Intelligent customization method and system for performance salary assessment of basic-level health center

Abstract

The embodiment of the invention provides an intelligent customization method and system for performance salary assessment of a grass-roots health center, and belongs to the technical field of performance assessment. The method comprises the following steps: matching in an index configuration template library according to post deviation degree to obtain a target recommendation index and a target index weight; and performing data acquisition according to the target recommendation index to obtain a structured data set. And performing confidence coefficient calculation according to the structured data, the field mode template, the historical qualified data statistical template and the post deviation degree to obtain a confidence coefficient score. And obtaining the coverage rate confidence of each target recommendation index according to the confidence score and a preset score threshold. And performing weighted fusion calculation according to the target index weight, the employee index score, the coverage rate confidence and the post deviation degree to obtain a comprehensive performance score. And a performance assessment scheme is obtained according to the comprehensive performance score and the bonus set value, so that the accuracy of performance assessment is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of performance evaluation, and particularly relates to an intelligent customization method and system for performance evaluation of primary health centers. BACKGROUND

[0002] Performance generally refers to the effect and quality of an individual or team in completing work tasks within a certain period of time. In enterprises and organizations, performance is generally used to measure the work performance of employees so as to evaluate, motivate and manage them. In the related art, performance evaluation usually relies on a static evaluation template to bind job responsibilities and indicators to job names or job positions, which is easy to cause mismatch between jobs and indicators and is difficult to cope with the increasingly complex and dynamic evolution of responsibilities. At the same time, data collection relies on manual extraction, arrangement and accounting from multiple business systems, which not only consumes time but also is prone to errors, resulting in low accuracy of performance evaluation. Therefore, how to improve the accuracy of performance evaluation has become a technical problem to be solved. SUMMARY

[0003] The main purpose of the embodiments of the present application is to provide an intelligent customization method and system for performance evaluation of primary health centers, aiming to improve the accuracy of performance evaluation.

[0004] To achieve the above-mentioned purpose, a first aspect of the embodiments of the present application provides an intelligent customization method for performance evaluation of primary health centers, which comprises:

[0005] obtaining a job deviation degree;

[0006] matching in a preset index configuration template library according to the job deviation degree to obtain a target index configuration template; wherein the target index configuration template comprises a target recommended index and a corresponding target index weight;

[0007] looking up in a preset index mapping table according to the target recommended index to obtain a collection task scheduling table;

[0008] performing data collection according to the collection task scheduling table to obtain a structured data set;

[0009] performing confidence calculation according to the structured data, a preset field mode template, a preset historical qualified data statistical template and the job deviation degree to obtain a confidence score;

[0010] obtaining a coverage confidence of each target recommended index according to the confidence score and a preset score threshold;

[0011] performing weighted fusion calculation according to the target index weight, a preset employee index score, the coverage confidence and the job deviation degree to obtain a comprehensive performance score;

[0012] obtaining the performance evaluation scheme according to the comprehensive performance score and a preset bonus setting value.

[0013] In some embodiments, after the performance evaluation scheme is obtained according to the comprehensive score and the preset bonus setting value, the method further comprises:

[0014] constructing a score explanation table according to the target recommended indicators, the target indicator weights, the employee indicator scores, the coverage confidence, the post deviation, and a preset post responsibility mapping set;

[0015] performing consistency scoring according to the coverage confidence, the target recommended indicators, and the post responsibility mapping set to obtain consistency scoring data;

[0016] displaying the score explanation table and the consistency scoring data.

[0017] In some embodiments, the post deviation is obtained by:

[0018] obtaining operation log data of the employee;

[0019] constructing a normalized behavior vector according to the operation log data;

[0020] performing difference calculation according to the normalized behavior vector and a preset historical average behavior vector to obtain the post deviation.

[0021] In some embodiments, the target indicator configuration template is obtained by matching the post deviation in a preset indicator configuration template library, comprising:

[0022] performing matching calculation according to the post deviation in the indicator configuration template library to obtain an initial matching score of each indicator configuration template; wherein the indicator configuration template library comprises a plurality of the indicator configuration templates, and the indicator configuration template comprises recommended indicators and corresponding indicator weights;

[0023] taking the highest initial matching score as a target matching score, and the target indicator configuration template corresponding to the target matching score.

[0024] In some embodiments, the initial matching score of each indicator configuration template is obtained by performing matching calculation according to the post deviation in the indicator configuration template library, comprising:

[0025] the matching calculation formula is:

[0026]

[0027] wherein, s ijrepresents the initial matching score of the i th employee selecting the j th set of index configuration templates, represents the normalized behavior vector of the i th employee, w j represents the j th set of index configuration templates, δ i represents the post deviation, D (p (i), j) represents the post matching penalty term, p (i) represents the post of the i th employee, λ 1 and λ 2 represent the regularization term control coefficients.

[0028] In some embodiments, the confidence score is calculated according to the structured data, the preset field mode template, the preset historical qualified data statistical template and the post deviation, and the confidence score is obtained, comprising:

[0029] The confidence calculation formula is:

[0030]

[0031] Wherein, represents the confidence score of the i th employee at time t and the k th target recommended index, represents the structured data of the i th employee at time t and the k th target recommended index, M k represents the field mode template of the k th target recommended index, represents the historical qualified data statistical template, δ i represents the post deviation of the i th employee, comp (·) represents the field completeness function, align (·) represents the structure alignment function, and α, β and γ represent the adjustment coefficients.

[0032] In some embodiments, the comprehensive performance score is calculated by weighted fusion calculation according to the target index weight, the preset employee index score, the coverage confidence, and the post deviation, comprising:

[0033] The weighted fusion calculation formula is:

[0034]

[0035] Wherein, P i represents the comprehensive performance score of the i th employee, represents the target index weight of the k th target recommended index of the i th employee, represents the employee index score of the k th target recommended index of the i th employee, represents the coverage confidence of the k th target recommended index of the i th employee, δ i represents the post deviation of the i th employee, the k th target recommended index of the i th employee, Ω p(i) represents the post responsibility range, and γ represents the post matching penalty term coefficient, mi represents the total number of personalized performance indicators assigned to the i-th employee, represents an indicator function.

[0036] To achieve the above object, a second aspect of the embodiment of the present application provides an intelligent customization system for performance salary assessment of a primary health center, the system comprising:

[0037] The acquisition module is configured to acquire the post deviation degree.

[0038] The matching module is configured to match in a preset index configuration template library according to the post deviation degree to obtain a target index configuration template, wherein the target index configuration template comprises target recommended indexes and corresponding target index weights.

[0039] The searching module is configured to search in a preset index mapping table according to the target recommended indexes to obtain a collection task scheduling table.

[0040] The acquisition module is configured to acquire the post deviation degree.

[0041] The confidence module is configured to perform confidence degree calculation according to the structured data, a preset field mode template, a preset historical qualified data statistical template and the post deviation degree to obtain a confidence score.

[0042] The coverage module is configured to obtain a coverage confidence degree of each target recommended index according to the confidence score and a preset score threshold.

[0043] The calculation module is configured to perform weighted fusion calculation according to the target index weights, a preset employee index score, the coverage confidence degree and the post deviation degree to obtain a comprehensive performance score.

[0044] The performance module is configured to obtain a performance assessment scheme according to the comprehensive performance score and a preset bonus setting value.

[0045] To achieve the above object, a third aspect of the embodiment of the present application provides an electronic device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the method of the first aspect when executing the computer program.

[0046] To achieve the above object, a fourth aspect of the embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method of the first aspect.

[0047] The intelligent customization method and system for performance salary assessment of primary health centers provided by the application obtain a post deviation degree, match the post deviation degree in a preset index configuration template library, and obtain a target index configuration template. The target index configuration template includes a target recommended index and a corresponding target index weight. The target recommended index is looked up in a preset index mapping table to obtain a collection task scheduling table. Data is collected according to the collection task scheduling table to obtain a structured data set. Confidence is calculated according to the structured data, a preset field mode template, a preset historical qualified data statistical template, and the post deviation degree to obtain a confidence score. The confidence score and a preset score threshold are used to obtain a coverage confidence of each target recommended index. The target index weight, an employee index score, the coverage confidence, and the post deviation degree are used for weighted fusion calculation to obtain a comprehensive performance score. The comprehensive performance score and a preset bonus setting value are used to obtain a performance assessment scheme, and the accuracy of performance assessment is improved. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 is a flowchart of the intelligent customization method for performance salary assessment of primary health centers provided by the embodiment of the application;

[0049] Figure 2 is a flowchart of step S101 in Figure 1

[0050] Figure 3 is a flowchart of step S102 in Figure 1

[0051] Figure 4 is a flowchart of the intelligent customization method for performance salary assessment of primary health centers provided by another embodiment of the application;

[0052] Figure 5 is a structural schematic diagram of the intelligent customization system for performance salary assessment of primary health centers provided by the embodiment of the application;

[0053] Figure 6 is a hardware structural schematic diagram of an electronic device provided by the embodiment of the application. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical scheme and advantages of the application more clear, the application is further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the application and do not limit the application.

[0055] ​​It is to be noted that although the functional modules are divided in the system schematic diagram, and the logical order is shown in the flowchart, in some cases, the steps shown or described can be performed in a manner different from the module division in the system or the order in the flowchart. The terms "first", "second", and the like in the description and claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this application is for the purpose of describing the embodiments of the application only, and is not intended to limit the application.

[0057] Performance is usually used to measure the work performance of employees, and on this basis, to motivate and manage them, so as to improve the work efficiency of employees. Performance evaluation usually relies on static evaluation templates to bind job responsibilities and indicators to job names or post establishment, which is easy to cause mismatch between posts and indicators, and is difficult to cope with the increasingly complex and dynamic evolution of responsibilities. At the same time, data collection relies on manual extraction, sorting and accounting from multiple business systems, which not only consumes time but also is prone to errors, resulting in low accuracy of performance evaluation.

[0058] Based on this, the embodiments of the present application provide an intelligent customization method and system for performance salary evaluation of primary health centers, which aims to automatically build post behavior portraits and calculate post deviation by multi-dimensional modeling of the operation behavior of employees in each business subsystem, in order to depict the difference between actual responsibilities and nominal posts. Then based on post deviation, the target indicator configuration template in the indicator configuration template library that is adapted to it is matched. Combined with behavior similarity, responsibility constraints and post penalty mechanism, an individualized performance evaluation scheme is generated. In the data processing link, based on the target indicator configuration template, the collection path is automatically scheduled, the API or operation log of each business system is called, and through structured cleaning and confidence calculation, intelligent extraction and reliable verification of the data required for evaluation are realized. In the performance score calculation process, the indicator confidence and post responsibility matching item are introduced, and the non-duty indicators or low-confidence data are automatically de-weighted, so as to improve the fairness and rationality of scoring, and improve the accuracy of performance evaluation. Finally, the comprehensive performance score is linked to the income through the internal distribution mechanism of the bonus pool, and the whole-link explanation table of scoring source, weight structure, and responsibility consistency is provided, so that employees can verify the scoring process item by item, realize the real explainable, traceable and appealable performance score.

[0059] The intelligent customization method and system for performance salary evaluation of primary health centers provided by the embodiments of the present application are specifically described through the following embodiments. First, the intelligent customization method for performance salary evaluation of primary health centers in the embodiments of the present application is described.

[0060] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. The artificial intelligence (AI) is to use a digital computer or a machine controlled by a digital computer to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results.

[0061] The artificial intelligence basic technology generally includes technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction system, mechatronics, etc. The artificial intelligence software technology mainly includes computer vision technology, robot technology, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning, etc.

[0062] The intelligent customization method for performance wage assessment of primary health centers provided by the embodiments of the present application can be applied to a terminal, can be applied to a server end, and can also be software running in the terminal or the server end. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc.; the server end can be configured as an independent physical server, can be configured as a server cluster or a distributed system composed of multiple physical servers, can also be configured as a cloud server providing basic cloud computing services such as cloud service, cloud database, cloud computing, cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN, and big data and artificial intelligence platform, etc.; and the software can be an application program implementing the intelligent customization method for performance wage assessment of primary health centers, etc., but is not limited to the above forms.

[0063] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment, in which tasks are performed by remote processing devices connected by a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0064] Please refer to Figure 1 , Figure 1is a flowchart of an intelligent customization method for performance wage assessment of primary health centers provided by the embodiment of the application. The method in the figure can include but is not limited to steps S101 to S108.

[0065] In step S101, a post deviation degree is obtained.

[0066] In step S102, a target index configuration template is obtained by matching the post deviation degree in a preset index configuration template library. The target index configuration template includes a target recommended index and a corresponding target index weight.

[0067] In step S103, a collection task scheduling table is obtained by searching a preset index mapping table according to the target recommended index.

[0068] In step S104, a structured data set is obtained by data collection according to the collection task scheduling table.

[0069] In step S105, a confidence score is obtained by confidence calculation according to the structured data, a preset field mode template, a preset historical qualified data statistical template and the post deviation degree.

[0070] In step S106, a coverage confidence of each target recommended index is obtained according to the confidence score and a preset score threshold.

[0071] In step S107, a comprehensive performance score is obtained by weighted fusion calculation according to the target index weight, the employee index score, the coverage confidence and the post deviation degree.

[0072] In step S108, a performance assessment scheme is obtained according to the comprehensive performance score and a preset bonus setting value.

[0073] Please refer to Figure 2 In some embodiments, step S101 can include but is not limited to steps S201 to S203:

[0074] In step S201, operation log data of an employee is obtained.

[0075] In step S202, a normalized behavior vector is constructed according to the operation log data.

[0076] In step S203, a post deviation degree is obtained by difference calculation according to the normalized behavior vector and a preset historical average behavior vector.

[0077] In step S201 of some embodiments, employee operation log data recorded in each business subsystem is obtained. In an example, the business subsystems specifically include:

[0078] Electronic Medical Record (EMR) log: records the timestamps, module numbers, and patient numbers of each employee's access to medical record modules (e.g., initial visit, follow-up visit, physical examination record, prescription entry, etc.);

[0079] Public Health Record (PHR) log: records the completion time and number of tasks such as resident follow-up plan execution, health record creation and maintenance, and index entry;

[0080] Attendance and scheduling system data: records information such as daily clock-in and clock-out times, shift types, and job rotation positions of employees;

[0081] Auxiliary business system log: includes module access sequence, page dwell time, and interaction object count recorded by the system background.

[0082] Operation log data is collected daily through interfaces or automated collection programs in the internal network to form an operation behavior raw data pool indexed by "employee-time". For example, a primary care physician named Li, in May 2025, logged into the EMR system at 8:30 am and accessed the "physical examination record", "prescription module", and "test application" a total of 12 times, during which he also logged into the PHR system and completed 2 hypertension follow-up records, and signed out at 17:20. The system background statistics his total active time for that day as 6 hours and 47 minutes.

[0083] In step S201 of some embodiments, to quantify the behavior characteristics of each employee, a behavior vector of a unified format needs to be constructed based on the operation log data. The behavior vector v i As shown in the following formula (1):

[0084]

[0085] wherein, represents the daily average access frequency of the i-th employee in the electronic medical record system, which is derived from the EMR module access log and counts the total number of daily access actions. represents the number of records completed by the i-th employee in the public health system every day, which is derived from the statistics of "task submission events" in the PHR system. represents the total active time period length of the i-th employee in the system every day, which is calculated by summing the event segments with an interval time less than 10 minutes between consecutive operations. represents the number of business module types (e.g., "follow-up entry", "physical examination management", "prescription system", a total of 3 types) visited by the i-th employee every week, which is derived from the count of unique module identifiers in the access log. The interaction task completion rate of the i-th employee, i.e., the proportion of tasks such as "resident health questionnaire" or "satisfaction telephone follow-up" completed by the employee in the cycle, is obtained from the task allocation and feedback system.

[0086] The above behavior vector dimensions are standardized to the [0, 1] interval, obtaining a normalized behavior vector with unified dimensions and comparability.

[0087] In step S203 of some embodiments, to identify whether there is a deviation between the actual behavior pattern of the employee and his / her post responsibility, the normalized behavior vector and the preset historical average behavior vector are used to calculate the difference, obtaining the post deviation degree. As shown in the following formula (2):

[0088]

[0089] Wherein, δ i The post deviation degree of the i-th employee, the larger the value, the more obvious the deviation, which is used to assist in determining whether to consider the cross-post feature or the responsibility expansion trend when selecting the index. The normalized behavior vector of the i-th employee, The historical average behavior vector of the post p(i) to which the i-th employee belongs, i.e., the average vector calculated from the behavior data of multiple employees in the same post in history.

[0090] For example, a person registered as an "administrative assistant" frequently participates in public health archive maintenance and resident follow-up tasks in his / her work behavior, showing a high And The δ i value of the i-th employee will be significantly higher than the average value of the same post, indicating that the actual work deviates from the current administrative responsibility, and the medical prevention participation degree should be considered in the subsequent performance evaluation index.

[0091] Through the above steps S201 to S203, the post behavior portrait of each employee is constructed to support the accurate generation of subsequent personalized performance indicators. Unlike the traditional evaluation system which divides responsibilities by post name or organizational structure, this step reflects the post responsibilities based on operation log data, extracts the actual responsibility dimensions such as medical operation, public health task, and administrative affairs presented by employees in work through systematic collection, feature quantization, and behavior vectorization processing of employee behavior in multiple source business subsystems, and constructs a high-resolution behavior portrait. Through post behavior portrait modeling and post deviation degree measurement, the work responsibilities are described quantitatively rather than nominally, so that the subsequent evaluation index configuration is truly based on "what has been done" rather than "which post it belongs to". At the same time, the data usage does not rely on manual reporting or fixed templates, but directly mines objective behavior evidence from existing business subsystems.

[0092] Referring to Figure 3 In some embodiments, step S102 can include, but is not limited to, steps S301 to S302:

[0093] In step S301, an initial matching score of each index configuration template is obtained by matching calculation according to the post deviation degree in the index configuration template library; wherein the index configuration template library includes a plurality of index configuration templates, and the index configuration template includes recommended indexes and corresponding index weights;

[0094] In step S302, the highest initial matching score is taken as a target matching score, and the index configuration template corresponding to the target matching score is a target index configuration template.

[0095] In step S301 of some embodiments, the index configuration template library is set in advance Each w j is a five-dimensional performance index vector, corresponding to a set of actually available evaluation scheme configurations. These configurations come from historical evaluation practices and system settings, and are consistent with the dimension structure to facilitate behavior adaptation degree calculation. To prevent short-term fluctuations in employee behavior from leading to incorrect judgments of indexes, the system also maintains a set of post standard behavior cluster centers for auxiliary constraints on post identity.

[0096] The core calculation goal is to select an index configuration template that best matches the actual behavior of employee e i from the index configuration template library so that it matches the actual behavior profile of the employee and does not violate the post responsibility limit. To this end, a matching score function is designed for matching calculation, as shown in the following formula (3):

[0097]

[0098] where s ij represents the initial matching score of the i th employee selecting the j th set of index configuration templates, represents the normalized behavior vector of the i th employee. w j represents the j th set of index configuration templates, δ i represents the post deviation degree. p(i) represents the post of the i th employee, D(p(i),j) represents the post matching penalty term, and represents whether the j th index configuration template is adapted to the post p(i). It is defined as the Euclidean distance between the post standard behavior center and w j . λ1 and λ2 represent regular term control coefficients, λ1 controls the behavior deviation suppression intensity, and λ2 controls the post penalty weight.

[0099] It should be noted that formula (3) implements a triple control mechanism: firstly, the behavior-indicator similarity item ensures that the recommended scheme fits the actual work content; secondly, the deviation index decay item ensures that employees with obvious job drift are not matched to the regular job template; and thirdly, the job penalty item prevents the system from recommending assessment schemes that are completely mismatched with the job (such as matching nurses to the doctor indicator group), thereby ensuring the legality of performance results and business compliance.

[0100] In the implementation, D(p(i),j) is computed by matching the behavioral cluster centers of the nominal job p(i). With w j The L2 distance is implemented without the need for a separate, complex model. For example, if an employee's nominal position is "public health worker," and w j For a "medical operation-driven" indicator scheme (e.g., outpatient volume accounts for 70% of the weight), the job penalty item D(p(i),j) will be much higher than that of a matching public health scheme, thus causing s ij Suppressed.

[0101] In step S302 of some embodiments, the system processes each employee e i Calculate the initial matching score s sequentially ij Select the w corresponding to the highest score j. j This serves as a target metric configuration template. The target metric configuration template includes recommended target metrics and their corresponding weights, used for subsequent performance calculations. Let it be denoted as... Among them, R i This represents the target metric configuration template for the i-th employee. Let represent the k-th target recommendation metric for the i-th employee. This represents the target indicator weight corresponding to the k-th target recommendation indicator for the i-th employee. For example, a target indicator configuration template might be {"Outpatient visits: 30%", "Hypertension follow-up rate: 40%", "Satisfaction survey: 30%"}.

[0102] Through the above steps S301 to S302, the normalized behavior vector is utilized. and its job deviation δ i Based on the actual business practices of performance appraisal, a personalized performance indicator matching mechanism that dynamically adapts to the characteristics of employee responsibilities is constructed. Unlike the traditional static binding of "position-indicator" that cannot dynamically adapt to the evolution of responsibilities, this embodiment focuses on introducing a job behavior-driven indicator generation process. By designing an indicator similarity scoring model with deviation adjustment factors and job-related penalty items, intelligent matching and ranking of indicator configuration schemes are completed, providing an indicator structure that is adaptable to job behavior for subsequent performance accounting.

[0103] In steps S103 and S104 of some embodiments, an index mapping table Ψ(·) is pre-set, which stores the source system and page operation path of the data required by each index. According to the target recommendation index In the index mapping table Ψ(·), the source system and page operation path of the data required by each index are quickly located to form a collection task scheduling table And data collection is performed according to the collection task scheduling table. To improve the crawling efficiency under low-config hardware, the system uses a normalized behavior vector as the priority basis for path sorting, for example The higher, the more frequently the employee medical module is used, and the index related to the EMR system is preferentially scheduled.

[0104] In the execution of the data collection process, the data segment corresponding to each target recommendation index is recorded as The system uses a reinforced RPA combined with context-enhanced logic extraction components to perform automatic collection tasks. The original data collected is first converted into a unified format to output standardized data records Then it enters the cleaning link to obtain structured data The structured data corresponding to all target recommendation indexes of each employee form a structured data set.

[0105] In step S105 of some embodiments, the confidence score is calculated according to the structured data, the pre-set field mode template, the pre-set historical qualified data statistical template, and the post deviation, to obtain a confidence score. The confidence calculation formula is shown in the following formula (4):

[0106]

[0107] wherein, represents the confidence score of the i-th employee at time t with the k-th target recommendation index, represents the structured data of the i-th employee at time t with the k-th target recommendation index. M k represents the field mode template of the k-th target recommendation index, such as the field name set, the data structure type, etc. represents the historical qualified data statistical template, which is used to calculate the matching degree of the current data with the standard style. δ i represents the post deviation of the i-th employee, comp(·) represents the field completeness function, which is used to measure whether the data field is complete. align(·) represents the structure alignment function, such as based on Jaccard or structure cosine similarity. α, β and γ represent adjustment coefficients, usually set α = 0.4, β = 0.6, γ = 0.2, to ensure appropriate punishment for the post deviation.

[0108] It should be noted that the introduction of the post deviation degree δ i As a deduction item, it improves the abnormal identification ability of employees with ambiguous responsibilities. For example, an employee nominally in an administrative position will be given a lower confidence score if he collects a large amount of medical task-related data without authorization, so as to prevent his data from being used to account for performance items that he should not bear, thereby preventing "exceeding authority to brush performance".

[0109] In step S106 of some embodiments, based on the preset scoring threshold τ k Determine whether the data is available: when the confidence score , is considered valid and is added to the valid data set X i . The coverage confidence of each target recommendation indicator is defined by the following formula (5):

[0110]

[0111] Among them, denotes the coverage confidence of the kth target recommendation indicator of the ith employee, T k denotes the number of time slices of data that can be captured by the employee for the target recommendation indicator within the period. denotes the indicator function, which returns 1 if the judgment condition is true, and 0 otherwise. denotes the confidence score of the ith employee at time t for the kth target recommendation indicator, τ k denotes the scoring threshold.

[0112] Through it can assist in determining whether manual recording or complaint process intervention is needed, and the system can also weight the subsequent performance score according to the confidence.

[0113] In step S107 of some embodiments, the employee indicator score represents the performance level of the employee e i in the actual work on the recommended indicator , which is calculated according to the structured data . Specifically, is the result of periodic reduction calculation of the structured data according to the accounting logic of the indicator (such as completion rate, average value, number of times, etc.). For example: for the "hypertension follow-up completion rate" indicator,

[0114] Specifically, according to the target indicator weight, the employee indicator score, the coverage confidence, and the post deviation degree, a weighted fusion calculation is performed to obtain a comprehensive performance score. The weighted fusion calculation formula is shown in the following formula (6):

[0115]

[0116] wherein P i represents the comprehensive performance score of the i-th employee, represents the target indicator weight of the k-th target recommendation indicator of the i-th employee. represents the employee indicator score of the k-th target recommendation indicator of the i-th employee, represents the coverage confidence of the k-th target recommendation indicator of the i-th employee. δ i represents the post deviation degree of the i-th employee, represents the k-th target recommendation indicator of the i-th employee. Ω p(i) represents the post responsibility range of the i-th employee, γ represents the post matching penalty term coefficient, which is used to prevent score unfairness caused by responsibility overreach. m i represents the total number of personalized performance indicators assigned to the i-th employee. represents the indicator function, which is 1 when the indicator does not belong to the post responsibility range Ω p(i) of the i-th employee.

[0117] It should be noted that formula (6) not only considers the indicator weight and performance, but also adds data confidence and responsibility matching factors, so that non-post core behaviors are moderately punished, reducing the phenomenon of "indicator brushing score"; at the same time, the data quality difference is automatically weighted, avoiding the interference of low-quality data on the score.

[0118] In step S108 of some embodiments, the final performance evaluation scheme b i adopts a relative performance proportional distribution mechanism, with a preset bonus setting value B g as the upper limit, and the performance evaluation scheme is calculated as shown in the following formula (7):

[0119]

[0120] wherein b i represents the performance evaluation scheme of the i-th employee, P i represents the comprehensive performance score of the i-th employee. B g represents the bonus setting value, P j represents the comprehensive performance score of the j-th employee, G represents the employee group participating in the same bonus setting value B g distribution. This scheme has extremely high reproducibility in operation: only the historical team bonus setting value B g needs to be retrieved, and then the scores of each employee are normalized and proportionally mapped.

[0121] In an example, if the target index configuration template of a public health worker e1 includes "hypertension follow-up completion rate" (weight 40%), "resident health record maintenance" (weight 30%), and "service satisfaction questionnaire completion rate" (weight 30%). After collecting and cleaning the effective data, the employee index scores are 0.9, 0.85, and 0.6 respectively; the confidence scores are 1.0, 0.9, and 0.6 respectively, and "questionnaire satisfaction" is not the core responsibility of the post, δ1 = 0.3, γ = 0.5, then the comprehensive performance score P1 of e1 is calculated by the following formula (8):

[0122] P1 = 0.4 · 0.9 · 1.0 + 0.3 · 0.85 · 0.9 + 0.3 · 0.6 · (0.6 - 0.5 · 0.3) = 0.36 + 0.2295 + 0.081 = 0.6705, (8)

[0123] If the set value B g of the group bonus of e1 is 10,000 yuan, and the total score is ∑P j = 5.4, then the performance evaluation scheme is shown in the following formula (9):

[0124]

[0125] The steps S101 to S108 shown in the embodiments of the present application obtain the post deviation degree, match the target index configuration template in the preset index configuration template library according to the post deviation degree, and obtain the target index configuration template. The target index configuration template includes target recommended indexes and corresponding target index weights. The target recommended indexes are looked up in the preset index mapping table to obtain a collection task scheduling table, and the structured data set is obtained by collecting data according to the collection task scheduling table. The confidence score is obtained by calculating the confidence according to the structured data, the preset field mode template, the preset historical qualified data statistical template, and the post deviation degree. The coverage confidence of each target recommended index is obtained according to the confidence score and the preset score threshold. The comprehensive performance score is obtained by weighted fusion calculation according to the target index weight, the employee index score, the coverage confidence, and the post deviation degree. The performance evaluation scheme is obtained according to the comprehensive performance score and the preset bonus setting value, and the accuracy of the performance evaluation is improved.

[0126] Please refer to Figure 4 In some embodiments, after step S108, the intelligent customized method for performance wage evaluation of the primary health center can further include but is not limited to steps S401 to S403:

[0127] Step S401, constructing a score explanation table according to the target recommended index, the target index weight, the employee index score, the coverage confidence, the post deviation degree, and the preset post responsibility mapping set;

[0128] Step S402, consistency score is scored according to coverage confidence, target recommendation index and post responsibility mapping set, and consistency score data is obtained;

[0129] Step S403, the scoring explanation table and the consistency score data are displayed.

[0130] In step S401 of some embodiments, to realize the traceable performance mechanism, a scoring explanation table is constructed The table records the employee e i The composition details of each performance score in the cycle, and each record is a six-tuple, as shown in the following formula (10):

[0131]

[0132] Among them, denotes the kth target recommendation index of the ith employee, denotes the employee index score of the kth target recommendation index of the ith employee, denotes the target index weight of the kth target recommendation index of the ith employee. δ i denotes the post deviation degree of the ith employee, Ω p(i) denotes the post responsibility range of the ith employee.

[0133] Each entry provides additional information such as whether the index belongs to the post responsibility (responsibility out-of-bound identifier), index confidence, post deviation degree, and is displayed in a structured table form in the performance management front end, so that employees can clearly see the origin and influence weight of each score.

[0134] In step S402 and step S403 of some embodiments, to realize the scoring rationality verification, consistency score data T i is introduced to measure whether the current comprehensive performance score composition is compliant and transparent. The calculation of the consistency score data is shown in the following formula (11):

[0135]

[0136] Among them, T i denotes the consistency score data of the ith employee, denotes the coverage confidence of the kth target recommendation index of the ith employee. m i denotes the number of target recommendation indexes of the employee, denotes the responsibility consistency indication function (1 indicates that the index belongs to its post). denotes the kth target recommendation index of the ith employee. Ω p(i) denotes the post responsibility range of the ith employee.

[0137] The higher the consistency score data is, the more reliable the data in the current performance composition is and the higher the matching degree of the index responsibility is. The system will provide a "score explanation quality" mark based on this during performance publicity, such as "high consistency score: T=0.92".

[0138] In an example, a certain doctor e 12 , whose comprehensive performance score P 12 =0.85, has the following performance composition:

[0139] Index 1 (hypertension follow-up completion rate): s (1) =0.95, w (1) =0.4, η (1) =0.9, which belongs to the post responsibility;

[0140] Index 2 (satisfaction telephone follow-up rate): s (2) =0.75, w (2) =0.3, η (2) =0.6, which is not his responsibility;

[0141] Index 3 (prescription rationality score): s (3) =0.8, w (3) =0.3, η (3) =1.0, which belongs to the responsibility.

[0142] The system automatically generates a score explanation table The employee can click to view the table and its source original record. The consistency score data T 12 =(0.9+0+1.0) / 3=0.63, and the system marks it as "medium consistency score".

[0143] In the steps S401 to S403 shown in the embodiment, by constructing the traceability mechanism of performance score results and the transparent management of bonus allocation process, the core pain points of "unclear calculation and unclear explanation" of performance in grassroots institutions are solved. Unlike the traditional way of only publishing the final score and bonus result, the embodiment realizes the item-by-item verification of the source of each performance score by structured recording and formula tracing.

[0144] Please refer to Figure 5 The embodiment of the application also provides an intelligent customized system for performance wage assessment of grassroots health centers, which can realize the intelligent customized method for performance wage assessment of grassroots health centers as described above. The system comprises:

[0145] The acquisition module 501 is configured to acquire the post deviation degree.

[0146] The matching module 502 is configured to match the post deviation degree in a preset index configuration template library to obtain a target index configuration template; the target index configuration template includes a target recommended index and a corresponding target index weight;

[0147] The searching module 503 is configured to search the target recommended index in a preset index mapping table to obtain a collection task scheduling table.

[0148] The collection module 504 is configured to collect data according to the collection task scheduling table to obtain a structured data set.

[0149] The confidence module 505 is configured to perform confidence calculation according to the structured data, a preset field mode template, a preset historical qualified data statistical template and the post deviation degree to obtain a confidence score.

[0150] The coverage module 506 is configured to obtain a coverage confidence of each target recommended index according to the confidence score and a preset score threshold.

[0151] The calculation module 507 is configured to perform weighted fusion calculation according to the target index weight, a preset employee index score, the coverage confidence and the post deviation degree to obtain a comprehensive performance score.

[0152] The performance module 508 is configured to obtain a performance evaluation scheme according to the comprehensive performance score and a preset bonus setting value.

[0153] The specific implementation of the intelligent customization system for the performance salary evaluation of the primary health center is basically the same as the specific embodiments of the intelligent customization method for the performance salary evaluation of the primary health center, and thus will not be repeated here.

[0154] The embodiments of the present application further provide an electronic device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the intelligent customization method for the performance salary evaluation of the primary health center when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer and the like.

[0155] Please refer to Figure 6 , Figure 6 The hardware structure of the electronic device of another embodiment is illustrated, which includes:

[0156] The processor 601 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, and is configured to execute related programs to implement the technical solutions provided by the embodiments of the present application.

[0157] The memory 602 can be implemented by a ROM (Read Only Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory), and the like. The memory 602 can store an operating system and other application programs. When the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are stored in the memory 602 and are called and executed by the processor 601 to implement the intelligent customization method for performance wage assessment of primary health centers in the embodiments of the present application.

[0158] The input / output interface 603 is configured to realize information input and output.

[0159] The communication interface 604 is configured to realize the communication interaction between the device and other devices. The communication can be realized by a wired manner (for example, a USB, a network cable, or the like) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, or the like).

[0160] The bus 605 is configured to transmit information between various components (for example, the processor 601, the memory 602, the input / output interface 603, and the communication interface 604) of the device.

[0161] The processor 601, the memory 602, the input / output interface 603, and the communication interface 604 are connected to each other through the bus 605 to realize the communication connection between the device.

[0162] The embodiments of the present application further provide a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the intelligent customization method for performance wage assessment of primary health centers.

[0163] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include a high-speed random access memory and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory disposed remotely from the processor, which can be connected to the processor through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0164] The embodiments described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0165] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and can include more or fewer steps than shown in the figures, or combine certain steps, or different steps.

[0166] The system embodiments described above are only schematic, and the units described as separate components can or can not be physically separate, that is, can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0167] Those skilled in the art can understand that all or some of the steps in the above disclosed method, the functional modules / units in the system and the device can be implemented as software, firmware, hardware and appropriate combinations thereof.

[0168] The terms "first", "second", "third", "fourth" and the like used in the description of the present application and the above figures, if any, are used to distinguish similar objects, and do not necessarily have to describe a particular order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0169] It should be understood that, in the application, "at least one" refers to one or more, and "multiple" refers to two or more. "And / or" is used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, "A and / or B" can represent three cases of only A, only B and A and B existing at the same time, wherein A and B can be singular or plural. The character " / " generally represents an "or" relationship between the front and rear associated objects. "At least one of the following" or the like means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c can represent a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b and c can be single or multiple.

[0170] In several embodiments provided in the application, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiments described above are only illustrative, for example, the division of the above units is only a logical function division, and actual implementation can have another division mode, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed systems or units can be indirect coupling or communication connection through some interfaces, systems or units, which can be electrical, mechanical or other forms.

[0171] The units described above as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0172] In addition, each functional unit in each embodiment of the application can be integrated into a processing unit, or each unit can exist physically, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0173] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in other words, the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program storage media.

[0174] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, and are not limited to the scope of the embodiments of the present application. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the embodiments of the present application.

Claims

1. A smart, customized method for performance-based salary assessment in primary healthcare centers, characterized in that: The method includes: Obtain the job deviation rate; The target indicator configuration template is obtained by matching the job deviation degree with a preset indicator configuration template library; wherein, the target indicator configuration template includes target recommended indicators and corresponding target indicator weights; The target recommendation index is searched in a preset index mapping table to obtain the data collection task scheduling table; Data is collected according to the aforementioned data collection task schedule to obtain a structured dataset; The confidence score is calculated based on the structured data, the preset field pattern template, the preset historical qualified data statistics template, and the job deviation. The coverage confidence level of each target recommendation indicator is obtained based on the confidence score and the preset scoring threshold. The comprehensive performance score is obtained by weighting and integrating the target indicator weights, preset employee indicator scores, coverage confidence scores, and job deviation scores. A performance appraisal scheme is derived based on the overall performance score and the preset bonus setting.

2. The method according to claim 1, characterized in that, After obtaining the performance appraisal scheme based on the comprehensive score and the preset bonus setting value, the method further includes: A scoring interpretation table is constructed based on the target recommendation index, the target index weight, the employee index score, the coverage confidence level, the job deviation, and the preset job responsibility mapping set; Consistency scoring is performed based on the coverage confidence level, the target recommendation index, and the job responsibility mapping set to obtain consistency score data; The rating interpretation table and the consistency rating data are displayed.

3. The method according to claim 1, characterized in that, The acquisition of job deviation includes: Obtain employee operation log data; Construct a normalized behavior vector based on the operation log data; The job deviation is obtained by calculating the difference between the normalized behavior vector and the preset historical average behavior vector.

4. The method according to claim 1, characterized in that, The step of matching the target indicator configuration template in a preset indicator configuration template library based on the job deviation includes: The initial matching score for each indicator configuration template is obtained by matching and calculating the deviation of the job position in the indicator configuration template library; wherein, the indicator configuration template library includes multiple indicator configuration templates, and each indicator configuration template includes recommended indicators and corresponding indicator weights; The highest initial matching score is taken as the target matching score, and the indicator configuration template corresponding to the target matching score is the target indicator configuration template.

5. The method according to claim 4, characterized in that, The step of matching and calculating the initial matching score for each indicator configuration template based on the job deviation in the indicator configuration template library includes: The matching calculation formula is as follows: Among them, s ij This represents the initial matching score when the i-th employee selects the j-th set of indicator configuration templates. w represents the normalized behavior vector of the i-th employee. j This represents the j-th set of indicator configuration templates, δ i Let D(p(i),j) represent the job deviation degree, D(p(i),j) represent the job matching penalty term, p(i) represent the job of the i-th employee, and λ1 and λ2 represent the regularization control coefficients.

6. The method according to claim 1, characterized in that, The confidence score is calculated based on the structured data, a preset field pattern template, a preset historical qualified data statistics template, and the job deviation, including: The confidence level is calculated using the following formula: in, Let represent the confidence score of the i-th employee at time t relative to the k-th target recommendation indicator. M represents the structured data of the i-th employee at time t and the k-th target recommendation metric. k This represents the field pattern template for the k-th target recommendation metric. This represents a template for statistical analysis of historical qualified data, δ i represents the job deviation of the i-th employee, comp(·) represents the field completeness function, align(·) represents the structure alignment function, and α, β, and γ represent adjustment coefficients.

7. The method according to claim 1, characterized in that, The comprehensive performance score is obtained by weighting and integrating the target indicator weights, preset employee indicator scores, coverage confidence scores, and job deviations, including: The weighted fusion calculation formula is as follows: Among them, P i Let represent the overall performance score of the i-th employee. This represents the target indicator weight for the k-th target recommendation indicator for the i-th employee. This represents the employee metric score for the i-th employee in the k-th target recommendation metric. δ represents the coverage confidence level of the k-th target recommendation indicator for the i-th employee. i This represents the job deviation degree of the i-th employee. The k-th target recommendation indicator for the i-th employee, Ω p(i) The job description represents the scope of job responsibilities, γ represents the job matching penalty coefficient, and m represents the job responsibilities scope. i This represents the total number of personalized performance indicators assigned to the i-th employee. This indicates an indicator function.

8. An intelligent, customized system for performance-based salary assessment in primary healthcare centers, characterized in that: The system includes: The acquisition module is used to obtain the job deviation. The matching module is used to match the job deviation degree in a preset indicator configuration template library to obtain a target indicator configuration template; wherein, the target indicator configuration template includes a target recommended indicator and a corresponding target indicator weight; The search module is used to search in a preset index mapping table according to the target recommended index to obtain the data collection task scheduling table; The data acquisition module is used to acquire data according to the data acquisition task schedule table to obtain a structured dataset; The confidence module is used to calculate the confidence score based on the structured data, the preset field pattern template, the preset historical qualified data statistical template, and the job deviation. The coverage module is used to obtain the coverage confidence of each target recommendation indicator based on the confidence score and a preset scoring threshold. The calculation module is used to perform weighted fusion calculation based on the target indicator weight, the preset employee indicator score, the coverage confidence level, and the job deviation to obtain a comprehensive performance score; The performance module is used to generate a performance appraisal plan based on the overall performance score and the preset bonus setting value.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the intelligent customization method for performance-based salary assessment of primary healthcare centers as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the intelligent customization method for performance-based salary assessment of primary health centers as described in any one of claims 1 to 7.

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