processing model can be a star-level global evaluation model.

[0073] If any of the operational indicator data to be evaluated corresponding to the department is not a percentage and the type is quantifiable data, then the evaluation level of the operational indicator data to be evaluated corresponding to each department is determined according to the data volume of the operational indicator data to be evaluated corresponding to each department, and the third type of data processing model corresponding to the operational indicator data to be evaluated is determined according to the evaluation level. Among them, the third type of data processing model can be a star-level regional evaluation model.

[0074] Alternatively, if any of the operational indicator data to be evaluated corresponding to the department is not a percentage, the data processing model corresponding to each operational indicator data to be evaluated is determined to be a fourth type of data processing model. The fourth type of data processing model may be a star rating evaluation model.

[0075] In this embodiment, the operational indicator data to be evaluated can be generally divided into percentage data, non-percentage data and quantifiable data based on the type to which it belongs; wherein the percentage type data can include the cost reduction rate, the non-percentage data can include the per capita revenue, and the quantifiable data can include patient satisfaction. Different data are difficult to process with a unified data processing model due to their different types, and in order to reduce the complexity of the model and the consumption of computing resources, when selecting the data processing model, the data volume of each operational indicator data to be evaluated can also be used as one of the bases for selection.

[0076] In an optional embodiment, in a possible implementation manner, in step 130, according to the data processing model corresponding to each operation indicator data to be evaluated and each operation indicator data to be evaluated, obtaining the classification result of each operation indicator data to be evaluated may include:

[0077] Each operating indicator data to be evaluated is input into a data processing model corresponding to each operating indicator data to be evaluated, and an evaluation value of each operating indicator data to be evaluated is obtained.

[0078] According to the evaluation value of each operation indicator data to be evaluated, the classification result of each operation indicator data to be evaluated is obtained.

[0079] Among them, for any operation indicator data to be evaluated, if the data processing model corresponding to the operation indicator data to be evaluated is a first-type data processing model, the following relationship is satisfied between the operation indicator data to be evaluated and the evaluation value:

[0080] Z1=((Nx)+1)

[0081] Wherein, N is the total amount corresponding to the operation indicator data to be evaluated; x is the ranking of the operation indicator data to be evaluated.

[0082] If the data processing model corresponding to the to-be-evaluated operating indicator data is a second-type data processing model, the to-be-evaluated operating indicator data and the evaluation value satisfy the following relationship:

[0083] Z2=(Q-INT(x / (N / Q)-T))*(N / Q)

[0084] Wherein, Q is the evaluation level; T is the integer adjustment value; x is the ranking of the operation indicator data to be evaluated; and N is the total amount corresponding to the operation indicator data to be evaluated.

[0085] If the data processing model corresponding to the to-be-evaluated operating indicator data is a third-category data processing model, the to-be-evaluated operating indicator data and the evaluation value satisfy the following relationship:

[0086] Z3=(Q-INT(x / (N / Q)-T))-1)*(N*(ND) / (Q-1))+N*D

[0087] Among them, D is the minimum evaluation rate.

[0088] If the data processing model corresponding to the to-be-evaluated operating indicator data is the fourth type of data processing model, the to-be-evaluated operating indicator data and the evaluation value satisfy the following relationship:

[0089] DJ=INT(x / (N / Qn)-T)+1

[0090] Z4=SUBSTR(Fn,(DJ-1)*3+1,2)

[0091] Among them, DJ represents the number of stars after quantification; Qn represents the preset number of stars; and Fn represents the number of stars corresponding to each value in the operational indicator data to be evaluated.

[0092] In this embodiment, for the star rating model, the number of stars corresponding to it is determined according to each operational indicator data to be evaluated. For any operational indicator data to be evaluated, if its data volume is greater than the preset first threshold, it is determined that the number of stars corresponding to the evaluated operational indicator data is nine stars; if its data volume is not greater than the preset first threshold, and is less than the second preset threshold, it is determined that the number of stars corresponding to the evaluated operational indicator data is seven stars; if its data volume is not greater than the preset second threshold, and is less than the third preset threshold, it is determined that the number of stars corresponding to the evaluated operational indicator data is five stars; if its data volume is not greater than the preset third threshold, it is determined that the number of stars corresponding to the evaluated operational indicator data is four stars. Among them, the first threshold> the second threshold> the third threshold. Exemplarily, the first threshold can be 100, the second threshold can be 50, and the third threshold can be 30. It should be understood that the above data is only for illustration and does not constitute any limitation on the actual solution.

[0093] In this embodiment, the proportional evaluation model does not set the number of stars and scores. The evaluation objects are ranked in order and a certain algorithm is used to automatically score the evaluation objects, thereby conducting a quantitative evaluation of the evaluation objects.

[0094] The star-rating global assessment model determines the number of stars based on the number of assessment objects, and then determines the assessment objects into different star levels according to a certain algorithm, automatically setting a specific score between 0 and the assessment number for each star, thereby conducting a quantitative assessment of the assessment objects.

[0095] The star-rated regional assessment model determines the number of stars based on the number of assessment objects, and then determines the assessment objects into different star levels according to a certain algorithm. It automatically sets a specific score for each star level that is equal to the minimum assessment rate and the assessment number, thereby conducting a quantitative assessment of the assessment objects.

[0096] The star rating fixed value evaluation model sets the star rating and score, determines the evaluation object into different star ratings according to a certain algorithm, and automatically extracts the corresponding star score for the evaluation object, thereby conducting a quantitative evaluation of the evaluation object.

[0097] The following is an explanation of the calculation process of each data processing model through specific examples:

[0098] For the star-level global evaluation model, assuming that there are 30 evaluation objects (the amount of operational indicator data to be evaluated), they are divided into 5 stars, with a difference of 6 points between two stars, and the scores from 1 star to 5 stars are 6, 12, 18, 24, and 30; the department where the evaluation object (operation indicator data to be evaluated) is located is Department 1, and the fourth indicator is ranked seventh. Correspondingly, its corresponding evaluation value is:

[0099] Z2=(Q-INT(x / (N / Q)-T))*(N / Q)

[0100] Z2=(5-INT(7 / (30 / 5)-0.001))*(30 / 5)=24.

[0101] For the star-rated regional evaluation model, assuming that there are 30 evaluation objects (the amount of operational indicator data to be evaluated), they are divided into 5 stars, the lowest evaluation value is 40% (12 points), the difference between two stars is 4.5 points ((30-12) / (5-1)), and the scores from 1 star to 5 stars are 12, 16.5, 21, 25, 5, 30; the department where the evaluation object (operation indicator data to be evaluated) is located is Department 1, and the fourth indicator is ranked seventh. Correspondingly, its corresponding evaluation value is:

[0102] Z3=(Q-INT(x / (N / Q)-T))-1)*(N*(ND) / (Q-1))+N*D

[0103] Z3=25.5.

[0104] For the star rating evaluation model, assume that there are 30 evaluation objects (the amount of operational indicator data to be evaluated), and according to the 5-star rating, the scores from 5 stars to 1 star are 30, 25, 18, 10, and 0; the department where the evaluation object (operation indicator data to be evaluated) is located is Department 1, and the ranking of the fourth indicator is seventh. Correspondingly, its corresponding evaluation value is:

[0105] Qn=(5,4,3,2,1);

[0106] Fn=(30, 25, 18, 10, 0);

[0107] DJ=INT(x / (N / Qn)-T)+1

[0108] Z4=SUBSTR(Fn,(DJ-1)*3+1,2)

[0109] Take the two characters starting from the 4th character in Fn and assign them to Z.

[0110] Then Z=25.

[0111] In an optional embodiment, in step 120, the data volume and type of the operation indicator data to be evaluated corresponding to each department are analyzed to determine the data processing model corresponding to each operation indicator data to be evaluated, which can also be achieved by the following steps:

[0112] According to the types of the to-be-evaluated operation indicator data corresponding to each department, the to-be-evaluated operation indicator data corresponding to each department are classified to obtain the first type of to-be-evaluated operation indicator data corresponding to each department.

[0113] For any first-category operational indicator data to be evaluated, based on its corresponding data volume, determine the data processing model corresponding to the first-category operational indicator to be evaluated, so as to determine the data processing model corresponding to each operational indicator data to be evaluated; wherein the data processing model is a neural network model.

[0114] In this embodiment, the first type of operating indicator data to be evaluated may include percentage data, non-percentage data and quantifiable data. Accordingly, each operating indicator data to be evaluated may be divided into percentage data, non-percentage data and quantifiable data according to the type of each operating indicator data to be evaluated.

[0115] For each first-class operational indicator data to be evaluated, it can be divided into small-data-volume first-class operational indicator data to be evaluated, medium-data-volume first-class operational indicator data to be evaluated, and large-data-volume first-class operational indicator data to be evaluated according to its corresponding data volume, and the corresponding data processing model is determined according to the classification result. In this embodiment, the data processing model can be a neural network model.

[0116] In an optional embodiment, in step 130, obtaining the classification results of each operation indicator data to be evaluated according to the evaluation value of each operation indicator data to be evaluated may include:

[0117] The evaluation value of each operational indicator data to be evaluated is multiplied by the preset weight to obtain the operational management effect analysis data of each department; wherein, the operational management effect analysis data of each department includes the indicator score of each operational indicator data to be evaluated in each department and the comprehensive score of each department; the indicator score of each operational indicator data to be evaluated in each department is obtained based on the multiplication of the evaluation value of each operational indicator data to be evaluated by the preset weight; the comprehensive score of each department is the sum of the indicator scores of each operational indicator data to be evaluated in the department.

[0118] According to the preset order, each department is sorted according to its corresponding operation management effect analysis data, and based on the sorting results, each department is divided into multiple categories to obtain the classification results of the operation management data of each department in the hospital to be analyzed.

[0119] In this embodiment, for each department, the proportion of each operational indicator data to be evaluated is different. Therefore, the evaluation value of each operational indicator data to be evaluated can be multiplied by the preset weight, and the product can be used as the indicator score of each operational indicator data to be evaluated; for a department, the indicator scores corresponding to all the operational indicator data to be evaluated included therein are added together to obtain the comprehensive score of the department.

[0120] For any department, the higher the comprehensive score, the better it is. Therefore, if sorting is performed according to the comprehensive score, it can be performed in descending order. If sorting is performed according to the indicator scores of each operational indicator data to be evaluated, it is necessary to determine whether to sort in ascending or descending order based on the nature of each operational indicator data to be evaluated.

[0121] Finally, the ranking results can be divided according to the proportion to determine the category of each department; or, the ranking results can be divided according to the corresponding operation and management effect analysis data to determine the category of each department. Among them, dividing each department into multiple categories means dividing each department into excellent, good, poor, etc., which can be divided according to needs to determine the corresponding department of each category and the category of each department, and then determine the effectiveness of the operation and management method of the hospital to be analyzed.

[0122] In an optional embodiment, after obtaining the analysis results of the operation management data of each department in the hospital to be analyzed according to the classification results of each operation indicator data to be evaluated in step 140, the following may also be included:

[0123] The analysis results of the operational management data of each department in the hospital to be analyzed are visualized.

[0124] In this embodiment, a ladder diagram and a text analysis report are obtained based on the classification results of the operational indicator data to be evaluated in each department after arrangement, and then the ladder diagram is visualized. This may include the comprehensive evaluation ranking of each department determined based on the operational indicator data to be evaluated included in each department, and may also include the indicator ranking of each department determined based on the number of operational indicators to be evaluated. When displaying, the ranking of each department in each indicator can be displayed according to the operation of the staff. In addition, the ladder diagram can also display its corresponding actual data, calculated evaluation value and corresponding ranking when it is selected.

[0125] Alternatively, the overall evaluation object and individual evaluation object can be analyzed and commented on according to the comprehensive ranking of the department, the ranking of each indicator and the benchmarking reference value of the department indicator warehouse. Generate a text analysis report to provide users with access, viewing and printing of analysis results. Exemplarily, the text analysis report can be: Department 1 evaluated a total of 30 indicators in the department indicator evaluation in August 2024, with a comprehensive score of 888 points, and the evaluation star rating was the first among the 5-star departments. Among them, 5 5-star indicators accounted for 25%, all of which have reached the benchmarking reference value and need to be maintained; 7 4-star indicators accounted for 35%, of which 3 indicators reached the benchmarking reference value, and the remaining 4 still need to strengthen management: the difference of indicator B is XXX; the difference of indicator E is XX...; 3-star indicators...

[0126] In addition to ladder charts and text analysis reports, bar charts and radar charts can also be used for visual display. Bar charts are suitable for comparing the differences between different departments on a single indicator, while radar charts can comprehensively present the overall performance of departments on multiple indicators. The two complement each other and provide managers with a more comprehensive and intuitive basis for decision-making. The following are specific implementation methods:

[0127] First, let's explain how to visualize it in the form of a bar chart:

[0128] Take the department as the horizontal axis and select key indicators such as cost-expense ratio and per capita revenue as the vertical axis. For the cost-expense ratio, the actual cost-expense ratio data of each department can be sorted out; for the per capita revenue, the corresponding data of each department can also be summarized.

[0129] Use professional drawing software (such as Excel, Tableau, etc.) to create a bar chart. If you focus on cost control, you can draw a bar chart of the cost-expense ratio of each department. The height of the bar corresponds to the value of the cost-expense ratio, so that managers can intuitively compare the cost situation of each department. The lower the bar, the better the cost control. If you focus on the profitability of the department, draw a bar chart of per capita revenue. The higher the bar, the stronger the revenue-generating ability. Differentiate departments by setting different colors to enhance the readability of the chart.

[0130] Add a mouse hover prompt function to the bar chart. When the mouse moves over the column, the detailed data of the department (such as the specific cost-to-expense ratio value, per capita revenue value, etc.) and the ranking information among all departments are displayed, which makes it convenient for managers to quickly obtain detailed data.

[0131] Then explain how to visualize it in the form of a radar chart:

[0132] Determine multiple key indicators to be displayed, such as asset utilization efficiency, patient satisfaction, cost reduction rate, etc., and standardize the data of each department on these indicators to keep them in the same dimension and value range. For example, appropriately convert the percentage data of asset utilization efficiency, the score data of patient satisfaction, the percentage data of cost reduction rate, etc.

[0133] Use the drawing tool to draw a radar chart, with each department corresponding to a polygon. Each vertex of the polygon represents an indicator, and the position of the vertex in the radial direction of the radar chart is determined by the standardized value of the corresponding indicator of the department. If a department performs well in the asset utilization efficiency indicator, its corresponding vertex in the radar chart will be far away from the center; if it performs poorly in the cost reduction rate indicator, the corresponding vertex will be close to the center.

[0134] Indicator explanations and data descriptions are provided next to the radar chart or through interactive methods (such as a pop-up prompt box when clicking on the department polygon) to help managers understand the meaning of each indicator and the advantages and disadvantages of each department on different indicators, so as to comprehensively evaluate the overall operation status of the department.

[0135] Figure 3 is a flowchart of a hospital operation management data analysis method provided by another embodiment of the present invention. Figure 3 To explain:

[0136] S101: Establish an indicator warehouse based on the indicator evaluation plan and set indicator evaluation attributes to store various department operation indicators.

[0137] In this embodiment, Figure 3 The provided method can be applied to a system that includes an indicator warehouse, which includes relevant data of each department in the entire hospital.

[0138] S102: Establish an indicator evaluation model warehouse and a star indicator evaluation model, and establish the calculation relationship between the indicators.

[0139] In this embodiment, the indicator warehouse may include the name of the indicator, its type, benchmark reference value, indicator direction (ascending or descending), preset weight, and the model corresponding to the indicator.

[0140] Exemplarily, the indicator warehouse may be as shown in Table 1.

[0141] Table 1 Index warehouse

[0142]

[0143]

[0144] S103: Establish a department operation indicator warehouse, collect department indicator data through the HRP system interface, and perform data cleaning.

[0145] In this embodiment, the department operation indicator warehouse may include the department name, numerator value, denominator value, indicator value and corresponding ranking. Taking "asset utilization efficiency" as an example, the department operation indicator warehouse is shown in Table 2.

[0146] Table 2 Department Operation Index Warehouse

[0147] Serial number Department Numerator value Denominator value Index value Ranking 1 Department 1 47.49 2402.63 1.98％ 18 2 Department 2 721.25 4136.25 17.44％ 1 3 Department 3 -66.43 3086.71 -2.15％ 23 4 Department 4 1030.52 8042.97 12.81％ 7 5 Department 5 633.31 4583.86 13.82％ 2 …… …… …… …… …… 30 Department 30 -162.12 12109.70 -1.34％ 20

[0148] In Table 2, the total number of evaluation objects is 30 departments; this indicator is a positive indicator and is arranged in descending order, from large to small. For example, "17.4%" is the largest and ranks "1".

[0149] S104: Evaluate the department indicator data according to the indicator model and store the results in the evaluation effect warehouse.

[0150] In this embodiment, taking the "asset utilization efficiency" indicator as an example, referring to Table 1 and Table 2, the evaluation results calculated are shown in Table 3:

[0151] Table 3 Evaluation results

[0152]

[0153] Table 3 shows the evaluation results of asset utilization efficiency in each department, the corresponding star rating, and the final indicator score.

[0154] S105: Reorganize the evaluation results according to the ranking order of departments.

[0155] Each department is sorted according to the comprehensive score and the corresponding indicator value of each indicator. The ranking results of each department are shown in Table 4:

[0156] Table 4 Ranking results of each department

[0157]

[0158] Table 4 shows the comprehensive score of each department, the comprehensive ranking of each department determined based on the comprehensive score, the indicator value of each indicator in each department, the indicator score, and the department ranking determined based on each indicator.

[0159] S106: Generate a department index evaluation ladder diagram and text analysis report.

[0160] In order to intuitively display the ranking results of each department, a ladder chart and text analysis report can be generated based on the ranking results of each department. The ladder chart can be referred to Figure 2 shown.

[0161] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0162] In summary, the embodiments of the present invention have the following advantages: centralized department operation indicator analysis, simplified report query frequency, and improved work efficiency. By intelligently evaluating the effects of department operation indicators, qualitative and quantitative data are converted into unified evaluation values, thereby improving the recognition of department operation effects. According to the total number of departments with different operation indicators, the department operation indicator evaluation model is automatically matched or according to the user-defined evaluation model, and intelligent data processing technology is used to analyze and evaluate the department operation indicators through intelligent algorithms and calculation models. Intelligent department indicator evaluation avoids cumbersome manual evaluation methods, and the evaluation process is more rigorous, transparent, fast and accurate. Automatically generate a visual indicator warehouse, and use a visual method to intuitively reflect the ranking and evaluation results of various department operation indicators. It is convenient for managers to grasp the position of department operation capabilities in hospital management, which is conducive to benchmarking management and continuous improvement of departments.

[0163] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0164] Figure 4 The schematic diagram of the structure of the hospital operation management data analysis device provided by the embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0165] like Figure 4 As shown, the hospital operation management data analysis device 4 includes:

[0166] The acquisition module 41 is used to obtain the operational indicator data to be evaluated of each department in the hospital to be analyzed;

[0167] A determination module 42 is used to analyze the data volume and type of the operation indicator data to be evaluated corresponding to each department, and determine the data processing model corresponding to each operation indicator data to be evaluated;

[0168] The classification module 43 is used to obtain the evaluation value of each operation indicator data to be evaluated according to the data processing model corresponding to each operation indicator data to be evaluated and each operation indicator data to be evaluated;

[0169] The analysis module 44 is used to obtain the analysis results of the operation management data of each department in the hospital to be analyzed according to the evaluation values ​​of each operation indicator data to be evaluated.

[0170] In a possible implementation, the determination module 42 is specifically configured to:

[0171] For any operation indicator data to be evaluated corresponding to any department, if any operation indicator data to be evaluated corresponding to the department is a percentage, it is determined that the data processing model corresponding to the operation indicator data to be evaluated is a first-type data processing model;

[0172] If any of the to-be-evaluated operational indicator data corresponding to the department is not a percentage, then the evaluation level of the to-be-evaluated operational indicator data corresponding to each department is determined according to the data volume of the to-be-evaluated operational indicator data corresponding to each department; and the second type of data processing model corresponding to the to-be-evaluated operational indicator data is determined according to the evaluation level;

[0173] If any of the to-be-evaluated operational indicator data corresponding to the department is not a percentage and is of quantifiable data type, then the evaluation level of the to-be-evaluated operational indicator data corresponding to each department is determined according to the data volume of the to-be-evaluated operational indicator data corresponding to each department, and the third type of data processing model corresponding to the to-be-evaluated operational indicator data is determined according to the evaluation level;

[0174] Alternatively, if any of the to-be-evaluated operational indicator data corresponding to the department is not a percentage, then the data processing model corresponding to each to-be-evaluated operational indicator data is determined to be a fourth type of data processing model.

[0175] In a possible implementation, the first type of data processing model is a proportional evaluation model;

[0176] The second type of data processing model is the star-level global evaluation model;

[0177] The third type of data processing model is the star-rated regional evaluation model;

[0178] The fourth type of data processing model is the star rating evaluation model.

[0179] In a possible implementation, the classification module 43 is specifically configured to:

[0180] Input each to-be-evaluated operational indicator data into a data processing model corresponding to each to-be-evaluated operational indicator data to obtain an evaluation value of each to-be-evaluated operational indicator data;

[0181] According to the evaluation value of each operation indicator data to be evaluated, the classification result of each operation indicator data to be evaluated is obtained;

[0182] Among them, for any operation indicator data to be evaluated, if the data processing model corresponding to the operation indicator data to be evaluated is a first-type data processing model, the following relationship is satisfied between the operation indicator data to be evaluated and the evaluation value:

[0183] Z1=((Nx)+1)

[0184] Wherein, N is the total amount corresponding to the operation indicator data to be evaluated; x is the ranking of the operation indicator data to be evaluated;

[0185] If the data processing model corresponding to the to-be-evaluated operating indicator data is a second-type data processing model, the to-be-evaluated operating indicator data and the evaluation value satisfy the following relationship:

[0186] Z2=(Q-INT(x / (N / Q)-T))*(N / Q)

[0187] Among them, Q is the evaluation level; T is the integer adjustment value; x is the ranking of the operating indicator data to be evaluated; N is the total amount corresponding to the operating indicator data to be evaluated;

[0188] If the data processing model corresponding to the to-be-evaluated operating indicator data is a third-category data processing model, the to-be-evaluated operating indicator data and the evaluation value satisfy the following relationship:

[0189] Z3=(Q-INT(x / (N / Q)-T))-1)*(N*(ND) / (Q-1))+N*D

[0190] Among them, D is the minimum evaluation rate;

[0191] If the data processing model corresponding to the to-be-evaluated operating indicator data is the fourth type of data processing model, the to-be-evaluated operating indicator data and the evaluation value satisfy the following relationship:

[0192] DJ=INT(x / (N / Qn)-T)+1

[0193] Z4=SUBSTR(Fn,(DJ-1)*3+1,2)

[0194] Among them, DJ represents the number of stars after quantification; Qn represents the preset number of stars; and Fn represents the number of stars corresponding to each value in the operational indicator data to be evaluated.

[0195] In a possible implementation, the classification module 43 is specifically configured to:

[0196] The evaluation value of each operational indicator data to be evaluated is multiplied by the preset weight to obtain the operational management effect analysis data of each department; wherein the operational management effect analysis data of each department includes the indicator score of each operational indicator data to be evaluated in each department and the comprehensive score of each department; the indicator score of each operational indicator data to be evaluated in each department is obtained based on the multiplication of the evaluation value of each operational indicator data to be evaluated by the preset weight; the comprehensive score of each department is the sum of the indicator scores of each operational indicator data to be evaluated in the department;

[0197] According to the preset order, each department is sorted according to its corresponding operation management effect analysis data, and based on the sorting results, each department is divided into multiple categories to obtain the classification results of the operation management data of each department in the hospital to be analyzed.

[0198] In a possible implementation, the analysis module 44 is further configured to:

[0199] The analysis results of the operational management data of each department in the hospital to be analyzed are visualized.

[0200] In a possible implementation, the determination module 42 is specifically configured to:

[0201] According to the types of the operation indicator data to be evaluated corresponding to each department, the operation indicator data to be evaluated corresponding to each department are classified to obtain the first type of operation indicator data to be evaluated corresponding to each department;

[0202] For any first-category operational indicator data to be evaluated, based on its corresponding data volume, determine the data processing model corresponding to the first-category operational indicator to be evaluated, so as to determine the data processing model corresponding to each operational indicator data to be evaluated; wherein the data processing model is a neural network model.

[0203] Figure 5 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 5 As shown, the electronic device 5 of this embodiment includes: a processor 50, a memory 51, and a computer program 52 stored in the memory 51 and executable on the processor 50. When the processor 50 executes the computer program 52, the steps in the above-mentioned various hospital operation management data analysis method embodiments are implemented, such as Figure 1 Alternatively, when the processor 50 executes the computer program 52, the functions of each module / unit in the above-mentioned device embodiments are realized, for example, Figure 4 The functions of each module / unit are shown.

[0204] Exemplarily, the computer program 52 may be divided into one or more modules / units, which are stored in the memory 51 and executed by the processor 50 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program 52 in the electronic device 5. For example, the computer program 52 may be divided into Figure 4 The modules / units shown.

[0205] The electronic device 5 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 5 may include, but is not limited to, a processor 50 and a memory 51. Those skilled in the art will appreciate that Figure 5 It is only an example of the electronic device 5 and does not constitute a limitation of the electronic device 5. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0206] The processor 50 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0207] The memory 51 may be an internal storage unit of the electronic device 5, such as a hard disk or memory of the electronic device 5. The memory 51 may also be an external storage device of the electronic device 5, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 5. Further, the memory 51 may also include both an internal storage unit of the electronic device 5 and an external storage device. The memory 51 is used to store the computer program and other programs and data required by the electronic device. The memory 51 may also be used to temporarily store data that has been output or is to be output.

[0208] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0209] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0210] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0211] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0212] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0213] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0214] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various hospital operation management data analysis method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium.

[0215] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A hospital operation management data analysis method, characterized in that: include: Obtain the operational indicator data to be evaluated for each department in the hospital to be analyzed; Analyze the data volume and type of the operational indicator data to be evaluated for each department, and determine the data processing model corresponding to each operational indicator data to be evaluated; According to the data processing model corresponding to each of the operation indicator data to be evaluated and each of the operation indicator data to be evaluated, a classification result of each of the operation indicator data to be evaluated is obtained; According to the classification results of each operational indicator data to be evaluated, the analysis results of the operational management data of each department in the hospital to be analyzed are obtained.

2. The hospital operation management data analysis method according to claim 1, characterized in that: The data volume and type of the operation indicator data to be evaluated corresponding to each department are analyzed to determine the data processing model corresponding to each operation indicator data to be evaluated, including: For any operation indicator data to be evaluated corresponding to any department, if any operation indicator data to be evaluated corresponding to the department is a percentage, it is determined that the data processing model corresponding to the operation indicator data to be evaluated is a first-type data processing model; If any of the to-be-evaluated operation indicator data corresponding to the department is not a percentage, then the evaluation level of the to-be-evaluated operation indicator data corresponding to each department is determined according to the data volume of the to-be-evaluated operation indicator data corresponding to each department; and according to the evaluation level, the second type of data processing model corresponding to the to-be-evaluated operation indicator data is determined; If any of the to-be-evaluated operational indicator data corresponding to the department is not a percentage and is of quantifiable data type, then the evaluation level of the to-be-evaluated operational indicator data corresponding to each department is determined according to the data volume of the to-be-evaluated operational indicator data corresponding to each department, and the third type of data processing model corresponding to the to-be-evaluated operational indicator data is determined according to the evaluation level; Alternatively, if any of the to-be-evaluated operational indicator data corresponding to the department is not a percentage, then the data processing model corresponding to each to-be-evaluated operational indicator data is determined to be a fourth type of data processing model.

3. The hospital operation management data analysis method according to claim 2, characterized in that: The first type of data processing model is a proportional evaluation model; The second type of data processing model is a star-level global evaluation model; The third type of data processing model is a star-rated regional evaluation model; The fourth type of data processing model is a star rating evaluation model.

4. The hospital operation management data analysis method according to claim 2, characterized in that: The classification results of each operation indicator data to be evaluated are obtained according to the data processing model corresponding to each operation indicator data to be evaluated and each operation indicator data to be evaluated, including: Input each to-be-evaluated operational indicator data into a data processing model corresponding to each to-be-evaluated operational indicator data to obtain an evaluation value of each to-be-evaluated operational indicator data; According to the evaluation value of each operation indicator data to be evaluated, the classification result of each operation indicator data to be evaluated is obtained; Among them, for any operation indicator data to be evaluated, if the data processing model corresponding to the operation indicator data to be evaluated is a first-type data processing model, the following relationship is satisfied between the operation indicator data to be evaluated and the evaluation value: Z1=((Nx)+1) Wherein, N is the total amount corresponding to the operation indicator data to be evaluated; x is the ranking of the operation indicator data to be evaluated; If the data processing model corresponding to the to-be-evaluated operating indicator data is a second-type data processing model, the to-be-evaluated operating indicator data and the evaluation value satisfy the following relationship: Z2=(Q-INT(x / (N / Q)-T))*(N / Q) Among them, Q is the evaluation level; T is the integer adjustment value; x is the ranking of the operating indicator data to be evaluated; N is the total amount corresponding to the operating indicator data to be evaluated; If the data processing model corresponding to the to-be-evaluated operating indicator data is a third-category data processing model, the to-be-evaluated operating indicator data and the evaluation value satisfy the following relationship: Z3=(Q-INT(x / (N / Q)-T))-1)*(N*(ND) / (Q-1))+N*D Among them, D is the minimum evaluation rate; If the data processing model corresponding to the to-be-evaluated operating indicator data is the fourth type of data processing model, the to-be-evaluated operating indicator data and the evaluation value satisfy the following relationship: DJ=INT(x / (N / Qn)-T)+1 Z4=SUBSTR(Fn,(DJ-1)*3+1,2) Among them, DJ represents the number of stars after quantification; Qn represents the preset number of stars; and Fn represents the number of stars corresponding to each value in the operational indicator data to be evaluated.

5. The hospital operation management data analysis method according to claim 4, characterized in that: The classification results of the operation indicator data to be evaluated are obtained according to the evaluation values ​​of the operation indicator data to be evaluated, including: The evaluation value of each operational indicator data to be evaluated is multiplied by a preset weight to obtain the operational management effect analysis data of each department; wherein the operational management effect analysis data of each department includes the indicator score of each operational indicator data to be evaluated in each department and the comprehensive score of each department; the indicator score of each operational indicator data to be evaluated in each department is obtained based on the evaluation value of each operational indicator data to be evaluated multiplied by a preset weight; the comprehensive score of each department is the sum of the indicator scores of each operational indicator data to be evaluated in the department; According to a preset order, each department is sorted according to its corresponding operation management effect analysis data, and based on the sorting result, each department is divided into multiple categories to obtain the classification results of the operation management data of each department in the hospital to be analyzed.

6. The hospital operation management data analysis method according to claim 1, characterized in that: After obtaining the analysis results of the operation management data of each department in the hospital to be analyzed according to the classification results of each operation indicator data to be evaluated, the following steps are included: The analysis results of the operation management data of each department in the hospital to be analyzed are visualized.

7. The hospital operation management data analysis method according to claim 1, characterized in that: Alternatively, analyzing the data volume and type of the operational indicator data to be evaluated corresponding to each department to determine the data processing model corresponding to each operational indicator data to be evaluated includes: According to the types of the operation indicator data to be evaluated corresponding to each department, the operation indicator data to be evaluated corresponding to each department are classified to obtain the first type of operation indicator data to be evaluated corresponding to each department; For any first-category operational indicator data to be evaluated, based on its corresponding data volume, determine the data processing model corresponding to the first-category operational indicator to be evaluated, so as to determine the data processing model corresponding to each operational indicator data to be evaluated; wherein, the data processing model is a neural network model.

8. A hospital operation management data analysis device, characterized in that: include: The acquisition module is used to obtain the operational indicator data to be evaluated of each department in the hospital to be analyzed; A determination module is used to analyze the data volume and type of the operational indicator data to be evaluated corresponding to each department, and determine the data processing model corresponding to each operational indicator data to be evaluated; A classification module, used to obtain classification results of each operation indicator data to be evaluated according to the data processing model corresponding to each operation indicator data to be evaluated and each operation indicator data to be evaluated; The analysis module is used to obtain the analysis results of the operation management data of each department in the hospital to be analyzed according to the evaluation values ​​of each operation indicator data to be evaluated.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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