A method and system for evaluating the overall situation of a hospital's qualitative indicators

Abstract

A method and system for evaluating the overall situation of a hospital's qualitative indicators, which relates to the technical field of hospital situation evaluation. The method includes: generating an overall situation evaluation task based on all business content information of the target hospital, splitting the overall evaluation task into department self-evaluation tasks and expert sampling evaluation tasks, obtaining target departments and target experts, sending the department self-evaluation tasks and expert sampling evaluation tasks to relevant objects, collecting two types of scoring results, calculating the average scoring deviation degree between the department self-evaluation score and the expert sampling score, judging the average scoring deviation degree of the two types of scores. If the average scoring deviation degree is higher than the preset stability threshold, then calculate the quantitative scores of each business indicator of the whole hospital based on the department self-evaluation score, which can reduce the workload of the expert group and ensure that the result has strong objectivity.

Description

Technical Field

[0001] This application relates to the technical field of hospital situation assessment, and particularly to a method and system for assessing the overall situation of the whole hospital with qualitative indicators. Background Art

[0002] With the improvement of medical standards, the service quality of hospitals has received increasing attention. How to effectively monitor and evaluate the service quality of hospitals has become an important topic.

[0003] Related technologies often involve setting up expert groups to visit and investigate the specific situations of each department related to the indicators, and then the expert groups make an overall summary and evaluation for the specific indicators. However, this method often leads to a large workload for the expert groups and the results are highly subjective. Summary of the Invention

[0004] This application provides a method and system for assessing the overall situation of the whole hospital with qualitative indicators, which is used to generate an overall situation assessment task based on all the business content information of the target hospital, and split the overall assessment task into a self-assessment task for each department and an expert sampling assessment task. Compared with the expert group assessment method in the prior art, it can reduce the workload of the expert group, and use the self-assessment of each department to make the assessment scope more comprehensive, avoid the assessment blind spots in the existing solutions, obtain the target departments and target experts, send the self-assessment task for each department and the expert sampling assessment task to the relevant objects, collect the two types of scoring results, calculate the average scoring deviation degree between the self-assessment score of each department and the expert sampling score, judge the average scoring deviation degree of the two types of scores. If the average scoring deviation degree is higher than the preset stability threshold, then calculate the quantitative scores of each business indicator of the whole hospital based on the self-assessment score of each department, which can reduce the workload of the expert group and ensure that the results are highly objective.

[0005] In the first aspect, this application provides a method for assessing the overall situation of the whole hospital with qualitative indicators, which is applied to a system for assessing the overall situation of the whole hospital with qualitative indicators. The method includes: generating an overall situation assessment task based on all the business content information of the target hospital, and all this business content information corresponds one-to-one with all business indicators;

[0006] Splitting the overall situation assessment task into a self-assessment task for each department and an expert sampling assessment task. The self-assessment task for each department corresponds one-to-one with all these business indicators, and the expert sampling assessment task corresponds one-to-one with a preset number of sampled business indicators among all these business indicators. The sampled business indicators are selected from these business indicators;

[0007] Obtaining all the target departments related to these business indicators and all the target experts related to these sampled business indicators;

[0008] The department self-evaluation task is sent to all target departments to obtain the current department self-evaluation score collection within the preset time period;

[0009] The expert sampling evaluation task is sent to all target experts to obtain the current expert sampling score collection within the preset time period;

[0010] Calculate the average score deviation of the sampled business indicator based on the current department self-assessment score collection and the current expert sampling score collection;

[0011] If the degree of deviation of the average score is higher than the preset stability threshold, the current hospital-wide comprehensive quantitative score collection of all business indicators will be calculated based on the department's self-evaluation score collection.

[0012] In the above embodiment, an overall situation assessment task is generated based on all business content information of the target hospital, and the overall assessment task is divided into a department self-assessment task and an expert sampling assessment task. Compared with the expert group assessment method in the prior art, the workload of the expert group can be reduced, and the department self-assessment is used to make the assessment scope more comprehensive, avoiding the assessment blind spots in the existing scheme, and obtaining the target department and the target expert. By issuing the department self-assessment task and the expert sampling assessment task to relevant objects, the two types of scoring results are collected, and the average score deviation degree of the department self-assessment score and the expert sampling score is calculated. The average score deviation degree of the two types of scores is judged. If the average score deviation degree is higher than the preset stability threshold, the quantitative score of each business indicator of the whole hospital is calculated based on the department self-assessment score, which can reduce the workload of the expert group and ensure that the results are highly objective.

[0013] In conjunction with some embodiments of the first aspect, in some embodiments, the step of generating an overall situation assessment task based on all business contents of the target hospital, wherein all the business content information corresponds to all business indicators one by one, specifically includes:

[0014] Obtain the organizational structure data and business content information of the target hospital;

[0015] Analyze all departments based on the organizational structure data;

[0016] Analyze the business indicators corresponding to each department based on the business content information;

[0017] Generate an overall situation assessment task based on the business indicator.

[0018] In the above embodiment, the organizational structure and business content are automatically analyzed, and departments and business indicators are automatically matched, so as to realize the intelligent generation of the overall evaluation task. Compared with manually determining the evaluation task, this data-driven automatic generation method can ensure the comprehensiveness of the evaluation task setting, so that the business content of all departments can be assessed, avoiding blind spots in the evaluation, and automatically parsing the correspondence between business content and indicators, which also ensures the accuracy of the evaluation task.

[0019] In combination with some embodiments of the first aspect, in some embodiments, after the step of calculating the average score deviation degree of the sampled business indicator according to the current department self-assessment score collection and the current expert sampling score collection, the method further includes:

[0020] If the average score deviation is not higher than the preset stability threshold, determine the deviation business indicator corresponding to the score deviation that is greater than the preset stability threshold;

[0021] Regenerate the optimized department self-assessment task and optimized expert sampling assessment task corresponding to the deviation business indicator;

[0022] The optimized department self-evaluation task is distributed to all target departments to obtain a collection of self-evaluation scores of deviated departments;

[0023] The optimization expert sampling evaluation task is distributed to all target experts to obtain a collection of deviation expert sampling scores;

[0024] Calculate the degree of deviation of the optimized score of the deviation business indicator based on the collection of self-evaluation scores of the deviation department and the collection of sample scores of the deviation experts;

[0025] If the degree of deviation of the optimization score is higher than the preset stability threshold, the hospital's comprehensive quantitative score collection of all business indicators with the deviation will be calculated based on the collection of self-evaluation scores of the deviation department.

[0026] In the above embodiment, multiple rounds of optimization and adjustment are implemented for indicators with scoring deviations. By readjusting the evaluation tasks, the results of different scoring subjects are gradually converged, which effectively reduces the randomness and subjectivity of the scoring and improves the reliability of the evaluation. At the same time, a preset scoring deviation threshold is set. When there are still obvious differences in the scores after multiple rounds of adjustment, quantitative scores can be generated separately through department self-scoring, which avoids the situation where evaluation data for these indicators are missing and ensures the integrity of the evaluation results.

[0027] In conjunction with some embodiments of the first aspect, in some embodiments, before the step of splitting the overall situation assessment task into a department self-assessment task and an expert sampling assessment task, the method further includes:

[0028] Calculate the importance of all business indicators based on the business content information and hospital business system information to obtain a collection of importance levels;

[0029] The ranking within the importance set sets the sampling probability of all business indicators, and the sampling probability is positively correlated with the importance ranking.

[0030] In the above embodiment, indicators related to the core or important business of the hospital can be identified, and these important indicators can be evaluated more fully by setting different sampling probabilities, so that the evaluation results can be more focused on the core competitiveness of the hospital. This can better reflect the actual level of service quality of the hospital in key business areas than simple random sampling.

[0031] In conjunction with some embodiments of the first aspect, in some embodiments, the step of calculating the importance of all business indicators according to the business content information specifically includes:

[0032] Count the frequency of each business indicator appearing in the business content information;

[0033] Count the usage frequency of each business indicator in the hospital's business system information;

[0034] The importance of all the business indicators is calculated according to the frequency information and the usage frequency information to obtain an importance collection.

[0035] In the above embodiment, the importance of the indicators is comprehensively considered from the two dimensions of business content and system use. High-frequency keywords in business content can better reflect key business concerns, and the frequency of system use can also directly reflect the importance of indicators in actual operation. Statistics and integration of these two aspects of frequency information can make the evaluation of indicator importance more comprehensive and accurate. Compared with only one frequency, this evaluation method can avoid evaluation bias caused by the particularity of the indicator in a certain aspect, improve the reliability of the importance results, and provide a more reliable basis for the subsequent sampling probability setting.

[0036] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of calculating the current hospital-wide comprehensive quantitative score collection of all the business indicators based on the department's self-assessment score collection if the score deviation degree is higher than the preset stability threshold, the method further includes:

[0037] Obtain a historical collection of comprehensive quantitative scores of the entire hospital, where the historical collection of comprehensive quantitative scores of the entire hospital includes a preset number of collections of comprehensive quantitative scores of the entire hospital within a preset time period;

[0038] Calculate the historical average comprehensive quantitative score of the hospital for all business indicators based on the collection of historical comprehensive quantitative scores of the hospital;

[0039] If the current comprehensive quantitative score of the hospital for the current business indicator is lower than the historical average comprehensive quantitative score of the hospital, a warning message will be issued to all departments related to the current business indicator.

[0040] In the above embodiment, when the quantitative score shows a significant negative change, the abnormal situation can be quickly identified and an early warning can be issued in time. Compared with the traditional regular assessment, this can achieve immediate discovery and response to quality problems. At the same time, calculating the historical average score as a benchmark can eliminate the impact of score volatility to the greatest extent, making the early warning more accurate and reliable, and avoiding the occurrence of false alarms. The establishment of this early warning mechanism enables the hospital to promptly rectify the service links that have declined and avoid further expansion of quality problems.

[0041] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of calculating the historical average hospital-wide comprehensive quantitative scores of all the business indicators based on the historical hospital-wide comprehensive quantitative score collection, the method further includes:

[0042] If the current hospital-wide comprehensive quantitative score of the current business indicator is not lower than the historical average hospital-wide comprehensive quantitative score, the current hospital-wide comprehensive quantitative score will be sent to all departments related to the current business indicator.

[0043] In the above embodiment, when the business indicator level of the department is normal or has increased, the department can see the specific quantitative performance and continue to maintain or further improve the quality level. This comprehensive closed-loop feedback mechanism avoids passive response by focusing only on problem indicators, allowing the department to more comprehensively examine the status of its own service quality and carry out quality improvements for all indicators.

[0044] In the second aspect, an embodiment of the present application provides a hospital qualitative indicator overall situation assessment system for the whole hospital, the hospital qualitative indicator overall situation assessment system for the whole hospital comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, the one or more processors call the computer instructions to enable the hospital qualitative indicator overall situation assessment system for the whole hospital to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0045] In the third aspect, an embodiment of the present application provides a computer program product comprising instructions. When the above-mentioned computer program product is run on a hospital qualitative indicator overall situation assessment system for the whole hospital, the above-mentioned hospital qualitative indicator overall situation assessment system for the whole hospital executes the method described in the first aspect and any possible implementation method of the first aspect.

[0046] Fourthly, an embodiment of the present application provides a computer-readable storage medium, including instructions, which, when running on the hospital qualitative index overall situation assessment system, cause the hospital qualitative index overall situation assessment system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0047] It can be understood that the hospital qualitative index overall situation assessment system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, and will not be elaborated here.

[0048] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0049] 1. By generating an overall situation assessment task based on all business content information of the target hospital, and splitting the overall assessment task into a department self-assessment task and an expert sampling assessment task, compared with the expert group assessment method in the prior art, the workload of the expert group can be reduced, and the assessment scope can be made more comprehensive by using department self-assessment, avoiding the assessment blind spots in the existing solutions. The target department and target experts are obtained, and by sending the department self-assessment task and the expert sampling assessment task to the relevant objects, two types of scoring results are collected, and the average scoring deviation degree between the department self-assessment score and the expert sampling score is calculated. If the average scoring deviation degree is higher than the preset stability threshold, the quantitative scores of each business indicator of the whole hospital are calculated based on the department self-assessment score, which can reduce the workload of the expert group and ensure that the result has strong objectivity.

[0050] 2. By automatically analyzing the organizational structure and business content, and automatically matching departments with business indicators, the intelligent generation of the overall assessment task is realized. Compared with manually determining the assessment task, this data-driven automatic generation method can ensure the comprehensiveness of the assessment task setting, so that the business content of all departments is evaluated, avoiding assessment blind spots, and automatically analyzing the corresponding relationship between business content and indicators also ensures the accuracy of the assessment task.

[0051] 3. By realizing multi-round optimization adjustment of the indicators with scoring deviations, by readjusting the assessment task, the results of different scoring subjects are gradually made consistent, effectively reducing the randomness and subjectivity of scoring, improving the reliability of the assessment. At the same time, a preset scoring deviation threshold is set. When there are still obvious differences in scoring after multi-round adjustment, the quantitative scores can be generated separately by the department self-assessment score, avoiding the situation of missing assessment data for these indicators and ensuring the integrity of the assessment results. Description of the Drawings

[0052] Figure 1 It is a schematic flowchart of the evaluation method for the overall situation of the hospital's qualitative indicators in the embodiments of the present application;

[0053] Figure 2 It is another schematic flowchart of the evaluation method for the overall situation of the hospital's qualitative indicators in the embodiments of the present application;

[0054] Figure 3 It is another schematic flowchart of the evaluation method for the overall situation of the hospital's qualitative indicators in the embodiments of the present application;

[0055] Figure 4 It is another schematic flowchart of the evaluation method for the overall situation of the hospital's qualitative indicators in the embodiments of the present application;

[0056] Figure 5 It is a schematic structural diagram of an entity device of the hospital qualitative indicator overall situation evaluation system in the embodiments of the present application. Detailed implementation manners

[0057] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application, the singular forms "a", "an", "the above", "the", and "this" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations including one or more of the listed items.

[0058] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0059] For ease of understanding, the method provided in this embodiment is described in terms of a process below. Please refer to Figure 1 , which is a schematic flowchart of the evaluation method for the overall situation of the hospital's qualitative indicators in the embodiments of the present application.

[0060] S101. Generate an overall situation evaluation task based on all the business content information of the target hospital, and all the business content information corresponds to all the business indicators one by one.

[0061] Among them, the business content information refers to the various medical service contents in the daily operation of the hospital, including outpatient service, inpatient service, examinations and tests, etc.; the business indicators are the various indicators used to evaluate the service quality of the hospital, such as the visiting environment, medical equipment, service attitude, etc.; the overall situation assessment task is the assessment task of all the business contents and service indicators of the hospital. This step is to match the business content of the target hospital with the corresponding service quality indicators after obtaining it, so as to form an assessment task.

[0062] Specifically, when evaluating the service quality of a hospital, it is necessary to first clarify the business scope of the hospital, such as service contents like outpatient service, emergency service, inpatient service, etc., and then determine the various indicators that need to be evaluated for quality assessment based on these contents, such as the visiting environment, equipment configuration, service attitude of the staff, etc. Through the one-to-one correspondence between the business content and the indicators, an assessment task for the overall situation of the hospital can be generated, that is, corresponding quality indicators need to be set for each service content provided by the hospital for comprehensive assessment.

[0063] In some embodiments, the system uses natural language processing technology to parse semi-structured text materials such as the organizational structure and business procedures in the hospital database, extracts the business content information, uses the vector representation based on the business content, matches it with the vector representations of each indicator in the quality management system standard, extracts relevant indicators according to the matching degree, and uses the rule engine technology to automatically set the indicator requirements for quality assessment according to the corresponding rules between the business content and the indicators.

[0064] S102. Split the overall situation assessment task into a self-assessment task for each department and an expert sampling assessment task. The self-assessment task for each department corresponds one-to-one with all the business indicators, and the expert sampling assessment task corresponds one-to-one with a preset number of sampled business indicators selected from all the business indicators. The sampled business indicators are selected from the business indicators.

[0065] Among them, the self-assessment task for each department is the task for each department to conduct self-evaluation on all the service indicators of its own department; the expert sampling assessment task is the task for experts to evaluate some sampled indicators. This step decomposes the overall assessment task into two assessment methods: self-assessment by each department and expert sampling.

[0066] Specifically, to reduce the assessment workload, the generated overall situation assessment task can be split. Each department is required to conduct self-evaluation on all the business indicators of its own department to form a self-assessment task for each department; at the same time, randomly select some indicators for the expert group to evaluate to form an expert sampling task.

[0067] In some embodiments, the department self-evaluation tasks can be automatically generated according to the overall situation assessment tasks, and part of the indicators can be extracted from all the indicators by using a random algorithm to generate the expert sampling evaluation tasks. Optionally, the indicator ratio of the expert evaluation can be determined according to the hospital requirements, and the system default sampling algorithm based on indicator weights can be used to provide a customized sampling method to meet personalized needs. It can be understood that other methods can also be used for task splitting and indicator matching, which are not limited herein.

[0068] S103. Obtain all target departments related to the business indicator and all target experts related to the sampled business indicator.

[0069] Among them, the target department refers to the department within the hospital related to the business indicator, and the target expert refers to the evaluation expert related to the sampled business indicator. This step is to obtain the relevant departments and experts for performing the evaluation tasks.

[0070] Specifically, after generating the evaluation tasks, it is necessary to clarify the departments and experts participating in the evaluation. For each business indicator, find the department corresponding to the indicator through the hospital information system as the target department; for the sampled business indicators, determine the experts familiar with these indicators as the target experts. By obtaining the information of these two types of entities, objects are provided for subsequent task distribution to perform department self-evaluation and expert sampling evaluation.

[0071] In some embodiments, the departments related to the indicators can be located by querying the department information in the hospital information system, and the experts with matching professional fields can be accessed by visiting the expert database. Optionally, the departments that often participate in the evaluation of relevant indicators can be determined by analyzing the hospital's historical evaluation data; using the medical knowledge graph, expert candidates can be intelligently matched according to the business indicator fields. It can be understood that other methods can also be used to determine the target departments and experts, which are not limited herein.

[0072] S104. Send the department self-evaluation tasks to all the target departments to obtain the current department self-evaluation score collection within a preset time period.

[0073] Among them, the department self-evaluation score collection is a set of scores obtained by the target departments for self-evaluating the business indicators of their own departments. This step is to assign the department self-evaluation tasks to the relevant departments for execution and obtain the self-evaluation results of each department.

[0074] Specifically, after clarifying the target departments, the system can send the department self-evaluation tasks to the relevant departments through the network or other means. The departments conduct self-evaluation and assessment of the service indicators of their own departments according to the tasks issued, complete the self-evaluation within the preset time, and report the assessment results.

[0075] In some embodiments, the task notifications can be sent in the form of emails; a network platform can be set up for the departments to fill in the self-evaluation results; and the self-evaluation scores can be collected by using the mobile terminal APP.

[0076] S105: Send the expert sampling evaluation task to all target experts to obtain the current expert sampling score collection within the preset time period.

[0077] The expert sampling score collection is a set of scores obtained by the target experts scoring some sampled business indicators. This step is to assign the sampling evaluation task to experts to perform and obtain the expert scoring results.

[0078] Specifically, after the target experts are identified, the system can send the sampling assessment task to the relevant experts through the Internet and other means. The experts conduct assessments based on the sampling indicators, complete the scoring within the preset time, and report the assessment results. The system summarizes the scores reported by each expert to form a collection of expert sampling scores within this round of assessment cycle.

[0079] In some embodiments, task notifications may be sent via email, and a network platform may be set up for experts to fill in their ratings; and mobile terminal APPs may be used to collect ratings.

[0080] S106. Calculate the average score deviation of the sampled business indicator based on the current department self-assessment score collection and the current expert sampling score collection.

[0081] Among them, the average score deviation degree is a statistic of the difference between the department's self-assessment score and the expert's score for the same sampling business indicator. This step is to calculate the deviation degree between the department's self-assessment score and the expert's score.

[0082] Specifically, after obtaining the collection of department self-evaluation scores and expert sampling scores, the system can perform a difference analysis on the scores of the same sampling indicator given by the two. By calculating the mean, variance and other statistical data of the department self-evaluation scores and expert scores, the degree of deviation between the two scoring results can be quantified. This provides a basis for whether the subsequent evaluation is stable.

[0083] In some embodiments, the average of the score differences can be calculated as the average deviation degree; the standard deviation of the score differences is used to represent the score consistency. Optionally, the correlation coefficient between the scores can be calculated to determine the matching degree; and the score difference threshold range can be set to qualitatively describe the deviation. It is understandable that the score deviation degree can also be calculated by other statistical means, which are not limited here.

[0084] S107. If the deviation of the average score is higher than the preset stability threshold, the current hospital-wide comprehensive quantitative score collection of all business indicators is calculated based on the department's self-assessment score collection.

[0085] Among them, the hospital's comprehensive quantitative score collection is a quantitative score collection of all business indicator evaluation results. This step is to generate quantitative scores based only on department self-evaluation results when the score deviation does not meet the stability conditions.

[0086] Specifically, the system can preset a threshold for the degree of scoring deviation as a criterion for judging the stability of the scoring. If the deviation of the two scores exceeds the threshold, it means that there is a significant difference between the expert sampling score and the department's self-evaluation, and the score is unstable. At this time, it is impossible to combine the scores of the two for quantitative scoring. In order to ensure that all indicators are quantitatively scored, only the scoring results of the department's self-evaluation can be used to quantify the scores of all indicators in the hospital, forming a comprehensive quantitative score collection for the entire hospital in this round.

[0087] In some embodiments, the department's self-assessment scores can be directly used for equivalent mapping scores; each indicator can be scored according to the department's self-assessment evaluation level. Optionally, the department's self-assessment scores can be adjusted based on expert sampling; the quantitative model can be adjusted based on the expert sampling results. It is understandable that other methods can also be used to perform quantitative scoring of the entire hospital based only on the department's self-assessment scores, which is not limited here.

[0088] In some embodiments, the department self-evaluation task includes five levels set according to the commonly used scoring method in the medical industry, represented by 1-5, 5 scores, the higher the score, the higher the degree of compliance, and the department self-evaluates and scores the indicators assigned to it one by one. For example, department D scores indicator K as S1, and then the expert group visits and investigates the departments by sampling. In order to ensure that the number of samples can meet the subsequent calculations, it is stipulated that: for each indicator, the number of sampled departments should be greater than 5% of the total number of departments assigned to the indicator; for each department, the number of sampled indicators should be greater than 5% of the total number of indicators assigned to the department, and the expert group obtains the expert group's sampling evaluation score S2 for indicator K of department D. The department scores all the assigned indicators, and the expert group scores the sampled department for each indicator. Due to the task splitting, the task volume of each department and the expert group will not be too large. The initial scores S1 and S2 are both integers, and the subsequent calculation process retains decimals. For the expert group sampled sample Y, calculate S2 (expert group evaluation score)-S1 (department self-evaluation score) , get the difference V, V may be negative, then get the difference V of all sampled samples of department D, calculate the average value AVG_V of department D about V, AVG_V can measure the accuracy of department self-evaluation, AVG_V represents the average score difference, if AVG_V is higher than the preset stability threshold, subsequent calculations can be performed to calculate the comprehensive score S3 (S3=S1+AVG_V) of each assigned indicator K of department D, if S3>5, then take 5, if S3<1, then take 1, for indicator K, Calculate the mean AVG_S3 of the S3 data of each department. For indicator K, calculate the standard deviation T of the S3 data of each department. T can measure the discreteness of the scores of each department for the same indicator. The larger the standard deviation, the more unstable the overall situation. For indicator K, calculate the final evaluation score S=AVG_S3-T / 4 (4 is the maximum value 5-minimum value 1 in the scoring rule). Finally, obtain the comprehensive quantitative score S of the qualitative indicator K for the whole hospital, and then calculate the comprehensive quantitative scores of the whole hospital corresponding to all business indicators one by one.

[0089] The following is a supplement to the scenario of this embodiment.

[0090] After combining the above scenarios, the following is a more detailed description of the process of the method provided by this implementation. Figure 2 , which is another flow chart of the method for evaluating the overall situation of the hospital using qualitative indicators in an embodiment of the present application.

[0091] S201. Count the frequency information of each business indicator appearing in the business content information.

[0092] Among them, the business content information refers to the text materials such as business procedures and operation manuals obtained by the system from each department of the hospital; the occurrence frequency information refers to the number of times a business indicator appears in the above text materials. This step is for the system to count the occurrence frequency of each business indicator in the business content text information.

[0093] Specifically, the system can use natural language processing technology to automatically parse various types of business content text materials obtained, identify the business indicator keywords appearing in the text, and count the number of times each business indicator keyword appears, as the occurrence frequency of each business indicator in all business content text information.

[0094] In some embodiments, the system can count the number of times an indicator appears through a keyword matching algorithm; use a semantic parsing model to calculate the matching degree between the indicator and the text content. Optionally, the system can analyze high-frequency indicator words through a word frequency statistics method; use deep learning to identify the appearance of indicator words. It can be understood that the system can also obtain the occurrence frequency information of business indicators in the text through other statistical methods.

[0095] S202. Count the usage frequency information of each business indicator in the hospital business system information.

[0096] Among them, the hospital business system information refers to the structured data extracted by the system from various hospital business management systems; the usage frequency information refers to the number of times a business indicator appears in this structured data. This step is for the system to count the usage frequency of each business indicator in the hospital business system data.

[0097] Specifically, the system can analyze the structured business data in various hospital business systems, identify the business indicator information contained in the data, and count the specific number of times each business indicator appears in the business system data, as the usage frequency of each business indicator in the hospital business system information.

[0098] In some embodiments, the system can query the database to count the proportion of indicator data; use a data interface to access the system to extract the indicator usage amount. Optionally, the system can count the number of times a system log indicator is mentioned through log analysis; use a data mining algorithm to analyze the indicator usage pattern. It can be understood that the system can also obtain the usage frequency information of business indicators in the hospital system data through other methods.

[0099] S203. Calculate the importance degree of all the business indicators according to the frequency information and the usage frequency information, and obtain the importance degree set.

[0100] Among them, the frequency information refers to the number of occurrences of business indicators obtained by system statistics in the business content text information; the usage frequency information refers to the number of times the business indicators are used in the business system data; the importance degree collection is a list sorted by the importance degree of business indicators. This step is for the system to calculate the importance degree of each business indicator based on the statistically obtained frequency and usage frequency.

[0101] Specifically, the system can comprehensively consider the information in two dimensions of the occurrence frequency and usage frequency of business indicators, adopt a certain algorithm to calculate the importance degree of each business indicator, and generate a list of business indicators sorted by importance degree as the importance degree collection. The higher the frequency and usage frequency of a business indicator, the higher its importance degree.

[0102] In some embodiments, the system can calculate the index importance degree through a linear weighting algorithm; use a machine learning model to evaluate the index weight. Optionally, the system can set up a multi-dimensional scorecard to score and rank the indicators; use association rules to analyze the important relevance of the indicators. It can be understood that the system can also calculate the importance degree of business indicators based on statistical frequency in other ways.

[0103] S204. Set the sampling probability for all business indicators within the importance degree collection, and this sampling probability is positively correlated with the importance degree ranking.

[0104] Among them, the sampling probability refers to the probability that a business indicator is randomly selected by the system for evaluation. This step is for the system to set the sampling probability according to the importance degree ranking of business indicators.

[0105] Specifically, the system can, according to the result of sorting the importance degree of business indicators, set the probability of each business indicator being selected for evaluation according to a certain algorithm. The higher the importance degree ranking of a business indicator, the higher the probability of its being selected. In this way, indicators with high importance can obtain more evaluations and provide more reliable scoring results.

[0106] In some embodiments, the system can set the sampling weight through linear mapping; use an exponential function model to calculate the sampling probability. Optionally, the system can also generate a probability distribution through a statistical sampling algorithm; construct a sampling probability model based on historical data. It can be understood that the system can also set the sampling probability of business indicators based on the importance degree in other ways.

[0107] S205. Split the overall situation evaluation task into a department self-evaluation task and an expert sampling evaluation task. The department self-evaluation task corresponds one-to-one to all these business indicators, and the expert sampling evaluation task corresponds one-to-one to a preset number of sampled business indicators among all these business indicators, and the sampled business indicators are randomly selected from among these business indicators.

[0108] It can be understood that this step is similar to step S102 and will not be elaborated here.

[0109] After combining the above scenarios, the following further describes the more specific process of the method provided in this embodiment. Please refer to Figure 3 , which is another process schematic diagram of the hospital qualitative index overall situation evaluation method in the embodiment of the present application.

[0110] S301. Calculate the average score deviation degree of the sampled business indicators according to the current department self-evaluation score collection and the current expert sampling score collection.

[0111] Among them, the average score deviation degree refers to the degree of difference between the department self-evaluation score and the expert score calculated by the system through statistical methods; the current department self-evaluation score collection is the self-evaluation scores of each department on business indicators collected by the system in this round; the current expert sampling score collection is the scores of experts on the sampled business indicators collected by the system in this round. This step is for the system to calculate the average score deviation degree of the sampled business indicators based on the two scoring results.

[0112] Specifically, the primary task of the system after receiving the department self-evaluation and expert scores is to compare whether there is an obvious difference between the two results. The system can apply statistical algorithms to calculate the average deviation degree of the scores of the same sampled business indicators based on the current department self-evaluation score collection and the expert score collection, as a quantitative statistic for the consistency of these sampled business indicator scores.

[0113] In some embodiments, the system can calculate the average value of the score differences; use the standard deviation of the score differences to represent the score consistency. Optionally, the system can also calculate the score correlation coefficient to determine the matching degree; set a threshold range for the score differences for qualitative description. It can be understood that the system can also calculate the average score deviation degree of the sampled business indicators through other statistical algorithms.

[0114] S302. If the average score deviation degree is not higher than the preset stability threshold, determine the deviation business indicators corresponding to the score deviation degrees greater than the preset stability threshold.

[0115] Among them, the preset stability threshold is the maximum allowable score deviation predefined by the system; the deviation business indicators are the business indicators with the score differences between the two scoring results exceeding the threshold. This step is for the system to determine which business indicators have excessive score deviations and regard them as deviation business indicators.

[0116] Specifically, the system will pre-set a quantified scoring deviation threshold. If the scoring deviation of the sampled business metrics is lower than this threshold, the scoring result can be considered stable enough. Conversely, if there is a situation where the deviation is greater than the threshold, the system can judge from the quantified degree of deviation which sampled business metrics have a significant scoring deviation exceeding the threshold, and identify these metrics as deviation business metrics for subsequent key scoring adjustment.

[0117] In some embodiments, the system can define deviation metrics based on preset parameters; dynamically adjust the boundaries to judge deviation metrics. Optionally, the system can also obtain deviation metrics through association rule analysis; construct a machine learning model for deviation determination. It can be understood that the system can also determine which sampled business metrics have a scoring deviation exceeding the preset stability threshold through other means.

[0118] S303. Regenerate the optimized department self-evaluation task and the optimized expert sampling evaluation task corresponding to the deviation business metric.

[0119] Among them, the optimized department self-evaluation task is the department self-evaluation task optimized for the deviation business metric; the optimized expert sampling evaluation task is the expert sampling evaluation task optimized for the deviation business metric. This step is for the system to regenerate the optimized evaluation tasks for the judged deviation business metrics.

[0120] Specifically, for the determined business metrics with scoring deviations, the system needs to readjust the corresponding evaluation task requirements, hoping to reduce the scoring deviation through this optimization adjustment. The system can reset the evaluation ideas and key aspects of the department self-evaluation for the deviation metrics; re-determine the focus of the expert evaluation, and generate highly targeted optimized department self-evaluation tasks and expert sampling evaluation tasks to guide the reduction of the scoring deviation.

[0121] In some embodiments, the system can increase the generation of result instance requirements to generate optimized tasks; provide evaluation cases to refine the self-evaluation requirements. Optionally, the system can also generate optimized tasks by modifying the scoring criteria; adjust the scoring weights to emphasize the key points. It can be understood that the system can also regenerate optimized evaluation tasks for deviation business metrics through other means.

[0122] S304. Send the optimized department self-evaluation task to all the target departments to obtain a collection of self-evaluation scores of the deviation departments.

[0123] Among them, the collection of self-evaluation scores of the deviation departments is the scoring result obtained for the optimized department self-evaluation task. This step is for the system to send the optimized self-evaluation task to the relevant departments and collect the results.

[0124] Specifically, for the optimized department self-evaluation task, the system needs to issue it to the relevant target departments again to obtain the new results of the department self-evaluation after this targeted adjustment. The system can issue the optimized self-evaluation task to the corresponding target departments through means such as a similar network platform as before, and collect the self-evaluation score results proposed by the departments for the deviation business indicators within the specified time to form the collection of self-evaluation scores of the deviation departments in this round.

[0125] In some embodiments, the system can issue the optimization task by email; set up a web form to collect the optimized self-evaluation results. Optionally, the system can also use a mobile APP to issue and collect the self-evaluation task; construct a self-evaluation data API to obtain the results. It can be understood that the system can also issue the optimized department self-evaluation task through other channels and collect the corresponding scoring data.

[0126] S305. Issue the optimized expert sampling evaluation task to all the target experts to obtain the collection of sampling scores of the deviation experts.

[0127] Among them, the optimized expert sampling evaluation task is an expert sampling evaluation task regenerated for the deviation business indicators; the collection of sampling scores of the deviation experts is the expert scoring results obtained for the optimization task. This step is for the system to issue the optimized sampling evaluation task to the relevant experts and collect the results.

[0128] Specifically, for the regenerated optimized expert sampling evaluation task, the system also needs to issue it to the corresponding target experts, so that they can score the sampling business indicators according to the key points of the optimized task. The system can distribute the optimization task to the relevant experts in a similar way as before, and collect the scoring data provided by the experts for the deviation business indicators within the specified time to form the collection of sampling scores of the deviation experts in this round.

[0129] In some embodiments, the system can issue the optimization task in the form of an email; provide a web form for the experts to fill in the scores. Optionally, the system can also use a mobile APP to issue and collect the scoring task; set up a scoring data API to obtain the results. It can be understood that the system can also issue the optimized expert evaluation task through other channels and obtain the corresponding scoring data

[0130] S306. Calculate the optimization scoring deviation degree of the deviation business indicator according to the collection of self-evaluation scores of the deviation departments and the collection of sampling scores of the deviation experts.

[0131] Among them, the optimization scoring deviation degree is the scoring difference degree calculated for the two optimized scoring results. This step is for the system to calculate the new optimization scoring deviation situation of the deviation business indicator based on the two scores obtained after optimization.

[0132] Specifically, after collecting the new results of the self-evaluation scores of the deviation departments and the deviation expert scores, the system needs to recalculate the score deviation of the two to determine whether the previous score deviation has been optimized and adjusted, or whether there is still a significant difference. The system can apply a statistical algorithm similar to the previous one, based on the two newly obtained sets of score data, to recalculate the score deviation degree of the deviation business indicator as the evaluation result of the score consistency optimization effect.

[0133] In some embodiments, the system can recalculate the average of the score difference and use the standard deviation method to check the consistency optimization. Optionally, the system can also determine the improvement of the matching degree based on the correlation coefficient and compare and analyze the optimization effect with the original deviation degree. It is understandable that the system can also calculate the new score deviation degree after the deviation business indicator is optimized by other statistical means.

[0134] S307. If the degree of deviation of the optimization score is higher than the preset stability threshold, the hospital's comprehensive quantitative score collection of all the deviation business indicators is calculated based on the collection of self-evaluation scores of the deviation department.

[0135] Among them, the degree of deviation of the optimized score is the degree of deviation calculated by the system for the optimized score results; the preset stability threshold is the maximum allowable score deviation value predefined by the system; the comprehensive quantitative score collection of the whole hospital is the quantitative score of the deviation business indicator calculated only based on the self-evaluation results of the department. This step is for the system to determine whether the score deviation after optimization still exceeds the threshold. If so, the quantitative score is generated only based on the self-evaluation results of the department.

[0136] Specifically, the system compares the degree of deviation of the optimized score with the preset stability threshold to determine whether the optimization effect meets the standard. If the degree of deviation of the score of the deviation business indicator is still higher than the threshold after optimization, it means that there is still a significant difference between the department's self-evaluation and the expert's score, and they cannot be used together. At this time, the system can only generate the comprehensive quantitative score of the whole hospital for these deviation business indicators based on the collection of self-evaluation scores of the deviation departments through quantitative calculation algorithms as the evaluation results of these indicators.

[0137] In some embodiments, the system can directly take the self-assessment scores of the deviant departments for quantitative mapping and scoring; score according to the preset score range corresponding to the self-assessment evaluation level. Optionally, the system can also fine-tune the quantitative model based on the expert sampling results; and use interpolation to adjust the self-assessment scores. It is understandable that the system can also generate the whole hospital quantitative score of the corresponding deviation business indicator based only on the self-assessment scores of the deviation departments in other ways.

[0138] After combining the above scenarios, the following is a more detailed description of the process of the method provided by this implementation. Figure 4 , which is another flow chart of the method for evaluating the overall situation of the hospital using qualitative indicators in an embodiment of the present application.

[0139] S401. If the deviation of the average score is higher than the preset stability threshold, the current hospital-wide comprehensive quantitative score set of all business indicators is calculated based on the department's self-assessment score set.

[0140] It can be understood that this step is similar to step S107 and will not be described in detail here.

[0141] S402, obtaining a historical collection of comprehensive quantitative scores of the entire hospital, where the historical collection of comprehensive quantitative scores of the entire hospital includes a preset number of collections of comprehensive quantitative scores of the entire hospital within a preset time period.

[0142] The historical comprehensive quantitative score collection of the whole hospital refers to the results of the most recent rounds of quantitative scoring stored in the system; the preset number and preset time period are the acquisition quantity and time range preset by the system. This step is for the system to obtain the results of several rounds of quantitative scoring in the most recent period of time.

[0143] Specifically, the system can preset the number of recent quantitative score results to be obtained, such as the latest 8 rounds of results. It can also preset a time period, such as the results within the last year. Based on these two parameters, the system will extract the specified number of quantitative score collections within the most recent period from the quantitative score data historically stored in the system as the historical quantitative score collection of the entire hospital.

[0144] In some embodiments, the system can filter and obtain partial data by setting a timestamp window; directly query the required data according to the evaluation round identifier. Optionally, the system can also use a sliding time window to obtain the latest data in real time; build a scoring history database to implement custom queries. It is understandable that the system can also obtain a preset number of historical quantitative scores of the entire hospital in the recent period of time through other methods.

[0145] S403. Calculate the historical average hospital comprehensive quantitative scores of all business indicators based on the historical hospital comprehensive quantitative scores.

[0146] The historical average comprehensive quantitative score of the hospital refers to the average score of each business indicator calculated by the system based on the historical quantitative scores. This step is to calculate the historical average quantitative score of each business indicator based on the historical quantitative scores of the recent period.

[0147] Specifically, after obtaining a preset number of historical hospital-wide quantitative score collections in the recent period, the system can count the scores of each business indicator in these historical quantitative scoring rounds and calculate the average score of each business indicator in the historical period. This average score can be used as a reference value for the historical quantitative score of each business indicator.

[0148] In some embodiments, the system may simply calculate the arithmetic mean of the historical scores of each indicator; and use a scoring weight algorithm to calculate the weighted average score. Optionally, the system may also apply a time decay function to calculate the historical score average; and predict the expected average score through a machine learning algorithm. It is understandable that the system may also calculate the historical average quantitative score of each business indicator based on the historical quantitative score through other statistical methods.

[0149] S404. If the current hospital-wide comprehensive quantitative score of the current business indicator is lower than the historical average hospital-wide comprehensive quantitative score, a warning message is issued to all departments related to the current business indicator.

[0150] Among them, the warning information refers to the prompt information that the system sends to the relevant departments that the business indicator score has dropped. In this step, if the system determines that the current business indicator score is lower than its historical average score, it will automatically send a warning to the relevant departments.

[0151] Specifically, the system can compare the quantitative score of the current round of business indicators with the historical average score calculated for the indicator. If the current score of a business indicator is significantly lower than its historical average, it means that the score of the business indicator has dropped abnormally, and the relevant departments need to be alerted. The system can automatically send warning information to the relevant departments corresponding to the business indicator through emails, messages, etc., to indicate the decline in the indicator score.

[0152] In some embodiments, the system can set a warning threshold to determine whether to send a prompt; use the score change trend to predict whether a warning is needed. Optionally, the system can also generate warning reports regularly; build a warning information push mechanism. It is understandable that the system can also use other methods to issue warning prompts when the indicator is lower than the average score.

[0153] S405. If the current hospital-wide comprehensive quantitative score of the current business indicator is not lower than the historical average hospital-wide comprehensive quantitative score, the current hospital-wide comprehensive quantitative score is sent to all departments related to the current business indicator.

[0154] Among them, the current comprehensive quantitative score of the whole hospital is the result of the current round of quantitative scoring calculated by the system; the historical average comprehensive quantitative score of the whole hospital is the average value of the historical quantitative scores of the business indicators calculated by the system. This step is that when the system determines that the current score of the indicator is not lower than the average score, the current actual score will be fed back to the relevant department.

[0155] Specifically, when the system discovers that the current quantitative score of a certain business indicator is not lower than its historical average score, it is necessary to also feedback the current actual quantitative score to the relevant departments for reference. The system can actively push the calculated current overall hospital comprehensive quantitative score result to each department related to this business indicator in the form of emails, messages, etc., so that these departments can see the actual quantitative score situation.

[0156] In some embodiments, the system can simultaneously feedback the current score in the warning email; set up a dedicated quantitative score query interface. Optionally, the system can also generate a quantitative score report and push it regularly; construct a multi-channel quantitative score information release mechanism. It can be understood that the system can also send the current score of the indicator to the corresponding department in a timely manner through other means.

[0157] Next, the hospital qualitative indicator overall hospital situation evaluation system in the embodiments of the present invention application will be described from the perspective of hardware processing. Please refer to Figure 5 , which is a schematic structural diagram of an entity device of the hospital qualitative indicator overall hospital situation evaluation system in the embodiments of the present application.

[0158] It should be noted that Figure 5 The structure of the hospital qualitative indicator overall hospital situation evaluation system shown is only an example and should not bring any limitations to the functions and usage scope of the embodiments of the present invention.

[0159] As Figure 5 shown, the hospital qualitative indicator overall hospital situation evaluation system includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 502 or the program loaded from the storage section 508 into the random access memory (RAM) 503, such as executing the methods described in the above embodiments. In the RAM 503, various programs and data required for system operation are also stored. The CPU 501, ROM 302, and RAM 503 are connected to each other through a bus 504. The input / output (I / O) interface 505 is also connected to the bus 504.

[0160] The following components are connected to the I / O interface 505: an input section 506 including an audio input device, a button switch, etc.; an output section 507 including a liquid crystal display (LCD), an audio output device, an indicator light, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. The drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed so that a computer program read from it can be installed into the storage section 508 as needed.

[0161] Specifically, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication section 509, and / or installed from the removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, various functions defined in the present invention are executed.

[0162] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or combined with an instruction execution system, device, or apparatus.

[0163] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. In this context, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code, and the above-mentioned module, segment of a program, or part of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings.

[0164] Specifically, the hospital qualitative index overall situation evaluation system of this embodiment includes a processor and a memory. A computer program is stored on the memory. When the computer program is executed by the processor, it implements the hospital qualitative index overall situation evaluation method provided in the above-mentioned embodiment.

[0165] On the other hand, the present invention also provides a computer-readable storage medium. This storage medium may be included in the hospital qualitative index overall situation evaluation system described in the above-mentioned embodiment; or it may exist independently and not be assembled into the hospital qualitative index overall situation evaluation system. The above storage medium carries one or more computer programs. When the above one or more computer programs are executed by a processor of the hospital qualitative index overall situation evaluation system, the hospital qualitative index overall situation evaluation system is enabled to implement the hospital qualitative index overall situation evaluation method provided in the above-mentioned embodiment.

[0166] As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application.

[0167] As used in the above embodiments, depending on the context, the term "when..." may be interpreted to mean "if...", or "after...", or "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if detecting (the stated condition or event)" may be interpreted to mean "if determining...", or "in response to determining...", or "when detecting (the stated condition or event)", or "in response to detecting (the stated condition or event)".

[0168] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by hardware instructed by a computer program. This program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The foregoing storage medium includes: various media such as ROM or random access memory RAM, magnetic disks, or optical discs that can store program codes.

Claims

1. A method for evaluating the overall situation of the whole hospital in terms of qualitative indicators, characterized in that, Applied to the hospital qualitative indicator overall situation evaluation system of the whole hospital, the method includes: Generate an overall situation assessment task based on all the business content information of the target hospital, where all the business content information corresponds to all the business indicators one by one; Calculate the importance of all business indicators based on the business content information and hospital business system information to obtain a collection of importance levels; The ranking in the importance collection is used to set the sampling probability of all business indicators, and the sampling probability is positively correlated with the importance ranking; The overall situation assessment task is divided into a department self-assessment task and an expert sampling assessment task, wherein the department self-assessment task corresponds one-to-one to all the business indicators, and the expert sampling assessment task corresponds one-to-one to a preset number of sampled business indicators among all the business indicators, and the sampled business indicators are selected from the business indicators; Acquire all target departments related to the business indicator and all target experts related to the sampled business indicator; The department self-evaluation task is sent to all the target departments to obtain a collection of current department self-evaluation scores within a preset time period; The expert sampling evaluation task is sent to all the target experts to obtain the current expert sampling score collection within the preset time period; Calculate the average score deviation of the sampled business indicator based on the current department self-assessment score collection and the current expert sampling score collection; If the average score deviation is not higher than the preset stability threshold, determine the deviation business indicator corresponding to the score deviation that is greater than the preset stability threshold; Regenerate the optimized department self-assessment tasks and optimized expert sampling assessment tasks corresponding to the deviation business indicators; The optimized department self-evaluation task is distributed to all the target departments to obtain a collection of self-evaluation scores of the deviated departments; The optimized expert sampling evaluation task is distributed to all the target experts to obtain a collection of deviation expert sampling scores; Calculate the degree of deviation of the optimized score of the deviation business indicator according to the collection of self-evaluation scores of the deviation department and the collection of sample scores of the deviation experts; If the degree of deviation of the optimization score is higher than the preset stability threshold, the hospital-wide comprehensive quantitative score collection of all the deviation business indicators is calculated based on the collection of self-evaluation scores of the deviation departments; If the degree of deviation of the average score is higher than the preset stability threshold, the current hospital-wide comprehensive quantitative score collection of all the business indicators is calculated based on the department's self-assessment score collection.

2. The method according to claim 1, characterized in that, The step of generating an overall situation assessment task based on all the business contents of the target hospital, wherein all the business content information corresponds to all the business indicators one by one, specifically includes: Obtaining organizational structure data and business content information of the target hospital; Analyze all departments according to the organizational structure data; Analyze the business indicators corresponding to each department according to the business content information; An overall situation assessment task is generated based on the business indicators.

3. The method according to claim 1, characterized in that The step of calculating the importance of all business indicators according to the business content information specifically includes: Counting the frequency of each business indicator appearing in the business content information; Counting the usage frequency information of each business indicator in the hospital business system information; The importance of all the business indicators is calculated according to the frequency information and the usage frequency information to obtain an importance collection.

4. The method according to claim 1, characterized in that After the step of calculating the current hospital-wide comprehensive quantitative score collection of all the business indicators based on the department self-assessment score collection if the score deviation degree is higher than the preset stability threshold, the method further includes: Obtaining a historical collection of comprehensive quantitative scores of the entire hospital, wherein the historical collection of comprehensive quantitative scores of the entire hospital includes a collection of comprehensive quantitative scores of the entire hospital within a preset number of preset time periods; Calculate the historical average comprehensive quantitative scores of the entire hospital for all the business indicators based on the historical comprehensive quantitative scores of the entire hospital; If the current hospital-wide comprehensive quantitative score of the current business indicator is lower than the historical average hospital-wide comprehensive quantitative score, a warning message will be issued to all departments related to the current business indicator.

5. The method according to claim 4, wherein After the step of calculating the historical average hospital-wide comprehensive quantitative scores of all the business indicators based on the historical hospital-wide comprehensive quantitative score collection, the method further includes: If the current hospital comprehensive quantitative score of the current business indicator is not lower than the historical average hospital comprehensive quantitative score, The current comprehensive quantitative score of the entire hospital is sent to all departments related to the current business indicators.

6. A hospital qualitative index overall situation evaluation system, characterized in that The hospital qualitative indicator overall situation assessment system for the whole hospital includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the hospital qualitative indicator overall situation assessment system to execute the method described in any one of claims 1-5.

7. A computer-readable storage medium, comprising instructions, characterized in that, When the instruction is executed on the hospital qualitative indicator overall situation evaluation system, the hospital qualitative indicator overall situation evaluation system executes the method as described in any one of claims 1-5.

8. A computer program product, characterized in that, When the computer program product is run on a hospital qualitative indicator overall situation evaluation system, the hospital qualitative indicator overall situation evaluation system executes the method as described in any one of claims 1-5.

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