Transverse evaluation method and system for diagnosis and treatment capabilities of multiple hospitals

Abstract

The invention provides a transverse evaluation method and system for diagnosis and treatment capabilities of multiple hospitals, belongs to the technical field of medical big data analysis and hospital performance evaluation, and solves the problem of score distortion caused by data heterogeneity, weight immobilization and the like in the prior art. The method comprises the following steps: preprocessing detection data of a plurality of hospitals on various diagnosis types to obtain a standard diagnosis rate of each hospital on each diagnosis type; calculating the relative proportion of each hospital in the corresponding diagnosis type according to the standard diagnosis rates of all hospitals in the same diagnosis type; according to the relative proportion of all hospitals in the same diagnosis type, determining the comprehensive weight of the corresponding diagnosis type by using an entropy weight method; according to the detection data and the standard diagnosis rate of each hospital on each diagnosis type and the comprehensive weight of each diagnosis type, calculating a comprehensive diagnosis and treatment ability score of the corresponding hospital; and according to the comprehensive diagnosis and treatment capability scores of all hospitals, carrying out transverse evaluation on the diagnosis and treatment capabilities of the hospitals.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical big data analysis and hospital performance evaluation, and particularly relates to a transverse evaluation method and system for diagnosis and treatment capabilities of multiple hospitals. BACKGROUND

[0002] With the development of precision medicine, the performance of medical institutions in various cancer screening, diagnosis and treatment needs a set of scientific and comparable quantitative evaluation tools. Traditional evaluation methods rely on single indicators (such as total positive rate) or subjective scoring, which cannot reflect the differences in incidence of different cancers, the influence of sample size, and the bias caused by the directionality (positive / negative) of indicators.

[0003] The existing technology mainly has the following problems: ignoring the natural incidence weight of different tumors in the population, which may lead to insufficient attention to some tumors with low incidence but high danger, thereby affecting the scientificity and comprehensiveness of the overall evaluation; lacking a dynamic weighting mechanism for internal variability of indicators, as the internal variability of indicators may change over time, environment or population, static weighting method cannot flexibly adapt to these changes, reducing the accuracy and applicability of evaluation; failing to effectively integrate the relationship between different levels, which may lead to poor information transmission or important levels being ignored, affecting the systematicness and logic of the overall evaluation; the normalization processing of indicators lacks a unified mathematical basis support, is easily disturbed by outliers, further weakening the reliability and consistency of the evaluation results.

[0004] Therefore, how to construct an evaluation method of diagnosis and treatment capabilities across medical institutions that can automatically correct data skewness, reasonably allocate weights, is a problem to be solved at present. SUMMARY

[0005] In view of the above analysis, the embodiments of the present application aim to provide a transverse evaluation method and system for diagnosis and treatment capabilities of multiple hospitals, to solve the score distortion problem caused by data heterogeneity, weight fixation and the like in the prior art.

[0006] In one aspect, the present application discloses a transverse evaluation method for diagnosis and treatment capabilities of multiple hospitals, which comprises:

[0007] Pretreating the detection data of multiple hospitals on various diagnosis types to obtain the standard diagnosis rate of each hospital on each diagnosis type; According to the standard diagnosis rates of all hospitals on the same diagnosis type, calculating the relative proportion of each hospital on the corresponding diagnosis type; according to the relative proportions of all hospitals on the same diagnosis type, determining the comprehensive weight of the corresponding diagnosis type by using the entropy weight method; Based on the testing data and standard diagnostic rate of each hospital for each diagnostic type, as well as the comprehensive weight of each diagnostic type, the comprehensive diagnostic and treatment capability score of the corresponding hospital is calculated. A cross-hospital evaluation of diagnostic and treatment capabilities was conducted based on the comprehensive diagnostic and treatment capability scores of all hospitals.

[0008] Based on the above solution, the present invention also makes the following improvements: Furthermore, the testing data includes the total number of people tested and the number of positive cases; the standard diagnostic rate for each hospital in each diagnostic type is obtained by performing the following operations: Based on the test data of each hospital for each diagnostic type, the diagnostic probability of each hospital for each diagnostic type is obtained; The diagnostic probabilities of all hospitals for the same diagnostic type are normalized to obtain the standard diagnostic rate of each hospital for the corresponding diagnostic type.

[0009] Furthermore, the overall weight of the corresponding diagnostic type is determined by performing the following operations: Calculate the information entropy of the corresponding diagnostic type based on the relative proportion of all hospitals in the same diagnostic type; The comprehensive weight of each diagnostic type is determined based on the information entropy of each diagnostic type.

[0010] Furthermore, the comprehensive weight of the corresponding diagnostic type is determined in the following ways: No. Entropy weight of diagnostic types Represented as: (1) in, Indicates the first Information entropy of different diagnostic types Indicates the total number of hospitals; The first of all hospitals Total number of people tested for each diagnostic type Represented as: (2) in, They represent the first The hospital in Total number of people tested for each diagnostic type; No. Weights after diagnostic type correction Represented as: (3) No. Comprehensive weight of diagnostic types Represented as: (4) in, This indicates the total number of diagnostic types.

[0011] Furthermore, the comprehensive diagnostic and treatment capability score of the corresponding hospital is calculated by performing the following operations: Each diagnostic type is used as a secondary indicator, and each primary indicator includes several secondary indicators. Based on the standard diagnostic rate and comprehensive weight of each hospital on each secondary indicator, the objective performance score of the corresponding hospital is obtained. Based on the test data of each hospital on each secondary indicator, the corresponding hospital's diagnosis and treatment capability score is obtained; Based on each hospital's objective performance score and treatment capability score, a comprehensive treatment capability score for each hospital is obtained.

[0012] Furthermore, a cross-hospital evaluation of diagnostic and treatment capabilities was conducted by performing the following operations: All participating hospitals were ranked in descending order of their comprehensive diagnostic and treatment capability scores. The higher the comprehensive diagnostic and treatment capability score, the better the horizontal evaluation result of the corresponding hospital.

[0013] Furthermore, the objective performance score for the corresponding hospital is obtained by performing the following operations: For each hospital, the standard diagnostic rate of all secondary indicators under each primary indicator is weighted and summed according to the comprehensive weight of all secondary indicators under each primary indicator to obtain the score of the corresponding primary indicator. The scores of all primary indicators are then weighted and summed to obtain the objective performance score of the corresponding hospital.

[0014] Furthermore, the corresponding hospital's diagnostic and treatment capability score is obtained by performing the following operations: For the The hospital, the The first primary indicator The number of positive cases and the total number of tests for each secondary indicator are respectively expressed as follows: , ;No. The hospital in The ratio of the mean of each primary indicator Represented as: (5) in, Indicates the first The number of secondary indicators under each primary indicator; No. The hospital in Scale coefficient under each primary indicator Represented as: (6) No. The diagnosis and treatment ability score of the i-th hospital is expressed as: (7) wherein, is the scale weight coefficient of the i-th primary index, is the total number of primary indexes. Further, the comprehensive diagnosis and treatment ability score of the i-th hospital

[0015] is expressed as: (8) wherein, , respectively represent the weight coefficients of the objective performance score and the diagnosis and treatment ability score; is the objective performance score of the i-th hospital. On the other hand, the present application also discloses a horizontal evaluation system for diagnosis and treatment ability of multiple hospitals, which comprises:

[0016] a data preprocessing module, configured to preprocess the detection data of multiple hospitals on various diagnosis types to obtain a standard diagnosis rate of each hospital on each diagnosis type; an entropy weight method weighting module, configured to calculate a relative proportion of each hospital on a corresponding diagnosis type according to the standard diagnosis rates of all hospitals on the same diagnosis type, and determine a comprehensive weight of the corresponding diagnosis type by using the entropy weight method according to the relative proportions of all hospitals on the same diagnosis type; a hierarchical fusion scoring module, configured to calculate a comprehensive diagnosis and treatment ability score of a corresponding hospital according to the detection data and the standard diagnosis rate of each hospital on each diagnosis type, and the comprehensive weight of each diagnosis type; a horizontal evaluation module, configured to perform horizontal evaluation of the diagnosis and treatment ability of multiple hospitals according to the comprehensive diagnosis and treatment ability scores of all hospitals.

[0017] Compared with the prior art, the present application can at least achieve one of the following beneficial effects: The horizontal evaluation method and system for diagnosis and treatment ability of multiple hospitals provided by the present application have the following beneficial effects: 1. Data heterogeneity correction capability: by preprocessing the detection data, the diagnosis probabilities of different hospitals on various diagnosis types are normalized to obtain a standard diagnosis rate, effectively eliminating the data heterogeneity caused by differences in detection population structure, sample size, etc. between different hospitals, laying a unified data foundation for subsequent horizontal comparison and avoiding possible deviations when directly using original data for evaluation.

[0018] ​​​2. Dynamic Weighting Mechanism: An entropy-based weighting module is introduced, calculating information entropy based on the standard diagnostic rates of all hospitals for each diagnostic type, and then determining the comprehensive weight of each diagnostic type. This dynamic weighting method can automatically adjust the weights according to the variability within the indicators. Compared with traditional static weighting, it is more adaptable to the dynamic characteristics of data changing with time, environment, or population, making the weighting allocation more objective and scientific, and improving the accuracy of the assessment.

[0019] 3. Hierarchical Comprehensive Scoring System: The hierarchical integrated scoring module uses diagnosis type as a secondary indicator and combines it with primary indicators for hierarchical weighted summation. This not only integrates the relationships between different levels, making information transmission smoother and avoiding the problem of important levels being ignored, but also comprehensively reflects the hospital's diagnostic and treatment level by combining objective performance score with diagnostic and treatment capability score (comprehensively considering mean ratio and scale coefficient), overcoming the limitations of single indicator evaluation.

[0020] 4. Reliability and comparability of evaluation results: The entire evaluation process, from data preprocessing and weight determination to score calculation, is based on a clear mathematical model and unified processing methods, such as the introduction of entropy weight calculation, mean ratio, and scale coefficient. This provides a solid mathematical foundation for indicator normalization, effectively reduces the interference of outliers, ensures the reliability of evaluation results and the comparability of evaluation results between different hospitals, and provides medical management departments with a scientific and objective basis for decision-making.

[0021] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0022] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. Figure 1 A flowchart of a method for cross-hospital diagnostic and treatment capabilities provided in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the structure of the cross-hospital diagnostic and treatment capability evaluation system provided in Embodiment 2 of the present invention. Detailed Implementation

[0023] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0024] Specific embodiment 1 of the present invention discloses a method for cross-hospital diagnostic and treatment capabilities, the flowchart of which is shown below. Figure 1 As shown, the specific explanation is as follows.

[0025] Step S1: Preprocess the test data from multiple hospitals for various diagnostic types to obtain the standard diagnostic rate for each hospital for each diagnostic type.

[0026] In this embodiment, the detection data includes the total number of people tested and the number of positive cases.

[0027] Step S11: Based on the test data of each hospital for each diagnostic type, obtain the diagnostic probability of the corresponding hospital for the corresponding diagnostic type.

[0028] Specifically, no. The hospital in Diagnostic probability of different diagnostic types Represented as: (1) in, , They represent the first The hospital in The number of positive cases for each diagnostic type, and the total number of people tested. In the specific implementation process, if... The calculation of formula (1) is no longer performed; the value is directly set. It is 0.

[0029] In this embodiment, the diagnostic type to be studied can be determined according to the actual situation. For example, the diagnostic type (corresponding to the secondary indicator code in Table 1) and its corresponding diagnostic probability (corresponding to the secondary indicator name in Table 1) can be set as follows (see Table 1). It should be noted that this embodiment sets multiple primary indicators, each primary indicator contains several secondary indicators (i.e., the diagnostic type here), and the two-layer nested scoring structure is shown in Table 1.

[0030] Table 1 Two-layer nested scoring structure

[0031] Step S12: Normalize the diagnostic probabilities of all hospitals for the same diagnostic type to obtain the standard diagnostic rate of each hospital for the corresponding diagnostic type.

[0032] Specifically, in this embodiment, diagnostic indicators are divided into two categories: positive indicators and negative indicators. Positive indicators are those with higher values, which are more desirable; negative indicators are those with lower values, which are more desirable, such as mortality rate. The relevant classifications can be found in Table 1 above.

[0033] If the first If the first diagnostic type is a positive indicator, then the second... The hospital in Standard diagnostic rate for each diagnostic type Represented as: (2) If the first If the first diagnostic type is a negative indicator, then the second... The hospital in Standard diagnostic rate for each diagnostic type Represented as: (3) in, This indicates the total number of hospitals.

[0034] Adaptive normalization of positive and negative indicators: The system can automatically identify various types of indicators and intelligently transform them according to their characteristics. This function not only effectively improves data processing efficiency but also significantly enhances the system's versatility and adaptability, enabling it to better meet the needs of diverse scenarios. Through this flexible normalization method, both positive and negative indicators can be analyzed and compared under a unified standard, thus providing more accurate data support for decision-making.

[0035] Step S2: Calculate the relative weight of each hospital in the corresponding diagnostic type based on the standard diagnostic rate of all hospitals in the same diagnostic type; determine the comprehensive weight of the corresponding diagnostic type using the entropy weight method based on the relative weight of all hospitals in the same diagnostic type.

[0036] Step S21: Calculate the relative proportion of each hospital in the corresponding diagnostic type based on the standard diagnostic rate of all hospitals in the same diagnostic type.

[0037] No. The hospital in The relative proportion of each diagnostic type (positive contribution proportion) Represented as: (4) Step S22: Calculate the information entropy of the corresponding diagnostic type based on the relative proportion of all hospitals in the same diagnostic type.

[0038] No. Information entropy of various diagnostic types Represented as: (5) The standardized entropy value ranges from [0,1]. The larger the value, the more uniform the distribution and the weaker the ability of the indicator to distinguish.

[0039] Step S23: Determine the comprehensive weight of each diagnostic type based on the information entropy of each diagnostic type.

[0040] No. Entropy weight of diagnostic types Represented as: (6) The first of all hospitals Total number of people tested for each diagnostic type Represented as: (7) No. Weights after diagnostic type correction Represented as: (8) No. Comprehensive weight of diagnostic types Represented as: (9) in, This indicates the total number of diagnostic types.

[0041] The method for calculating the comprehensive weight of diagnostic types provided in this embodiment combines "epidemiological weight" and "information richness weight." Simultaneously, a weight correction model based on the number of cases is implemented: → → The three-tiered correction pathway ensures that rare tumor types are not overly affected by occasional high positive rates in their scores. Step S3: Calculate the comprehensive diagnostic and treatment capability score of each hospital based on the test data and standard diagnostic rate for each diagnostic type, as well as the comprehensive weight of each diagnostic type.

[0042] Each diagnostic type is treated as a secondary indicator. The scores of the secondary indicators are combined with the weights of their corresponding primary indicators to generate a weighted score, which is then used to form three core scores: objective performance score, diagnostic and treatment capability score, and final treatment plan score. The entire process supports the automatic identification and processing of positive indicators (the higher the better) and negative indicators (the lower the better), and has good scalability and robustness. The specific implementation process is described below.

[0043] Step S31: Each diagnostic type is used as a secondary indicator, and each primary indicator includes several secondary indicators; based on the standard diagnostic rate and comprehensive weight of each hospital on each secondary indicator, the objective performance score of the corresponding hospital is obtained.

[0044] Specifically, for each hospital, the standard diagnostic rate of all secondary indicators under each primary indicator is weighted and summed according to the comprehensive weight of all secondary indicators under each primary indicator to obtain the score of the corresponding primary indicator. The scores of all primary indicators are then weighted and summed to obtain the objective performance score of the corresponding hospital.

[0045] For the The hospital, the The first primary indicator Each secondary indicator is represented as At this time, the first The first primary indicator The comprehensive weight of each secondary indicator is expressed as follows: At this time, the first The hospital in Scores of each primary indicator Represented as: (10) in, Indicates the first The number of secondary indicators under each primary indicator.

[0046] No. Objective performance of the hospital Represented as: (11) in, This represents the total number of primary indicators. Indicates the first The weight coefficient of each primary indicator.

[0047] Step S32: Based on the test data of each hospital on each secondary indicator, obtain the corresponding hospital's diagnosis and treatment capability score.

[0048] For the The hospital, the The first primary indicator The number of positive cases and the total number of tests for each secondary indicator are respectively expressed as follows: , . No. The hospital in The ratio of the mean of each primary indicator Represented as: (12) No. The hospital in Scale coefficient under each primary indicator Represented as: (13) In this embodiment, the scale coefficient reflects the overall service volume of the sample institution.

[0049] No. The hospital's diagnostic and treatment capabilities score Represented as: (14) in, Indicates the first The size weight coefficient of each primary indicator, In the specific implementation process, the criticality of each primary indicator in the diagnosis and treatment process, the completeness of data collection, and the opinions of industry experts can be comprehensively considered to assign differentiated scale weight coefficients to different primary indicators. The sum of the scale weight coefficients of all primary indicators must be 1 to ensure the standardization of calculations and the comparability of results. The scale weight coefficients determined in this way can more accurately combine the actual service volume of the hospital and make an objective evaluation of its diagnosis and treatment capabilities.

[0050] Step S33: Based on the objective performance score and treatment capability score of each hospital, obtain the comprehensive treatment capability score of the corresponding hospital.

[0051] No. The comprehensive diagnosis and treatment capabilities score of a hospital Represented as: (15) in, , These represent the weighting coefficients for the objective performance score and the diagnostic and treatment ability score, respectively. In the specific implementation process, multiple rounds of Delphi method discussions can be organized among medical experts to quantitatively evaluate the weighting coefficients of objective performance scores and treatment capacity scores, taking into account the functional positioning of hospitals at different levels, regional medical resource allocation plans, and key points of medical quality and safety management. To improve the overall regional treatment level, the weighting of the objective performance score can be increased to highlight core quality indicators; to promote the development of primary healthcare institutions, the weighting of the treatment capacity score can be increased to encourage increased service volume and coverage. Simultaneously, a dynamic adjustment mechanism should be introduced, periodically calibrating the weighting coefficients based on factors such as medical policy orientation and changes in the disease spectrum during the evaluation period, to ensure that the comprehensive treatment capacity score accurately reflects the actual treatment level of hospitals at different stages.

[0052] Scale-quality decoupled scoring framework: By separating "objective performance score" and "diagnosis and treatment capability score", a fair comparison can be made between large hospitals and small and medium-sized medical institutions.

[0053] Step S4: Conduct a cross-hospital evaluation of the comprehensive diagnostic and treatment capabilities of all hospitals based on their overall diagnostic and treatment capability scores.

[0054] In this embodiment, the comprehensive treatment capability score is a dual-dimensional indicator, reflecting not only the "quality" of a hospital's treatment but also its "scale." These two dimensions complement each other, forming a comprehensive assessment of the hospital's overall treatment level. In practice, to more clearly reflect the differences between hospitals, the comprehensive treatment capability scores of all participating hospitals are arranged in descending order. A higher score indicates a better overall evaluation result. This ranking method visually demonstrates the relative strengths and weaknesses of different hospitals in terms of treatment capabilities. Top-ranked hospitals are generally considered to possess high levels of both treatment quality and scale, while as the ranking decreases, the corresponding hospital's treatment capability evaluation results also gradually decline, thus forming a hierarchical capability assessment system. This ranking method not only helps identify high-level medical institutions but also provides important reference for subsequent optimization of resource allocation and improvement of medical service quality.

[0055] Specific embodiment 2 of the present invention discloses a cross-hospital diagnostic and treatment capability evaluation system, the structural schematic diagram of which is shown below. Figure 2 As shown, the specific details are as follows: the system includes a data preprocessing module, an entropy weighting module, a hierarchical fusion scoring module, and a horizontal evaluation module; among which, The data preprocessing module is used to preprocess test data from multiple hospitals for various diagnostic types to obtain the standard diagnostic rate for each hospital for each diagnostic type. The entropy weighting module is used to calculate the relative weight of each hospital in the corresponding diagnostic type based on the standard diagnostic rate of all hospitals in the same diagnostic type; and to determine the comprehensive weight of the corresponding diagnostic type based on the relative weight of all hospitals in the same diagnostic type using the entropy weighting method. The hierarchical fusion scoring module is used to calculate the comprehensive diagnosis and treatment capability score of each hospital based on the detection data and standard diagnostic rate of each diagnostic type, as well as the comprehensive weight of each diagnostic type. The cross-hospital evaluation module is used to conduct a cross-hospital evaluation of the diagnostic and treatment capabilities of multiple hospitals based on the comprehensive diagnostic and treatment capability scores of all hospitals.

[0056] The specific implementation process of this invention can be found in the above method embodiments, and will not be repeated here.

[0057] Since this embodiment is based on the same principle as the above method embodiments, this system also has the corresponding technical effects of the above method embodiments.

[0058] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0059] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for cross-hospital diagnostic and treatment capabilities, characterized in that, The method includes: Preprocessing of test data from multiple hospitals across various diagnostic types yields the standard diagnostic rate for each hospital in each diagnostic type. Based on the standard diagnostic rate of all hospitals for the same diagnostic type, calculate the relative proportion of each hospital in the corresponding diagnostic type; based on the relative proportion of all hospitals in the same diagnostic type, determine the comprehensive weight of the corresponding diagnostic type using the entropy weight method. Based on the testing data and standard diagnostic rate of each hospital for each diagnostic type, as well as the comprehensive weight of each diagnostic type, the comprehensive diagnostic and treatment capability score of the corresponding hospital is calculated. A cross-hospital evaluation of diagnostic and treatment capabilities was conducted based on the comprehensive diagnostic and treatment capability scores of all hospitals.

2. The method for cross-hospital diagnostic and treatment capabilities according to claim 1, characterized in that, The testing data includes the total number of people tested and the number of positive cases; the standard diagnostic rate for each hospital in each diagnostic type is obtained by performing the following operations: Based on the test data of each hospital for each diagnostic type, the diagnostic probability of each hospital for each diagnostic type is obtained; The diagnostic probabilities of all hospitals for the same diagnostic type are normalized to obtain the standard diagnostic rate of each hospital for the corresponding diagnostic type.

3. The method for cross-hospital diagnostic and treatment capabilities according to claim 2, characterized in that, The overall weight of the corresponding diagnostic type is determined by performing the following operations: Calculate the information entropy of the corresponding diagnostic type based on the relative proportion of all hospitals in the same diagnostic type; The comprehensive weight of each diagnostic type is determined based on the information entropy of each diagnostic type.

4. The method for cross-hospital diagnostic and treatment capabilities according to claim 3, characterized in that, The overall weight of the corresponding diagnostic type is determined in the following way: No. Entropy weight of diagnostic types Represented as: （1） in, Indicates the first Information entropy of different diagnostic types Indicates the total number of hospitals; The first of all hospitals Total number of people tested for each diagnostic type Represented as: （2） in, They represent the first The hospital in Total number of people tested for each diagnostic type; No. Weights after diagnostic type correction Represented as: （3） No. Comprehensive weight of diagnostic types Represented as: （4） in, This indicates the total number of diagnostic types.

5. The method for cross-hospital diagnostic and treatment capabilities according to claim 4, characterized in that, The comprehensive medical service capability score of the corresponding hospital is calculated by performing the following operations: Each diagnostic type is used as a secondary indicator, and each primary indicator includes several secondary indicators. Based on the standard diagnostic rate and comprehensive weight of each hospital on each secondary indicator, the objective performance score of the corresponding hospital is obtained. Based on the test data of each hospital on each secondary indicator, the corresponding hospital's diagnosis and treatment capability score is obtained; Based on each hospital's objective performance score and treatment capability score, a comprehensive treatment capability score for each hospital is obtained.

6. The method for cross-hospital diagnostic and treatment capabilities according to claim 5, characterized in that, A cross-hospital evaluation of diagnostic and treatment capabilities was conducted by performing the following steps: all participating hospitals were ranked in descending order of their comprehensive diagnostic and treatment capability scores. The higher the comprehensive diagnostic and treatment capability score, the better the cross-hospital evaluation result.

7. The method for cross-hospital diagnostic and treatment capabilities according to claim 5 or 6, characterized in that, The objective performance score for the corresponding hospital is obtained by performing the following operations: For each hospital, the standard diagnostic rate of all secondary indicators under each primary indicator is weighted and summed according to the comprehensive weight of all secondary indicators under each primary indicator to obtain the score of the corresponding primary indicator. The scores of all primary indicators are then weighted and summed to obtain the objective performance score of the corresponding hospital.

8. The method for cross-hospital diagnostic and treatment capabilities according to claim 7, characterized in that, The following steps are performed to obtain a score for the corresponding hospital's diagnostic and treatment capabilities: For the The hospital, the The first primary indicator The number of positive cases and the total number of tests for each secondary indicator are respectively expressed as follows: , ;No. The hospital in The ratio of the mean of each primary indicator Represented as: （5） in, Indicates the first The number of secondary indicators under each primary indicator; No. The hospital in Scale coefficient under each primary indicator Represented as: （6） No. The hospital's diagnostic and treatment capabilities score Represented as: （7） in, Indicates the first The size weight coefficient of each primary indicator, This indicates the total number of primary indicators.

9. The method for cross-hospital diagnostic and treatment capabilities according to claim 8, characterized in that, No. The comprehensive diagnosis and treatment capabilities score of a hospital Represented as: （8） in, , These represent the weighting coefficients for the objective performance score and the diagnostic and treatment ability score, respectively. Indicates the first The objective performance of the hospital.

10. A cross-hospital diagnostic and treatment capability evaluation system, characterized in that, The system includes: The data preprocessing module is used to preprocess test data from multiple hospitals for various diagnostic types to obtain the standard diagnostic rate for each hospital for each diagnostic type. The entropy weighting module is used to calculate the relative weight of each hospital in the corresponding diagnostic type based on the standard diagnostic rate of all hospitals in the same diagnostic type; and to determine the comprehensive weight of the corresponding diagnostic type based on the relative weight of all hospitals in the same diagnostic type using the entropy weighting method. The hierarchical fusion scoring module is used to calculate the comprehensive diagnosis and treatment capability score of each hospital based on the detection data and standard diagnostic rate of each diagnostic type, as well as the comprehensive weight of each diagnostic type. The cross-hospital evaluation module is used to conduct a cross-hospital evaluation of the diagnostic and treatment capabilities of multiple hospitals based on the comprehensive diagnostic and treatment capability scores of all hospitals.

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