Performance indicator analysis method and system based on evidence synthesis theory
Through the performance index analysis method based on evidence synthesis theory, the problem of insufficient accuracy in hospital performance evaluation is solved, a more scientific and transparent evaluation process is achieved, and the hospital management efficiency and service quality are improved.
Patent Information
- Application Number
- CN202411420581.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-12
- Publication Date
- 2025-01-10
AI Technical Summary
The prior art has problems such as insufficient accuracy in hospital performance evaluation, complex system construction and maintenance, difficulty in real-time feedback and adjustment, and impact on prediction accuracy and stability.
Using the performance index analysis method based on evidence synthesis theory, by constructing a performance index evaluation system, determining the identification framework, setting mapping rules, mapping the original data of the hospital outpatient department into evidence body, and using preset synthesis rules for evaluation, including Murphy synthesis rules and D-S Rules for synthesis of evidence theory to achieve evaluation of hospital and department performance.
It improves the accuracy and transparency of hospital performance evaluation, overcomes the personal bias of evaluators, enhances fairness and scientific decision-making, and improves hospital management efficiency and service resilience.
Smart Images
Figure CN119296744A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of performance evaluation, and specifically to a performance indicator analysis method and system based on evidence synthesis theory. Background Art
[0002] Hospital performance evaluation is a quantitative and qualitative assessment and analysis of the hospital's operating conditions, operating benefits, and operator performance over a certain period of time in order to make an objective evaluation. Its nature is reflected in its systematicity, integration, scientificity, and dynamism. In related technologies, TOPSIS is usually used to construct a weighted evaluation matrix and calculate the comprehensive evaluation value; the combination weight of the evaluation index is calculated through the hierarchical analysis method and the entropy weight method, and the BP neural network model based on the genetic algorithm is used to dynamically select the best evaluation value and optimize the combination weight.
[0003] However, the use of methods such as hierarchical analysis method, entropy weight method, TOPSIS and BP neural network model based on genetic algorithm will make the construction and maintenance of the entire system relatively complicated, requiring professional knowledge and skills, increasing the difficulty and cost of implementation. In practical applications, it may be difficult to achieve real-time feedback and adjustment due to the time consumption of data processing and model training; in the face of extreme or abnormal situations, its prediction accuracy and stability may be affected. Summary of the invention
[0004] In view of the deficiencies in the prior art, the present application provides a performance indicator analysis method and system based on evidence synthesis theory, which solves the problem of insufficient accuracy in current performance evaluation.
[0005] To achieve the above objectives, this application is implemented through the following technical solutions:
[0006] In the first aspect, an embodiment of the present application provides a performance indicator analysis method based on the theory of evidence synthesis, which includes: constructing a performance indicator evaluation system, the performance indicator evaluation system includes a target layer and a multi-level indicator area, the target layer corresponds to the overall performance of the hospital; determining an identification framework, the identification framework is Θ = {H1, H2, H3, H4, H5} = {excellent, good, medium, low, poor}; according to the distribution characteristics of each indicator value, setting multiple quantiles corresponding to the constructed mapping rules; the multiple quantiles are equivalent to excellent level, good level, medium level, low level and poor level, respectively; based on the mapping rules, the original hospital outpatient data corresponding to each indicator obtained is mapped to an evidence body to obtain a first reliability distribution; based on the first reliability distribution, a preset synthesis rule is used to evaluate the hospital performance.
[0007] According to the first aspect of the embodiment of the present application, the multi-level indicator area is composed of a first-level indicator layer, a second-level indicator layer and a third-level indicator layer; the first-level indicator layer corresponds to department performance; the second-level indicator layer corresponds to medical quality and operational efficiency; the third-level indicator layer includes multiple third-level indicators corresponding to medical quality and operational efficiency, respectively.
[0008] According to the first aspect of the embodiment of the present application, the three-level indicators corresponding to medical quality include: number of discharges, admission and discharge diagnosis compliance rate, average length of stay, cure rate, improvement rate, average hospitalization expenses, proportion of hospitalization consumables, and cost of medical materials consumed per 100 yuan of medical income; the three-level indicators corresponding to operational efficiency include bed occupancy rate, hospital infection rate and proportion of hospitalization drugs.
[0009] According to the first aspect of the embodiment of the present application, the aforementioned evaluation of hospital performance is based on the first reliability distribution and adopts a preset synthesis rule, including: synthesizing the first reliability distribution corresponding to each indicator through the preset Murphy synthesis rule to obtain the second reliability distribution of the medical quality and operational efficiency of each department; using the DS evidence theory synthesis rule to synthesize the second reliability distribution of each department respectively, and obtain the total reliability distribution corresponding to each department to represent the comprehensive performance of each department; based on the Murphy synthesis rule, synthesizing the total reliability distribution corresponding to each department to obtain the overall performance level of the hospital.
[0010] According to the first aspect of the embodiment of the present application, multiple quantiles include the maximum value, 75th quantile, 50th quantile, 25th quantile and minimum value of the indicator; the maximum value, 75th quantile, 50th quantile, 25th quantile and minimum value of the indicator correspond to excellent grade, good grade, medium grade, low grade and poor grade, respectively.
[0011] According to the first aspect of the embodiment of the present application, the DS evidence theory synthesis rule includes a basic credibility allocation stage and a Dempster synthesis stage; in the basic credibility allocation stage, the set function m:2 Θ →[0,1] satisfies:
[0012]
[0013] Where m is called the basic credibility assignment BPA on the recognition framework Θ; for any m(A) is called the basic credibility of A and is used to characterize the degree of support for proposition A by evidence.
[0014] According to the first aspect of the embodiment of the present application, in the Dempster synthesis stage, for Proposition m(A) satisfies the expression:
[0015]
[0016] Among them, m1(A1)m2(A2)···m n (A n ) is the n basic credibility assignments on the recognition framework Θ; m(A) represents m1(A1)m2(A2)···m n (A n ) is the degree of joint support of the corresponding evidence for proposition A, and K is the normalization coefficient and is used to characterize the degree of conflict between the evidences.
[0017] In the second aspect, an embodiment of the present application provides a performance indicator analysis system based on evidence synthesis theory, the system comprising a construction module, a determination module, a setting module, a mapping module and an evaluation module; wherein the construction module is used to construct a performance indicator evaluation system, the performance indicator evaluation system comprising a target layer and a multi-level indicator area, the target layer corresponds to the overall performance of the hospital; the determination module is used to determine an identification framework, the identification framework is Θ = {H1, H2, H3, H4, H5} = {excellent, good, medium, low, poor}; the setting module is used to set a plurality of quantiles corresponding to the constructed mapping rules according to the distribution characteristics of each indicator value; the plurality of quantiles are equivalent to excellent grade, good grade, medium grade, low grade and poor grade, respectively; the mapping module is used to map the obtained hospital outpatient raw data corresponding to each indicator into an evidence body based on the mapping rules, and obtain a first reliability distribution; the evaluation module is used to evaluate the hospital performance based on the first reliability distribution and using a preset synthesis rule.
[0018] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the performance indicator analysis method based on evidence synthesis theory in the aforementioned first aspect is implemented.
[0019] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a program or instruction, which, when executed by a processor, implements the performance indicator analysis method based on evidence synthesis theory in the aforementioned first aspect.
[0020] This application provides a performance indicator analysis method and system based on evidence synthesis theory, which has the following beneficial effects compared with the existing technology:
[0021] In order to conduct performance indicator analysis, this application constructs a performance indicator evaluation system including a target layer and a multi-level indicator area, and sets up an identification framework. After obtaining the original data of the hospital outpatient department, the original data is mapped and processed into an evidence body to obtain the initial first reliability distribution. By synthesizing the first reliability distribution, the evaluation of hospital performance is achieved. Based on the preset mapping rules and preset synthesis rules, this application provides clear evaluation standards and indicators, overcomes the objectivity defects caused by the personal bias and judgment of the evaluator in the hospital performance evaluation; enhances the transparency and fairness of performance evaluation, enhances the scientific nature of decision-making, and improves hospital management efficiency and service resilience. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0023] Figure 1 It is a flowchart of a performance indicator analysis method based on evidence synthesis theory provided in an embodiment of the present application;
[0024] Figure 2 yes Figure 1 An exemplary process diagram of S150;
[0025] Figure 3 is an exemplary schematic diagram of a performance indicator evaluation system provided in an embodiment of the present application;
[0026] Figure 4 It is a structural diagram of a performance indicator analysis system based on evidence synthesis theory provided in an embodiment of the present application;
[0027] Figure 5 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0028] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0029] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.
[0030] The embodiments of the present application solve the problem of insufficient accuracy in current performance evaluation by providing a performance indicator analysis method and system based on evidence synthesis theory.
[0031] The technical solution in the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows:
[0032] Hospital performance evaluation is to conduct quantitative and qualitative assessment and analysis of the hospital's operating conditions, operating benefits, and operator performance over a certain period of time in order to make an objective evaluation. This evaluation process aims to reflect the requirements of implementing the public welfare nature of public medical and health institutions and maintaining public health, reflect the service and management process, focus on service results, and highlight target management and comprehensive quality management.
[0033] The purpose of hospital performance evaluation methods is to promote scientific and standardized hospital management and improve the quality and efficiency of medical services; its nature is reflected in systematicity, integration, scientificity and dynamism. Through scientific and reasonable performance evaluation, hospitals can achieve optimal allocation of resources, improve the work efficiency and enthusiasm of employees, and ultimately promote the sustainable and healthy development of hospitals.
[0034] In the related art, TOPSIS is usually used to construct a weighted evaluation matrix and calculate the comprehensive evaluation value; and a BP neural network model based on a genetic algorithm is constructed to dynamically select the best evaluation value and optimize the combination weight. The present invention calculates the combination weight of the evaluation index by using the hierarchical analysis method and the entropy weight method, and adopts the BP neural network model based on the genetic algorithm to dynamically select the best evaluation value and optimize the combination weight.
[0035] However, the use of methods such as hierarchical analysis method, entropy weight method, TOPSIS and BP neural network model based on genetic algorithm will make the construction and maintenance of the entire system relatively complicated, requiring professional knowledge and skills, increasing the difficulty and cost of implementation.
[0036] In practical applications, it may be difficult to achieve real-time feedback and adjustment due to the time consumption of data processing and model training; although the BP neural network model based on genetic algorithm has good adaptability, its prediction accuracy and stability may be affected when facing extreme or abnormal situations. In addition, differences between different hospitals may also affect the versatility and applicability of the model. In addition, in actual operation, how to balance subjectivity and objectivity is still a challenge. For example, the application of hierarchical analysis method and entropy weight method involves certain subjective judgments.
[0037] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.
[0038] The following first introduces a performance indicator analysis method based on evidence synthesis theory provided in an embodiment of the present application.
[0039] The present application embodiment provides a flow chart of a performance indicator analysis method based on evidence synthesis theory, such as Figure 1 As shown, the performance indicator analysis method based on evidence synthesis theory may include the following steps S110-S150.
[0040] S110. Construct a performance indicator evaluation system. The performance indicator evaluation system includes a target layer and multi-level indicator areas. The target layer corresponds to the overall performance of the hospital.
[0041] S120. Determine a recognition framework, where the recognition framework is Θ = {H1, H2, H3, H4, H5} = {excellent, good, medium, low, poor}.
[0042] S130. According to the distribution characteristics of each indicator value, a plurality of quantiles corresponding to the constructed mapping rule are set; the plurality of quantiles are respectively equivalent to excellent grade, good grade, medium grade, low grade and poor grade.
[0043] S140. Mapping the obtained original hospital outpatient data corresponding to each indicator into an evidence body based on a mapping rule to obtain a first reliability distribution.
[0044] S150. Based on the first reliability distribution, the hospital performance is evaluated using a preset synthesis rule.
[0045] The above is a specific implementation method of a performance indicator analysis method based on evidence synthesis theory provided in an embodiment of the present application. It can be understood that, in order to conduct performance indicator analysis, the present application constructs a performance indicator evaluation system including a target layer and a multi-level indicator area, and sets up an identification framework. After obtaining the original data of the hospital outpatient department, the original data is mapped and processed into an evidence body to obtain an initial first reliability distribution. By synthesizing the first reliability distribution, the evaluation of the hospital performance is achieved. The entire analysis and processing process is relatively simple, overcoming resource limitations and avoiding the consumption of a large amount of manpower and financial resources due to an overly complex and unreasonable evaluation process.
[0046] Based on this, this application provides clear evaluation criteria and indicators based on preset mapping rules and preset synthesis rules, overcoming the objectivity defects caused by the personal bias and judgment of the evaluators in hospital performance evaluation; enhancing the transparency and fairness of performance evaluation, enhancing the scientific nature of decision-making, and improving hospital management efficiency and service resilience.
[0047] In an example, the multiple quantiles include the maximum value, 75th quantile, 50th quantile, 25th quantile and minimum value of the indicator; the maximum value, 75th quantile, 50th quantile, 25th quantile and minimum value of the indicator correspond to excellent grade, good grade, medium grade, low grade and poor grade, respectively.
[0048] In some embodiments, please refer to Figure 3 The multi-level indicator area consists of the first-level indicator layer, the second-level indicator layer and the third-level indicator layer; the first-level indicator layer corresponds to department performance; the second-level indicator layer corresponds to medical quality and operational efficiency; the third-level indicator layer includes multiple third-level indicators corresponding to medical quality and operational efficiency respectively. The third-level indicators corresponding to medical quality include: number of discharged patients, admission and discharge diagnosis compliance rate, average length of stay, cure rate, improvement rate, average hospitalization cost, proportion of hospitalization consumables, and medical material cost consumed per 100 yuan of medical income; the third-level indicators corresponding to operational efficiency include bed occupancy rate, hospital infection rate and proportion of hospitalization drugs.
[0049] In the embodiments of this application, it can be understood that this application selects specific, key, and quantifiable indicators to conduct a comprehensive evaluation of performance, which helps the hospital management to clearly understand the key issues and bottlenecks in hospital operations. Through the evaluation of hospital department performance, managers can promptly identify problems and take corresponding measures, thereby improving the hospital's management efficiency and operational level, and can improve hospital management efficiency and service resilience.
[0050] In some embodiments, Figure 2 As shown, the aforementioned evaluation of hospital performance based on the first reliability distribution using a preset synthesis rule, that is, the aforementioned S150 may specifically include the following steps:
[0051] S210. The first reliability distributions corresponding to the various indicators are synthesized using a preset Murphy synthesis rule to obtain second reliability distributions of the medical quality and operational efficiency of each department.
[0052] S220. Use the DS evidence theory synthesis rules to synthesize the second reliability distribution of each department separately, and obtain the total reliability distribution corresponding to each department to represent the comprehensive performance of each department.
[0053] S230. Based on Murphy's synthesis rule, the total reliability distribution corresponding to each department is synthesized to obtain the overall performance level of the hospital.
[0054] In the embodiments of the present application, it can be understood that the present application uses the evidence synthesis method in performance evaluation to provide decision makers with a more comprehensive and objective evidence basis. This helps to formulate more scientific and reasonable policies and measures, improve the accuracy and effectiveness of decision-making, and enhance the scientific nature of decision-making.
[0055] In one example, the DS evidence theory synthesis rule includes a basic credibility allocation stage and a Dempster synthesis stage; in the basic credibility allocation stage, the set function m:2 Θ →[0,1] satisfies:
[0056]
[0057] Where m is called the basic credibility assignment BPA on the recognition framework Θ; for any m(A) is called the basic credibility of A and is used to characterize the degree of support for proposition A by evidence.
[0058] In the Dempster synthesis stage, for Proposition m(A) satisfies the expression:
[0059]
[0060] Among them, m1(A1)m2(A2)···m n (A n ) is the n basic credibility assignments on the recognition framework Θ; m(A) represents m1(A1)m2(A2)···m n (A n ) is the degree of joint support of the corresponding evidence for proposition A, and K is the normalization coefficient and is used to characterize the degree of conflict between the evidences.
[0061] In some embodiments, the foregoing mapping rules satisfy the expression:
[0062] γ n+1，j＝ 1-γn，j ,
[0063] Among them, h n，j≤hj≤hn+1，j ;
[0064] γ k,j =0, where k=1,…,N,k≠n,n+1
[0065] Assume h i is the original data of the ith indicator, equivalent to the performance level H n , r n,i Indicates that indicator i is rated as BPA of performance level n. Through the mapping rule, the reliability distribution of indicator i can be obtained as S i (h i )={(h n，i , Y n，i ),n=1,…,N}.
[0066] In some embodiments, the Dempster synthesis rule may produce unreasonable synthesis results when synthesizing conflicting evidence; therefore, before synthesizing conflicting evidence, the evidence source may be pre-processed to avoid paradoxes;
[0067] Assuming there are n pieces of evidence, first take the basic credibility distribution BPA of the n pieces of evidence and calculate the average to get m avg , and then use the DS synthesis rule to avg The basic credibility distribution of the proposition can be obtained by synthesizing n-1 times; the Murphy synthesis formula is defined as follows:
[0068] m1=m avg ⊕m avg
[0069] M i =m i-1 ⊕m avg , where 2≤i≤n-1.
[0070] In some embodiments, the present application provides a performance indicator analysis system 300 based on evidence synthesis theory, such as Figure 4 As shown, the system 300 may include the following modules:
[0071] A construction module 310 is used to construct a performance indicator evaluation system, the performance indicator evaluation system includes a target layer and a multi-level indicator area, and the target layer corresponds to the overall performance of the hospital;
[0072] A determination module 320 is used to determine a recognition framework, where the recognition framework is Θ={H1, H2, H3, H4, H5}={excellent, good, medium, low, poor};
[0073] A setting module 330 is used to set a plurality of quantiles corresponding to the constructed mapping rules according to the distribution characteristics of each indicator value; the plurality of quantiles are respectively equivalent to excellent grade, good grade, medium grade, low grade and poor grade;
[0074] A mapping module 340 is used to map the obtained hospital outpatient raw data corresponding to each indicator into an evidence body based on a mapping rule to obtain a first reliability distribution;
[0075] The evaluation module 350 is used to evaluate the hospital performance based on the first reliability distribution using a preset synthesis rule.
[0076] According to an embodiment of the present application, any multiple modules among the construction module 310, the determination module 320, the setting module 330, the mapping module 340 and the evaluation module 350 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module.
[0077] In some embodiments, the evaluation module 350 may be specifically used to:
[0078] The first reliability distribution corresponding to each indicator is synthesized through the preset Murphy synthesis rule to obtain the second reliability distribution of the medical quality and operation efficiency of each department;
[0079] Using the synthesis rule of DS evidence theory, the second reliability distribution of each department is synthesized separately, and the total reliability distribution corresponding to each department is obtained to represent the comprehensive performance of each department;
[0080] Based on Murphy's synthesis rule, the total reliability distribution corresponding to each department is synthesized to obtain the overall performance level of the hospital.
[0081] Figure 4 Each module in the system shown has the function of implementing each step in the aforementioned performance indicator analysis method based on evidence synthesis theory, and can achieve its corresponding technical effect. For the sake of concise description, it will not be repeated here.
[0082] In some embodiments, the present application provides an electronic device, the structure diagram of the electronic device is as follows Figure 5 shown.
[0083] The electronic device may include a processor 410 and a memory 420 storing computer program instructions.
[0084] Specifically, the processor 410 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.
[0085] The memory 420 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 420 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 420 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 420 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 420 is a non-volatile solid-state memory.
[0086] The memory 420 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Therefore, generally, the memory 420 includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it can perform the operations described in any one of the performance indicator analysis methods based on evidence synthesis theory in the above-mentioned embodiments.
[0087] The processor 410 implements any one of the performance indicator analysis methods based on evidence synthesis theory in the above embodiments by reading and executing computer program instructions stored in the memory 420 .
[0088] In one example, the electronic device may further include a communication interface 430 and a bus 400. Figure 5 As shown, the processor 410, the memory 420, and the communication interface 430 are connected via a bus 400 and communicate with each other.
[0089] The communication interface 430 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.
[0090] Bus 400 includes hardware, software or both, and the parts of online data flow billing equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-end bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 400 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.
[0091] In addition, in combination with the performance indicator analysis method based on evidence synthesis theory in the above embodiments, the embodiment of the present application can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any one of the performance indicator analysis methods based on evidence synthesis theory in the above embodiments is implemented.
[0092] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.
[0093] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.
[0094] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.
[0095] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.
[0096] In summary, compared with the prior art, this application has the following beneficial effects:
[0097] 1. In order to conduct performance indicator analysis, this application constructs a performance indicator evaluation system including a target layer and a multi-level indicator area, and sets up an identification framework. After obtaining the original data of the hospital outpatient department, the original data is mapped and processed into an evidence body to obtain an initial first reliability distribution. By synthesizing the first reliability distribution, the evaluation of hospital performance is achieved. The entire analysis and processing process is relatively simple, overcoming resource limitations and avoiding the consumption of a large amount of manpower and financial resources due to an overly complex and unreasonable evaluation process.
[0098] 2. Based on preset mapping rules and preset synthesis rules, this application provides clear evaluation criteria and indicators, which can provide decision makers with a more comprehensive and objective evidence basis, overcome the objectivity defects caused by the personal bias and judgment of evaluators in hospital performance evaluation; enhance the transparency and fairness of performance evaluation, enhance the scientific nature of decision-making, and improve hospital management efficiency and service resilience.
[0099] 3. This application selects specific, key, and quantifiable indicators to conduct a comprehensive evaluation of performance, which helps the hospital management to clearly understand the key issues and bottlenecks in hospital operations. Through the evaluation of hospital department performance, managers can identify problems in a timely manner and take corresponding measures, thereby improving the hospital's management efficiency and operational level.
[0100] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A performance indicator analysis method based on evidence synthesis theory, characterized in that: include: Constructing a performance indicator evaluation system, wherein the performance indicator evaluation system includes a target layer and a multi-level indicator area, wherein the target layer corresponds to the overall performance of the hospital; Determine a recognition framework, wherein the recognition framework is Θ={H1, H2, H3, H4, H5}={excellent, good, medium, low, poor}; According to the distribution characteristics of each indicator value, multiple quantiles corresponding to the constructed mapping rules are set; the multiple quantiles are respectively equivalent to excellent grade, good grade, medium grade, low grade and poor grade; Based on the mapping rule, the original hospital outpatient data corresponding to each indicator is mapped into an evidence body to obtain a first reliability distribution; Based on the first reliability distribution, a preset synthesis rule is used to evaluate hospital performance.
2. The performance indicator analysis method based on evidence synthesis theory as claimed in claim 1 is characterized in that: The multi-level indicator area is composed of a first-level indicator layer, a second-level indicator layer and a third-level indicator layer; the first-level indicator layer corresponds to department performance; the second-level indicator layer corresponds to medical quality and operational efficiency; the third-level indicator layer includes multiple third-level indicators corresponding to the medical quality and the operational efficiency respectively.
3. The performance indicator analysis method based on evidence synthesis theory as claimed in claim 2 is characterized in that: The three-level indicators corresponding to the medical quality include: number of discharges, diagnosis compliance rate for admission and discharge, average length of stay, cure rate, improvement rate, average hospitalization cost, proportion of hospitalization consumables, and cost of medical materials consumed per 100 yuan of medical income; the three-level indicators corresponding to the operational efficiency include bed occupancy rate, hospital infection rate, and proportion of hospitalization drugs.
4. The performance indicator analysis method based on evidence synthesis theory as claimed in claim 2 is characterized in that: The step of evaluating hospital performance based on the first reliability distribution using a preset synthesis rule includes: The first reliability distributions corresponding to the various indicators are synthesized by using a preset Murphy synthesis rule to obtain the second reliability distributions of the medical quality and the operational efficiency of each department respectively; Using the synthesis rule of DS evidence theory, the second reliability distribution of each department is synthesized respectively, and the total reliability distribution corresponding to each department is obtained to represent the comprehensive performance of each department; Based on the Murphy synthesis rule, the total reliability distribution corresponding to each department is synthesized to obtain the overall performance level of the hospital.
5. The performance indicator analysis method based on evidence synthesis theory as claimed in claim 1, characterized in that: The multiple quantiles include an indicator maximum value, a 75th quantile, a 50th quantile, a 25th quantile and a minimum value; the indicator maximum value, the 75th quantile, the 50th quantile, the 25th quantile and the minimum value correspond to the excellent grade, the good grade, the medium grade, the low grade and the poor grade, respectively.
6. The performance indicator analysis method based on evidence synthesis theory as claimed in claim 1, characterized in that: The DS evidence theory synthesis rule includes a basic credibility allocation stage and a Dempster synthesis stage; in the basic credibility allocation stage, the set function m:2 Θ →[0,1] satisfies: Where m is called the basic credibility assignment BPA on the recognition framework Θ; for any m(A) is called the basic credibility of A and is used to characterize the degree of support for proposition A by evidence.
7. The performance indicator analysis method based on evidence synthesis theory as claimed in claim 6 is characterized in that: In the Dempster synthesis stage, for Proposition m(A) satisfies the expression: Among them, m1(A1)m2(A2)···m n (A n ) is the n basic credibility assignments on the recognition framework Θ; m(A) represents m1(A1)m2(A2)···m n (A n ) is the degree of joint support of the corresponding evidence for proposition A, and K is the normalization coefficient and is used to characterize the degree of conflict between the evidences.
8. A performance indicator analysis system based on evidence synthesis theory, characterized in that: include: A construction module is used to construct a performance indicator evaluation system, wherein the performance indicator evaluation system includes a target layer and a multi-level indicator area, wherein the target layer corresponds to the overall performance of the hospital; A determination module is used to determine a recognition framework, wherein the recognition framework is Θ={H1, H2, H3, H4, H5}={excellent, good, medium, low, poor}; A setting module, used to set a plurality of quantiles corresponding to the constructed mapping rules according to the distribution characteristics of each indicator value; the plurality of quantiles are respectively equivalent to excellent grade, good grade, medium grade, low grade and poor grade; A mapping module, used to map the obtained original hospital outpatient data corresponding to each indicator into an evidence body based on the mapping rule to obtain a first reliability distribution; An evaluation module is used to evaluate hospital performance based on the first reliability distribution using a preset synthesis rule.
9. An electronic device, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the performance indicator analysis method based on evidence synthesis theory as described in any one of claims 1 to 7 is implemented.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores programs or instructions, and when the programs or instructions are executed by the processor, the performance indicator analysis method based on evidence synthesis theory as described in any one of claims 1 to 7 is implemented.