Hospital logistics management mode evaluation method based on AHP-fuzzy comprehensive evaluation method

Abstract

The application discloses a hospital logistics management mode evaluation method based on an AHP-fuzzy comprehensive evaluation method, relates to the technical field of hospital management, and comprises the following steps: S1, index system construction; S2, weight determination; S3, fuzzy evaluation; and S4, result output.In the application, the logistics staff satisfaction is included in the core evaluation dimension, three-dimensional evaluation of man-performance-quality is realized, 13 subdivided indexes are covered, the overall operation condition of the hospital logistics management mode is comprehensively and systematically reflected, the evaluation result is more complete and representative, the AHP and the FCE method are combined, the logic of subjective judgment is utilized by the AHP, the objectivity of fuzzy information is utilized by the FCE, the complex logistics management condition is converted into specific scores, the evaluation result is intuitive and comparable, and the core weak link is determined by combining the weight analysis, so that data-driven decision-making bases are provided for hospital logistics management optimization, resource allocation and system reform.

Description

Technical Field

[0001] This invention relates to the field of hospital management technology, specifically to an evaluation method for hospital logistics management models based on the AHP-fuzzy comprehensive evaluation method. Background Technology

[0002] As my country's medical system reform continues to deepen and the operational model of public hospitals is constantly being optimized and upgraded, logistics management, as a crucial support for the overall hospital operation system, is facing an urgent need to transform from traditional extensive management to refined, digitalized, and intelligent management. Public hospital logistics encompasses multiple key aspects such as personnel support, material supply, equipment maintenance, environmental management, and safety and emergency response. Its management level directly affects the quality of medical services, patient experience, and overall hospital operational efficiency.

[0003] Currently, many public hospitals still have significant shortcomings in evaluating their logistics management models, mainly manifested in the strong subjectivity of the evaluation process and the lack of systematic quantitative indicators. Traditional evaluation methods often rely excessively on single financial indicators (such as budget execution rate and cost control level) or use only simple qualitative descriptions (such as vague statements like "good management" or "average service"). They fail to organically integrate and comprehensively consider the three core dimensions of logistics staff satisfaction, logistics department performance evaluation, and logistics work quality. The resulting evaluations are difficult to accurately and comprehensively reflect the overall operation and inherent strengths and weaknesses of the logistics management model, nor can they accurately identify shortcomings and weaknesses in management, let alone provide targeted scientific basis for management optimization and decision-making improvement. Therefore, there is an urgent need for an evaluation method that can comprehensively consider multiple factors and is both scientific and practical. Summary of the Invention

[0004] The purpose of this invention is to provide an evaluation method for hospital logistics management models based on the AHP-fuzzy comprehensive evaluation method. This addresses the problem that traditional evaluation methods often rely excessively on single financial indicators or use only simple qualitative descriptions, failing to organically integrate and comprehensively consider the three core dimensions of logistics staff satisfaction, logistics department performance evaluation, and logistics work quality. Consequently, the evaluation results cannot truly and comprehensively reflect the overall operation and inherent advantages and disadvantages of the logistics management model, and cannot provide targeted scientific basis for management optimization and decision improvement.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for evaluating hospital logistics management models based on AHP-fuzzy comprehensive evaluation, comprising the following steps:

[0006] S1. Indicator System Construction: Establish an indicator system that includes three primary indicators and thirteen secondary indicators;

[0007] S2. Weight Determination: Using AHP, the combined weights of the primary and secondary indicators are determined by constructing a judgment matrix, calculating eigenvectors, and performing consistency checks.

[0008] S3, Fuzzy Evaluation: Set a set of comments Based on the score range, a fuzzy evaluation model is established to determine the membership degree and construct the membership degree matrix. ;

[0009] S4. Output Results: Combine the weights with... Perform fuzzy computation, determine the evaluation level based on the principle of maximum membership degree, and output the comprehensive evaluation score.

[0010] Preferred, primary indicators The satisfaction of logistics staff is assessed using three qualitative indicators: salary and benefits, job content and environment, and career development and promotion.

[0011] Primary indicators The performance evaluation indicators for the logistics department consist of five sub-indicators: departmental budget execution rate, medical and patient satisfaction, departmental work plan completion rate, employee turnover rate, and annual training participation rate.

[0012] Primary indicators The evaluation indicators for the quality of logistics work include five sub-indicators: personnel structure, degree of implementation of regulations, degree of archiving, equipment operation compliance rate, and emergency management qualification rate.

[0013] Preferably, the specific process for determining the indicator weights in S2 includes:

[0014] S21: Construct an expert questionnaire using the 1-9 level scaling method, and invite experts with associate senior or higher professional titles to compare the importance of the indicators pairwise.

[0015] S22: Collect questionnaire data and construct a judgment matrix;

[0016] S23: Calculate the largest eigenvalue of the judgment matrix and its corresponding eigenvector, and use them as the single sorting weight;

[0017] S24: Perform a consistency check; when the consistency ratio... When <0.1, the determination matrix is ​​valid;

[0018] S25: The effective data from multiple experts are aggregated using the arithmetic mean method to obtain the combined weights of each indicator.

[0019] Preferably, in the 1-9 level scaling method of S21, 1 represents equal importance, 3 represents slightly important, 5 represents significantly important, 7 represents strongly important, 9 represents extremely important, and 2, 4, 6, and 8 represent the intermediate values ​​of adjacent judgments.

[0020] Preferably, in S25, the resulting combination weights are: Among them, salary and benefits have the highest weighting, at 0.15896; Among them, the departmental budget execution rate has the highest weight, at 0.15974; .

[0021] Preferably, when establishing the fuzzy evaluation model in S3, the comment set Specifically, it includes:

[0022] S31: Collection of Comments Corresponding to The score ranges for each level are: Excellent (4.5–5.0), Good (3.5–4.4), Average (2.5–3.4), Poor (1.5–2.4), and Very Poor (1.0–1.4), with corresponding score vectors. ;

[0023] S32: For quantitative indicators, establish membership functions based on historical hospital data or industry standards; for qualitative indicators, determine membership degrees using expert scoring and construct a membership degree matrix. .

[0024] Preferably, in S32, when establishing the membership function for the quantitative index:

[0025] The formulas for calculating positive indicators (budget execution rate, plan completion rate, training participation rate, equipment compliance rate, and emergency response qualification rate) are as follows:

[0026] ;

[0027] The formula for calculating the reverse indicator (employee turnover rate) is as follows:

[0028] .

[0029] Preferably, the combined weights in S4 are... The expression for performing fuzzy operations is as follows:

[0030] ;

[0031] in, This is a vector representing the comprehensive evaluation results. For the weight vector of the indicator combination, This is a fuzzy evaluation matrix.

[0032] Preferably, in S4, the final comprehensive score is obtained through weighted calculation:

[0033] ;

[0034] in, This is the transpose of the score vector for the comment set.

[0035] Preferably, when using the maximum membership principle to determine the level in S4, if the membership of two levels is equal, the level with the higher score is taken; if the score of a secondary indicator is less than 2.5 points, it is determined to be a weak indicator.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] 1. In this invention, "satisfaction of logistics staff" is incorporated into the core evaluation dimension, breaking the limitations of traditional focus on only financial or hard indicators. It realizes a three-dimensional evaluation of "people-performance-quality", covering 13 sub-indicators, comprehensively and systematically reflecting the overall operation of the hospital's logistics management model, making the evaluation results more complete and representative. Combining AHP and FCE methods, it utilizes both AHP to handle the logic of subjective judgment and FCE to handle the objectivity of fuzzy information, transforming the complex logistics management situation into specific scores, making the evaluation results intuitive and comparable.

[0038] 2. In this invention, by calculating the scores of secondary indicators, a threshold of 2.5 points (lower middle limit) is used to accurately identify the weak indicators. Combined with weight analysis, the core weak links are identified, which solves the pain point of traditional evaluation that "only gives conclusions but does not point out problems". This provides a data-driven decision-making basis for hospital logistics management optimization, resource allocation and system reform. Attached Figure Description

[0039] Figure 1 This is a system diagram of the evaluation index structure of a hospital logistics management model evaluation method based on AHP-fuzzy comprehensive evaluation method according to the present invention.

[0040] Figure 2 This is a flowchart of a hospital logistics management model evaluation method based on AHP-fuzzy comprehensive evaluation method according to the present invention;

[0041] Figure 3 This is a flowchart illustrating the AHP weight determination process for a hospital logistics management model evaluation method based on the AHP-fuzzy comprehensive evaluation method, as described in this invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Example: Refer to Figures 1-3The invention presents an evaluation method for hospital logistics management based on the AHP-fuzzy comprehensive evaluation method. Public Hospital A is selected as the evaluation object. This hospital adopts a "semi-closed, semi-socialized" logistics management model. The method of this invention diagnoses and evaluates the current status of the hospital's logistics management, including the following steps:

[0044] Step 1: Constructing the indicator system: Construct an evaluation indicator system, which includes three primary indicators and thirteen secondary indicators.

[0045] The selection of indicators followed two principles: first, scientific validity, meaning the indicators should accurately reflect the objective situation of public hospital logistics management, align with the "semi-closed, semi-socialized" model, and be independent and non-overlapping; second, feasibility, meaning the data sources for the indicators should be clear, the statistical definitions should be precise, the evaluation process should be reproducible, no special tools or unpublished data should be required, and the indicators should be suitable for the actual operational scenarios of hospitals. After literature review, expert consultation, and feasibility verification, the primary indicators were ultimately determined to be: logistics staff satisfaction, logistics department performance evaluation indicators, and logistics work quality evaluation indicators, namely... , , ;

[0046] Further refine the secondary indicators:

[0047] The following three qualitative indicators are set:

[0048] Compensation and benefits: including five dimensions: basic salary, performance-based salary, social security and housing provident fund, year-end bonus, and subsidies; a five-point scale questionnaire is used, which is filled out by logistics staff and the average value is taken.

[0049] Work content and environment: including four dimensions: work intensity, job matching degree, office environment, and safety protection; a five-level questionnaire, filled out by logistics staff, and the average value is taken.

[0050] Development and Promotion: Includes three dimensions: promotion channels, training opportunities, and career planning guidance; a 5-level questionnaire, filled out by logistics staff, with the average score taken.

[0051] It includes 5 indicators:

[0052] Departmental budget execution rate (quantitative): Calculation formula = Annual actual expenditure / Annual budget × 100%; Data is taken from the hospital's annual financial statement, and the statistical period is the calendar year.

[0053] Medical staff and patient satisfaction (qualitative): Based on the annual satisfaction survey data from the hospital's quality control department, the frequency is counted according to five levels: excellent, good, average, poor, and very poor.

[0054] Departmental work plan completion rate (quantitative): Calculation formula = Annual completed work items / Annual planned work items × 100%; Data taken from the logistics department's annual work summary report.

[0055] Employee turnover rate (quantitative, inverse indicator): Calculation formula = number of employees leaving in a year / average number of employees on duty in a year × 100%; data is taken from the personnel department's personnel ledger.

[0056] Annual training participation rate (quantitative): Calculation formula = (Actual number of trainees per year / Number of trainees required per year) × 100%; Data is taken from the personnel department's training records.

[0057] It includes 5 indicators:

[0058] Personnel structure (qualitative): includes four dimensions: age structure, education structure, professional title structure, and job ratio; and is scored on five levels after on-site verification by 10 experts.

[0059] The degree of implementation of the system (qualitative): includes three dimensions: system completeness, process compliance, and implementation standardization; 10 experts conducted on-site verification and scored it on 5 levels.

[0060] Archiving level (qualitative): includes three dimensions: document completeness, classification standardization, and ease of access; scored on 5 levels after on-site verification by 10 experts.

[0061] Equipment operation compliance rate (quantitative): Calculation formula = annual qualified equipment operating time / annual total equipment operating time × 100%; data is taken from the equipment department's operation and maintenance records.

[0062] Emergency management pass rate (quantitative): Calculation formula = Number of qualified emergency drills per year / Total number of emergency drills per year × 100%; Data is taken from the logistics emergency management ledger.

[0063] Step 2: Weight Determination: The weights are determined using the Analytic Hierarchy Process (AHP). The specific process is as follows:

[0064] a. Expert Survey: A questionnaire was designed using a 1-9 scale, where 1 indicates equal importance, 3 indicates slightly important, 5 indicates significantly important, 7 indicates strongly important, 9 indicates extremely important, and 2, 4, 6, and 8 represent the median values ​​of adjacent judgments. Five hospital management experts with associate senior professional titles or above (including experts from the Provincial Health Commission and the logistics director of a tertiary hospital, etc.) were invited to conduct pairwise comparisons and scores on the importance of each indicator.

[0065] b. Matrix Construction and Testing: Collect questionnaire data, construct a judgment matrix, and calculate the largest eigenvalue of the judgment matrix. and the corresponding eigenvectors.

[0066] When constructing the judgment matrix, according to the reciprocal rule of the scaling method, the expert scores are transformed into a reciprocal judgment matrix. Taking the first-level indicator as an example, after experts compare each indicator pairwise, a third-order reciprocal judgment matrix is ​​constructed, with its diagonal elements being 1, satisfying the following condition. (i.e., indicators) relative to indicators The importance of indicators relative to indicators (The importance of each other is reciprocal)

[0067] Normalize the judgment matrix: First, sum the matrix column by column, then divide each element by the sum of its corresponding column, and then calculate the average of the elements in each row (i.e., row sum normalization) to obtain the eigenvectors. Calculate the largest eigenvalue.

[0068] ;

[0069] In the formula, This is the original judgment matrix. For the weight vector, is the order of the matrix.

[0070] c. Consistency check: Calculate the consistency index and random consistency ratio , set when At that time, it is considered that the judgment matrix has satisfactory consistency and the weight allocation is effective;

[0071] and The calculation formula is as follows:

[0072] ;

[0073] in, The average random consistency index has the following values: =1 =0; =2 =0; =3 =0.58; =4 =0.90; =5 o'clock =1.12; =6 o'clock =1.24; =7 o'clock =1.32; =8 o'clock =1.41; =9 o'clock =1.45; if If the score is ≥0.1, the consistency is not up to standard and the score needs to be reassessed by experts.

[0074] d. Weighted aggregation: After 5 experts complete their scoring, the remaining experts are eliminated. For invalid questionnaires with a value ≥0.1, the remaining valid weights are aggregated using the arithmetic mean method to obtain the final combined weights of each indicator.

[0075] e. Indicator Weight Results: Calculate the combined weights of the primary and secondary indicators separately:

[0076] When calculating the combined weights of primary indicators, let the total number of indicators be... The number of valid experts (passing the consistency test), the first The weight vector given by the experts is The final combined weights are the arithmetic mean:

[0077] ;

[0078] Among them, for The weighted average is 0.42222. The weighted average is 0.44856. The weighted average is 0.12922.

[0079] When calculating the combined weights of secondary indicators, the primary indicator is set. The global weight is Its subordinate The local weights of the secondary indicators are: The global combined weight of the secondary indicators is then:

[0080] ;

[0081] When the first-level weight The partial weight of compensation and benefits Therefore, the overall weight of compensation and benefits is approximately 0.42222 × 0.3765 ≈ 0.15896; similarly, the local weight of departmental budget execution rate is... If we set it to 0.3561, then its global weight = 0.44856 × 0.3561 ≈ 0.15974.

[0082] Step 3: Fuzzy Evaluation: Setting up a set of comments Based on the score range, the membership degree is determined through membership functions (quantitative indicators) or expert scoring / questionnaire surveys (qualitative indicators), and a membership degree matrix is ​​constructed. .

[0083] Establishing a fuzzy evaluation model: defining a set of evaluation comments , respectively corresponding The score ranges for each level are: Excellent (4.5–5.0), Good (3.5–4.4), Average (2.5–3.4), Poor (1.5–2.4), and Very Poor (1.0–1.4), with corresponding score vectors. .

[0084] Membership degree determination: For quantitative indicators (such as budget execution rate and equipment operation compliance rate), membership functions are established based on historical hospital data or industry standards; for qualitative indicators (such as work environment and system implementation), expert scoring or questionnaire surveys are used to determine their membership degree vectors, and a membership degree matrix is ​​constructed. .

[0085] (1) Expert scoring method: For each qualitative indicator, 10 experts with relevant professional backgrounds are organized. Each expert independently selects one level according to the evaluation set. The number of experts selected for each level is counted. Then the membership degree of each level is the number of people selected for that level divided by the total number of experts 10. Thus, the 1×5 membership degree row vector corresponding to the qualitative indicator is obtained.

[0086] (2) Questionnaire survey method: For indicators directly derived from logistics personnel (such as "salary and benefits", "work content and environment" and "development and promotion"), a 5-point scale questionnaire was used and filled out by all logistics personnel. After collecting the questionnaires, the frequency of selection for each rating level (excellent, good, average, poor, and very poor) was counted, and the membership degree was determined by the proportion of the frequency of that rating level to the total number of respondents.

[0087] (3) The "satisfaction of medical staff and patients" data provided by the hospital quality control department has been statistically analyzed according to five levels: excellent, good, medium, poor and bad. The frequency of each level can be directly normalized and used as the membership vector.

[0088] For quantitative indicators, a continuous membership function is constructed based on the nature of the indicator (positive or negative). The actual measured value is transformed into a membership vector belonging to five rating levels. The standard thresholds for positive indicators are set as follows: Excellent corresponds to ≥95%, Good corresponds to 90%-95%, and Average, Poor, and Very Poor correspond to lower ranges respectively. The standard thresholds for negative indicators are set as follows: Excellent corresponds to ≤5%, Good corresponds to 5%-10%, and Very Poor corresponds to >10%. The calculation formula is as follows:

[0089] Positive indicators (such as budget execution rate, plan completion rate, training participation rate, equipment compliance rate, and emergency response qualification rate):

[0090] ;

[0091] Converse indicators (such as employee turnover rate):

[0092] ;

[0093] Substituting the actual data of each quantitative indicator in the hospital into the above function, the membership degree value corresponding to each indicator for five levels—excellent, good, average, poor, and very poor—is calculated, forming a 1×5 row vector. This process is repeated for each quantitative indicator, generating one row per indicator. Finally, the row vectors of all qualitative indicators are concatenated vertically with the row vectors of all quantitative indicators in order of indicator sequence, forming a complete 13×5 fuzzy membership degree matrix. .

[0094] Step 4: Output Results: Calculate the overall score through fuzzy synthesis and output the evaluation results.

[0095] Fuzzy composition operation: combining weight vectors With membership matrix Perform fuzzy synthesis:

[0096] ;

[0097] in, This is the vector of comprehensive evaluation results.

[0098] Score Calculation: Multiply the result vector by the comment score vector to calculate the final comprehensive score. :

[0099] ;

[0100] in, This is the transpose of the score vector for the comment set.

[0101] The principle of maximum membership degree is used to determine the comprehensive evaluation level: when two levels have the same membership degree value, the level with the higher score is selected first to ensure the positive guidance of the evaluation results; at the same time, a secondary indicator score below 2.5 points (lower limit of the medium level) is set as a weak indicator.

[0102] Evaluation Results: Based on this method, the comprehensive evaluation score for the logistics management model of Public Hospital A is 3.41408. According to the principle of maximum membership, this comprehensive score corresponds to a medium evaluation level, reflecting that the hospital's overall logistics management is at a medium level and has not yet reached the good standard. Further analysis of the scores of each secondary indicator revealed that the scores of salary and benefits and departmental budget execution rate are significantly lower than the medium threshold, representing core weaknesses.

[0103] The results fully validate that this method can achieve accurate diagnosis from overall evaluation to the identification of shortcomings, effectively solving the problems of traditional evaluation being difficult to quantify and unable to accurately identify weak links. It provides scientific and quantitative decision support for the hospital to optimize the salary incentive mechanism, strengthen budget control, and improve the level of refined logistics management.

[0104] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating hospital logistics management models based on AHP-fuzzy comprehensive evaluation, characterized in that: Includes the following steps: S1. Indicator System Construction: Establish an indicator system that includes three primary indicators and thirteen secondary indicators; S2. Weight Determination: Using AHP, the combined weights of primary and secondary indicators are determined by constructing a judgment matrix, calculating eigenvectors, and performing consistency checks. S3, Fuzzy Evaluation: Set a set of comments Based on the score range, a fuzzy evaluation model is established to determine the membership degree and construct the membership degree matrix. ; S4. Output Results: Combine the weights with... Perform fuzzy computation, determine the evaluation level based on the principle of maximum membership degree, and output the comprehensive evaluation score.

2. The hospital logistics management model evaluation method based on AHP-fuzzy comprehensive evaluation method according to claim 1, characterized in that: In S1, the primary indicator To assess the satisfaction of logistics staff, three qualitative indicators are set: salary and benefits, job content and environment, and career development and promotion. Primary indicators The performance evaluation indicators for the logistics department are divided into five sub-indicators: departmental budget execution rate, medical staff and patient satisfaction, departmental work plan completion rate, employee turnover rate, and annual training participation rate. Primary indicators The evaluation indicators for the quality of logistics work include five sub-indicators: personnel structure, degree of implementation of regulations, degree of archiving, equipment operation compliance rate, and emergency management qualification rate.

3. The hospital logistics management model evaluation method based on AHP-fuzzy comprehensive evaluation method according to claim 1, characterized in that: The specific process for determining the indicator weights in S2 includes: S21: Construct an expert questionnaire using the 1-9 level scaling method, and invite experts with associate senior or higher professional titles to compare the importance of the indicators pairwise. S22: Collect questionnaire data and construct a judgment matrix; S23: Calculate the largest eigenvalue of the judgment matrix and its corresponding eigenvector, and use them as the single sorting weight; S24: Perform a consistency check; when the consistency ratio... When <0.1, the determination matrix is ​​valid; S25: The effective data from multiple experts are aggregated using the arithmetic mean method to obtain the combined weights of each indicator.

4. The hospital logistics management model evaluation method based on AHP-fuzzy comprehensive evaluation method according to claim 3, characterized in that: In the 1-9 scale of S21, 1 indicates equal importance, 3 indicates slightly important, 5 indicates obviously important, 7 indicates strongly important, 9 indicates extremely important, and 2, 4, 6, and 8 represent the median values ​​of adjacent judgments.

5. The hospital logistics management model evaluation method based on AHP-fuzzy comprehensive evaluation method according to claim 3, characterized in that: In S25, the resulting combined weights are the weights of the first-level indicators: , , .

6. The hospital logistics management model evaluation method based on AHP-fuzzy comprehensive evaluation method according to claim 1, characterized in that: When establishing a fuzzy evaluation model in S3, the comment set Specifically, it includes: S31: Collection of Comments Corresponding to The score ranges for each level are: Excellent (4.5–5.0), Good (3.5–4.4), Average (2.5–3.4), Poor (1.5–2.4), and Poor (1.0–1.4). S32: For quantitative indicators, calculate the membership degree based on the membership function; for qualitative indicators, determine the membership degree through expert scoring or questionnaire surveys, and construct a membership degree matrix. .

7. The hospital logistics management model evaluation method based on AHP-fuzzy comprehensive evaluation method according to claim 6, characterized in that: In S32, when establishing membership functions for quantitative indicators: The formula for calculating positive indicators is: ； The formula for calculating the contrarian indicator is: 。 8. The evaluation method for hospital logistics management model based on AHP-fuzzy comprehensive evaluation method according to claim 1, characterized in that: Combination weights in S4 The expression for performing fuzzy operations is as follows: ; in, This is a vector representing the comprehensive evaluation results. For the weight vector of the indicator combination, This is a fuzzy evaluation matrix.

9. The hospital logistics management model evaluation method based on AHP-fuzzy comprehensive evaluation method according to claim 1, characterized in that: In S4, the final overall score is calculated using a weighted average: ； in, This is the transpose of the score vector for the comment set.

10. The hospital logistics management model evaluation method based on AHP-fuzzy comprehensive evaluation method according to claim 9, characterized in that: When using the maximum membership principle to determine the level in S4, if the membership of two levels is equal, the level with the higher score is taken; if the score of a secondary indicator is less than 2.5, it is determined to be a weak indicator.

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