Full-process traceability management system for orthopedic consumables

Abstract

The invention relates to the technical field of consumable traceability, and particularly discloses a whole-process traceability management system for orthopedic consumables, which comprises a traceability construction module, a material screening module, a difference judgment module, a correlation analysis module, a matching recommendation module and the like. The traceability construction module is used for generating a unique code for the orthopedic consumables and constructing a traceability chain; the material screening module is used for determining key monitoring consumables according to the intraoperative replacement frequency and the postoperative concurrent frequency; the difference judgment module is used for analyzing the difference of postoperative concurrent frequencies of different post-operation doctors; the correlation analysis module is used for judging the correlation between the mismatching of the key monitoring consumables and the complication risk; and the matching recommendation module is used for constructing a matching recommendation model and recommending proper orthopedic surgeons to the key monitoring consumables. According to the invention, process tracking and traceability, problem positioning and responsibility definition can be realized, and optimization of physician resource allocation and dynamic traceability management can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of consumable traceability, and particularly relates to a full-process traceability management system for orthopedic consumables. Background Art

[0002] With the continuous development of medical technology, orthopedic consumables are increasingly widely used in orthopedic surgeries. Currently, there are still many deficiencies in the management of orthopedic consumables, making it difficult to trace the entire process of orthopedic consumables.

[0003] A Chinese patent with the application number CN116994736A discloses an orthopedic consumable management system and method, including: a management terminal, an intermediate logistics terminal, and a usage terminal; among them, the intermediate logistics terminal is used to obtain the order information of the usage terminal, generate logistics information according to the order information and product information; it is also used to obtain receiving information and update the order status according to the receiving information; the usage terminal is used to obtain the photographed images of orthopedic consumables after receiving the orthopedic consumables corresponding to the order information, compare the preset template with the physical object to complete the inspection, and update the consumable usage information.

[0004] The prior art does not perform full-process traceability management on orthopedic consumables, resulting in the inability to locate the responsible link for postoperative complications or quality problems. It is difficult to determine whether there are errors in the production, circulation, surgical use, or other links. If a technology of constructing a traceability chain through a unique code is used to concatenate the full-process data of consumables from production to postoperative follow-up, process tracking and traceability, problem location, and responsibility definition can be achieved.

[0005] The prior art does not comprehensively consider the intraoperative replacement frequency and postoperative complication frequency to determine the key monitoring consumables, and cannot achieve early risk warning. Consumables with quality risks or safety hazards cannot be detected and processed in a timely manner. If a technology of using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine the key monitoring consumables based on the intraoperative replacement frequency and postoperative complication frequency is used, high-risk consumables can be screened out, thereby improving medical safety.

[0006] The prior art does not analyze the differences in postoperative complication frequencies among physicians with different professional titles, and cannot optimize the allocation of physician resources according to the correlation between physicians and complications. When arranging surgical physicians, it is impossible to reasonably match suitable physicians with specific consumables. If the postoperative complication frequencies of physicians with different professional titles are classified and analyzed, and a matching recommendation model is constructed to recommend orthopedic surgeons according to the consumable matching degree and postoperative complication frequency, it is beneficial to improve the utilization efficiency of medical resources.

[0007] Therefore, the present invention provides a full-process traceability management system for orthopedic consumables. Summary of the Invention

[0008] The purpose of the present invention is to provide a full-process traceability management system for orthopedic consumables to solve the above background problems.

[0009] The object of the present invention can be achieved by the following technical solutions:

[0010] A full-process traceability management system for orthopedic consumables, comprising the following modules:

[0011] Traceability construction module: used for uniformly coding orthopedic consumables and constructing a consumable traceability chain;

[0012] Material screening module: used for numerically analyzing the historical orthopedic consumable data of the consumable traceability chain to obtain the intraoperative replacement frequency and postoperative complication frequency, and conducting statistical analysis based on the intraoperative replacement frequency and postoperative complication frequency to determine key monitored consumables;

[0013] Difference discrimination module; used for classifying and analyzing the postoperative complication frequencies of different physicians, determining whether there are significant differences in the postoperative complication frequencies of physicians with different titles, and formulating a traceability strategy;

[0014] Correlation analysis module: if there are significant differences in the postoperative complication frequencies of physicians with different titles, conduct correlation analysis on the postoperative complication frequencies of physicians with different titles to determine whether there is a positive correlation between the consumable mismatch of key monitored consumables and the increased complication risk;

[0015] Matching recommendation module: if there is a positive correlation, construct a consumable matching model based on the consumable matching degree and postoperative complication frequency, and recommend orthopedic surgeons for key monitored consumables.

[0016] As a further solution of the present invention: the determination method of the key monitored consumables is as follows:

[0017] Obtain the historical orthopedic consumable data of the consumable traceability chain, and extract the intraoperative replacement frequency and postoperative complication frequency of implantable orthopedic consumables;

[0018] Conduct statistical analysis on the intraoperative replacement frequency and postoperative complication frequency to obtain a comprehensive proximity;

[0019] Based on the comprehensive proximity, conduct sorting analysis on implantable orthopedic consumables to determine key monitored consumables.

[0020] As a further solution of the present invention: the acquisition method of the comprehensive proximity is as follows:

[0021] Based on the intraoperative replacement frequency and postoperative complication frequency, perform normalization processing to construct a decision matrix;

[0022] Use the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to numerically analyze the decision matrix to obtain the relative proximity of the intraoperative replacement frequency and the relative proximity of the postoperative complication frequency;

[0023] Perform weighted summation processing on the relative proximity of the intraoperative replacement frequency and the relative proximity of the postoperative complication frequency to obtain a comprehensive proximity.

[0024] As a further solution of the present invention, the method for determining whether there is a significant difference in the postoperative complication frequency of doctors with different professional titles is as follows:

[0025] Obtain the postoperative complication frequencies of different doctors in the same batch of key monitoring consumables, quantify the responsibilities of doctors based on the postoperative complication frequencies, and obtain the doctor responsibility coefficients;

[0026] Group and classify and calculate the doctor responsibility coefficients according to professional titles, and classify the doctor responsibility coefficients of the same professional title into the same group;

[0027] Conduct numerical analysis on the doctor responsibility coefficients of different groups to obtain the homogeneity of variance and effect size of different groups;

[0028] If the homogeneity of variance and effect size of different groups both meet the preset standard range, it is considered that there is a significant difference in the postoperative complication frequencies of doctors with different professional titles.

[0029] As a further solution of the present invention, the method for obtaining the effect size is as follows:

[0030] Based on the doctor responsibility coefficients of different groups, calculate the sum of squares between groups and the sum of squares within groups of the doctor responsibility coefficients;

[0031] Calculate the sum of squares between groups and the sum of squares within groups through a ratio formula to obtain the effect size.

[0032] As a further solution of the present invention, the method for obtaining the homogeneity of variance is as follows:

[0033] Calculate the homogeneity of variance of different groups based on the homogeneity of variance test method.

[0034] As a further solution of the present invention, the method for formulating the traceability strategy is as follows:

[0035] If there is no significant difference in the postoperative complication frequencies of doctors with different professional titles, obtain the postoperative complication frequencies of multiple batches of key monitoring consumables from the historical orthopedic consumable data in the consumable traceability chain;

[0036] If the postoperative complication frequencies of multiple batches of key monitoring consumables are concentrated in the same orthopedic consumable manufacturing enterprise, locate the orthopedic consumable manufacturer in the consumable traceability chain and mark it as the key traceability enterprise;

[0037] Send a warning message to the key monitoring enterprise through the consumable traceability chain, and continuously monitor the postoperative complication frequency of the key traceability enterprise.

[0038] As a further solution of the present invention, the method for determining whether there is a positive correlation between the consumable mismatch of key monitoring consumables and the increased risk of complications is as follows:

[0039] Obtain the consumable matching degree of postoperative complications generated by key monitored consumables in different orthopedic surgeries;

[0040] Quantify the professional title levels of physicians, and construct a logistic regression model based on the quantified professional titles of physicians and consumable matching degrees;

[0041] Based on the odds value of the consumable matching degree calculated by the logistic regression model, if the odds value of the consumable matching degree is in the preset odds value classification, it is considered that the mismatch of the key monitored consumables is positively correlated with the increased risk of complications.

[0042] As a further solution of the present invention: The construction method of the logistic regression model is:

[0043] Construct a logistic regression model log through the formula: log = β0 + β1*Pd + β2*Zc, where Pd is the consumable matching degree, Zc is the quantified professional title level, and β0, β1, and β2 are the parameters of the logistic regression model;

[0044] Use the maximum likelihood estimation method to solve the optimal solutions of β0, β1, and β2.

[0045] As a further solution of the present invention: The method for recommending orthopedic surgeons for key monitored consumable matching is:

[0046] Obtain the consumable matching degree of key monitored consumables and the postoperative complication frequencies of different physicians from the consumable traceability chain;

[0047] Construct a consumable matching model based on the consumable matching degree and the postoperative complication frequencies of different physicians;

[0048] Based on the consumable matching model, recommend orthopedic surgeons for key monitored consumable matching.

[0049] Advantages of the present invention:

[0050] (1) Generate a unique code for orthopedic consumables and construct a traceability chain covering production, circulation, surgical use, and postoperative follow-up and other links; when postoperative complications or quality problems occur, the specific production batch, circulation link, and usage scenario can be located by virtue of the unique code, and the data of the entire life cycle of orthopedic consumables can be connected in series to realize the whole-process tracking and traceability of orthopedic consumables, providing data support for subsequent material screening, difference discrimination, and correlation analysis, and providing a basis for medical decision-making.

[0051] (2) Comprehensively considering the intraoperative replacement frequency and postoperative complication frequency, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used to determine the key monitored consumables. If the postoperative complication frequencies of the key monitored consumables in multiple batches are concentrated in the same manufacturing enterprise, the system will mark this enterprise as a key traceability enterprise, send a warning prompt to the key traceability enterprise, and continuously monitor its postoperative complication frequency to strengthen the supervision of high-risk enterprises and control the quality of consumables from the source.

[0052] (3) Quantify the postoperative complication frequencies of different physicians for the same batch of key monitored consumables to obtain the physician responsibility coefficient. Group and classify the physician responsibility coefficients according to professional titles, and judge whether there are significant differences in the postoperative complication frequencies of physicians with different professional titles through the homogeneity of variance test and effect size calculation. If there are significant differences, it indicates that the complications may be related to the proficiency of physicians' operations. The hospital can optimize the allocation of physician resources accordingly to reduce the probability of surgical complications. If the mismatch of key monitored consumables is positively correlated with the increased risk of complications, a consumable matching model is constructed based on the consumable matching degree and postoperative complication frequency to recommend suitable orthopedic surgeons for key monitored consumables, achieving a balance between risk and resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The present invention will be further described below with reference to the accompanying drawings.

[0054] Figure 1 is a module diagram of a full-process traceability management system for an orthopedic consumable of the present invention;

[0055] Figure 2 is a flowchart of the acquisition method of key monitored consumables in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the protection scope of the present invention.

[0057] Example 1: Please refer to Figure 1 as shown, a full-process traceability management system for an orthopedic consumable includes the following modules:

[0058] Traceability construction module: used to uniformly code orthopedic consumables and construct a consumable traceability chain;

[0059] In some embodiments, the product identification and production identification of orthopedic consumables are obtained, where the product identification includes: the brand, specification, and model of the orthopedic consumable; the production identification includes: the batch number, production date, and expiration date of the orthopedic consumable.

[0060] It should be noted that orthopedic consumables include: internal fixation and trauma consumables for fixing or repairing bones, artificial joint consumables for reconstruction or replacement of specific parts, and medical consumables for assisting surgeries;

[0061] Based on the production identification of orthopedic consumables, generate the product identification code DI. Based on the production identification of orthopedic consumables, use the time series coding algorithm to construct the production identification code PI;

[0062] Exemplarily, if the model of an orthopedic screw is "FX-2023" and the manufacturer is "AAA", then DI can be encoded as: DI = AAA_FX-2023_ISO5832, where ISO 15223 is an international standard;

[0063] If the production batch of the screw is "B2305", the production date is "2023-05-01", and the expiration date is "2030-05-01", then PI can be encoded as PI = B2305_20230501_20300501;

[0064] Construct the unique device identifier UDI of orthopedic consumables through the formula: UDI = Base64(SHA256(DI||PI));

[0065] Where SHA256 is a hash function used to perform hash encryption on the concatenated product identification code DI and production identification code PI to obtain a binary hash value, and Base64 converts the binary hash value into a readable string of the unique device identifier UDI of orthopedic consumables;

[0066] According to the different usage links of orthopedic consumables, establish a consumable traceability chain in the order of usage links;

[0067] Based on the consumable traceability chain, when orthopedic consumables are circulated in each usage link, associate the unique device identifier UDI of orthopedic consumables with the usage records and store them in the consumable traceability chain;

[0068] It should be noted that the privacy information in the usage records is subject to desensitization encryption processing. The privacy information includes but is not limited to: the names and IDs of patients and medical staff;

[0069] Among them, the role of constructing the consumable traceability chain is as follows:

[0070] Role 1: Process tracking and traceability. Through the unique device identifier UDI of consumables, connect the whole life cycle links of consumables from production to after-surgery, namely production, circulation, surgical use, and postoperative follow-up links, to achieve the process tracking and traceability of orthopedic consumables;

[0071] Function 2: Data supports decision-making. The material screening provides historical data, obtaining the physician responsibility coefficient for cause judgment and analysis, and providing a data basis for the correlation analysis module to verify the correlation between consumable matching degree and complications;

[0072] Function 3: Problem location and responsibility definition. In case of postoperative complications or quality problems, the specific batch, production enterprise, and circulation link can be located through the unique code UDI of the consumable, clarifying the responsibility attribution;

[0073] Exemplarily, orthopedic consumables are divided into production links, circulation links, surgical use links, and postoperative traceability links.

[0074] Material screening module: Used to perform numerical analysis on the historical orthopedic consumable data in the consumable traceability chain, obtaining the intraoperative replacement frequency and postoperative complication frequency. Based on the intraoperative replacement frequency and postoperative complication frequency, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is used to determine the key monitoring consumables;

[0075] From the historical orthopedic consumable data in the consumable traceability chain, obtain the number of times implantable orthopedic consumables are replaced during surgery within the monitoring period, and the number of cases of postoperative complications associated with orthopedic consumables;

[0076] Based on the number of times implantable orthopedic consumables are replaced during surgery, calculate the intraoperative replacement frequency;

[0077] Based on the number of cases of postoperative complications, calculate the postoperative complication frequency;

[0078] It should be noted that the intraoperative replacement frequency is extracted from the data of the surgical use link in the consumable traceability chain, reflecting the adaptability and reliability of orthopedic consumables in the surgical scenario. A high replacement frequency may indicate problems in product design, specification matching, or logistics;

[0079] The postoperative complication frequency is calculated from the postoperative traceability link in the consumable traceability chain, reflecting the long-term safety and biocompatibility of the consumable. A relatively high postoperative complication frequency indicates problems with material performance, surgical operation, or patient individual differences;

[0080] It should be noted that implantable orthopedic consumables include internal fixation and trauma consumables for fixing or repairing bones, and artificial joint consumables for reconstruction or replacement of specific parts;

[0081] Obtain the intraoperative replacement frequency and postoperative complication frequency of multiple implantable orthopedic consumables, mark the intraoperative replacement frequency as f, and the postoperative complication frequency as p;

[0082] It should be further noted that the functions of obtaining the intraoperative replacement frequency and postoperative complication frequency are:

[0083] Function 1: Screening key monitoring consumables. Normalize the intraoperative replacement frequency and postoperative complication frequency of multiple implantable orthopedic consumables through the TOPSIS method, calculate the comprehensive proximity, and identify the consumable with the lowest comprehensive proximity as the key monitoring object;

[0084] Function 2: Quantifying the quality risk of consumables. The intraoperative replacement frequency reflects the adaptability and reliability of the consumable in the surgical scenario. High-frequency replacement may indicate product design defects, specification matching problems, or abnormalities in the logistics link;

[0085] The postoperative complication frequency reflects the long-term safety and biocompatibility of the consumable. High-frequency complications may be related to material properties, surgical procedures, or patient individual differences;

[0086] Function 3: Problem tracing and responsibility definition. Provide data support for subsequent cause judgment to help distinguish whether the complication is caused by the quality of the consumable or the operation of the physician;

[0087] Perform Min-Max normalization on the intraoperative replacement frequency and postoperative complication frequency to construct a decision matrix;

[0088] As Figure 2 shown, based on the decision matrix, use the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to determine the key monitoring consumables;

[0089] Specifically, determine the positive ideal solution f = 0, p = 0, and the negative ideal solution f = f max , p = p max ;

[0090] where f max and p max respectively represent the maximum value of the intraoperative replacement frequency and the maximum value of the postoperative complication frequency in the decision matrix;

[0091] Through the formula: Obtain the relative proximity N f of the intraoperative replacement frequency, where D f － and D f + respectively represent the distance from the intraoperative replacement frequency to the negative ideal solution and the distance from the intraoperative replacement frequency to the positive ideal solution;

[0092] Through the formula: Obtain the relative proximity N p of the postoperative complication frequency, where D p - , D p + respectively represent the distance from the postoperative complication frequency to the negative ideal solution and the distance from the postoperative complication frequency to the positive ideal solution;

[0093] The relative proximity N of the intraoperative replacement frequency p and the relative proximity N of the postoperative complication frequency f are subjected to weighted summation processing to obtain the comprehensive proximity;

[0094] Among them, the relative proximity N of the intraoperative replacement frequency p and the relative proximity N of the postoperative complication frequency f have weights of 0.64 and 0.36 respectively;

[0095] Obtain the comprehensive proximities of multiple implantable orthopedic consumables and perform sorting processing from small to large to obtain the implantable orthopedic consumable with the smallest comprehensive proximity value as the key monitoring consumable;

[0096] It should be noted that the role of determining the key monitoring consumable is as follows:

[0097] Function 1: Precise traceability and responsibility definition. The UDI code of the key monitoring consumable can quickly locate to the production batch, circulation link and usage scenario. If complications in multiple batches are concentrated in the same enterprise, the system will label the enterprise;

[0098] Function 2: Risk warning and quality control. By comprehensively evaluating the intraoperative replacement frequency and postoperative complication frequency through the OPSIS method, identify the key monitoring consumables to achieve early warning.

[0099] The technical solution of this embodiment is: uniformly code orthopedic consumables, construct a consumable traceability chain, perform numerical analysis on the historical orthopedic consumable data of the consumable traceability chain to obtain the intraoperative replacement frequency and postoperative complication frequency, and perform statistical analysis based on the intraoperative replacement frequency and postoperative complication frequency to determine the key monitoring consumables, which is beneficial to locating the responsible link of postoperative complications or quality problems, identifying the key monitoring consumables, and realizing early risk warning.

[0100] Example 2: Please refer to Figure 1 as shown, a full-process traceability management system for orthopedic consumables further includes the following modules:

[0101] Difference discrimination module; used to classify and analyze the postoperative complication frequencies of different physicians, judge whether there are significant differences in the postoperative complication frequencies of physicians with different professional titles, and formulate a traceability strategy;

[0102] Obtain the postoperative complication frequencies of different physicians in the key monitoring consumables of the same batch, marked as R i , where i is the number of the physician;

[0103] Through the formula: Obtain the physician responsibility coefficient As i , where is the average value of the postoperative complication frequencies of different physicians in the key monitoring consumables;

[0104] It should be noted that the focus of monitoring the postoperative complication frequency of different physicians in the consumables and the mean of the postoperative complication frequency of different physicians are based on the premise of the same batch and the same type of orthopedic surgery;

[0105] Obtain the professional titles of different doctors and assign the doctor responsibility coefficient As according to the professional titles. i Group and classify the physicians with the same professional title into groups. i be grouped together;

[0106] By formula: Obtaining effect size η 2 , where SSB is the group sum of squares of the physician responsibility coefficient, and SSW is the within-group sum of squares of the physician responsibility coefficient;

[0107] It should be noted that the group sum of squares represents the differences between physicians in different groups, and the within-group sum of squares represents the differences between physicians in the same group;

[0108] The variance homogeneity p of the physician responsibility coefficients of different groups was calculated based on the variance homogeneity test method;

[0109] If the variance p and effect size η of the physician responsibility coefficients in different groups are homogeneous 2 If all of them meet the preset standard range, it is considered that there are significant differences in the frequency of postoperative complications among physicians with different professional titles;

[0110] For example, if the p-value is ≥ 0.05, it means that there is no statistical evidence to reject the null hypothesis, and the variance homogeneity is considered to be satisfied. The effect size η 2 If the p-value is ≥0.059, it is determined that there is a significant difference in the frequency of postoperative complications among physicians with different professional titles; the null hypothesis of the homogeneity of variance test is that the variances of the groups are equal. If the p-value is ≥0.05, it means that there is no statistical evidence to reject the null hypothesis, and the homogeneity of variance can be considered to meet the conditions;

[0111] It should be noted that the effect size reflects the actual significance of the difference between groups, and the effect size η 2 =0.01 is a small effect, 0.06 is a medium effect, and 0.14 is a large effect;

[0112] If the variance homogeneity p or effect size of the physician responsibility coefficients of different groups does not meet the preset standard range, then there is no significant difference in the postoperative complication frequency among physicians with different professional titles. The postoperative complication frequency of multiple batches of key monitoring consumables can be obtained from the historical orthopedic consumables data in the consumables traceability chain;

[0113] If the postoperative complication frequency of multiple batches of key monitoring consumables is concentrated on the same orthopedic consumables manufacturer, the orthopedic consumables manufacturer should be located in the consumables traceability chain and marked as a key traceability enterprise;

[0114] It should be noted that in some embodiments, the K-means or DBSCAN algorithm is used to cluster different production batches and different production enterprises of key monitored consumables in the postoperative complication frequency, and a mapping relationship is established between the key monitored consumables after clustering and orthopedic consumable production enterprises. If the key monitored consumables after clustering are concentrated in the same orthopedic consumable production enterprise, the orthopedic consumable production enterprise is marked as the key traceability enterprise;

[0115] An early warning prompt is sent to the key monitored enterprise through the consumable traceability chain, and the postoperative complication frequency of the key traceability enterprise is continuously monitored;

[0116] It should be further noted that the role of judging the significant difference in the postoperative complication frequency of doctors with different professional titles is as follows:

[0117] Role 1: Responsibility attribution and risk positioning. If the postoperative complication frequency of high-title doctors is significantly lower than that of low-title doctors, it indicates that the complication may be related to the doctor's operation proficiency. If the difference is significant, the doctor factor is analyzed first to reduce the misjudgment as a consumable quality problem and reduce the unnecessary traceability of the production enterprise;

[0118] Role 2: Optimize the allocation of doctor resources. Through the correlation between doctors and complications, the system can recommend doctors targeted;

[0119] Role 3: Trigger the correlation analysis process. When the difference between different doctors is significant, the correlation between the consumable matching degree and the complication risk is quantified through a logistic regression model;

[0120] Role 4: Reduce the possibility of ineffective analysis: If the difference in professional titles is not significant, directly turn to the clustering analysis of the production enterprise to improve the traceability efficiency.

[0121] Correlation analysis module: If there is a significant difference in the postoperative complication frequency of doctors with different professional titles, conduct a correlation analysis on the postoperative complication frequency of doctors with different professional titles to judge whether the mismatch of key monitored consumables is positively correlated with the increase in complication risk;

[0122] If there is a significant difference in the postoperative complication rate of doctors with different professional titles, obtain the consumable matching degree of key monitored consumables in different orthopedic surgeries that cause postoperative complications;

[0123] Quantify the professional title level of doctors, and build a logistic regression model based on the quantified professional title of doctors and the consumable matching degree;

[0124] Build a logistic regression model log through the formula: log = β0 + β1 * Pd + β2 * Zc, where Pd is the consumable matching degree, Zc is the quantified professional title level, and β0, β1, and β2 are the parameters of the logistic regression model;

[0125] Use the maximum likelihood estimation method to solve the optimal solutions of β0, β1, and β2, through the formula: OR = e β1 Obtain the odds ratio OR of the consumable matching degree;

[0126] It should be noted that the maximum likelihood estimation method constructs a likelihood function, takes the logarithm to convert it into a log-likelihood function, then takes the partial derivative of the parameters and sets the derivative to zero, solves the system of equations to obtain the parameter values that maximize the log-likelihood, so as to estimate the model parameters β0, β1, and β2, and maximize the probability of the observed data appearing;

[0127] Based on the odds ratio OR of the consumable matching degree, judge whether there is a positive correlation between the mismatch of the key monitoring consumables and the increased risk of complications;

[0128] Match the odds ratio OR of the consumable matching degree with the preset odds ratio grading table. If the odds ratio OR of the consumable matching degree is within the preset odds ratio grading, it is considered that there is a positive correlation between the mismatch of the key monitoring consumables and the increased risk of complications;

[0129] It should be noted that the odds ratio OR can intuitively reflect the specific multiple of the increased risk of complications caused by the mismatch of consumables. If the OR value is within the preset odds ratio grading, for example, OR ≥ 1.5, it is determined as a positive correlation and triggers the matching recommendation analysis;

[0130] If the odds ratio OR of the consumable matching degree is not within the preset odds ratio grading, it cannot be considered that there is a positive correlation between the mismatch of the key monitoring consumables and the increased risk of complications;

[0131] It should be noted that if the OR value is higher than 1, it is considered that there is a positive correlation between the mismatch of the key monitoring consumables and the increased risk of complications;

[0132] Among them, the role of judging the positive correlation between the mismatch of the key monitoring consumables and the increased risk of complications is:

[0133] Function 1: Provide data support for constructing a consumable matching model. Calculate the odds ratio of the consumable matching degree through a logistic regression model. If the conditions are met, construct a consumable matching model and give priority to recommending key monitoring consumables for physicians with a low complication frequency;

[0134] Function 2: Support clinical decision-making and procurement. For consumables with a continuously high odds ratio of consumable matching degree, the hospital can suspend procurement or include them in the elimination list, and give priority to selecting alternative products with a high matching degree.

[0135] Matching recommendation module: If it is positively correlated, based on the consumable matching degree and the postoperative complication frequency, construct a consumable matching model and recommend key monitoring consumables to orthopedic surgeons;

[0136] If there is a positive correlation between the mismatch of key monitoring consumables and the increased risk of complications, obtain the consumable matching degree of key monitoring consumables from the consumable traceability chain, as well as the postoperative complication frequencies of different physicians;

[0137] Based on the consumable matching degree and the postoperative complication frequencies of different physicians, construct a consumable matching model through big data algorithms;

[0138] It should be noted that in some embodiments, based on the consumable matching degree and the postoperative complication frequencies of physicians in historical data, use the gradient boosting decision tree (XGBoost / LightGBM) algorithm to construct a complication risk prediction model, analyze the contribution degree of features quantitatively through SHAP values, and generate personalized recommendations in combination with the collaborative filtering algorithm: preferentially recommend physicians with low complication frequencies for high-matching consumables, forcefully match high-title physicians for low-matching consumables, and continuously optimize the recommendation strategy through reinforcement learning;

[0139] Based on the consumable matching model, match and recommend orthopedic surgeons for key monitoring consumables;

[0140] It should be further noted that the role of matching and recommending orthopedic surgeons for key monitoring consumables is as follows:

[0141] Role 1: Reduce the risk of complications and improve medical safety. By predicting the complication probabilities of different physicians using key monitoring consumables through the model, preferentially recommend combinations of low-risk physicians to reduce the risk of complications caused by unskilled operations;

[0142] Role 2: Optimize the allocation of physician resources and improve efficiency. Preferentially recommend physicians with low complication frequencies for high-matching consumables to make full use of high-quality medical resources; forcefully match high-title physicians for low-matching consumables to balance risks and resources;

[0143] Role 3: Achieve dynamic traceability management. Bind the recommendation records of key monitoring consumables with the UDI of consumables and physician information to support tracing the responsibility for complications.

[0144] Role 4: Continuous postoperative monitoring: Conduct long-term follow-up verification on the recommendation results and continuously optimize the recommendation strategy.

[0145] The technical solution of this embodiment is as follows: Classify and analyze the postoperative complication frequencies of different physicians, determine whether there are significant differences in the postoperative complication frequencies of physicians with different titles, and formulate a traceability strategy; if there are significant differences in the postoperative complication frequencies of physicians with different titles, conduct a correlation analysis on the postoperative complication frequencies of different physicians to determine whether there is a positive correlation between the mismatch of key monitoring consumables and the increased risk of complications; if there is a positive correlation, based on the consumable matching degree and the postoperative complication frequencies, construct a consumable matching model, match and recommend orthopedic surgeons for key monitoring consumables, which is beneficial to improving the utilization efficiency of medical resources.

[0146] The above has described an embodiment of the present invention in detail, but the above content is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the application of the present invention should still fall within the scope covered by the present invention.

Claims

1. A full-process traceability management system for orthopedic consumables, characterized in that: It includes the following modules: Traceability construction module: used to uniformly code orthopedic consumables and construct a consumable traceability chain; Material screening module: used to perform numerical analysis on the historical orthopedic consumable data of the consumable traceability chain to obtain the intraoperative replacement frequency and postoperative complication frequency, and conduct statistical analysis based on the intraoperative replacement frequency and postoperative complication frequency to determine the key monitoring consumables; Difference discrimination module; Used to classify and analyze the postoperative complication frequencies of different physicians, determine whether there are significant differences in the postoperative complication frequencies of physicians with different professional titles, and formulate a traceability strategy; Association analysis module: If there are significant differences in the postoperative complication frequencies of physicians with different professional titles, conduct an association analysis on the postoperative complication frequencies of physicians with different professional titles to determine whether there is a positive correlation between the consumable mismatch of the key monitoring consumables and the increased complication risk; Matching recommendation module: If there is a positive correlation, construct a consumable matching model based on the consumable matching degree and postoperative complication frequency, and recommend orthopedic surgeons for the key monitoring consumables.

2. The full-process traceability management system for an orthopedic consumable according to claim 1, characterized in that: The method for determining the key monitoring consumables is as follows: Obtain the historical orthopedic consumable data of the consumable traceability chain, and extract the intraoperative replacement frequency and postoperative complication frequency of implantable orthopedic consumables; Conduct statistical analysis on the intraoperative replacement frequency and postoperative complication frequency to obtain the comprehensive proximity; Based on the comprehensive proximity, conduct a ranking analysis on implantable orthopedic consumables to determine the key monitoring consumables.

3. The whole-process traceability management system for an orthopedic consumable according to claim 2, characterized in that: The method for obtaining the comprehensive proximity is as follows: Based on the intraoperative replacement frequency and postoperative complication frequency, perform normalization processing to construct a decision matrix; Use the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to perform numerical analysis on the decision matrix to obtain the relative proximity of the intraoperative replacement frequency and the relative proximity of the postoperative complication frequency; Perform weighted summation processing on the relative proximity of the intraoperative replacement frequency and the relative proximity of the postoperative complication frequency to obtain the comprehensive proximity.

4. The full-process traceability management system for an orthopedic consumable according to claim 1, wherein: The method for determining whether there are significant differences in the postoperative complication frequencies of physicians with different professional titles is as follows: Obtain the postoperative complication frequencies of different physicians in the same batch of key monitoring consumables, and quantify the responsibilities of physicians based on the postoperative complication frequencies to obtain the physician responsibility coefficients; Group and classify the physician responsibility coefficients according to professional titles, and classify the physician responsibility coefficients of the same professional title into the same group; Perform numerical analysis on the physician responsibility coefficients of different groups to obtain the homogeneity of variance and effect size of different groups; If the homogeneity of variance and effect size of different groups both meet the preset standard range, it is considered that there are significant differences in the postoperative complication frequencies of physicians with different professional titles.

5. The full-process traceability management system for an orthopedic consumable according to claim 4, characterized in that: The method for obtaining the effect size is as follows: Based on the physician responsibility coefficients of different groups, calculate the sum of squares between groups and the sum of squares within groups of the physician responsibility coefficients; Calculate the sum of squares between groups and the sum of squares within groups through a ratio formula to obtain the effect size.

6. The full-process traceability management system for an orthopedic consumable according to claim 4, characterized in that: The method for obtaining the homogeneity of variance is as follows: Calculate the homogeneity of variance of different groups based on the homogeneity of variance test method.

7. The full-process traceability management system for an orthopedic consumable according to claim 1, characterized in that: The method for formulating the traceability strategy is as follows: If there are no significant differences in the postoperative complication frequencies of physicians with different professional titles, obtain the postoperative complication frequencies of multiple batches of key monitoring consumables from the historical orthopedic consumable data of the consumable traceability chain; If the postoperative complication frequencies of multiple batches of key monitored consumables are concentrated in the same orthopedic consumable manufacturing enterprise, locate the orthopedic consumable manufacturer in the consumable traceability chain and mark it as a key traceability enterprise; Send a warning prompt to the key monitored enterprise through the consumable traceability chain, and continuously monitor the postoperative complication frequency of the key traceability enterprise.

8. The full-process traceability management system for an orthopedic consumable according to claim 1, wherein: The method for judging whether the mismatch of the key monitored consumables is positively correlated with the increased risk of complications is as follows: Obtain the consumable matching degrees of the key monitored consumables that cause postoperative complications in different orthopedic surgeries; Quantify the professional title levels of physicians, and construct a logistic regression model based on the quantified professional titles of physicians and consumable matching degrees; Calculate the odds value of the consumable matching degree based on the logistic regression model. If the odds value of the consumable matching degree is within the preset odds value grading, it is considered that the mismatch of the key monitored consumables is positively correlated with the increased risk of complications.

9. The full-process traceability management system for an orthopedic consumable according to claim 8, characterized in that: The construction method of the logistic regression model is as follows: Construct a logistic regression model log through the formula: log = β0 + β1*Pd + β2*Zc, where Pd is the consumable matching degree, Zc is the quantified professional title level, and β0, β1, and β2 are the parameters of the logistic regression model; Use the maximum likelihood estimation method to solve the optimal solutions of β0, β1, and β2.

10. The full-process traceability management system for an orthopedic consumable according to claim 1, characterized in that: The method for recommending orthopedic surgeons who match the key monitored consumables is as follows: Obtain the consumable matching degree of the key monitored consumables and the postoperative complication frequencies of different physicians from the consumable traceability chain; Construct a consumable matching model based on the consumable matching degree and the postoperative complication frequencies of different physicians; Based on the consumable matching model, recommend orthopedic surgeons who match the key monitored consumables.

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