Data processing method for automatic replenishment of medical consumables

Abstract

The embodiment of the invention relates to a data processing method for automatic replenishment of medical consumables, and the method comprises the steps: a consumable catalogue standardization processing step: transmitting a collected medical consumable catalogue to a UD I service platform, and carrying out the data cleaning processing and automatic coding processing, and obtaining a standardized medical consumable catalogue; a consumable use data acquisition step: acquiring original medical consumable use data according to the standardized medical consumable catalog, and analyzing and processing the medical consumable data to obtain the medical consumable use data; and an automatic replenishment data generation step: carrying out replenishment data analysis processing according to the medical consumable use data and the standardized medical consumable catalog to generate replenishment data.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a data processing method for automatic replenishment of medical consumables. Background Technology

[0002] As a crucial component of healthcare services, the accuracy and timeliness of medical consumables management directly impact medical quality and cost control. Unique Device Identifiers (UDIs), serving as "electronic ID cards" for consumables, enable end-to-end traceability. However, the lack of comprehensive implementation of UDIs for low-value consumables hinders their full-process management. Furthermore, low-value consumables, due to their diverse categories, high usage volume, frequent use, and large inventory levels, lack effective management methods that simultaneously address the needs of reducing management costs, improving inventory turnover, and accurate replenishment forecasting. Currently, a popular approach for low-value consumables is a fixed-quantity management model. This model determines a fixed quantity of each type of low-value consumable in a specific package or container based on clinical department usage habits and historical consumption data. This fixed quantity serves as the management unit, enabling precise control over low-value consumables.

[0003] Currently, in the management of low-value consumables, some hospitals use a combination of traditional manual registration and simple information systems. Low-value consumables are typically purchased, stored, and distributed in whole boxes or cartons, lacking detailed unit-based management. This makes it difficult to accurately track the flow of individual consumables and fails to fully utilize UDI (User-Defined Inventory) for precise traceability, resulting in low efficiency in quality traceability and inventory checks. Some hospitals have introduced information systems, but these suffer from low coding efficiency and inadequate coding execution, failing to achieve automatic replenishment based on actual usage. This often leads to problems such as inventory backlogs of some consumables and supply disruptions of others, making it difficult to meet the real-time needs of clinical departments. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by providing a data processing method that can fully utilize the characteristics of UDI, combine hospital consumables unit management with pre-coding, achieve one code per unit, manage the entire process using UDI management strategies, and achieve automatic replenishment of consumables based on unit consumption.

[0005] To achieve the above objectives, the present invention provides a data processing method for automatic replenishment of medical consumables, comprising:

[0006] A data processing method for automatic replenishment of medical consumables includes:

[0007] The standardization process for the medical consumables catalog involves transmitting the collected medical consumables catalog to the UDI service platform, where data cleaning and automatic coding are performed to obtain a standardized medical consumables catalog.

[0008] The steps for acquiring medical consumable usage data are as follows: obtain the original medical consumable usage data according to the standardized medical consumable catalog, and analyze and process the medical consumable data to obtain medical consumable usage data.

[0009] The automatic replenishment data generation step involves analyzing and processing replenishment data based on the medical consumables usage data and the standardized medical consumables catalog to generate replenishment data.

[0010] Preferably, the standardization process for the consumables catalog specifically includes:

[0011] The consumables catalog collection process involves collecting data on multiple primary consumables.

[0012] The consumable data transmission step involves sending multiple sets of the first consumable data to the UDI service platform according to a preset format.

[0013] The consumable data cleaning step involves cleaning multiple sets of the first consumable data according to a preset matching strategy to obtain multiple sets of first cleaned data; wherein, the first cleaned data corresponds to the first consumable data.

[0014] The consumable data coding step involves attaching a first code to the first cleaning data to obtain first standardized consumable data, and multiple sets of the first standardized consumable data form a standardized medical consumable catalog.

[0015] More preferably, the first consumable data includes at least one field of data;

[0016] The preset matching strategy includes field priority rules, matching methods, field weights, and preset matching thresholds; wherein, the field weights correspond one-to-one with the multiple field data.

[0017] More preferably, the consumable data cleaning step specifically includes:

[0018] According to the field priority rules and the matching method, each field of the first consumable data is matched with each field of the first standard data to obtain the field similarity corresponding to each field data.

[0019] The first matching value of the first consumable data is obtained by calculating and processing based on the similarity of each field and the weight of each field.

[0020] Determine the relationship between the first matching value and the preset matching threshold, and determine the first cleaning data based on the determination result.

[0021] More preferably, determining the relationship between the first matching value and the preset matching threshold, and determining the first cleaned data based on the determination result, specifically includes:

[0022] When the first matching value is greater than or equal to the preset matching threshold, the first consumable data is determined to be the first cleaning data;

[0023] When the first matching value is less than the preset matching threshold, the first standard data is determined to be the first cleaned data.

[0024] More preferably, before performing the consumable data encoding step, the method further includes a first encoding acquisition step, specifically:

[0025] Search the UDI database for a UDI code that matches the first cleaning data;

[0026] When a first UDI code matching the first cleaning data is found, the first UDI code is determined as the first code;

[0027] If no UDI code matching the first cleaning data is found, a first code is generated according to the preset encoding rules and the first cleaning data.

[0028] Preferably, the standardized medical consumables catalog includes multiple sets of first standardized consumables data, and the steps for obtaining consumables usage data specifically include:

[0029] Based on the first standardized consumable data, the consumable usage data of each department within the first preset time period is obtained to obtain the original medical consumable usage data;

[0030] The original medical consumables usage data is cleaned and aggregated to obtain the medical consumables usage data; wherein, the medical consumables usage data includes first consumables usage data corresponding to the first standardized consumables data, and the first consumables usage data includes the department's maximum daily consumption, the department's average daily consumption, the maximum daily consumption, the average daily consumption, and time series data;

[0031] The time series data is smoothed using a preset smoothing method to obtain long-term consumption trend data; wherein, the long-term consumption trend data reflects the gradual change pattern of consumable demand over time.

[0032] The seasonal characteristics of the long-term consumption trend data are analyzed by seasonal decomposition method to separate the seasonal factors corresponding to the first standardized consumable data.

[0033] More preferably, the automatic replenishment data generation step specifically includes:

[0034] Set the replenishment cycle corresponding to the first standardized consumable data based on the warehouse layout, warehouse size, and delivery route;

[0035] The safety stock corresponding to the first standardized consumable data is obtained by calculating and processing the replenishment cycle, the maximum daily consumption of the department, the average daily consumption of the department, and the seasonal factor.

[0036] Determine whether the day is a replenishment day based on the preset time and replenishment cycle;

[0037] When a day is determined to be a replenishment day, a replenishment trigger threshold is calculated based on the average daily consumption, replenishment cycle, and safety stock data, and a replenishment task is determined based on the replenishment trigger threshold.

[0038] When a replenishment task is triggered, the replenishment quantity data is calculated based on the replenishment limit, inventory quantity, quantity in transit, and safety stock.

[0039] The replenishment quantity data is appended to the first standardized consumable data to obtain the replenishment data.

[0040] More preferably, the first standardized consumable data includes unit content, and is calculated based on the replenishment cycle, the department's maximum daily consumption, the department's average daily consumption, and seasonal factors. The formula for the safety stock corresponding to the first standardized consumable data is as follows:

[0041] Safety stock = (Department's maximum daily consumption - Department's average daily consumption) × Replenishment cycle × Seasonal factor ÷ Unit content.

[0042] More preferably, the formula for calculating the replenishment trigger threshold based on average daily consumption, replenishment cycle, and safety stock data is as follows:

[0043] Replenishment trigger threshold = average daily consumption × replenishment cycle + safety stock.

[0044] This invention provides a data processing method for automatic replenishment of medical consumables. It uses a unitized and UDI-integrated mode to automatically replenish medical consumables. This invention deeply integrates unitized management of medical consumables with Unique Device Identifier (UDI). By assigning a code to each unit, it can accurately locate the information of each consumable unit in the entire process of procurement, storage, and use, which greatly improves the precision of management. The method provided by this invention not only changes the previous situation of lacking accurate identification in the management of low-value consumables, but also solves the technical defects of traditional management in tracking the flow of individual consumables. Attached Figure Description

[0045] Figure 1A flowchart illustrating a data processing method for automatic replenishment of medical consumables provided in an embodiment of the present invention;

[0046] Figure 2 A schematic diagram illustrating the structure of an example of a preset encoding rule provided by the present invention;

[0047] Figure 3 A flowchart for the standardization process of the consumables catalog provided in this embodiment of the invention;

[0048] Figure 4 A flowchart for acquiring consumable usage data provided in an embodiment of the present invention;

[0049] Figure 5 This is a flowchart illustrating the automatic replenishment data generation process provided in an embodiment of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0051] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0052] The data processing method for automatic replenishment of medical consumables provided in this invention deeply integrates unitized management of medical consumables with Unique Device Identifier (UDI), changing the low coding efficiency and inadequate coding execution in the prior art, and realizing automatic replenishment based on a one-unit-one-code UDI management strategy for the entire process.

[0053] The technical solution of the present invention will be described below with reference to the accompanying drawings and specific embodiments.

[0054] Figure 1 A flowchart illustrating a data processing method for automatic replenishment of medical consumables provided in an embodiment of the present invention is shown below. Figure 1 As shown, the data processing method for automatic replenishment of medical consumables according to the present invention includes the following steps:

[0055] S1000, the standardization process for the medical consumables catalog, involves transmitting the collected medical consumables catalog to the UDI service platform, and performing data cleaning and automatic coding to obtain a standardized medical consumables catalog.

[0056] Specifically, the standardization of consumable catalogs involves cleaning hospital consumable catalogs using the national UDI database to create unified DI codes. Based on these unified DI codes, errors caused by duplicate replenishment of the same physical consumable under multiple codes can be effectively avoided. Figure 3 A flowchart for the standardization process of the consumable catalog provided in the embodiments of the present invention is shown below. Figure 3 As shown in the embodiment of the present invention, the standardization process of the consumables catalog includes the following steps:

[0057] S1100, Consumables Catalog Collection Steps, collects multiple primary consumables data.

[0058] In this step, the full data of consumables in use is accurately extracted from the hospital's existing material management system (such as HIS, SPD, etc.). This includes data on various consumables, including consumable name, specifications, unit of measurement, manufacturer and registration certificate number. In addition, the data may also include related departments in the hospital that use the consumables, usage frequency in the past 3 months and purchase price / limit price, etc.

[0059] After saving each record according to a data type such as a list, multiple first consumable data sets are formed. Each first consumable data set includes at least one field; wherein the field data includes at least one of the following: consumable name, specifications, unit of measurement, manufacturer, and registration certificate number. In an optional embodiment of the present invention, the field data may further include information such as the hospital department using the first consumable data, the frequency of use in the past 3 months, and the purchase price / limit price. The medical consumable catalog is composed of multiple first consumable data sets, that is, the medical consumable catalog includes multiple first consumable data sets.

[0060] S1200, Consumables Data Transmission Step: Sends multiple first consumables data to the UDI service platform according to a preset format.

[0061] In this step, the preset format is a pre-set format according to the data access specifications of the UDI service platform. For example, the acquired multiple first consumable data, such as those in Excel template format, are converted to conform to the interface transmission requirements of the UDI service platform, and the converted first consumable data is imported into the UDI service platform. In this embodiment of the invention, the UDI service platform is a service platform that executes the data processing method provided by this invention, and can be a server or other device. The data storage format in the UDI service platform conforms to the standard medical consumable data format in the national UDI database.

[0062] S1300, Consumables Data Cleaning Step: Cleaning multiple first consumables data according to a preset matching strategy to obtain multiple first cleaned data.

[0063] In this step, matching rules are set for cleaning, including automatic matching, setting field similarity weights, and manual cleaning strategies. Based on the set matching strategies, a batch cleaning task is triggered on the UDI service platform. The UDI service platform system automatically matches the first consumable data one by one and provides cleaning suggestions based on the matching degree. Based on the system's suggestions, batch cleaning and association with DI codes can be completed with one click, or the cleaning results can be manually processed as needed.

[0064] In a preferred embodiment of the present invention, pre-set cleaning rules, i.e., a preset matching strategy, include field priority rules, matching methods, field weights, and preset matching thresholds. Each field weight corresponds one-to-one with the field data; that is, each field data has a corresponding field weight. For example, in an optional embodiment of the present invention, the field priority rules are sorted from highest to lowest priority as follows: consumable name, manufacturer, registration certificate number, specification, and unit of measurement; the matching method is keyword matching; the field weights for each field are: consumable name 50%, manufacturer 20%, registration certificate number 5%, specification 15%, and unit of measurement 10%; and the preset matching threshold is 0.8. Of course, when using the data processing method provided by the present invention, the field priority rules, matching methods, field weights, and preset matching thresholds can be set according to one's own situation and usage scenario.

[0065] In a preferred embodiment of the present invention, the cleaning process specifically includes the following steps:

[0066] S1310, according to the field priority rules and matching method, match each field data of the first consumable data with each field data of the first standard data to obtain the field similarity corresponding to each field data. Among them, the first standard data is standard medical consumable data obtained from the national UDI database, which includes multiple first standard data.

[0067] S1311, calculate and process the first matching value of the first consumable data based on the similarity and weight of each field.

[0068] S1312, determine the relationship between the first matching value and the preset matching threshold, and determine the first cleaned data based on the determination result. In an optional embodiment of the present invention, this step specifically includes:

[0069] S1313, when the first matching value is greater than or equal to the preset matching threshold, the first consumable data is determined as the first cleaning data.

[0070] S1314, when the first matching value is less than the preset matching threshold, the first standard data is determined as the first cleaned data.

[0071] By performing the above steps S1301 to S1314 on multiple first consumable data respectively, multiple first cleaning data corresponding to each first consumable data can be obtained.

[0072] The following is a detailed explanation of the consumable data cleaning steps of the present invention using a specific example. In a specific example of the present invention, it is assumed that the first consumable data includes the fields "disposable mask", "175×90 (MM)", "piece", "Manufacturer A", and "National Medical Device Approval Number H12345678" for the consumable name, specifications, unit of measurement, manufacturer, and registration certificate number. It is also assumed that the first standard data in the standard medical consumable data obtained from the National UDI includes the fields "disposable mask", "175×90 (MM)", "piece", "Manufacturer A", and "National Medical Device Approval Number H12345678" for the consumable name, specifications, unit of measurement, manufacturer, and registration certificate number. In the preset matching strategy, the field priority rule is set as follows: the order from highest to lowest priority is consumable name > manufacturer > registration certificate number > specifications > unit of measurement, and the field weights for consumable name, specifications, unit of measurement, manufacturer, and registration certificate number are pre-set to be 50%, 20%, 15%, 10%, and 5% respectively, based on the importance of the field data. The preset matching threshold is 0.8. In this example, the specific steps for matching the first consumable data according to the preset matching strategy, such as keyword matching, include:

[0073] First, the data in each field of the first consumable data is matched with the data in each field of the first standard data according to the field priority to obtain the field similarity of each field in the first consumable data. That is, according to the field priority rule of consumable name > manufacturer > registration certificate number > specification > unit of measurement, the field similarity of consumable name, specification, unit of measurement, manufacturer and registration certificate number in the first consumable data with each field in the first standard data is 1.0, 1.0, 1.0, 1.0 and 1.0 respectively.

[0074] Secondly, based on the similarity scores of 1.0, 1.0, 1.0, 1.0, and 1.0 for each field, and the weights of each field of 50%, 30%, 15%, 10%, and 5%, the following calculations were performed:

[0075] First matching value = 1.0 × 50% + 1.0 × 20% + 1.0 × 15% + 1.0 × 10% + 1.0 × 5% (Equation 1)

[0076] The first matching value was calculated to be 1.

[0077] Finally, by judging the relationship between the first matching value 1 and the preset matching threshold 0.8, it can be concluded that the first matching value is greater than the preset matching threshold. Therefore, the first consumable data is determined as the first cleaning data.

[0078] S1400, Consumables Data Encoding Step: A first code is added to the first cleaning data to obtain the first standardized consumables data. Multiple first standardized consumables data constitute a standardized medical consumables catalog.

[0079] In a preferred embodiment of the present invention, before performing this step, i.e. before performing the consumable data encoding step, the method further includes a first encoding acquisition step, specifically:

[0080] S1410, search the UDI database for a UDI code that matches the first cleaning data based on the first cleaning data.

[0081] S1411, determine whether a first UDI code matching the first cleaned data has been found, and execute step S1430 or S1440 according to the judgment result.

[0082] S1412, when a first UDI code matching the first cleaned data is found, the first UDI code is determined as the first code.

[0083] S1413, if no UDI code matching the first cleaned data is found, a first code is generated according to the preset encoding rule and the first cleaned data. The preset encoding rule is the GS1 / MA encoding rule.

[0084] Figure 2 A schematic diagram illustrating the structure of an example of the preset encoding rules provided by this invention, such as... Figure 2 As shown, the MA code is issued by the Zhongguancun Institute of Industrial and Information Technology (ZIIOT). The DI of the MA code consists of the issuing organization code, industry code, manufacturer code, packaging code, product code, and check digit. The first three parts are assigned by ZIIOT, while the latter three parts are assigned by the manufacturer to identify the specific model or specifications of the product.

[0085] In another optional embodiment of the present invention, before performing this step, i.e. before performing the consumable data encoding step, the method further includes a first encoding acquisition step, specifically:

[0086] S1420, Search the UDI database for a UDI code that matches the first cleaning data based on the first cleaning data.

[0087] S1421, Determine whether a first UDI code matching the first cleaned data is found, and add an encoding status label to the first cleaned data.

[0088] S1422, when a first UDI code matching the first cleaned data is found, the first UDI code is determined as the first code, and a "Matched official DI" code status label is added to the first cleaned data.

[0089] S1423, when no matching UDI code is found for the first cleaned data, add a "No matching official DI code" status label to the first cleaned data. The unmatched first cleaned data can follow the UDI management specifications and be self-coded using the MA coding method. For example, the issuing organization code MA.156 can be replaced with MA.XXX, the industry code can be replaced with general consumables, high-value consumables, reagents, etc., the manufacturer code can be replaced with the code of the consumable manufacturer in the hospital's material management system, the packaging code and product code can be self-coded similarly, and finally, the check digit can be padded using the parity check method.

[0090] After attaching a first code and / or a code status label to the first cleaned data, the first standardized consumable data is obtained. Through the above processing, it can be seen that the first code is essentially a UDI code.

[0091] In this embodiment of the invention, after the multiple first consumable data in the medical consumable catalog collected in step 1100 are processed by steps S1200 to S1400, multiple first standardized consumable data are obtained, and these first standardized consumable data constitute the standardized medical consumable catalog of the present invention.

[0092] S2000, Consumables Usage Data Acquisition Steps: Obtain raw medical consumables usage data according to the standardized medical consumables catalog, and analyze and process the medical consumables data to obtain medical consumables usage data.

[0093] Specifically, Figure 4 A flowchart for acquiring consumable usage data provided in an embodiment of the present invention, such as Figure 4 As shown, in this embodiment of the invention, the acquisition of consumable usage data includes the following steps:

[0094] S2100: Obtain consumable usage data for each department within a first preset time period based on the first standardized consumable data, and obtain the original medical consumable usage data.

[0095] In this invention, consumable usage data for each standard consumable trial data in the standardized medical consumable catalog is collected from various departments of a hospital within a first preset time period, based on the first code of each standard consumable trial data. The first preset time period is at least 12 months prior to the current date of execution of this invention. The first preset time period can also be set according to the specific circumstances of using the technical solution of this invention, such as the past 12 months, the past 18 months, the past 24 months, etc., or a given time interval such as January 2024 to June 2025.

[0096] In a preferred embodiment of the present invention, consumable usage data from various departments of a hospital over the past 12 months are collected to obtain raw medical consumable usage data, including daily usage quantity, usage date, corresponding consumable UDI, and department information. The collected data is denoted as q(i,j,k), where i is the UDI code index, j is the department index, and k is the date index. The UDI code corresponds to the first code of the first standard consumable data.

[0097] S2200 performs data cleaning and aggregation on the original medical consumables usage data to obtain medical consumables usage data.

[0098] Among them, the medical consumables usage data includes the first consumables usage data corresponding to the first standardized consumables data. The first consumables usage data includes the department's maximum daily consumption, the department's average daily consumption, and time series data.

[0099] In this embodiment of the invention, the data cleaning process is to remove outliers. Due to factors such as system entry errors and non-routine consumption data such as special emergency requisitions by departments, the collected consumable usage data may contain some abnormal data, so it is necessary to remove outliers from the consumable usage data.

[0100] In a preferred embodiment of the present invention, the daily usage data for each UDI and department combination is {q(i,j,1),q(i,j,2),...,q(i,j,n)}, where n≥1 and n is an integer, representing the number of valid days for the group within 12 months. The mean μ and standard deviation σ are calculated, and the formula for calculating the mean μ is as follows:

[0101]

[0102] Where n is the total number of data points, and q(i,j,k) is the daily usage on day k.

[0103] The formula for calculating the standard deviation σ is as follows:

[0104]

[0105] Where μ is the mean and q(i,j,k) is the daily usage on day k.

[0106] If |q(i,j,k)-μ|>3σ, then q(i,j,k) is marked as an outlier and removed from the original medical consumables usage data, ensuring that the remaining data {q(i,j,1)',q(i,j,2)',...,q(i,j,m)'}, where m≤n and m is an integer, represents valid daily consumption samples.

[0107] The raw medical consumables usage data, after removing outliers, is aggregated by week and month to generate time series data. This aggregation process, such as aggregating daily usage into weekly or monthly totals, yields monthly and weekly time series data.

[0108] In a preferred embodiment of the present invention, the total monthly consumption Q(i,j,month) for a single UDI and a single department is calculated using natural months, such as January, February, ..., December. Specifically, for the m-th month, the effective daily usage within that month is selected {q(i,j,k)|k∈the set of dates in the m-th month}, and summed to obtain Q(i,j,month). This calculation method ultimately generates a monthly time series {Q(i,j,month,1),Q(i,j,month,2),...,Q(i,j,month,12)}, which is monthly time series data that can be used to analyze monthly consumption trends, such as quarterly fluctuations and annual growth patterns.

[0109] In another preferred embodiment of the present invention, a weekly time series {Q(i,j,week,1), Q(i,j,week,2), ...,Q(i,j,week,52)} can be generated, that is, weekly time series data.

[0110] In this embodiment of the invention, for a single UDI, the maximum daily consumption of a department can be obtained by traversing the original medical consumables usage data, and data such as the average daily consumption, maximum daily consumption, and average daily consumption of the department can be calculated.

[0111] S2300 uses a preset smoothing method to smooth time series data to obtain long-term consumption trend data.

[0112] Among them, the long-term consumption trend data reflects the gradual change in the demand for consumables over time.

[0113] In this step, the preset smoothing method is a pre-set smoothing method. In a preferred embodiment of the present invention, the preset smoothing method is the moving average method. Using the moving average method to smooth time series data can eliminate short-term fluctuations and highlight long-term trends. After smoothing weekly or monthly time series data using the moving average method, long-term consumption trend data, such as linear growth trends, can be separated.

[0114] In a specific example of this invention, a simple moving average (SMA) can be used to smooth weekly or monthly time series data to extract long-term consumption trend data.

[0115] In another specific example of this invention, a weighted moving average (WMA) is used to smooth weekly or monthly time series data to separate long-term consumption trend data.

[0116] After smoothing time series data using the moving average method, long-term consumption trend data can be extracted, which can reflect the gradual change in demand for consumables over time.

[0117] S2400 analyzes the seasonal characteristics of long-term consumption trend data using the seasonal decomposition method to separate the seasonal factors corresponding to the first standardized consumable data.

[0118] In this step, the seasonal characteristics of the data are analyzed using the seasonal decomposition method. For consumables with obvious seasonality, such as the increased use of masks during the flu season, seasonal factors are isolated.

[0119] The raw medical consumables usage data is processed through steps S2200 to S2400 to obtain medical consumables usage data, which includes the first UDI, that is, the first code of the first standardized consumable data, and the department's maximum daily consumption, department's average daily consumption, maximum daily consumption, average daily consumption, and seasonal factor for the consumables corresponding to the first code in the first time interval. Specifically, the department's maximum daily consumption, department's average daily consumption, maximum daily consumption, average daily consumption, and seasonal factor for the first consumables usage data. After processing all the first standardized consumables data in the standardized medical consumables catalog through steps S2100 to S2400, the first consumables usage data corresponding one-to-one with all the first standardized consumables data in the standardized medical consumables catalog can be obtained.

[0120] The S3000 automatic replenishment data generation step analyzes and processes replenishment data based on medical consumables usage data and a standardized medical consumables catalog to generate replenishment data.

[0121] Specifically, Figure 5 The flowchart for automatic replenishment data generation provided in the embodiments of the present invention is as follows: Figure 5 As shown, in this embodiment of the invention, the automatic replenishment data generation includes the following steps:

[0122] S3100 sets the replenishment cycle corresponding to the first standardized consumable data based on warehouse layout, warehouse size, and delivery route.

[0123] The warehouse layout, size, and delivery routes are based on data collected in advance. The warehouse layout includes factors such as building distribution, warehouse size, and delivery routes. Replenishment cycles are set based on usage, and safety stock is calculated for each warehouse. The replenishment cycle is set to a specific interval, typically twice a week for departments with high usage and once a week or every two weeks for departments with low usage.

[0124] S3200 calculates the safety stock corresponding to the first standardized consumable data based on the replenishment cycle, the department's maximum daily consumption, the department's average daily consumption, and seasonal factors.

[0125] In a preferred embodiment of the present invention, the first standardized consumable data includes unit content. The data is calculated based on the replenishment cycle, the department's maximum daily consumption, the department's average daily consumption, and seasonal factors. The formula for determining the safety stock corresponding to the first standardized consumable data is as follows:

[0126] Safety stock = (Department's maximum daily consumption - Department's average daily consumption) × Replenishment cycle × Seasonal factor ÷ Unit content (Equation 4)

[0127] S3300 determines whether a day is a replenishment day based on preset time and replenishment cycle.

[0128] In this embodiment of the invention, the system triggers replenishment data analysis at a set time each day. The preset time can be 9:00 AM daily, etc., and can be set in advance according to the usage scenario. In a preferred embodiment of the invention, the preset time is 1:00 AM daily.

[0129] When the preset time arrives each day, determine whether it is a replenishment day based on the replenishment cycle and the current date.

[0130] S3400: When a day is determined to be a replenishment day, the replenishment trigger threshold is calculated based on the average daily consumption, replenishment cycle and safety stock data, and the replenishment trigger threshold is used to determine whether to trigger a replenishment task.

[0131] In a preferred embodiment of the present invention, the following triggering conditions for each consumable in the warehouse are calculated:

[0132] Current inventory quantity + quantity in transit < replenishment trigger threshold (Equation 5) Where, the formulas for calculating the replenishment trigger threshold, current inventory quantity, and quantity in transit are as follows:

[0133] Replenishment trigger threshold = Department average daily consumption × Replenishment cycle + Safety stock (Equation 6)

[0134]

[0135] Where i and j both represent the number of unit packages of a certain consumable specification (DI) in the department. For example, a certain specification of indwelling needle has three forms in the department: large package (50), medium package (30), and small package (10). In this case, i = j = (1, 2, 3) and m = n = 3.

[0136] Determine if the replenishment trigger threshold is greater than the current inventory quantity plus the quantity in transit. If the replenishment trigger threshold is greater than the current inventory quantity plus the quantity in transit, it means that replenishment is required, and the replenishment task is triggered. At this time, continue to execute the subsequent steps S3500.

[0137] The S3500 calculates replenishment quantity data based on the replenishment limit, inventory quantity, quantity in transit, and safety stock.

[0138] In a preferred embodiment of the present invention, the replenishment quantity data is calculated using the following formula:

[0139] Replenishment quantity = Maximum replenishment quantity - Current inventory quantity - Quantity in transit + Safety stock (Equation 9)

[0140] S3600 appends the replenishment quantity data to the first standardized consumable data to obtain the replenishment data.

[0141] After obtaining the replenishment quantity data, it is appended as a field to the first standardized consumable data to generate replenishment data.

[0142] For each first standardized consumable data in the standardized medical consumable catalog, perform the corresponding processing steps S3100 to S3600 to obtain the replenishment data for the standardized medical consumable catalog.

[0143] By following the steps above, data on each standardized medical consumable in the standardized medical consumable catalog, along with its corresponding replenishment quantity, can be automatically obtained at predetermined time points. This data can be output according to a predetermined output format using existing output methods for reference by relevant personnel. Alternatively, the data can be sent to suppliers via the network for automatic replenishment of the relevant medical consumables.

[0144] This invention provides a data processing method for automatic replenishment of medical consumables. It fully utilizes the characteristics of User-Defined Inventory (UDI) and combines it with unit-based management and pre-coding methods for hospital consumables to achieve one code per unit. The method uses a UDI management strategy for full-process management and relies on unit consumption to achieve automatic replenishment. It acquires and analyzes medical consumable usage data from various hospital departments, sets replenishment cycles based on the specific usage of various medical consumables, processes replenishment tasks according to preset times, and automatically calculates replenishment quantities. This data processing method significantly improves the efficiency of medical consumable management, saves on management personnel costs, reduces inventory costs, and enhances the accuracy of medical consumable quality traceability.

[0145] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0146] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented in hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0147] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A data processing method for automatic replenishment of medical consumables, characterized in that, The method comprises: The consumable catalog standardization processing step transmits the collected medical consumable catalog to the UDI service platform and performs data cleaning processing and automatic coding processing to obtain a standardized medical consumable catalog; The consumable use data acquisition step acquires original medical consumable use data according to the standardized medical consumable catalog, and analyzes and processes the medical consumable data to obtain medical consumable use data; The automatic replenishment data generation step analyzes and processes replenishment data according to the medical consumable use data and the standardized medical consumable catalog to generate replenishment data.

2. The data processing method according to claim 1, characterized in that, The consumable catalog standardization processing step specifically comprises: The consumable catalog collection step collects a plurality of first consumable data; The consumable data transmission step sends a plurality of the first consumable data to the UDI service platform according to a preset format; The consumable data cleaning step cleans a plurality of the first consumable data according to a preset matching strategy to obtain a plurality of first cleaning data; wherein the first cleaning data corresponds to the first consumable data; The consumable data coding step appends a first code to the first cleaning data to obtain first standardized consumable data, and a plurality of the first standardized consumable data form a standardized medical consumable catalog.

3. The data processing method according to claim 2, characterized in that, The first consumable data comprises at least one field data; The preset matching strategy comprises a field priority rule, a matching method, a field weight, and a preset matching threshold; wherein the field weight corresponds to the plurality of field data one by one.

4. The data processing method according to claim 3, characterized in that, The consumable data cleaning step specifically comprises: According to the field priority rule and the matching method, each field data of the first consumable data is matched with each field data in the first standard data respectively to obtain a field similarity corresponding to each field data respectively; According to each field similarity and each field weight, a first matching value of the first consumable data is calculated and processed; Determine the first cleaning data according to the judgment result.

5. The data processing method according to claim 4, characterized in that, Determine the first cleaning data according to the judgment result specifically comprises: When the first matching value is greater than or equal to the preset matching threshold, the first consumable data is determined as the first cleaning data; When the first matching value is less than the preset matching threshold, the first standard data is determined as the first cleaning data.

6. The data processing method according to claim 2, characterized in that, Before the consumable data coding step is executed, the method further comprises a first code acquisition step, specifically: According to the first cleaning data, find the UDI code matching the first cleaning data in the UDI database; When the first UDI code matching the first cleaning data is found, the first UDI code is determined as the first code; When no UDI code matching the first cleaning data is found, generate the first code according to the preset coding rule and the first cleaning data.

7. The data processing method of claim 1, wherein, The standardized medical consumable catalog comprises a plurality of first standardized consumable data, and the consumable use data acquisition step specifically comprises: According to the first standardized consumable data, consumable use data of each department in a first preset time period is acquired to obtain original medical consumable use data; The original medical consumable use data is subjected to data cleaning processing and data aggregation processing to obtain the medical consumable use data; wherein the medical consumable use data comprises first consumable use data corresponding to the first standardized consumable data, and the first consumable use data comprises department maximum daily consumption, department average daily consumption, maximum daily consumption, average daily consumption and time series data; The time series data is subjected to smoothing processing using a preset smoothing processing method to obtain long-term consumption trend data; wherein the long-term consumption trend data reflects the gradual change law of consumable demand over time; Seasonal characteristics of the long-term consumption trend data are analyzed by a seasonal decomposition method to separate out a seasonal factor corresponding to the first standardized consumable data.

8. The data processing method according to claim 7, characterized in that, The automatic replenishment data generation step specifically comprises: A replenishment cycle corresponding to the first standardized consumable data is set according to warehouse layout, warehouse size and distribution route; A safety stock corresponding to the first standardized consumable data is calculated and processed according to the replenishment cycle, the department maximum daily consumption, the department average daily consumption and the seasonal factor; It is judged whether the current day is a replenishment day according to a preset time and the replenishment cycle; When it is judged that the current day is a replenishment day, a replenishment trigger threshold is calculated according to the average daily consumption, the replenishment cycle and the safety stock data, and it is judged whether a replenishment task is triggered according to the replenishment trigger threshold; When it is judged that the replenishment task is triggered, a replenishment quantity data is calculated according to a replenishment upper limit, a stock quantity, an in-transit quantity and the safety stock; The replenishment quantity data is attached to the first standardized consumable data to obtain the replenishment data.

9. The data processing method according to claim 8, characterized in that, The first standardized consumable data comprises unit content, and a formula for calculating and processing the safety stock corresponding to the first standardized consumable data according to the replenishment cycle, the department maximum daily consumption, the department average daily consumption and the seasonal factor is: Safety stock = (department maximum daily consumption - department average daily consumption) x replenishment cycle x seasonal factor ÷ unit content.

10. The data processing method according to claim 9, characterized in that, A formula for calculating the replenishment trigger threshold according to the average daily consumption, the replenishment cycle and the safety stock data is: Replenishment trigger threshold = average daily consumption x replenishment cycle + safety stock.

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