Medical institution medical consumable access selection evaluation method and system

Abstract

The invention discloses a medical institution medical consumable access selection evaluation method and system, and relates to the technical field of medical systems. Comprising the following steps: responding to an evaluation request of a target consumable, generating an evaluation task according to an evaluation index and an evaluation object, and distributing the evaluation task to the corresponding evaluation object; the evaluation indexes comprise a clinical value, an economic value, a patient value and a management value, and performing importance scoring on each index to obtain a corresponding weight of each evaluation index; collecting scoring results of all the evaluation objects on the target consumable, and performing linear weighting on the scoring results of the evaluation indexes according to the corresponding weights to obtain an evaluation result of the target consumable; by constructing a multi-dimensional dynamic evaluation system, an intelligent data fusion mechanism and an automatic task distribution mechanism, the manual process cycle is greatly shortened, the limitation of traditional single-dimensional evaluation is broken through, a linear weighted model is utilized to fuse multi-dimensional scoring results, the scientificity and efficiency of medical consumable admission selection are improved, and the clinical application quality of consumables is improved.

Description

Technical Field

[0001] This application relates to the technical field of medical systems, and particularly to a method and system for evaluating the access and selection of medical consumables in medical institutions. Background Art

[0002] With the rapid development of medical technology, the types and functions of medical consumables have become increasingly complex, and their importance in clinical diagnosis and treatment has been significantly improved. Their access and selection are directly related to medical quality, patient safety, and hospital operation efficiency. Traditional access and selection methods mostly rely on expert experience judgment or single economic indicators, such as price negotiation, and there are problems of fragmented evaluation dimensions, weak data support, and lagging dynamic response. For example, the existing technology generally adopts a static scoring card model (such as the paper scoring form used by some hospitals), which is difficult to quantify the clinical operation adaptability of consumables; the supplier evaluation system only focuses on qualification compliance and lacks full-life-cycle monitoring of supply stability and quality reliability; in addition, the phenomenon of data islands is serious, and the HIS system usage records, instruction manual technical parameters, and supply chain data of consumables are not connected, resulting in one-sided decision-making basis.

[0003] Therefore, there is an urgent need for a method and system for evaluating the access and selection of medical consumables in medical institutions. Through a multi-dimensional dynamic evaluation system, different weights are assigned to different indicators to achieve accurate quantification of evaluation indicators. Through the construction of a structured indicator pool (clinical / economic / patient / management value), weight consistency verification based on the coefficient of variation, and a dynamic adjustment mechanism of a linear weighting model, the decision-making mode is transformed from experience-driven to data-driven, effectively improving the scientificity and efficiency of medical resource allocation. Summary of the Invention

[0004] Aiming at the problems that the current access and selection methods of medical consumables mostly rely on expert experience judgment or single economic indicators, with fragmented evaluation dimensions, weak data support, and lagging dynamic response, this application provides a method and system for evaluating the access and selection of medical consumables in medical institutions, and realizes the transformation of the decision-making mode from experience-driven to data-driven through a multi-dimensional dynamic evaluation system. The specific technical solutions are as follows: In the first aspect of this application, a method for evaluating the access and selection of medical consumables in medical institutions is provided, including: Responding to an evaluation request for a target consumable, generating an evaluation task according to evaluation indicators and evaluation objects, and distributing the evaluation task to the corresponding evaluation objects; The evaluation indicators include clinical value, economic value, patient value, and management value. The importance of each indicator is scored to obtain the corresponding weight of each evaluation indicator; Collecting the scoring results of all evaluation objects for the target consumable, and linearly weighting the scoring results of each evaluation indicator according to the corresponding weight to obtain the evaluation result of the target consumable.

[0005] In an embodiment of the present application, the evaluation index constructs a hierarchical structure model. The hierarchical structure model is hierarchically associated from the high level to the low level according to the order of the target layer, the intermediate layer, and the measure layer. The target layer is clinical value, economic value, patient value, and management value. One or more intermediate layers are constructed according to requirements, and one or more low-level indicators are set for each high-level indicator. Importance scores are given to the indicators in each level, and the weights of the indicators in each level are calculated based on the analytic hierarchy process.

[0006] In an embodiment of the present application, the intermediate layer and measure layer indicators are semantically vectorized using a BERT pre-trained model. By calculating the cosine similarity between the evaluation index name and the medical scenario keywords, the indicators with a cosine similarity greater than the preset threshold are included in the candidate set; Calculate the partial regression coefficient of each evaluation index. When the partial regression coefficient of the evaluation index is less than the set threshold, it is automatically excluded as an inefficient index.

[0007] In an embodiment of the present application, the weight of the evaluation index is determined using a mixed weight calculation model. The mixed weight calculation model includes static weight, objective weight, and dynamic weight. The static weight is correspondingly determined by conducting importance scoring on experts using the Delphi consultation method. The objective weight is determined according to the qualifications, price comparison, and number of safety incidents included in the medical consumables themselves. The dynamic weight is determined according to different usage scenarios of the medical consumables. Different usage scenarios include the operating room and the outpatient clinic.

[0008] In an embodiment of the present application, the measure layer corresponding to the clinical value includes the ease of operation. Obtain the scores of all evaluation objects on the ease of operation of the target consumables as the first score; When the first score is lower than the full score, vectorize the operation step text in the target consumable manual and extract the core operation features; establish a knowledge graph of the operation methods of the existing consumables in the medical institution, and store the operation steps, training duration, and operation error rate data of the historical consumables; Use the cosine similarity algorithm to calculate the similarity between the target consumable and the operation features of the existing consumables in the medical institution. When the similarity is greater than the preset value, obtain the second score. The sum of the first score and the second score is used as the scoring result of the ease of operation.

[0009] In an embodiment of the present application, before calculating the similarity between the target consumable and the existing consumables, it also includes a preliminary screening of the existing consumables to form a comparison set for the target consumable. The target consumable calculates the similarity only with the existing consumables in the comparison set. The screening conditions include: Usage department matching degree: According to the expected usage department of the target consumable, extract the historical usage records of the corresponding department from the existing consumable library; Consumable type matching degree: Conduct similar screening according to the medical device classification catalog; Purpose-related matching degree: Based on the fields in the consumable instruction manual, extract the keywords of the treatment purpose, and construct a purpose feature vector for initial similarity screening; Weight screening: Set the priority of the screening conditions as department matching degree > consumable type > purpose-related. Sort the comprehensive matching degrees of the existing consumables, and select the preset number of existing consumables with the highest matching degree to enter the final comparison set.

[0010] In an embodiment of the present application, the measure layer corresponding to the management value includes supplier behavior monitoring, and conduct behavior monitoring on all medical consumables supplied by the same supplier, specifically including: Service ability evaluation: Statistically calculate the delivery on-time rate and return and exchange response time of the supplier's historical orders, and calculate the service score through the Logistic regression model; Supply stability monitoring: Establish a risk warning model for supplier out-of-stock. When the inventory satisfaction rate is lower than the preset value for 3 consecutive months, deduct the scores of all medical consumables supplied by the supplier.

[0011] In an embodiment of the present application, the supplier behavior monitoring further includes quality stability monitoring, specifically including: By parsing the medical consumable instruction manual, extract the features of the parameters related to the usage duration, and construct a standard life feature vector; Mark the sterilization times threshold for reusable consumables to generate a dynamic life comparison table; Collect actual usage data, and record the clinical usage times, single-use duration, and sterilization cycle times of the consumables in real time; Establish a consumable full life cycle database, and associate multi-source data including surgical records and equipment logs to verify the authenticity of the usage; Stability comparison and analysis, calculate the deviation degree between the actual service life and the nominal value in the instruction manual: Scoring linkage mechanism, qualified consumables trigger positive incentives for suppliers, the management value scores of the consumables in the current supply catalog are uniformly increased, and the initial scores are increased by the benchmark value when new consumables are admitted later; Unqualified consumables implement punitive measures, the management value scores of the consumables in the current supply catalog are uniformly reduced, and the initial scores are reduced by the benchmark value when new consumables are admitted later.

[0012] In the second aspect of the present application, a medical consumable access selection and evaluation system for medical institutions is provided, including: Task generation module, in response to the evaluation request of the target consumable, generate an evaluation task according to the evaluation index and the evaluation object, and distribute the evaluation task to the corresponding evaluation object; Weight generation module, the evaluation indexes include clinical value, economic value, patient value, and management value. Score the importance of each index to obtain the corresponding weights of each evaluation index; The evaluation generation module collects the scoring results of all evaluation objects for the target consumables, linearly weights the scoring results of each evaluation index according to the corresponding weights, and obtains the evaluation result of the target consumables.

[0013] In an embodiment of the present application, the weight generation module further includes an index structure sub-module, which constructs a hierarchical structure model. The hierarchical structure model is hierarchically associated from high level to low level according to the order of the target layer, intermediate layer, and measure layer. The target layer is clinical value, economic value, patient value, and management value. One or more intermediate layers are constructed according to requirements, one or more low-level indicators are set for each high-level indicator, the importance of the indicators in each level is scored, and the weights of the indicators in each level are calculated based on the analytic hierarchy process.

[0014] In an embodiment of the present application, the index structure sub-module further includes an index elimination sub-module. The intermediate layer and measure layer indicators are semantically vectorized using the BERT pre-trained model. By calculating the cosine similarity between the evaluation index name and the medical scenario keywords, the indicators with a cosine similarity greater than the preset threshold are included in the candidate set; Calculate the partial regression coefficient of each evaluation index. When the partial regression coefficient of the evaluation index is less than the set threshold, it is automatically eliminated as an inefficient index.

[0015] In an embodiment of the present application, the weight generation module further includes a mixed weight sub-module. The weight of the evaluation index is determined using a mixed weight calculation model. The mixed weight calculation model includes static weight, objective weight, and dynamic weight. The static weight is correspondingly determined by scoring the importance of experts using the Delphi consultation method. The objective weight is determined according to the qualifications, price comparison, and number of safety incidents of the medical consumables themselves. The dynamic weight is determined according to different usage scenarios of the medical consumables. Different usage scenarios include the operating room and outpatient clinic.

[0016] In an embodiment of the present application, the weight generation module further includes an operation scoring sub-module. The measure layer corresponding to the clinical value includes the operation difficulty level. Obtain the scores of all evaluation objects for the operation difficulty level of the target consumables as the first score; When the first score is lower than the full score, vectorize the operation step text in the target consumable manual and extract the core operation features; establish a knowledge graph of the operation methods of existing consumables in medical institutions, and store the operation steps, training duration, and operation error rate data of historical consumables; Use the cosine similarity algorithm to calculate the similarity between the operation features of the target consumable and the existing consumables in medical institutions. When the similarity is greater than the preset value, obtain the second score. The sum of the first score and the second score is used as the scoring result of the operation difficulty level.

[0017] In an embodiment of the present application, the operation scoring sub-module further includes a consumable screening sub-module. Before calculating the similarity between the target consumable and the existing consumables, it also includes a preliminary screening of the existing consumables to form a comparison set for the target consumable. The target consumable calculates the similarity only with the existing consumables in the comparison set. The screening conditions include: Usage department matching degree: According to the expected usage department of the target consumable, extract the historical usage records of the corresponding department from the existing consumable library; Consumable type matching degree: Conduct similar screening according to the medical device classification catalog; Purpose correlation matching degree: Based on the fields in the consumable instruction manual, extract the keywords of the treatment purpose, and construct a purpose feature vector for preliminary similarity screening; Weight screening: Set the priority of the screening conditions as department matching degree > consumable type > purpose correlation. Sort the existing consumables according to the comprehensive matching degree, and select the preset number of existing consumables with the highest matching degree to enter the final comparison set.

[0018] In an embodiment of the present application, the weight generation module further includes a supplier monitoring sub-module. The measure layer corresponding to the management value includes supplier behavior monitoring, and conducts behavior monitoring on all medical consumables supplied by the same supplier. Specifically, it includes: Service ability evaluation: Statistically calculate the delivery on-time rate and return and replacement response time of the supplier's historical orders, and calculate the service score through a Logistic regression model; Supply stability monitoring: Establish a supplier out-of-stock risk warning model. When the inventory satisfaction rate is lower than the preset value for 3 consecutive months, deduct the scores of all medical consumables supplied by the supplier.

[0019] In an embodiment of the present application, the supplier monitoring sub-module further includes a quality stability sub-module. The supplier behavior monitoring also includes quality stability monitoring. Specifically, it includes: By parsing the medical consumable instruction manual, extract the features of the parameters related to the usage duration, and construct a standard life feature vector; Mark the sterilization times threshold for reusable consumables to generate a dynamic life comparison table; Collect actual usage data, and record the clinical usage times, single usage duration, and sterilization cycle times of the consumables in real time; Establish a consumable full life cycle database, and associate multi-source data including surgical records and equipment logs to verify the authenticity of usage; Stability comparison and analysis, calculate the deviation degree between the actual service life and the nominal value in the instruction manual: The scoring linkage mechanism triggers positive incentives for suppliers when consumables meet the standards, uniformly improves the management value scores of consumables in the current supply catalog, and increases the initial score by the benchmark value when new consumables are admitted in the future; for consumables that do not meet the standards, punitive measures are implemented, the management value scores of consumables in the current supply catalog are uniformly reduced, and the initial score is reduced by the benchmark value when new consumables are admitted in the future.

[0020] The present application has the following beneficial effects: 1. By constructing a multi-dimensional dynamic evaluation system and an intelligent data fusion mechanism, the scientificity, efficiency, and risk control ability of the medical consumable admission and selection are significantly improved. In terms of evaluation efficiency, the automated task distribution mechanism is adopted to greatly shorten the manual process cycle. A systematic evaluation system covering clinical value, economic value, patient value, and management value is constructed, breaking through the limitations of traditional single-dimensional evaluation. The Delphi method of expert consultation is combined to achieve scientific allocation and dynamic optimization of index weights. With the support of the expert consistency verification and data feedback mechanism, it is ensured that the weight setting not only conforms to the medical management policy orientation but also can respond to the changes in actual clinical needs. Through docking the hospital information system and the supplier management platform, multi-source data integration is realized, and multi-role review groups such as clinical department experts, medical systems, management specialists, and patient representatives are intelligently matched. With the help of the automated task distribution and warning mechanism, the evaluation response efficiency is significantly improved. At the same time, the linear weighted model is used to fuse the multi-dimensional scoring results, and the visual analysis tool is combined to intuitively present the product performance differences, and a hierarchical decision-making rule is established to balance the risk of introducing innovative consumables and the clinical benefits. Finally, on the basis of optimizing the full-life cycle cost control of consumables, the standardization and scientificity of the admission decision-making process are realized, effectively reducing medical management disputes and improving the clinical application quality of consumables.

[0021] 2. The hybrid weight calculation model for medical consumables access evaluation indicators realizes the unity of the stability, real-time performance, and scenario adaptability of the evaluation system through a three-dimensional collaborative mechanism of static weight, objective weight, and dynamic weight. The static weight constructed based on the Delphi method expert consultation generates the initial weight through multiple rounds of expert scoring and the Analytic Hierarchy Process (AHP), and combines consistency testing to solidify expert consensus, providing a stable evaluation benchmark for core dimensions such as clinical value and economic value, and ensuring the evaluation reliability of difficult-to-quantify indicators such as improved diagnostic speed or accuracy and ease of operation; the objective weight dynamically responds to consumable qualification status, price fluctuations, and safety risk data by connecting to the drug administration database, medical insurance payment system, and adverse event monitoring platform in real time. For example, when the qualification expires, the weight is automatically reduced, and when the price exceeds the threshold, the economic weight is adjusted, realizing data-driven real-time correction; the dynamic weight is configured differently for different usage scenarios. In the operating room scenario, the weight of ease of operation is increased and the weight of patient comfort is reduced to strengthen the evaluation of instrument stability. In the outpatient scenario, the weight of clinical operation risk is emphasized and the invasive index is weakened to adapt to the low-trauma demand. The precise adaptation of the weight is achieved through the scenario-based strategy library preset by the rule engine. This model supports the automated update of the objective weight through multi-source data fusion, combines the expert experience solidification of the static weight and the scenario strategy linkage of the dynamic weight, significantly improves the evaluation efficiency and decision-making transparency, and at the same time supports custom rule extension (such as the biocompatibility weight of orthopedic consumables is dynamically adjusted according to the implantation period) and cross-scenario collaborative analysis. It can not only effectively reduce the evaluation error caused by subjective deviation or data lag, but also optimize resource allocation for special scenarios such as emergency and surgery, achieving the scientific and precise evaluation of consumables access in risk control and cost-benefit balance, and constructing a flexible and extensible decision support system for medical institutions.

[0022] 3. For the evaluation of the operation difficulty level in the clinical value dimension, through a dual verification mechanism that integrates subjective scoring and objective data mining, a refined and scenario-based evaluation effect is achieved. Specifically, when the first score of the operation difficulty level of the target consumable is lower than the full score, the system automatically triggers an intelligent analysis process for operation characteristics: First, the operation step text in the target consumable instruction manual is vectorized to extract core operation characteristics (such as key action nodes like "alternate firing of double staplers" and "one-handed unlocking and separation"). At the same time, based on the knowledge graph of historical consumable operation methods in medical institutions (including data such as operation steps, training duration, and operation error rate), the cosine similarity algorithm is used to calculate the matching degree of the operation characteristics between the target consumable and the existing consumables in the comparison library. When the similarity between the target consumable and the existing consumables in the comparison set exceeds the preset threshold, the system automatically generates a second score. The higher the similarity, the higher the second score. The weighted sum of the highest second score and the first score is used as the final score of the operation difficulty level (if it exceeds the full score, it is calculated according to the full score). This implementation method has the inheritance of operation experience and is more in line with the actual operation scenario; further, to ensure the high relevance of the comparison library, avoid invalid comparisons of cross-department consumables, and improve the calculation efficiency, the system preferentially screens the existing consumables that match the department of use of the target consumable (such as staplers dedicated to the thoracic surgery department are only compared with consumables in the same department), have the same consumable type (classified according to class II / III medical devices), and have a high degree of use relevance (matched through the keyword vector of the treatment purpose), and sorts them according to the priority of department matching > consumable type > use relevance. The consumables with the top comprehensive similarity, such as the top 20%, are selected to form the final comparison set for resource optimization and allocation.

[0023] 4. The behavior monitoring system for medical consumables suppliers constructs a closed-loop full-life cycle management covering access, operation, and exit through a three-dimensional collaborative mechanism of service capacity evaluation, supply stability monitoring, and quality stability analysis. First, based on the Logistic regression model, the system quantifies the service capacity of suppliers, integrates multi-source data such as delivery on-time rate and response time for returns and exchanges to dynamically generate service scores. At the same time, it establishes a stockout risk warning model. Through the correlation analysis of inventory turnover rate and clinical consumption volume, for suppliers with a continuous inventory satisfaction rate not meeting the standard, the catalog consumable score linkage is deducted, and the alternative supplier switching process is automatically triggered to ensure supply continuity. At the quality monitoring level, by analyzing the consumable instruction manual to construct standard life characteristic vectors (such as the threshold of sterilization times and single-use duration), combined with surgical records and equipment logs to verify the authenticity of actual usage data, calculate the deviation degree between the actual service life and the nominal value, and implement dynamic score linkage accordingly to uniformly manage all consumables under the supplier. For qualified consumables, it triggers positive incentives for the supplier (improvement of management value score and increase in the benchmark score for new consumable access), while for unqualified consumables, punitive measures such as credit rating downgrade and tender restrictions are initiated. Through multi-source data cross-validation (connecting to the HIS system, supplier ERP, and sterilization equipment logs) and intelligent warning rules (such as automatic marking of abnormal inventory and warning of life deviation threshold), the system realizes the objective quantitative evaluation and closed-loop control of supplier behavior. It can not only dynamically calibrate the quality score based on the historical operation error rate but also ensure the traceability credibility by solidifying key data through blockchain storage technology. In clinical practice, it significantly optimizes the efficiency of supplier resource allocation, drives the increase in the order proportion of high-credit suppliers, and at the same time shortens the rectification cycle for quality problems, providing technical support for medical institutions to establish a scientific and transparent supplier management mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present disclosure and, together with the specification, are used to explain the principles of the present disclosure.

[0025] Figure 1 It is a schematic diagram of the electronic device structure for the hardware operating environment involved in the embodiments of the present application.

[0026] Figure 2 It is a flowchart of the steps of a method for selecting and evaluating the access of medical consumables in medical institutions provided by the embodiments of the present application.

[0027] Figure 3 It is a schematic diagram of the functional modules of a system for selecting and evaluating the access of medical consumables in medical institutions provided by the embodiments of the present application.

[0028] Figure 4 It is a module construction diagram of a system for selecting and evaluating the access of medical consumables in medical institutions provided by the embodiments of the present application.

[0029] Identifications in the figure: 1001 - Processor, 1002 - Communication bus, 1003 - User interface, 1004 - Network interface, 1005 - Memory. Detailed implementation manners

[0030] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the accompanying drawings and specific implementation manners. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts belong to the scope of protection of the present application.

[0031] The solution of the present application will be further described below with reference to the accompanying drawings.

[0032] As shown in Figure 1 the figure, the electronic device may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard). Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a Wireless-Fidelity (WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0033] Those skilled in the art can understand that Figure 1 the structure shown in

[0034] As shown in Figure 1 the figure, the memory 1005, as a storage medium, may include an operating system, a network communication module, a user interface module, and a data storage module.

[0035] In Figure 1In the electronic device shown, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the electronic device of the present invention can be arranged in the electronic device. The electronic device calls, through the processor 1001, a method for evaluating the admission and selection of medical consumables in a medical institution stored in the data storage module in the memory 1005, and executes a system for evaluating the admission and selection of medical consumables in a medical institution provided in an embodiment of the present application.

[0036] Based on the foregoing hardware operating environment and system architecture, in the first aspect of the present application, with reference to Figure 2 as shown, a method for evaluating the admission and selection of medical consumables in a medical institution is provided, including: Respond to an evaluation request for a target consumable, generate an evaluation task according to evaluation indicators and evaluation objects, and distribute the evaluation task to the corresponding evaluation objects; It should be noted that when the target consumable enters the initial screening stage of the hospital procurement catalog, for example, after the supplier qualification is reviewed and approved, or when the existing consumables need to be re-evaluated due to clinical complaints or technological iterations, the system automatically triggers an evaluation request; the evaluation objects include experts with corresponding professional knowledge and professional capabilities, and systems with data integration and analysis capabilities; task distribution is docked with the hospital SPD system through an API interface to automatically match the roles of evaluation objects and shorten the distribution cycle; The evaluation indicators include clinical value, economic value, patient value, and management value. Importance scores are given to each indicator to obtain the corresponding weights of each evaluation indicator; It should be noted that through expert consultation by the Delphi method, experts are required to give importance scores to the clinical value, economic value, patient value, and management value indicators on a scale of 0-5 points. According to the scoring ratio of each indicator, the corresponding weights of each score are obtained; Collect the scoring results of all evaluation objects for the target consumable, and perform linear weighting on the scoring results of each evaluation indicator according to the corresponding weights to obtain the evaluation result of the target consumable.

[0037] In this embodiment, by constructing a multi-dimensional dynamic evaluation system and an intelligent data fusion mechanism, the scientific nature, efficiency, and risk control ability of the selection of medical consumables for access are significantly improved. In terms of evaluation efficiency, an automated task distribution mechanism is adopted to greatly shorten the manual process cycle. A systematic evaluation system covering clinical value, economic value, patient value, and management value is constructed, breaking through the limitations of traditional single-dimensional evaluation. Combining the Delphi method for expert consultation, the scientific allocation and dynamic optimization of index weights are realized. With the support of the expert consistency verification and data feedback mechanism, it is ensured that the weight setting not only conforms to the medical management policy orientation but also can respond to the changes in actual clinical needs. By docking the hospital information system and the supplier management platform, multi-source data integration is achieved, and multi-role review groups such as clinical department experts, medical systems, management specialists, and patient representatives are intelligently matched. With the help of the automated task distribution and warning mechanism, the evaluation response efficiency is significantly improved. At the same time, a linear weighted model is used to fuse the multi-dimensional scoring results, and a visualization analysis tool is combined to intuitively present the product performance differences, and a hierarchical decision-making rule is established to balance the risk of introducing innovative consumables and the clinical benefits. Finally, on the basis of optimizing the full-life cycle cost control of consumables, the standardization and scientific nature of the access decision-making process are realized, effectively reducing medical management disputes and improving the quality of clinical application of consumables.

[0038] In an embodiment of the present application, the evaluation index constructs a hierarchical structure model. The hierarchical structure model is hierarchically associated from the high level to the low level according to the order of the target layer, the intermediate layer, and the measure layer. The target layer is clinical value, economic value, patient value, and management value. One or more intermediate layers are constructed according to requirements, and one or more low-level indicators are set for each high-level indicator. The importance of the indicators in each level is scored, and the weights of the indicators in each level are calculated based on the analytic hierarchy process.

[0039] It should be noted that the target layer is the primary factor to be considered to achieve the expected effect, that is, the clinical value, economic value, patient value, and management value of the consumables; the intermediate layer is the factor to be considered that affects the upper layer, corresponding to including safety, effectiveness, suitability, economy, patient quality of life, or patient psychology and the improvement of hospital service capabilities; the measure layer is the factor considered to achieve the upper layer goal, including the occurrence of adverse events, clinical operation risks, contraindications, evidence-based medicine evidence, etc.; In this embodiment, a hierarchical structure is established from large to small and from top to bottom according to different value dimensions; the indicators at the same level are compared pairwise using a 1-9 comparison scale to construct a comparison matrix, such as the Satty 1-9 comparison scale shown in Table 1.

[0040] Table 1 The comparison matrix is A. The matrix includes n indicators, and the weight score of the i-th indicator and the j-th indicator is a ij, where A represents a pairwise comparison judgment matrix, which is a positive reciprocal matrix composed of pairwise comparisons of the importance of indicators at the same level; a ij represents the element in the i-th row and j-th column of matrix A, indicating the importance score of the i-th indicator relative to the j-th indicator. The value range is 1-9 and its reciprocals. For example, 1 means equally important, 9 means i is extremely important compared to j, and 1 / 9 means j is extremely important compared to i. The calculation process is as follows: (1)Calculate the product: , calculate the geometric mean of the products of each element of the i-th indicator in the comparison matrix for subsequent weight calculation; (2)Initialize the weight coefficient: , perform the n-th root operation on M i to obtain the preliminary weight coefficient, which reflects the relative importance of the indicators within a single level; (3)Normalization processing: , normalize the initial weight coefficient so that its sum is 1 to obtain the final single-level weight vector; W i is the normalized weight of the i-th level indicator; (4)Calculate the maximum eigenvalue: , calculate the maximum eigenvalue by multiplying the comparison matrix A by the normalized weight vector w for consistency test, λ max represents the maximum eigenvalue of the matrix, which is a common symbol in mathematics and represents the main eigenvalue of the matrix; (5)Calculate the consistency index: , measure the consistency degree of the judgment matrix. The smaller the CI value, the higher the consistency. CI is the consistency index, which is the standard abbreviation of AHP; (6)Calculate the consistency ratio: , combined with the random consistency index RI (obtained by looking up the table according to the matrix order. For example, when n = 5, RI = 1.12). If CR < 0.1, the consistency test is passed; CR is the consistency ratio, and RI is the random consistency index, and its value is obtained by looking up the table.

[0041] After determining the weights of each indicator at different levels, next, determine the weights of different indicators in the entire indicator system. Calculate the combined weights of each indicator through indicator multiplication. The combined weight of the secondary indicators is equal to the weight coefficient of the primary indicator multiplied by the weight coefficient of the secondary indicator, and the combined weight of the tertiary indicators is equal to the weight coefficient of the secondary indicator multiplied by the weight coefficient of the tertiary indicator. At the implementation level, automatically integrate multi-source data by docking with the hospital information system, supplier management platform, and patient follow-up system, and generate a comprehensive evaluation result in combination with the intelligent task distribution and linear weighted scoring model (total score = Σ (sub-item score × weight)), effectively shortening the traditional multi-round expert demonstration cycle and reducing the error of manual intervention; In an embodiment of the present application, the intermediate layer and measure layer indicators are semantically vectorized using a BERT pre-trained model. By calculating the cosine similarity between the evaluation indicator name and the medical scenario keywords, the indicators with a cosine similarity greater than the preset threshold are included in the candidate set; Calculate the partial regression coefficients of each evaluation indicator. When the partial regression coefficient of an evaluation indicator is less than the set threshold, it is automatically excluded as an inefficient indicator.

[0042] In an embodiment of the present application, the weights of the evaluation indicators are determined using a hybrid weight calculation model. The hybrid weight calculation model includes static weights, objective weights, and dynamic weights. The static weights are correspondingly determined by scoring the importance of experts using the Delphi consultation method. The objective weights are determined according to the qualifications, price comparison, and number of safety incidents included in the medical consumables themselves. The dynamic weights are determined according to the different usage scenarios of the medical consumables. Different usage scenarios include the operating room and outpatient clinic.

[0043] In the embodiments of the present application, the hybrid weight calculation model for the medical consumables access evaluation indicators realizes the unity of the stability, real-time performance, and scenario adaptability of the evaluation system through a three-dimensional collaborative mechanism of static weights, objective weights, and dynamic weights. The static weights constructed based on the Delphi method expert consultation generate initial weights through multiple rounds of expert scoring and the Analytic Hierarchy Process (AHP), and combine consistency tests to solidify expert consensus, providing a stable evaluation benchmark for core dimensions such as clinical value and economic value, and ensuring the evaluation reliability of difficult-to-quantify indicators such as the improvement of diagnostic speed or accuracy and the ease of operation; the objective weights dynamically respond to the consumable qualification status, price fluctuations, and safety risk data by connecting to the drug administration database, medical insurance payment system, and adverse event monitoring platform in real time. For example, when the qualification expires, the weight is automatically reduced, and when the price exceeds the threshold, the economic weight is adjusted, realizing real-time correction driven by data; the dynamic weights are differentially configured for different usage scenarios. In the operating room scenario, the weight of the ease of operation is increased and the weight of patient comfort is reduced to strengthen the evaluation of the instrument stability. In the outpatient clinic scenario, the weight of clinical operation risk is emphasized and the invasive indicators are weakened to adapt to the low-trauma requirements. The precise adaptation of weights is achieved through the scenario-based strategy library preset by the rule engine. This model supports the automated update of objective weights through multi-source data fusion, combines the expert experience solidification of static weights and the scenario strategy linkage of dynamic weights, significantly improves the evaluation efficiency and decision-making transparency, and at the same time supports custom rule expansion (such as the biocompatibility weight of orthopedic consumables dynamically adjusts with the implantation period) and cross-scenario collaborative analysis. It can not only effectively reduce the evaluation errors caused by subjective biases or data lags, but also optimize resource allocation for special scenarios such as emergencies and surgeries, realizing the scientific and precise evaluation of medical consumables access in the balance of risk control and cost-benefit, and constructing a flexible and extensible decision support system for medical institutions.

[0044] In an embodiment of the present application, the measure layer corresponding to the clinical value includes the ease of operation. Obtain the scores of all evaluation objects on the ease of operation of the target consumables as the first score. When the first score is lower than the full score, vectorize the operation step text in the target consumable manual and extract the core operation features. Build a knowledge graph of the operation methods of the existing consumables in the medical institution, and store the operation steps, training duration, and operation error rate data of the historical consumables. Use the cosine similarity algorithm to calculate the similarity between the operation features of the target consumable and the existing consumables in the medical institution. When the similarity is greater than the preset value, obtain the second score, and the sum of the first score and the second score is used as the scoring result of the ease of operation.

[0045] In an embodiment of the present application, before calculating the similarity between the target consumable and the existing consumables, it also includes a preliminary screening of the existing consumables to form a comparison set for the target consumable. The target consumable only calculates the similarity with the existing consumables in the comparison set, and the screening conditions include: Usage department matching degree: According to the expected usage department of the target consumable, extract the historical usage records of the corresponding department from the existing consumable library. Consumable type matching degree: Perform homogeneous screening according to the medical device classification catalog. Purpose correlation matching degree: Based on the fields in the consumable manual, extract the treatment purpose keywords and construct a purpose feature vector for preliminary similarity screening. Weight screening: Set the priority of the screening conditions as department matching degree > consumable type > purpose correlation. Sort the existing consumables according to the comprehensive matching degree, and select the preset number of existing consumables with the highest matching degree to enter the final comparison set.

[0046] In this embodiment, for the assessment of the operation difficulty level under the clinical value dimension, a refined and scenario-based evaluation effect is achieved through a dual verification mechanism that integrates subjective scoring and objective data mining. Specifically, when the first score of the operation difficulty level of the target consumable is lower than the full score, the system automatically triggers an intelligent analysis process for operation features: First, the operation step text in the target consumable instruction manual is vectorized to extract core operation features (such as key action nodes like "alternate firing of double staplers" and "one-handed unlocking and separation"). At the same time, based on the knowledge graph of the historical consumable operation methods in medical institutions (including data such as operation steps, training duration, and operation error rate), the cosine similarity algorithm is used to calculate the matching degree of the operation features between the target consumable and the existing consumables in the comparison library. When the similarity between the target consumable and the existing consumables in the comparison set exceeds the preset threshold, the system automatically generates a second score. The higher the similarity, the higher the second score. The weighted sum of the highest second score and the first score is used as the final score of the operation difficulty level (if it exceeds the full score, it is calculated according to the full score). This embodiment has the inheritance of operation experience and is more in line with the actual operation scenario; further, to ensure the high relevance of the comparison library, avoid invalid comparisons of consumables across departments, and improve the calculation efficiency, the system preferentially screens the existing consumables that match the department where the target consumable is used (for example, a special stapler for the thoracic surgery department is only compared with consumables in the same department), have the same consumable type (classified according to class II / III medical devices), and have a high degree of use relevance (matched through the keyword vectors of the treatment purpose), and sorts them according to the priority of department matching > consumable type > use relevance, selects the consumables with the top comprehensive similarity, such as the top 20%, to form the final comparison set for optimized resource allocation.

[0047] In an embodiment of the present application, the measure layer corresponding to the management value includes the monitoring of supplier behavior, and the behavior of all medical consumables supplied by the same supplier is monitored together. Specifically, it includes: Service ability assessment: Statistically analyze the delivery on-time rate and return and replacement response time of the supplier's historical orders, and calculate the service score through a Logistic regression model; Supply stability monitoring: Establish a risk warning model for supplier stockouts. When the inventory satisfaction rate is lower than the preset value for 3 consecutive months, the scores of all medical consumables supplied by the supplier are deducted.

[0048] In an embodiment of the present application, the monitoring of supplier behavior also includes the monitoring of quality stability, specifically including: By analyzing the medical consumable instruction manual, extract the relevant parameters of the usage duration to construct a standard life feature vector; mark the sterilization times threshold for reusable consumables to generate a dynamic life comparison table; Actual usage data collection, real-time recording of the clinical usage times, single-use duration, and sterilization cycle times of consumables; establishing a full-life cycle database for consumables, and associating multi-source data including surgical records and equipment logs to verify the authenticity of usage. Stability comparative analysis, calculating the deviation degree between the actual service life and the nominal value in the instruction manual: Scoring linkage mechanism, where qualified consumables trigger positive incentives for suppliers, the management value scores of consumables in the current supply catalog are uniformly increased, and the initial scores increase by the benchmark value when new consumables are admitted later; unqualified consumables are subject to punitive measures, the management value scores of consumables in the current supply catalog are uniformly decreased, and the initial scores decrease by the benchmark value when new consumables are admitted later.

[0049] In this embodiment, the medical consumable supplier behavior monitoring system constructs a full-life cycle management closed-loop covering access, operation, and withdrawal through a three-dimensional collaborative mechanism of service capacity evaluation, supply stability monitoring, and quality stability analysis. This system first quantifies the service capacity of suppliers based on the Logistic regression model, integrates multi-source data such as delivery on-time rate and return and replacement response timeliness to dynamically generate service scores, and at the same time establishes a stockout risk warning model. Through the correlation analysis of inventory turnover rate and clinical consumption volume, for suppliers with a continuous inventory satisfaction rate not meeting the standard, a linkage deduction of the consumable scores in the catalog is implemented, and the alternative supplier switching process is automatically triggered to ensure supply continuity; at the quality monitoring level, by analyzing the consumable instruction manual to construct a standard life feature vector (such as sterilization times threshold, single-use duration), combined with surgical records and equipment logs to verify the authenticity of actual usage data, calculating the deviation degree between the actual service life and the nominal value, and implementing dynamic scoring linkage accordingly to uniformly manage all consumables under the supplier - qualified consumables trigger positive incentives for suppliers (management value score increase and new consumable admission benchmark score increase), and unqualified consumables initiate punitive measures such as credit rating downgrade and bidding restrictions. Through multi-source data cross-verification (connecting to the HIS system, supplier ERP, and sterilization equipment logs) and intelligent warning rules (such as automatically marking abnormal inventory, warning for life deviation threshold), this system realizes the objective quantitative evaluation and closed-loop control of supplier behavior, can dynamically calibrate the quality score based on the historical operation error rate, and can also solidify key data through blockchain evidence storage technology to ensure the credibility of traceability, significantly optimizing the supplier resource allocation efficiency in clinical practice, driving the increase in the order proportion of high-credit suppliers, shortening the quality problem rectification cycle at the same time, and providing technical support for medical institutions to establish a scientific and transparent supplier management mechanism.

[0050] In the second aspect of this application, referring to Figure 3 as shown, a medical consumable access selection and evaluation system for medical institutions is provided, including: The task generation module responds to the evaluation request of the target consumable, generates an evaluation task according to the evaluation indicators and evaluation objects, and distributes the evaluation task to the corresponding evaluation objects; The weight generation module, where the evaluation indicators include clinical value, economic value, patient value, and management value, performs importance scoring on each indicator to obtain the corresponding weights of each evaluation indicator; The evaluation generation module collects the scoring results of all evaluation objects on the target consumable, performs linear weighting on the scoring results of each evaluation indicator according to the corresponding weights, and obtains the evaluation result of the target consumable.

[0051] In an embodiment of the present application, with reference to Figure 4 As shown, the weight generation module further includes an index structure sub-module, which constructs a hierarchical structure model. The hierarchical structure model is hierarchically associated from high to low according to the order of the target layer, intermediate layer, and measure layer. The target layer is clinical value, economic value, patient value, and management value. One or more intermediate layers are constructed according to requirements, one or more low-level indicators are set for each high-level indicator, importance scoring is performed on the indicators in each level, and the weights of the indicators in each level are calculated based on the analytic hierarchy process.

[0052] In an embodiment of the present application, the index structure sub-module further includes an index elimination grandson module. The intermediate layer and measure layer indicators are semantically vectorized using the BERT pre-trained model. By calculating the cosine similarity between the evaluation indicator name and the medical scenario keywords, the indicators with a cosine similarity greater than the preset threshold are included in the candidate set; Calculate the partial regression coefficient of each evaluation indicator. When the partial regression coefficient of the evaluation indicator is less than the set threshold, it is automatically eliminated as an inefficient indicator.

[0053] In an embodiment of the present application, the weight generation module further includes a mixed weight sub-module. The weights of the evaluation indicators are determined using a mixed weight calculation model. The mixed weight calculation model includes static weight, objective weight, and dynamic weight. The static weight is correspondingly determined by performing importance scoring on experts using the Delphi consultation method. The objective weight is determined according to the qualifications, price comparison, and number of safety incidents included in the medical consumables themselves. The dynamic weight is determined according to different usage scenarios of the medical consumables. Different usage scenarios include the operating room and outpatient clinic.

[0054] In an embodiment of the present application, the weight generation module further includes an operation scoring sub-module. The measure layer corresponding to the clinical value includes the ease of operation. Obtain the scoring of all evaluation objects on the ease of operation of the target consumable as the first score; When the first score is lower than the full score, vectorize the operation step text in the target consumable instruction manual and extract the core operation features; establish a knowledge graph of the operation methods of the consumables already available in the medical institution, and store the operation steps, training duration, and operation error rate data of the historical consumables. Use the cosine similarity algorithm to calculate the similarity between the operation features of the target consumable and the consumables already available in the medical institution. When the similarity is greater than the preset value, obtain the second score, and the sum of the first score and the second score is used as the scoring result of the operation difficulty level.

[0055] In an embodiment of the present application, the operation scoring sub-module further includes a consumable screening sub-module. Before calculating the similarity between the target consumable and the existing consumables, it also includes a preliminary screening of the existing consumables to form a comparison set of the target consumables. The target consumable only calculates the similarity with the existing consumables in the comparison set, and the screening conditions include: Usage department matching degree: According to the expected usage department of the target consumable, extract the historical usage records of the corresponding department from the existing consumable library. Consumable type matching degree: Conduct similar screening according to the medical device classification catalog. Purpose association matching degree: Based on the fields in the consumable instruction manual, extract the treatment purpose keywords and construct a purpose feature vector for preliminary similarity screening. Weight screening: Set the priority of the screening conditions as the usage department matching degree is greater than the consumable type is greater than the purpose association. Sort the existing consumables according to the comprehensive matching degree, and select the preset number of existing consumables with the highest matching degree to enter the final comparison set.

[0056] In an embodiment of the present application, the weight generation module further includes a supplier monitoring sub-module. The measure layer corresponding to the management value includes supplier behavior monitoring, and conduct behavior monitoring on all medical consumables supplied by the same supplier, specifically including: Service ability evaluation: Statistically analyze the delivery on-time rate and return and replacement response time of the supplier's historical orders, and calculate the service score through the Logistic regression model. Supply stability monitoring: Establish a supplier out-of-stock risk warning model, and deduct the scores of all medical consumables supplied by the supplier when the inventory satisfaction rate is lower than the preset value for 3 consecutive months.

[0057] In an embodiment of the present application, the supplier monitoring sub-module further includes a quality stability sub-module. The supplier behavior monitoring also includes quality stability monitoring, specifically including: By parsing the medical consumable instruction manual, extract the features of the parameters related to the usage duration, construct a standard life feature vector; mark the sterilization times threshold for reusable consumables and generate a dynamic life comparison table. Collect actual usage data, and record the clinical usage times, single-use duration, and sterilization cycle times of consumables in real time; establish a full-life cycle database for consumables, and associate multi-source data including surgical records and equipment logs to verify the authenticity of usage. Conduct a stability comparative analysis, and calculate the deviation degree between the actual service life and the nominal value stated in the instruction manual: Score linkage mechanism: For qualified consumables, trigger positive incentives for suppliers, uniformly improve the management value scores of consumables in the current supply catalog, and increase the initial score by the benchmark value when new consumables are admitted subsequently; for unqualified consumables, implement punitive measures, uniformly reduce the management value scores of consumables in the current supply catalog, and reduce the initial score by the benchmark value when new consumables are admitted subsequently.

[0058] It should be noted that the specific implementation manner of a medical consumable access selection and evaluation system in an embodiment of the present application refers to the specific implementation manner of a medical consumable access selection and evaluation method proposed in the first aspect of the embodiment of the present application, which will not be elaborated here.

[0059] It should be noted that in this text, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that an article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such article or device. Without further limitation, the elements defined by the statement "including..." do not exclude the existence of additional identical elements in the article or device including the elements.

[0060] The above provides a detailed introduction to a medical consumable access selection and evaluation method. Specific examples are used in this text to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the medical consumable access selection and evaluation method of the present application and its core idea; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for selecting and evaluating medical consumables for medical institutions, characterized in that Including: In response to an evaluation request for a target consumable, generate an evaluation task according to evaluation indicators and evaluation objects, and distribute the evaluation task to the corresponding evaluation objects; The evaluation indicators include clinical value, economic value, patient value, and management value. Importance scores are given to each indicator to obtain the corresponding weights of each evaluation indicator; Collect the scoring results of all evaluation objects for the target consumable, and perform linear weighting on the scoring results of each evaluation indicator according to the corresponding weights to obtain the evaluation result of the target consumable.

2. The medical consumables access selection and evaluation method for medical institutions according to claim 1, characterized in that, The evaluation indicators construct a hierarchical structure model. The hierarchical structure model is hierarchically associated from the high level to the low level in the order of the target layer, the intermediate layer, and the measure layer. The target layer is the clinical value, economic value, patient value, and management value. One or more intermediate layers are constructed according to requirements. One or more low-level indicators are set for each high-level indicator. Importance scores are given to the indicators in each level, and the weights of the indicators in each level are calculated based on the analytic hierarchy process.

3. The medical consumables access selection and evaluation method for medical institutions according to claim 2, characterized in that The intermediate layer and measure layer indicators are semantically vectorized using the BERT pre-trained model. By calculating the cosine similarity between the evaluation indicator name and the medical scenario keywords, the indicators with a cosine similarity greater than the preset threshold are included in the candidate set; Calculate the partial regression coefficient of each evaluation indicator. When the partial regression coefficient of the evaluation indicator is less than the set threshold, it is automatically excluded as an inefficient indicator.

4. The medical consumables access selection and evaluation method for medical institutions according to claim 3, wherein, The weights of the evaluation indicators are determined using a hybrid weight calculation model. The hybrid weight calculation model includes static weights, objective weights, and dynamic weights. The static weights are correspondingly determined by conducting importance scoring on experts using the Delphi consultation method. The objective weights are determined according to the qualifications, price comparison, and number of safety incidents included in the medical consumables themselves. The dynamic weights are determined according to the different usage scenarios of the medical consumables. Different usage scenarios include the operating room and the outpatient clinic.

5. A method for selecting and evaluating the access of medical consumables in a medical institution according to any one of claims 1-4, characterized in that, The measure layer corresponding to the clinical value includes the ease of operation. Obtain the scores of all evaluation objects for the ease of operation of the target consumable as the first score; When the first score is lower than the full score, vectorize the operation step text in the target consumable manual and extract the core operation features; Build a knowledge graph of the operation methods of existing consumables in medical institutions, and store the operation steps, training duration, and operation error rate data of historical consumables; Use the cosine similarity algorithm to calculate the similarity between the operation features of the target consumable and the existing consumables in medical institutions. When the similarity is greater than the preset value, obtain the second score. The sum of the first score and the second score is used as the scoring result of the ease of operation.

6. The medical consumables access selection and evaluation method for medical institutions according to claim 5, wherein, Before calculating the similarity between the target consumable and the existing consumables, it also includes a preliminary screening of the existing consumables to form a comparison set for the target consumable. The target consumable calculates the similarity only with the existing consumables in the comparison set. The screening conditions include: Usage department matching degree: According to the expected usage department of the target consumable, extract the historical usage records of the corresponding department from the existing consumable library; Consumable type matching degree: Perform similar screening according to the medical device classification catalog; Purpose association matching degree: Based on the fields in the consumable manual, extract the treatment purpose keywords and construct a purpose feature vector for preliminary similarity screening; Weight screening: Set the screening condition priority as department matching degree > consumable type > usage association. Sort the existing consumables according to the comprehensive matching degree, and select the preset number of existing consumables with the highest matching degree to enter the final comparison set.

7. A medical consumables access selection and evaluation method for medical institutions according to any one of claims 1-4, characterized in that, The measure layer corresponding to the management value includes supplier behavior monitoring. Conduct behavior monitoring on all medical consumables supplied by the same supplier, specifically including: Service ability evaluation: Statistically calculate the delivery on-time rate and return and replacement response time of the supplier's historical orders, and calculate the service score through the Logistic regression model; Supply stability monitoring: Establish a risk warning model for supplier out-of-stock. When the inventory satisfaction rate is lower than the preset value for 3 consecutive months, deduct the scores of all medical consumables supplied by the supplier.

8. The medical consumables access selection and evaluation method for medical institutions according to claim 7, wherein, The supplier behavior monitoring also includes quality stability monitoring, specifically including: By analyzing the medical consumable instructions, extract the features of the parameters related to the usage duration, and construct a standard life feature vector; Mark the sterilization times threshold for reusable consumables to generate a dynamic life comparison table; Collect actual usage data, and record the clinical usage times, single-use duration, and sterilization cycle times of the consumables in real time; Establish a consumable full life cycle database, and associate multi-source data including surgical records and equipment logs to verify the authenticity of the usage. Stability comparison and analysis, calculate the deviation degree between the actual service life and the nominal value in the instruction manual: Score linkage mechanism, qualified consumables trigger positive incentives for suppliers, the management value scores of the consumables in the current supply catalog are uniformly improved, and the initial score increases by the benchmark value when new consumables are admitted later; Unqualified consumables implement punitive measures, the management value scores of the consumables in the current supply catalog are uniformly reduced, and the initial score decreases by the benchmark value when new consumables are admitted later.

9. A medical consumables access selection and evaluation system for medical institutions, characterized in that, Including: Task generation module, in response to the evaluation request of the target consumable, generate an evaluation task according to the evaluation index and the evaluation object, and distribute the evaluation task to the corresponding evaluation object; Weight generation module, the evaluation indexes include clinical value, economic value, patient value and management value. Score the importance of each index to obtain the corresponding weights of each evaluation index; Evaluation generation module, collect the score results of all evaluation objects on the target consumable, and perform linear weighting on the score results of each evaluation index according to the corresponding weights to obtain the evaluation result of the target consumable.

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