Prescription information intelligent management platform for hospital pharmacy

Abstract

The invention discloses a prescription information intelligent management platform for a hospital pharmacy, and particularly relates to the technical field of prescription information management. According to the method, by integrating multi-dimensional data of the pre-occupied stock, the medicine historical consumption and the outpatient visitors flow, the accurate medicine pre-estimated demand quantity is calculated by relying on time sequence set construction, fitting the trend through the least square method and converting the trend ratio into the additional coefficient and combining the basic consumption quantity and the flow coefficient, the limitation of traditional artificial experience pre-judgment is eliminated, and the accuracy of medicine pre-judgment is improved. Deviation between demand estimation and clinical actual demand is greatly reduced, and reliable data support is provided for pharmacy prescription information and inventory management.

Description

Technical Field

[0001] This invention relates to the field of prescription information management technology, and more specifically, to an intelligent prescription information management platform for hospital pharmacies. Background Technology

[0002] Under the general trend of digital and intelligent transformation in the healthcare industry, hospital pharmacies, as the core hub of the healthcare service system, have their prescription information and drug inventory management efficiency directly related to the timeliness of clinical diagnosis and treatment and the patient's medical experience.

[0003] Existing intelligent prescription information management platforms for hospital pharmacies still have the following shortcomings in practical applications: The management model relies heavily on manual records and experience-based predictions. On the demand side, pharmacies struggle to accurately integrate pre-occupied inventory (the demand for drugs corresponding to approved but not yet dispensed prescriptions), historical consumption patterns of different drug categories, and multi-dimensional data on outpatient traffic fluctuations, making it impossible to form a systematic assessment of drug consumption demand. On the other hand, relying solely on manual review to judge drug consumption trends can easily lead to misjudgments of rising or falling demand due to subjective experience biases, resulting in a disconnect between demand forecasts and actual clinical needs. On the supply side, pharmacy real-time inventory and drug transportation data are mostly stored in isolation and lack a linkage mechanism with demand-side data. In addition, when there is a shortage of medicines, the traditional allocation model also has the drawback of slow response. There is no scientific standard for classifying the urgency level, and it is impossible to quickly screen the inventory surplus of surrounding cooperative hospitals. Often, the cumbersome allocation process delays clinical medication, which not only affects the quality of diagnosis and treatment, but may also lead to medical disputes.

[0004] To address this, an intelligent prescription information management platform for hospital pharmacies was launched. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an intelligent prescription information management platform for hospital pharmacies.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The intelligent prescription information management platform for hospital pharmacies includes the following modules: The data acquisition module is used to obtain demand-side and supply-side parameters in real time; Among them, demand-side parameters include pre-occupied inventory data, historical consumption data of different drug categories, and outpatient traffic data; Supply-side parameters include the real-time inventory of various medicines in pharmacies and the known amount of medicines in transit; The pharmacy forecasting module is used to process demand-side parameters using forecasting logic, integrate and classify the processed data, output the estimated demand for various types of drugs in the next set time zone, and generate a demand report. The estimated demand for medicines = basic consumption × additional coefficient × flow coefficient; The supply linkage module is used to compare and analyze the demand reports of pharmacies in the next set time zone with the supply-side parameters, and then generate a pharmacy allocation establishment report. The pharmacy allocation report includes the hospital's actual available quantity of various medicines in the next set time zone, estimated demand, predicted shortage of medicines, planning evaluation coefficient, marking results, and allocation plan.

[0007] Specifically, the calculation logic of the additional coefficient; Extract the consumption quantities of various drugs over the past x time zones from historical consumption data in the demand-side parameters, and construct time-series sets for each drug type. ; i represents the serial number of each type of medicine in the pharmacy. This represents the quantity of drug class i consumed in the x-th time zone in the past; Will The points in the coordinate system are drawn together to form a polyline. The least squares method is used to fit linear equations, and the slope of the linear equations is used to determine the direction of the consumption trend of pharmaceuticals; the direction of the consumption trend includes the upward trend of demand and the downward trend of demand. If the slope is greater than 0, it is determined to be an upward trend in demand; If the slope is less than 0, it is determined to be a downward trend in demand; Mark the line formed by two adjacent sets of points in the line graph as a wavy line, draw the horizontal line corresponding to each wavy line, and obtain the angle formed by each wavy line and the corresponding horizontal line. Based on the direction of trend change and the type of angle, the corresponding outputs are the upward trend ratio and the downward trend ratio; Construct trend ratio-additional coefficient mapping rules corresponding to upward and downward demand trends respectively, and convert the trend ratio into an additional coefficient based on the trend change direction of the drug using the corresponding mapping rules.

[0008] Specifically, the output logic for the upward trend ratio and the downward trend ratio; Angle types include obtuse angles and acute angles; If the angle between a certain undulating line and the corresponding horizontal line is an obtuse angle, then take the rightmost point in the two adjacent sets of points as the starting point, and construct a line segment horizontally downward from the starting point until it is on the same horizontal line as the left point. Mark the constructed line segment as an ascending line. Identify the ascending lines constructed by each group of obtuse angles, accumulate the lengths of the ascending lines, and obtain the ascending intensity value of the drug. If the angle between a certain undulating line and the corresponding horizontal line is an acute angle, then take the left point in the two adjacent sets of points as the starting point, and construct a line segment horizontally downward from the starting point until it is on the same horizontal line as the right point. Mark the constructed line segment as a descending line. Identify the descending lines constructed by each group of obtuse angles, accumulate the lengths of the descending lines, and obtain the descending intensity value of the drug. If the trend change direction is an upward trend in demand, then the ratio of the upward strength value to the downward strength value is calculated and used as the upward trend ratio. If the demand trend is downward, the ratio of the downward intensity to the upward intensity is calculated and used as the downward trend ratio.

[0009] Specifically, the calculation logic of the flow coefficient; Extract outpatient traffic data from the previous time zone from the historical consumption data in the demand-side parameters, and calculate the average daily traffic data in the previous time zone to obtain the average daily traffic in the previous time zone. The outpatient flow ratio is obtained by calculating the ratio with the average daily flow as the numerator and the reference flow as the denominator. The reference flow is calculated by averaging the average daily flow of each historical time zone. By utilizing the constructed mapping rule between outpatient flow ratio and flow coefficient, the outpatient flow ratio of the hospital is transformed into the flow coefficient.

[0010] Specifically, a description of the basic consumption quantity and pre-occupied inventory data; The consumption of various drugs in the past two time zones was extracted and averaged to serve as the baseline consumption. The pre-occupied inventory data refers to the drug categories and quantities corresponding to prescription orders that have been approved but have not yet been dispensed.

[0011] Specifically, the process of calculating and determining the actual available quantity and the predicted lack of drugs; The real-time inventory and known in-transit volume of various medicines are extracted from the supply-side parameters, and after adding them together and subtracting the pre-occupied inventory data, the actual available quantity of various medicines in the next set time zone is obtained. The actual availability of various drugs in the next set time zone is compared with the estimated demand. If the actual availability of a certain drug is lower than the estimated demand, it is marked as a drug that is predicted to be in short supply.

[0012] Specifically, the calculation logic of the plan evaluation coefficient; For predicted drug shortages, the emergency rate of drug replenishment is obtained by subtracting the actual available quantity from the estimated demand and then dividing by the actual available quantity. Within a pre-built drug type database, the system retrieves and predicts the category of drugs that are missing, including emergency drugs, essential treatment drugs, routine drugs, and auxiliary drugs. Different categories are assigned a set of clinical importance scores; After comprehensively processing the predicted emergency rate of drug shortage and the clinical importance score, the planned assessment coefficient of the predicted drug shortage is output.

[0013] Specifically, the labeling results include recommended procurement drugs and recommended allocation drugs; The planned assessment coefficient for the predicted drug shortage is compared with the preset reference coefficient. If the planned assessment coefficient is lower than the reference coefficient, the predicted drug shortage is marked as a recommended drug for procurement. If the planned assessment coefficient is higher than the reference coefficient, it is marked as a recommended drug for allocation.

[0014] Specifically, the logic behind the allocation plan; For the recommended allocation of medicines, calculate the difference between the actual available quantity and the estimated demand, and take the absolute value as the required supplementary quantity; Draw a circle with the current hospital pharmacy as the center and the distance as the radius, identify the partner hospitals inside the circle, and screen partner hospitals with sufficient inventory as potential transfer sources; Obtain the allocation suitability coefficient for each potential transferor; For each potential transferor, the difference between the actual available quantity and the estimated demand is calculated as the surplus. The surplus is compared with the required replenishment quantity of the current hospital's recommended drug allocation. If the number of potential transferors whose surplus is greater than the required replenishment quantity is greater than 1, then more than 1 potential transferors are selected, and the potential transferor with the highest allocation suitability coefficient is output as the current hospital's recommended drug allocation plan. If there is no potential transferor with a surplus greater than the required replenishment, a combined transfer signaling is triggered, and the potential transferor with the highest output transfer suitability coefficient is taken as the primary transfer target. The remaining supplement amount is obtained by subtracting the surplus amount of the main allocation target from the required supplement amount. Among the remaining potential transferors, those with surplus amounts exceeding the remaining replenishment amounts are selected as candidate targets, and those with the highest allocation suitability coefficients among the candidate targets are selected as secondary allocation targets. The potential transferors corresponding to the primary and secondary transfer targets are used as the current hospital's recommended drug transfer plan.

[0015] Specifically, the calculation logic of the allocation suitability coefficient; Using formula Calculate the drug sufficiency rate of potential transferors. ;in and These represent the actual available quantity and the estimated demand for the corresponding medicines recommended for allocation by the potential transferor; Obtain the distance between the potential transferee and the current hospital; The allocation suitability coefficient is obtained by comprehensively processing the drug sufficiency rate and distance of each potential transferor. Logic for determining sufficient inventory; For each cooperating pharmacy that sends the drug codes for the drugs to be allocated, each cooperating pharmacy will receive the drug codes and provide feedback on the actual available quantity and the estimated demand for the drugs to be allocated. Cooperating hospitals that have an actual available quantity higher than the estimated demand are deemed to have sufficient inventory.

[0016] The technical effects and advantages of this invention are as follows: (1) By integrating multi-dimensional data such as pre-occupied inventory, historical drug consumption, and outpatient traffic, and relying on time series set construction, least squares fitting trend, trend ratio conversion additional coefficient, and combining basic consumption and traffic coefficient, accurate drug demand forecast is calculated, which gets rid of the limitations of traditional manual experience prediction, greatly reduces the deviation between demand forecast and actual clinical demand, and provides reliable data support for pharmacy prescription information and inventory management. (2) By linking the estimated demand for drugs with the supply-side data of real-time inventory and transportation volume, the shortage of drugs can be automatically identified and predicted; at the same time, based on the supply urgency rate and clinical importance score, a plan evaluation coefficient is generated to classify and mark the procurement or allocation of drugs in short supply. This not only avoids delays in diagnosis and treatment due to shortages of key drugs such as emergency drugs and treatment-essential drugs, but also reduces inventory backlog caused by excessive procurement of routine drugs, thus optimizing the allocation of pharmacy inventory resources. (3) For the proposed drugs, the required replenishment amount is accurately calculated. By defining the scope, cooperative hospitals with sufficient inventory are selected as potential transferors. The appropriate transfer coefficient is calculated by combining the drug sufficiency rate and distance. Then, based on the surplus, a single or combined transfer plan is automatically matched. This achieves rapid response and optimal selection for the transfer of drugs in short supply, shortens the transfer cycle, reduces emergency procurement costs, and ensures the timeliness and continuity of clinical drug use. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the intelligent prescription information management platform for hospital pharmacies according to the present invention; Figure 2 This is a line graph diagram illustrating the intelligent prescription information management platform for hospital pharmacies according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figure 1 As shown, the intelligent prescription information management platform for hospital pharmacies includes: The data acquisition module is used to acquire and integrate demand-side and supply-side parameters from the hospital information system (HIS), pharmacy management system and external data sources in real time; Among them, demand-side parameters include pre-occupied inventory data, historical consumption data of different drug categories, and outpatient traffic data; Historical consumption data for different drug categories can be obtained by extracting full prescription data from prescription information and filtering out invalid data to ensure the accuracy of consumption data: Prescription status filtering: Only retain prescriptions that are "approved + dispensed + issued" (prescriptions that have not been issued, rejected, or voided have not generated any actual consumption and should be removed). Associate basic drug information: Match the drug name, specifications, and dosage form in the prescription with a pre-built "drug type database" (including emergency drugs, treatment essential drugs, etc.) using a unique drug code to clarify the category of each drug; Extract key fields: retain core fields such as prescription number, drug code, drug category, dispensing time, and dispensing quantity (the quantity of the drug consumed per prescription).

[0020] The pre-occupied inventory data includes the drug categories and quantities corresponding to all prescription orders with a status of "approved but not yet dispensed"; Pre-occupied inventory data is based on prescription review data, namely the types and quantities of drugs required by the treatment plan after review. This data is used as "pre-occupied inventory" data, which includes the types and quantities of drugs corresponding to all prescriptions that are "approved by intelligent pre-review but have not yet been dispensed".

[0021] Supply-side parameters include the real-time inventory of various medicines in pharmacies and the known amount of medicines in transit; The pharmacy forecasting module is used to process demand-side parameters using forecasting logic, integrate and classify the processed data, output the estimated demand for various types of drugs in the next set time zone, and generate a demand report. Specifically: Extract the consumption quantities of various drugs over the past x time zones from historical consumption data in the demand-side parameters, and construct time-series sets for each drug type. ; i represents the serial number of each type of medicine in the pharmacy. This represents the quantity of drug class i consumed in the x-th time zone in the past; The time zone length can be 7 days or 30 days, and x is preset by technicians, and x > 5.

[0022] Will The points in the graph are plotted in the coordinate system and connected to form a polyline, with x time zones as the horizontal axis and the consumption amount of each time zone as the vertical axis; The least squares method is used to fit a linear equation: y = at + b; the slope of the linear equation is identified to determine the direction of the consumption trend of the drug; the direction of the consumption trend includes the upward trend and the downward trend of demand. If the slope is greater than 0, it is determined to be an upward trend in demand; If the slope is less than 0, it is determined to be a downward trend in demand; Mark the line formed by two adjacent sets of points in the line graph as a wavy line, draw the horizontal line corresponding to each wavy line, and obtain the angle formed by each wavy line and the corresponding horizontal line. Additional notes: The horizontal line is drawn starting to the left of the wavy line and extends horizontally to the right until it touches the wavy line.

[0023] If the angle between a certain undulating line and the corresponding horizontal line is an obtuse angle, then take the rightmost point in the two adjacent sets of points as the starting point, and construct a line segment horizontally downward from the starting point until it is on the same horizontal line as the left point. Mark the constructed line segment as an ascending line. Identify the ascending lines constructed by each group of obtuse angles, accumulate the lengths of the ascending lines, and obtain the ascending intensity value of the drug. If the angle between a certain undulating line and the corresponding horizontal line is an acute angle, then take the left point in the two adjacent sets of points as the starting point, and construct a line segment horizontally downward from the starting point until it is on the same horizontal line as the right point. Mark the constructed line segment as a descending line. Identify the descending lines constructed by each group of obtuse angles, accumulate the lengths of the descending lines, and obtain the descending intensity value of the drug. If the trend change direction is an upward trend in demand, then the ratio of the upward strength value to the downward strength value is calculated and used as the upward trend ratio. If the demand trend is downward, the ratio of the downward intensity value to the upward intensity value is calculated and used as the downward trend ratio. Additional note: When calculating the upward or downward trend ratio, if the denominator of a certain drug is 0, it will be automatically converted to an integer of 1.

[0024] Construct trend ratio-additional coefficient mapping rules corresponding to upward and downward demand trends respectively, and convert the trend ratio into an additional coefficient based on the trend change direction of the drug using the corresponding mapping rules; Mapping rule description: That is, constructing the range of ratios corresponding to the upward trend ratio under the direction of upward demand, and each range of ratios corresponds to an additional coefficient; under the direction of upward demand, the matching range of the additional coefficient is limited to 1.056-1.138, and the larger the upward trend ratio, the higher the corresponding additional coefficient. It should be noted that, in conjunction with the above supplementary explanation, if the denominator of a certain drug is 0 when calculating the upward trend ratio, then the additional coefficient of 1.138 will be output directly.

[0025] Construct the range of ratios corresponding to the downward trend ratio under the direction of declining demand, and each range of ratios corresponds to an additional coefficient; under the direction of declining demand, the matching range of the additional coefficient is limited to 0.894-0.991, and the larger the downward trend ratio, the lower the corresponding additional coefficient; It should be noted that, in conjunction with the above supplementary explanation, if the denominator of a certain drug is 0 when calculating the downward trend ratio, then the additional coefficient of 0.894 will be output directly.

[0026] Extract outpatient traffic data from the previous time zone from the historical consumption data in the demand-side parameters, and calculate the average daily traffic data in the previous time zone to obtain the average daily traffic in the previous time zone. The outpatient flow ratio is obtained by calculating the ratio with the average daily flow as the numerator and the reference flow as the denominator. The reference flow is calculated by averaging the average daily flow of each historical time zone. Additional information: There is a positive correlation between outpatient visit volume and drug consumption; that is, higher outpatient visit volume corresponds to higher drug consumption. In summary: An increase in the number of outpatients leads to an increase in the number of prescriptions issued, which in turn leads to an increase in drug consumption. The number of outpatients decreased, leading to a reduction in the number of prescriptions issued and a decrease in drug consumption.

[0027] Using the constructed mapping rule between outpatient flow ratio and flow coefficient, the outpatient flow ratio of the hospital is converted into a flow coefficient. Mapping rule description: This involves constructing intervals for each group of outpatient flow ratios, with each interval corresponding to a flow coefficient. The matching range of the flow coefficient is limited to 0.953-1.072. If the outpatient flow ratio is greater than one, the matched flow coefficient is above one, with a higher probability of matching 1.072. If the outpatient flow ratio is less than one, the matched flow coefficient is below one, with a higher probability of matching 0.953.

[0028] The consumption of various drugs in the past two time zones was extracted and averaged to serve as the baseline consumption. The estimated demand for medicines = basic consumption × additional coefficient × flow coefficient; By default, estimated demand is rounded to the nearest integer.

[0029] The supply linkage module is used to compare and analyze the demand reports of pharmacies in the next set time zone with the supply-side parameters, and then generate a pharmacy allocation establishment report. Specifically: The real-time inventory and known in-transit volume of various medicines are extracted from the supply-side parameters, and after adding them together and subtracting the pre-occupied inventory data, the actual available quantity of various medicines in the next set time zone is obtained. The actual availability of various drugs in the next set time zone is compared with the estimated demand. If the actual availability of a certain drug is lower than the estimated demand, it is marked as a drug that is predicted to be in short supply. For predicted drug shortages, the emergency rate of drug replenishment is obtained by subtracting the actual available quantity from the estimated demand and then dividing by the actual available quantity. Within a pre-built drug type database, the system retrieves and predicts the category of drugs that are missing, including emergency drugs, essential treatment drugs, routine drugs, and auxiliary drugs. Additional notes: Emergency medications include: adrenaline, dopamine, sodium nitroprusside, and atropine; characteristics: essential for emergency treatment, irreplaceable, and extremely time-sensitive; Essential medications for treatment, such as antibiotics, insulin, antihypertensive drugs, and anticoagulants; characteristics: essential for treatment, limited substitutes, affecting treatment efficacy; Common medications include: common cold medicine, vitamins, and topical medications; characteristics: many alternatives available, broad therapeutic window, and minimal impact. Supportive medications, such as nutritional support drugs, some traditional Chinese medicines, and health supplements; characteristics: not essential for treatment, and may be temporarily out of stock.

[0030] Different categories are assigned a set of clinical importance scores; the score range is set from 1 to 10, and the scores for emergency drugs are higher than those for essential drugs, which are higher than those for routine drugs, which are higher than those for auxiliary drugs. After comprehensively processing the predicted emergency rate of drug shortage and the clinical importance score, the planned assessment coefficient of the predicted drug shortage is output. That is, the predicted emergency rate of drug shortage and the clinical importance score are normalized and then labeled as... and Using the formula Output the plan evaluation coefficient after weighted calculation ;in and These are the weighting coefficients for the urgency rate of resupply and the clinical importance score, respectively. .

[0031] The planned assessment coefficient for the predicted drug shortage is compared with the preset reference coefficient. If the planned assessment coefficient is lower than the reference coefficient, the predicted drug shortage is marked as a recommended drug for procurement. If the planned assessment coefficient is higher than the reference coefficient, it will be marked as a recommended drug for allocation; To add further explanation, drugs with high K values ​​(allocation recommendations) are mostly "urgent and critical". Allocation from other hospitals can quickly fill the gap, avoid the clinical impact caused by waiting for procurement, and reduce the premium costs of emergency procurement. Drugs with low K values ​​(procurement recommendations) are mostly "non-urgent or non-critical," with ample waiting time, and inventory can be replenished through regular procurement and transportation.

[0032] For the recommended allocation of medicines, calculate the difference between the actual available quantity and the estimated demand, and take the absolute value as the required supplementary quantity; Draw a circle with the current hospital pharmacy as the center and the distance as the radius, identify the partner hospitals inside the circle, and screen partner hospitals with sufficient inventory as potential transfer sources; Screening for sufficient inventory: For each cooperating pharmacy, the pharmacy sends the drug code of the proposed drug. After receiving the drug code, each cooperating pharmacy reports the actual available quantity and the estimated demand of the proposed drug. Cooperating hospitals with actual available quantity higher than estimated demand are judged to have sufficient inventory. Additional explanation: If the actual available quantity of each cooperating hospital within the circle is lower than the estimated demand, or if the actual available quantity of each cooperating hospital is higher than the sum of the estimated demand but lower than the required supplement, then the circle will be reconstructed after the set distance is enlarged to screen cooperating hospitals. The degree of enlargement will be preset by the technical staff, for example, by 1 kilometer.

[0033] Using formula Calculate the drug sufficiency rate of potential transferors. ;in and These represent the actual available quantity and the estimated demand for the corresponding medicines recommended for allocation by the potential transferor; Obtain the distance between the potential transferee and the current hospital; The allocation suitability coefficient is obtained by comprehensively processing the drug sufficiency rate and distance of each potential transferor. That is, the drug sufficiency rate and distance of potential transferors are normalized and then marked as... and Using the formula Output the allocation suitability coefficient after weighted calculation. ;in and These are the weighting coefficients for drug availability and distance, respectively. .

[0034] For drugs recommended for allocation, the following rules are applied to determine the allocation plan, taking into account the allocation suitability coefficient of each potential transferor: For each potential transferor, the difference between the actual available quantity and the estimated demand is calculated as the surplus. The surplus is compared with the required replenishment quantity of the current hospital's recommended drug allocation. If the number of potential transferors whose surplus is greater than the required replenishment quantity is greater than 1, then more than 1 potential transferors are selected, and the potential transferor with the highest allocation suitability coefficient is output as the current hospital's recommended drug allocation plan. If there is no potential transferor with a surplus greater than the required replenishment, a combined transfer signaling is triggered, and the potential transferor with the highest output transfer suitability coefficient is taken as the primary transfer target. The remaining supplement amount is obtained by subtracting the surplus amount of the main allocation target from the required supplement amount. Among the remaining potential transferors, those with surplus amounts exceeding the remaining replenishment amounts are selected as candidate targets, and those with the highest allocation suitability coefficients among the candidate targets are selected as secondary allocation targets. The potential transferors corresponding to the primary and secondary transfer targets are used as the current hospital's recommended drug transfer plan. The actual available quantity, estimated demand, predicted shortage of various medicines, planning evaluation coefficient, marking results (i.e., recommended medicines for procurement or recommended medicines for allocation), and allocation plan of various medicines in the next set time zone of the hospital are filled into a pre-built report template to generate a pharmacy allocation establishment report and send it to medical staff.

[0035] The above formulas are all dimensionless calculations. Dimensionless calculations can be performed using various methods such as standardization, which will not be elaborated here. The formulas are derived from software simulations based on a large amount of collected data, and the preset parameters in the formulas can be set by those skilled in the art according to the actual situation.

[0036] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, ATA hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state ATA hard disk.

[0037] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0038] Those skilled in the art will 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, or a combination of computer software and electronic hardware. 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 implementation should not be considered beyond the scope of this application.

[0039] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0040] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0041] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0042] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable ATA hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0043] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An intelligent prescription information management platform for hospital pharmacies, characterized in that: Includes the following modules: The data acquisition module is used to obtain demand-side and supply-side parameters in real time; Among them, demand-side parameters include pre-occupied inventory data, historical consumption data of different drug categories, and outpatient traffic data; Supply-side parameters include the real-time inventory of various medicines in pharmacies and the known amount of medicines in transit; The pharmacy forecasting module is used to process demand-side parameters using forecasting logic, integrate and classify the processed data, output the estimated demand for various types of drugs in the next set time zone, and generate a demand report. The estimated demand for medicines = basic consumption × additional coefficient × flow coefficient; The supply linkage module is used to compare and analyze the demand reports of pharmacies in the next set time zone with the supply-side parameters, and then generate a pharmacy allocation establishment report. The pharmacy allocation report includes the hospital's actual available quantity of various medicines in the next set time zone, estimated demand, predicted shortage of medicines, planning evaluation coefficient, marking results, and allocation plan.

2. The intelligent prescription information management platform for hospital pharmacies according to claim 1, characterized in that: The calculation logic of the additional coefficient; Extract the consumption quantities of various drugs over the past x time zones from historical consumption data in the demand-side parameters, and construct time-series sets for each drug type. ; i represents the serial number of each type of medicine in the pharmacy. This represents the quantity of drug class i consumed in the x-th time zone in the past; Will The points in the coordinate system are drawn together to form a polyline. The least squares method is used to fit linear equations, and the slope of the linear equations is identified to determine the direction of the trend of drug consumption. The direction of changes in consumption trends includes both upward and downward demand trends; If the slope is greater than 0, it is determined to be an upward trend in demand; If the slope is less than 0, it is determined to be a downward trend in demand; Mark the line formed by two adjacent sets of points in the line graph as a wavy line, draw the horizontal line corresponding to each wavy line, and obtain the angle formed by each wavy line and the corresponding horizontal line. Based on the direction of trend change and the type of angle, the corresponding outputs are the upward trend ratio and the downward trend ratio; Construct trend ratio-additional coefficient mapping rules corresponding to upward and downward demand trends respectively, and convert the trend ratio into an additional coefficient based on the trend change direction of the drug using the corresponding mapping rules.

3. The intelligent prescription information management platform for hospital pharmacies according to claim 2, characterized in that: Output logic for upward trend ratio and downward trend ratio; Angle types include obtuse angles and acute angles; If the angle between a certain undulating line and the corresponding horizontal line is an obtuse angle, then take the rightmost point in the two adjacent sets of points as the starting point, and construct a line segment horizontally downward from the starting point until it is on the same horizontal line as the left point. Mark the constructed line segment as an ascending line. Identify the ascending lines constructed by each group of obtuse angles, accumulate the lengths of the ascending lines, and obtain the ascending intensity value of the drug. If the angle between a certain undulating line and the corresponding horizontal line is an acute angle, then take the left point in the two adjacent sets of points as the starting point, and construct a line segment horizontally downward from the starting point until it is on the same horizontal line as the right point. Mark the constructed line segment as a descending line. Identify the descending lines constructed by each group of obtuse angles, accumulate the lengths of the descending lines, and obtain the descending intensity value of the drug. If the trend change direction is an upward trend in demand, then the ratio of the upward strength value to the downward strength value is calculated and used as the upward trend ratio. If the demand trend is downward, the ratio of the downward intensity to the upward intensity is calculated and used as the downward trend ratio.

4. The intelligent prescription information management platform for hospital pharmacies according to claim 1, characterized in that: The calculation logic of the flow coefficient; Extract outpatient traffic data from the previous time zone from the historical consumption data in the demand-side parameters, and calculate the average daily traffic data in the previous time zone to obtain the average daily traffic in the previous time zone. The outpatient flow ratio is obtained by calculating the ratio using the average daily flow as the numerator and the reference flow as the denominator. The reference flow rate uses the historical daily average flow rate of each time zone as the reference value, and the average of each set of reference values ​​is calculated to obtain the reference flow rate. By utilizing the constructed mapping rule between outpatient flow ratio and flow coefficient, the outpatient flow ratio of the hospital is transformed into the flow coefficient.

5. The intelligent prescription information management platform for hospital pharmacies according to claim 1, characterized in that: Explanation of basic consumption quantity and pre-occupied inventory data; The consumption of various drugs in the past two time zones was extracted and averaged to serve as the baseline consumption. The pre-occupied inventory data refers to the drug categories and quantities corresponding to prescription orders that have been approved but have not yet been dispensed.

6. The intelligent prescription information management platform for hospital pharmacies according to claim 1, characterized in that: The process of calculating and determining the actual available quantity and the predicted shortage of medicines; The real-time inventory and known in-transit volume of various medicines are extracted from the supply-side parameters, and after adding them together and subtracting the pre-occupied inventory data, the actual available quantity of various medicines in the next set time zone is obtained. The actual availability of various drugs in the next set time zone is compared with the estimated demand. If the actual availability of a certain drug is lower than the estimated demand, it is marked as a drug that is predicted to be in short supply.

7. The intelligent prescription information management platform for hospital pharmacies according to claim 6, characterized in that: The calculation logic of the plan evaluation coefficient; For predicted drug shortages, the emergency rate of drug replenishment is obtained by subtracting the actual available quantity from the estimated demand and then dividing by the actual available quantity. Within a pre-built drug type database, the system retrieves and predicts the category of drugs that are missing, including emergency drugs, essential treatment drugs, routine drugs, and auxiliary drugs. Different categories are assigned a set of clinical importance scores; After comprehensively processing the predicted emergency rate of drug shortage and the clinical importance score, the planned assessment coefficient of the predicted drug shortage is output.

8. The intelligent prescription information management platform for hospital pharmacies according to claim 7, characterized in that: The tagging results include recommended drugs for procurement and recommended drugs for allocation; The planned assessment coefficient for the predicted drug shortage is compared with the preset reference coefficient. If the planned assessment coefficient is lower than the reference coefficient, the predicted drug shortage is marked as a recommended drug for procurement. If the planned assessment coefficient is higher than the reference coefficient, it is marked as a recommended drug for allocation.

9. The intelligent prescription information management platform for hospital pharmacies according to claim 8, characterized in that: The logic behind constructing the allocation plan; For the recommended allocation of medicines, calculate the difference between the actual available quantity and the estimated demand, and take the absolute value as the required supplementary quantity; Draw a circle with the current hospital pharmacy as the center and the distance as the radius, identify the partner hospitals inside the circle, and screen partner hospitals with sufficient inventory as potential transfer sources; Obtain the allocation suitability coefficient for each potential transferor; For each potential transferor, the difference between the actual available quantity and the estimated demand is calculated as the surplus. The surplus is compared with the required replenishment quantity of the current hospital's recommended drug allocation. If the number of potential transferors whose surplus is greater than the required replenishment quantity is greater than 1, then more than 1 potential transferors are selected, and the potential transferor with the highest allocation suitability coefficient is output as the current hospital's recommended drug allocation plan. If there is no potential transferor with a surplus greater than the required replenishment, a combined transfer signaling is triggered, and the potential transferor with the highest output transfer suitability coefficient is taken as the primary transfer target. The remaining supplement amount is obtained by subtracting the surplus amount of the main allocation target from the required supplement amount. Among the remaining potential transferors, those with surplus amounts exceeding the remaining replenishment amounts are selected as candidate targets, and those with the highest allocation suitability coefficients among the candidate targets are selected as secondary allocation targets. The potential transferors corresponding to the primary and secondary transfer targets are used as the current hospital's recommended drug transfer plan.

10. The intelligent prescription information management platform for hospital pharmacies according to claim 9, characterized in that... The calculation logic of the allocation suitability coefficient; Using formula Calculate the drug sufficiency rate of potential transferors. ;in and These represent the actual available quantity and the estimated demand for the corresponding medicines recommended for allocation by the potential transferor; Obtain the distance between the potential transferee and the current hospital; The allocation suitability coefficient is obtained by comprehensively processing the drug sufficiency rate and distance of each potential transferor. Logic for determining sufficient inventory; For each cooperating pharmacy that sends the drug codes for the drugs to be allocated, each cooperating pharmacy will receive the drug codes and provide feedback on the actual available quantity and the estimated demand for the drugs to be allocated. Cooperating hospitals that have an actual available quantity higher than the estimated demand are deemed to have sufficient inventory.

Images