A method for automatically generating out-of-stock medications based on medical facility pharmacy consumption and trends

Abstract

The application discloses a kind of based on medical institution pharmacy consumption and trend automatic generation of drug shortage method, belong to medical institution drug management and logistics field, it includes setting statistical cycle acquisition actual consumption, and generate daily predicted consumption with moving average algorithm and the rise trend of same ratio and ratio comparison.The system synchronously pulls pharmacy and drugstore real-time inventory, calculates the amount of drug shortage based on predicted value, dynamically calculated safety stock and current amount, and automatically generates a request list when reaching the minimum distribution threshold.Then, the list is sorted intelligently according to expiration date, procurement cost and supplier factors and actively pushed to the mobile end.After the goods are delivered, the system uses the actual data and turnover days calculated from the feedback to optimize the parameters of the prediction model and the safety stock coefficient in a closed loop iteration.The application realizes the precision and automation of drug supply through data-driven replenishment logic, which reduces the risk of supply interruption and inventory accumulation while ensuring clinical supply.

Description

Technical Field

[0001] This invention relates to the field of drug management and logistics in medical institutions, and in particular to a method for automatically generating drug shortage data based on consumption and trends in medical institution pharmacies. Background Technology

[0002] Currently, pharmacy replenishment in medical institutions primarily relies on manual inventory checks and paper-based requisitions. Pharmacy managers typically need to periodically check the medications in cabinets and secondary storage areas, manually counting remaining quantities to determine if replenishment is necessary, and filling out requisition forms based on their experience before submitting them to the pharmacy for approval and dispensing. This method not only consumes significant manpower but also often involves substantial time delays in the process review and dispensing stages.

[0003] While some hospitals have adopted information management systems for inventory early warning in related technologies, most only provide simple alerts based on fixed inventory thresholds. Warehouse staff can use the system to view the current inventory levels of each medicine, as well as basic attributes such as batch number, expiration date, and supplier, and formulate replenishment plans accordingly. However, such methods often lack in-depth utilization of historical data and struggle to fully consider fluctuations in actual pharmacy usage and the status of medicines nearing their expiration dates during the requisition process.

[0004] However, existing technologies still have significant shortcomings. First, traditional models cannot reflect the dynamic changes and consumption trends of actual pharmacy usage in real time and accurately. Faced with seasonal demand or sudden increases in usage, they are prone to unexpected shortages of urgently needed clinical medications, and also easily lead to the blind accumulation of slow-moving drugs. Second, manually determined requisition cycles and quantities are highly subjective and fail to scientifically incorporate year-on-year and month-on-month moving average trends for forecasting, resulting in unreasonable safety stock settings. Finally, the entire requisition process lacks a closed-loop management mechanism encompassing demand forecasting, two-way inventory verification, and actual dispensing feedback, making it difficult to meet the rapid response requirements of modern medical services in terms of timeliness and accuracy of pharmacy replenishment. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a method for automatically generating drug shortage data based on the consumption and trends of medical institution pharmacies. This method employs historical consumption analysis based on statistical periods, consumption trend prediction combined with year-on-year and month-on-month moving average algorithms, dynamic safety stock calculation, and a multi-dimensional priority ranking strategy. It can intelligently and automatically calculate pharmacy drug shortages and generate requisition lists. Through proactive push and closed-loop feedback mechanisms, it achieves real-time and accurate responses to pharmacy replenishment, effectively reducing the risk of clinical supply disruptions and significantly optimizing inventory turnover.

[0006] The above objectives can be achieved through the following approach: A method for automatically generating drug shortage data based on consumption and trends in medical institution pharmacies includes: constructing a consumption trend prediction model; regularly extracting drug consumption data from the past three years (year-on-year) and at least three recent month-on-month periods; performing data cleaning on drugs whose daily consumption does not meet the sample conditions; and generating a daily predicted consumption based on a weighted average of the calculated year-on-year and month-on-month reference consumption data; retrieving available pharmacy inventory and available warehouse inventory in real time, and calculating the safety stock for each drug; adding the daily predicted consumption to the safety stock and subtracting the available pharmacy inventory to obtain a preliminary drug shortage; generating a requisition list based on a comparison between the preliminary drug shortage and a preset minimum delivery quantity; allocating and sorting the drugs in the requisition list according to expiry date proximity, procurement cost, and supplier reliability; calculating the drug turnover days index based on the actual delivery data after delivery; and optimizing the preset safety factor and daily predicted consumption for subsequent periods.

[0007] Optionally, the year-on-year reference consumption includes: obtaining the consumption of the drug within the year-on-year period of the past three years, analyzing the trend of the percentage increase in the year-on-year consumption in each year and calculating the average of the percentage increase, and adding the average of the percentage increase to the year-on-year consumption benchmark of the previous year to obtain the year-on-year reference consumption.

[0008] Optionally, the month-on-month reference consumption includes: obtaining the consumption of the drug in at least the most recent three month-on-month periods and calculating the percentage increase trend of the month-on-month consumption in each period, taking the actual consumption in the most recent period and adding the average of the percentage increase trend to obtain the month-on-month reference consumption.

[0009] Optionally, the sample conditions include: the number of samples for year-on-year analysis and the number of samples for month-on-month analysis are both no less than three; if the number of samples in either dimension is less than three, it is determined that the drug does not meet the conditions for trend analysis and no daily predicted consumption is generated.

[0010] Optionally, the calculation of the safety stock of each drug includes: multiplying the average daily consumption of the drug, the lead time of delivery, and the safety factor to obtain the safety stock of each drug.

[0011] Optionally, generating the requisition list includes: if the initial shortage is greater than or equal to the minimum delivery quantity, then the drug is recorded in the requisition list; if the initial shortage is less than the minimum delivery quantity, then no requisition record for the drug is generated.

[0012] Optionally, the allocation order includes: the first priority is the proximity of the expiration date, with the shorter expiration date having the highest priority; when the expiration dates are the same, the second priority is the procurement cost, with the longer the number of days the drug has been in stock having the highest priority.

[0013] Optionally, the drug turnover days index includes: obtaining the outbound and inbound dates of each batch of drugs and calculating the batch's storage time; multiplying the batch's storage time by the inbound quantity of the batch; and then dividing by the total actual outbound quantity within the statistical period to obtain the drug turnover days index reflecting the drug circulation rate.

[0014] Optionally, the method further includes: analyzing the time interval during which the drug inventory at the time of reaching the drug shortage point in the pharmacy meets the pharmacy's dispensing consumption, and combining the drug turnover days index, predicting the reasonable safety stock of the drug in the pharmacy through three or more consecutive calculations, under the premise of ensuring that the pharmacy meets the basic daily dispensing needs, and adjusting the safety factor accordingly.

[0015] Compared with the prior art, the present invention has the following advantages: 1. This method achieves a high degree of automation and real-time response in the pharmacy replenishment process. By proactively tracking consumption and automatically calculating drug shortages, it completely changes the cumbersome traditional model that relied on manual periodic inventory checks and requisition forms. This significantly reduces the risk of underreporting or delays due to human error, ensuring that clinical medication needs receive timely and accurate feedback.

[0016] 2. Improved the accuracy of drug shortage requests and the rationality of inventory structure. By introducing a moving average forecasting model that combines year-on-year and month-on-month comparisons, the system can deeply explore drug consumption patterns and future demand trends. Combined with dynamically calculated safety stock and minimum delivery quantity rules, it can scientifically calculate and effectively avoid the risk of clinical supply disruptions while minimizing the losses and capital tied up in drug stockpiles.

[0017] 3. The system optimizes the allocation efficiency of pharmacy resources and the level of refined supply chain management. It intelligently sorts requisition lists based on multiple factors such as the proximity of drug expiration dates, procurement costs, and supplier delivery reliability. This ensures that drugs nearing their expiration date are prioritized and effectively controls procurement costs, establishing an intelligent decision-making system for medical institutions, from demand forecasting to priority allocation.

[0018] 4. A continuously evolving closed-loop management mechanism and dynamic parameter adjustment capability have been established. Utilizing closed-loop feedback from actual consumption and distribution data, the system can conduct in-depth analysis of drug turnover days and dynamically optimize the safety stock coefficient and predictive model parameters accordingly. This allows replenishment strategies to iterate autonomously as the clinical environment changes, ensuring the long-term stable and efficient operation of the medical institution's drug supply chain.

[0019] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating a method for automatically generating drug shortage data based on consumption and trends in a medical institution's pharmacy, according to an embodiment of the present invention.

[0022] Figure 2 This is a schematic diagram of a drug consumption trend prediction method based on the consumption and trends of a medical institution's pharmacy, according to an embodiment of the present invention.

[0023] Figure 3 This is a schematic diagram of a pharmacy drug shortage replenishment decision analysis method based on the consumption and trends of a medical institution's pharmacy, according to an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Reference Figure 1 One embodiment of the present invention proposes a method for automatically generating drug shortages based on the consumption and trends of medical institution pharmacies. It employs historical consumption analysis based on statistical periods, consumption trend prediction combined with year-on-year and month-on-month moving average algorithms, dynamic safety stock calculation, and multi-dimensional priority ranking strategy. It can intelligently and automatically calculate the amount of drug shortages in pharmacies and generate a requisition list. Through proactive push and closed-loop feedback mechanisms, it can achieve real-time and accurate response to pharmacy replenishment, effectively reduce the risk of clinical supply disruptions, and significantly optimize inventory turnover.

[0026] The method described in this embodiment specifically includes: A consumption trend prediction model is constructed. The consumption of drugs in the past three years and at least three months are extracted daily. For drugs whose daily consumption does not meet the sample conditions, data cleaning is performed. The calculated year-on-year reference consumption and month-on-month reference consumption are weighted and averaged to generate the daily predicted consumption. Real-time retrieval of available pharmacy inventory and available drug warehouse inventory, and calculation of safety stock for each drug; The initial drug shortage is obtained by adding the daily predicted consumption to the safety stock and then subtracting the available pharmacy stock. A requisition list is then generated based on the comparison between the initial drug shortage and the preset minimum delivery quantity. The items in the requisition list are allocated and sorted according to their expiration date proximity, procurement cost, and supplier reliability. After the goods are allocated, the drug turnover days index is calculated based on the actual allocation data. The preset safety factor and daily predicted consumption are then optimized for subsequent cycles.

[0027] Specifically, the statistical period is first set, and historical actual consumption data is extracted from the pharmacy management system. At midnight each day, a timed extraction of consumption data from the most recent three-year year-on-year period and at least three recent month-on-month periods is initiated, and data cleaning is performed to remove records that do not meet the sample conditions. A weighted average is applied based on the year-on-year and month-on-month reference consumption to generate a daily predicted consumption that reflects market patterns and sudden demand. The pharmacy's available inventory and the available inventory containing batch expiration dates in the drug warehouse are retrieved in real time. The daily predicted consumption is added to the dynamically calculated safety stock, and then the pharmacy's available inventory is subtracted to obtain the preliminary drug shortage. A requisition list is generated based on the comparison with the preset minimum delivery quantity. The drugs are sorted according to expiration date proximity, procurement cost, and supplier reliability. After delivery, the actual delivery data is recorded, and the drug turnover days index is calculated to achieve closed-loop optimization of the safety factor and generation parameters for subsequent periods. The digital weighted average calculation involving weights is shown in the following formula: ; Forecast daily consumption; This is a reference consumption figure for the same period last year; This is a reference consumption figure for the month-on-month comparison; These are the preset weighting coefficients.

[0028] The consumption trend prediction model is a technical component that combines year-on-year and month-on-month moving average algorithms to quantitatively predict future drug demand. Daily predicted consumption refers to a reference value generated by the system based on the composite prediction model for drug demand over a future period. Safety stock is the minimum reserve level to ensure the pharmacy's minimum daily consumption, dynamically calculated based on average daily consumption, lead time, and safety factor. The requisition list is a standardized list of drugs that meet replenishment criteria and are automatically compiled by the system.

[0029] For example, when dealing with a certain commonly used antibiotic, the backend calculation shows a year-on-year reference consumption of 100 boxes and a month-on-month reference consumption of 120 boxes. If the weighting factor is set to 0.5, the predicted consumption for the day is calculated using the following formula: The system generates a list based on current inventory data and proactively pushes it out, enabling precise triggering of replenishment tasks.

[0030] Optionally, the year-on-year reference consumption includes: Obtain the consumption of drugs within the same period of the past three years, analyze the trend of the percentage increase in consumption in each year and calculate the average percentage increase. Add the average percentage increase to the consumption benchmark of the previous year to obtain the reference consumption for the same period.

[0031] Specifically, in generating daily predicted consumption, long-term seasonal trends are extracted. The actual consumption of the drug over the past three years is obtained, and the percentage increase at the same time point in each year is analyzed. By calculating the average of these percentages, this average increase is added to the previous year's year-on-year consumption baseline to obtain the year-on-year reference consumption. ; This is a reference consumption figure for the same period last year; This represents the actual consumption for the same period of the previous year. This represents the percentage increase in consumption over the past nth year. The year-on-year reference consumption is a projected reference value obtained by adding the historical average increase to the previous year's year-on-year consumption baseline.

[0032] For example, if a certain drug's year-on-year consumption last year was 1000 boxes, and its year-on-year growth rates over the past three years were 5%, 8%, and 11%, then the average growth rate is 8%. This can be calculated using the formula: The box, this value is used as a reference for the same period last year.

[0033] Optionally, the month-on-month reference consumption includes: Obtain the consumption of the drug in at least the most recent three month-on-month periods and calculate the percentage increase trend of the month-on-month consumption in each period. Take the actual consumption in the most recent period and add the average of the percentage increase trend to obtain the month-on-month reference consumption.

[0034] Specifically, the system uses month-on-month analysis to capture recent trends in medication consumption. It retrieves drug consumption data from at least the most recent three month-on-month periods and calculates the percentage increase in consumption between adjacent periods. Then, it takes the actual consumption in the most recent period and adds the average of the aforementioned percentage increases to obtain the month-on-month reference consumption. ; This is a reference consumption figure for the month-on-month comparison; This represents the consumption amount in the most recent cycle. This represents the average increase over the most recent three month-on-month periods. The month-on-month reference consumption is a predicted reference value obtained by taking the actual consumption in the most recent period and adding the average month-on-month increase. For example... Figure 2 The diagram illustrates the prediction logic of the consumption trend prediction module. The dashed lines represent year-on-year reference consumption based on data from the same period in previous years and month-on-month reference consumption based on recent fluctuations, respectively. The solid line represents the final predicted reference consumption generated by the weighted average algorithm, demonstrating the model's comprehensive ability to capture both seasonal patterns and sudden demand surges.

[0035] For example, the consumption of a certain drug in the most recent cycle was 200 boxes, and the average increase over the past three cycle-by-cycle was 5%. The calculation using the formula is as follows: Boxes. This figure reflects in real time the increase in drug demand due to recent fluctuations in outpatient volume.

[0036] Optionally, the sample conditions include: The sample size for both year-on-year analysis and month-on-month analysis shall be no less than three. If the number of samples in any dimension is less than three, the drug is deemed not to meet the conditions for trend analysis, and no daily predicted consumption will be generated.

[0037] Specifically, to filter out prediction biases caused by insufficient samples, a data cleaning threshold is established. When the model extracts data at midnight each day, it determines whether the historical records of the drug meet the condition that the number of samples for year-on-year analysis and month-on-month analysis are both no less than three. If not, the drug is deemed unsuitable for trend analysis, and the step of generating the predicted consumption is skipped, thereby ensuring the scientific nature of replenishment decisions.

[0038] Among these, the sample condition refers to the minimum data volume threshold required to determine whether drug consumption data meets the requirements of statistical analysis. Data cleaning is a preprocessing operation performed before data processing to remove data that does not meet the sample statistical conditions or is abnormally interfering.

[0039] For example, a newly introduced drug has only two cycles of inventory entry, with a month-on-month sample size of two. Since two is less than three, the drug is identified as not meeting the sample criteria, and therefore no predicted value is generated for it on that day, effectively avoiding blind replenishment recommendations due to insufficient samples.

[0040] Optionally, the calculation of the safety stock of each medicine includes: The safety stock of each drug is calculated by multiplying its average daily consumption, lead time, and safety factor.

[0041] Specifically, the safety stock of each medicine is calculated using a dynamic formula. This involves using the average daily consumption calculated over the statistical period, combined with the lead time for delivery due to procurement and distribution processes, and introducing a safety factor to hedge against consumption fluctuations. The safety stock value is obtained by multiplying these three factors together. ; A represents the safety stock level; P represents the average daily consumption; P represents the lead time; and K represents the safety factor. The average daily consumption is the ratio of the total amount of medicines requisitioned within the statistical period to the number of days in that period. The lead time is the average time required from the time the pharmacy initiates a requisition to the actual delivery and placement of the medicines in the shelving.

[0042] For example, the average daily consumption of a certain medicine is 10 boxes, the lead time for delivery is 3 days, and the safety factor is set at 1.2. The calculation using the formula is: box.

[0043] Optionally, the generation of the claim list includes: If the initial shortage of medicine is greater than or equal to the minimum delivery quantity, then the medicine shall be recorded in the requisition list; If the initial shortage of medicine is less than the minimum delivery quantity, no requisition record for that medicine will be generated.

[0044] Specifically, the final task generation decision is executed, calculating the sum of daily predicted consumption and safety stock, and subtracting the pharmacy's current available inventory. If the resulting preliminary shortage is greater than or equal to the pre-set minimum delivery quantity, the replenishment request is deemed logistically efficient and recorded in the inventory; otherwise, no request record is generated. The minimum delivery quantity is a pre-set minimum threshold for avoiding piecemeal replenishment and improving logistical efficiency. Figure 3 The diagram illustrates the process for determining drug shortages. The first column represents the total demand, which is the sum of predicted consumption and dynamic safety stock. The second column represents the current available inventory in the pharmacy. The difference between the two is the calculated drug shortage amount, indicated by the third column. The dashed line in the diagram marks the minimum delivery threshold. Only when the calculated drug shortage exceeds this threshold will a replenishment task be officially triggered and recorded in the requisition list, ensuring that the replenishment operation complies with the principle of logistical economy.

[0045] For example, the initial shortage is calculated to be 56 boxes. If the pharmacy's minimum delivery quantity is 20 boxes, the system determines to trigger replenishment because 56 is greater than 20; if the shortage is only 15 boxes, it is below the minimum delivery point, and no requisition record is generated.

[0046] Optionally, the allocation order includes: The priority is based on proximity to the expiration date, with the shortest expiration date having the highest priority. When the expiration dates are the same, the next priority is the procurement cost, and the drug with the longest inventory days has the highest priority.

[0047] Specifically, the requisition list is intelligently sorted and ranked. The expiration date proximity is the primary ranking factor, ensuring that medications with shorter expiration dates are prioritized. When expiration dates are the same, the procurement cost factor is used secondarily. By comparing the number of days medications have been in stock, batches with longer storage times are given higher priority for dispatch to reduce the risk of capital tied up. The expiration date proximity factor is the length of time between the current date and the expiration date of the medication, following a first-come, first-served principle. The procurement cost factor is an evaluation indicator that quantifies the degree of capital tied up based on the length of time medications have been in stock.

[0048] For example, there are two batches of the same drug in the pharmacy: batch A has 3 months remaining until its expiration date, and batch B has 6 months remaining until its expiration date but has been in storage for 5 months. The system first prioritizes batch A based on the rule of prioritizing the batch with the shorter expiration date. If there is another batch C with the same 3-month expiration date but has been in storage for 2 months, then batch A will be prioritized before batch C because it has been in storage longer.

[0049] Optionally, the drug turnover days indicator includes: Obtain the outbound and inbound dates of each batch of medicines and calculate the batch's storage time. Multiply the batch's storage time by the inbound quantity of that batch, and then divide by the total actual outbound quantity within the statistical period to obtain the medicine turnover days index, which reflects the medicine circulation rate.

[0050] Specifically, the inventory turnover days index is used to assess inventory turnover quality. The outbound and inbound dates of each batch of medicines are obtained, and their storage time is calculated. This storage time is multiplied by the inbound quantity and divided by the total actual outbound quantity for that period to derive a technical indicator reflecting the turnover rate. ; T represents the drug turnover days indicator; The date of shipment; The date of receipt; This is the sum of the actual outbound quantities; This refers to the quantity of this batch entering the warehouse. The drug turnover days indicator is a technical measure of the average time from when a drug enters the pharmacy to when it is used and consumed.

[0051] For example, a batch of medicines took 5 days from warehousing to shipment, with 100 boxes received and a total of 500 boxes shipped during the period. The calculation is as follows: Days. This indicator is used to evaluate the speed of drug turnover in pharmacies.

[0052] Optionally, the method further includes: The analysis covers the time interval within which the drug inventory meets the pharmacy's dispensing needs when the pharmacy reaches the point of drug shortage. Combined with the drug turnover days index, the reasonable safety stock level of the drug in the pharmacy is predicted through more than three consecutive calculations, under the premise of ensuring that the pharmacy meets its basic daily dispensing needs. The safety factor is then adjusted accordingly.

[0053] Specifically, the system continuously tracks the actual timeframe within which remaining inventory can support medication dispensing when inventory falls to the point of pharmacy shortage. Combining this with the drug turnover days indicator, and while ensuring basic daily medication dispensing needs are met, the system automatically and dynamically optimizes the safety factor by comparing trends from three or more consecutive forecasts, making subsequent replenishment strategies more accurate. The pharmacy shortage point is defined as the critical quantity threshold at which drug inventory falls to trigger the automatic requisition logic. Dynamic optimization involves automatically correcting model parameters based on actual business feedback data to approximate the optimal level.

[0054] For example, the initial safety factor for a certain drug is set at 1.2. Through analysis of turnover data over three consecutive weeks, the system discovers that its inventory support period is far shorter than expected, posing a risk of supply disruption. The closed-loop module automatically adjusts the safety factor for this drug to 1.5, increasing the safety stock benchmark for the next cycle and thus enhancing supply security.

[0055] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0056] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A method for automatically generating drug shortage data based on consumption and trends in medical institution pharmacies, characterized in that: A consumption trend prediction model is constructed. The consumption of drugs in the past three years and at least three months are extracted daily. For drugs whose daily consumption does not meet the sample conditions, data cleaning is performed. The calculated year-on-year reference consumption and month-on-month reference consumption are weighted and averaged to generate the daily predicted consumption. Real-time retrieval of available pharmacy inventory and available drug warehouse inventory, and calculation of safety stock for each drug; The initial drug shortage is obtained by adding the daily predicted consumption to the safety stock and then subtracting the available pharmacy stock. A requisition list is then generated based on the comparison between the initial drug shortage and the preset minimum delivery quantity. The items in the requisition list are allocated and sorted according to their expiration date proximity, procurement cost, and supplier reliability. After the goods are allocated, the drug turnover days index is calculated based on the actual allocation data. The preset safety factor and daily predicted consumption are then optimized for subsequent cycles.

2. The method for automatically generating drug shortage data based on consumption and trends in medical institution pharmacies according to claim 1, characterized in that, The year-on-year reference consumption includes: Obtain the consumption of drugs within the same period of the past three years, analyze the trend of the percentage increase in consumption in each year and calculate the average percentage increase. Add the average percentage increase to the consumption benchmark of the previous year to obtain the reference consumption for the same period.

3. The method for automatically generating drug shortage data based on consumption and trends in medical institution pharmacies according to claim 1, characterized in that, The month-on-month reference consumption includes: Obtain the consumption of the drug in at least the most recent three month-on-month periods and calculate the percentage increase trend of the month-on-month consumption in each period. Take the actual consumption in the most recent period and add the average of the percentage increase trend to obtain the month-on-month reference consumption.

4. The method for automatically generating drug shortage data based on consumption and trends in medical institution pharmacies according to claim 1, characterized in that, The sample conditions include: The sample size for both year-on-year analysis and month-on-month analysis shall be no less than three. If the number of samples in any dimension is less than three, the drug is deemed not to meet the conditions for trend analysis, and no daily predicted consumption will be generated.

5. The method for automatically generating drug shortage data based on consumption and trends in medical institution pharmacies according to claim 1, characterized in that, The calculation of safety stock for each drug includes: The safety stock of each drug is calculated by multiplying its average daily consumption, lead time, and safety factor.

6. The method for automatically generating drug shortage data based on consumption and trends in medical institution pharmacies according to claim 1, characterized in that, The generated claim list includes: If the initial shortage of medicine is greater than or equal to the minimum delivery quantity, then the medicine shall be recorded in the requisition list; If the initial shortage of medicine is less than the minimum delivery quantity, no requisition record for that medicine will be generated.

7. The method for automatically generating drug shortage data based on consumption and trends in medical institution pharmacies according to claim 1, characterized in that, The allocation sorting includes: The priority is based on proximity to the expiration date, with the shortest expiration date having the highest priority. When the expiration dates are the same, the next priority is the procurement cost, and the drug with the longest inventory days has the highest priority.

8. The method for automatically generating drug shortage data based on consumption and trends in medical institution pharmacies according to claim 1, characterized in that, The drug turnover days indicator includes: Obtain the outbound and inbound dates of each batch of medicines and calculate the batch's storage time. Multiply the batch's storage time by the inbound quantity of that batch, and then divide by the total actual outbound quantity within the statistical period to obtain the medicine turnover days index, which reflects the medicine circulation rate.

9. The method for automatically generating drug shortage data based on consumption and trends in medical institution pharmacies according to claim 8, characterized in that, The method further includes: The analysis covers the time interval within which the drug inventory meets the pharmacy's dispensing needs when the pharmacy reaches the point of drug shortage. Combined with the drug turnover days index, the reasonable safety stock level of the drug in the pharmacy is predicted through more than three consecutive calculations, under the premise of ensuring that the pharmacy meets its basic daily dispensing needs. The safety factor is then adjusted accordingly.

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