Geographic sales allocation system for estimating the source of pharmaceutical sales

Abstract

This invention provides a correspondence relationship extraction system that automatically extracts rationally linked medical institutions by calculating the geographical distance between pharmacies / drugstores and medical institutions using their latitude and longitude information, and setting appropriate distance thresholds based on the patient's travel range according to whether the institution is in an urban or rural area, as well as a method for estimating sales by medical institution on a recording medium. [Solution] The information processing system 100 normalizes the address information of pharmacies and medical institutions 110, obtains latitude and longitude using a geographic information API or the like 120, extracts medical institutions within a predetermined distance from the pharmacy 150, calculates a score inversely proportional to the distance to the extracted medical institutions 160, and allocates the pharmacy sales to each medical institution according to the score ratio 170. Furthermore, by outputting the allocation results and linking them with an analysis support platform, a probability model, and a sales support visualization tool 180, it provides an information infrastructure that contributes to understanding prescription practices, sales evaluation, and the advancement of marketing measures.

Description

Technical Field

[0001] The present disclosure relates to an information processing system and method for clarifying the sales sources of pharmaceuticals by estimating the medical institutions that are the prescription origins of pharmaceuticals based on the sales data of pharmacies according to geographical distances.

Background Art

[0002] In pharmaceutical companies, it is important to grasp the sales status of pharmaceuticals for each medical institution, which is the end user, in order to effectively implement sales activities and marketing strategies. However, under the pharmaceutical separation system in Japan, when a prescription issued by a medical institution is brought into an out-of-hospital pharmacy, the relationship between the prescribing medical institution and the pharmacy is not directly specified in the data. Therefore, it is difficult to grasp which medical institution the sales at the pharmacy are attributable to, which has been a major obstacle for pharmaceutical companies in verifying the effectiveness of sales promotion activities and targeting.

[0003] Regarding this problem, as prior art, patents of IMS (currently IQVIA) (Patent Documents 1 and 2) have created a database of the correspondence relationship (prescribing pharmacy relationship) between pharmacies and medical institutions, and in Patent Document 2, a mechanism is proposed in which the user can visually set and modify the correspondence relationship and apportionment values together with the map display on the GUI. In addition, Patent Document 1 shows a process of attributing (increasing) pharmacy sales to the medical institution side using ratio information regarding pairs of pharmacies and medical institutions.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document

[0005] However, these conventional technologies required manual configuration and modification of the relationship between pharmacies and medical institutions, and the determination of correspondence relationships and apportionment rates relied on the user's subjective judgment, resulting in limitations in terms of objectivity, reproducibility, and automation. Furthermore, the manual configuration of large amounts of facility data presented challenges in implementation.

[0006] Traditional methods of analyzing pharmaceutical sales data involved manually setting and correcting the correspondence between pharmacies and medical institutions and allocating sales accordingly, resulting in problems such as complexity, subjectivity, and lack of reproducibility. In particular, under the system of separation of prescribing and dispensing, when prescriptions were taken to off-site pharmacies, the prescribing medical institution could not be identified in the sales data, hindering sales source analysis and sales strategy design for pharmaceutical companies and other related parties. [Overview of the Initiative] [Problems that the invention aims to solve]

[0007] To address these challenges, this invention provides a mechanism that automatically extracts medical institutions that are rationally linked by calculating the geographical distance between pharmacies / drugstores and medical institutions using their latitude and longitude information, and setting an appropriate distance threshold based on the patient's travel range depending on whether the area is urban or rural.

[0008] Furthermore, if multiple medical institutions are located within the pharmacy's radius, a score (e.g., the reciprocal of the distance) is calculated based on the distance to each medical institution, and the pharmacy's sales are allocated proportionally to this score, with the aim of estimating the source of sales more objectively and fairly. [Means for solving the problem]

[0009] The information processing system according to the present invention comprises the following multiple components in order to rationally and automatically estimate the source of sales using sales data of pharmaceuticals at pharmacies and geographical information of medical institutions, and to allocate sales to each medical institution. ·(1) Address normalization means By correcting and normalizing inconsistencies in address notation for pharmacies and medical institutions (e.g., block number / house number / hyphens / full-width / half-width characters, etc.), the accuracy of latitude and longitude conversion is improved. (2) Means for obtaining latitude and longitude A method for obtaining latitude and longitude from a normalized address using geographic information services. This can be done via API or based on a predefined master data. ·(3) City classification determination means Based on the location of the pharmacy, the system determines the urban classification (e.g., designated city, regional city, etc.) and uses this information to set distance thresholds in subsequent processing.

[0010] Based on the geographical information above, the relationship between pharmacies and medical institutions is automatically estimated and sales are allocated proportionally through the following process: ·(4) Distance calculation means Using latitude and longitude, the geographical distance between the pharmacy and each medical institution is calculated using spherical trigonometry (e.g., harsine distance). (5) Distance threshold setting and extraction means Using appropriate distance thresholds based on urban areas (e.g., 500m in urban areas, 2km in rural areas, etc.), the system automatically extracts medical facilities located around a pharmacy. (6) Target facility filtering means If necessary, the facilities to be included in the apportionment will be narrowed down based on the medical department, size, and type of facility (hospital / clinic, etc.) of the medical institution.

[0011] If multiple target medical institutions are selected, the revenue will be allocated proportionally using the following method: (7) Distance score calculation means A score is calculated for each medical institution using the reciprocal of the distance from the pharmacy, etc. (8) Score normalization method The obtained scores are normalized by summing them up and converted into a ratio where the whole is 1, thereby obtaining the apportionment ratio. (9) Method of sales allocation The sales revenue from each pharmacy is automatically allocated to each healthcare provider based on a normalized score. (10) Facility-specific sales output means Output the sales data divided by medical institutions with composition ratios, and make it in a format that can be linked to external demand prediction models, prescription probability models, business optimization models, etc. With this configuration, it is possible to estimate the sources of pharmaceutical sales and allocate sales without relying on manual judgments or personal operations, achieving both geographical rationality and reproducibility. The problem-solving means summarized above are as follows.

[0012] Of medical institutions and pharmacies The fluctuations in address data have been corrected and normalized. Store master data including location information do Memory means, Based on the aforementioned normalized address data, it is possible to automatically retrieve it using an API. Based on the latitude and longitude of the medical institutions and The aforementioned pharmacies, automatically extract the correspondence relationship with the medical institutions located within a predetermined distance starting from the pharmacy The aforementioned processing means, The calculation method was performed using a mathematical algorithm that includes the Hersine distance or Vincent distance. A correspondence database that records the extracted correspondence relationship, Link the correspondence relationship to an analysis support platform, and provide it in a format that can be used for analyzing the prescription trends of pharmaceuticals, optimizing business activities, and evaluating the cost-effectiveness of sales and marketing activities. A correspondence extraction system characterized by comprising linking means. also

Advantages of the Invention

[0013] According to the present invention, based on objective and reproducible geographical distances for the sales data of pharmacies, it is possible to automatically estimate and structure the relationship with the medical institutions that are the sources of prescriptions. As a result, the sources of pharmaceutical sales from pharmacies, which were previously unclear, can be grasped in terms of medical institutions, and the accuracy and efficiency of sales and marketing activities in pharmaceutical companies are greatly improved.

[0014] ​In addition, the present invention enables visualization of sales derived from out-of-hospital prescriptions, which were previously inaccessible to sales representatives, and provides a basis for more accurately evaluating the contribution of sales activities. Conventionally, sales allocation decisions were made subjectively, but with the present invention, quantitative and transparent evaluation based on geographical evidence becomes possible, ensuring the fairness of incentive design in the sales field and contributing to the improvement of the morale of the responsible personnel.

[0015] Furthermore, the output results of the present invention can be easily linked to other mathematical models such as prescription probability models and sales optimization models, and can also be expected to be applied to behavior prediction for each medical institution and future sales simulations. The present invention is extremely practical and industrially useful also in that it enables high-precision sales analysis using only company data without relying on conventional expensive external data.

[0016] The present invention particularly shows extremely high utility in areas such as formulation of sales strategies, verification of sales promotion effects, and optimization of human resource allocation in the pharmaceutical industry, and functions as a base technology for overcoming information constraints under the current system by expanding the scope of utilization of pharmacy sales data.

Brief Description of the Drawings

[0017] The drawings illustrate an embodiment of the present invention and are referred to for understanding its configuration and operating principle. [Figure 1] It is a block configuration diagram of the entire information processing system of the present invention. [Figure 2] It is a flowchart showing the main processing flow of the present invention. [Figure 3] It is a diagram showing an example of address normalization processing. [Figure 4] It is a diagram showing an example of processing for obtaining latitude and longitude from a normalized address. [Figure 5] It is a diagram showing an example of urban / rural area classification processing and switching of threshold distances. [Figure 6] It is a schematic diagram of processing for measuring the distance between a pharmacy and a medical institution. [Figure 7]This is a schematic diagram of the process for extracting medical institutions that are within a threshold distance. [Figure 8] This is an example of filtering medical institutions based on their medical department and facility size. [Figure 9] This figure shows the process for calculating distance scores and an example of that calculation. [Figure 10] This figure shows an example of score normalization and sales allocation. [Figure 11] This figure shows an example of the output of sales breakdown allocated by medical institution. [Figure 12] This figure shows an example of linking the output results according to the present invention to an external model. [Figure 13] This is a block diagram showing the basic hardware configuration of a typical computer 90 that constitutes an information processing device.

[0018] An embodiment of the present invention will be described with reference to the drawings. [Modes for carrying out the invention]

[0019] Figure 1 is a block diagram showing the overall configuration of the geographical sales allocation system for estimating the source of pharmaceutical sales according to the present invention. This information processing system (100) uses pharmacy sales data and geographical information of medical institutions to automatically estimate the medical institutions that are the source of the pharmacy sales, and appropriately allocates those sales to multiple medical institutions. This system mainly includes address normalization means (110), latitude and longitude acquisition means (120), city classification determination means (130), distance calculation means (140), medical institution extraction and filtering means (150), distance score calculation and normalization means (160), sales allocation means (170), and output and model linkage means (180).

[0020] Component 1: From data preparation to distance acquisition (110 to 150) The address normalization method (110) standardizes variations in the notation of addresses for pharmacies and medical institutions (e.g., full-width / half-width characters, omission of block / number / house number, differences in delimiters, etc.). This improves the accuracy of latitude and longitude conversion in subsequent steps and ensures matching reliability. The latitude and longitude acquisition means (120) is responsible for acquiring the latitude and longitude of a normalized address using a geographic information service (e.g., Google Maps API). The latitude and longitude are used as basic data for distance calculation processing.

[0021] Component 2: Score, Proportional Distribution, Output (160 to 180) The distance score calculation and normalization means (160) calculates a score for each extracted medical institution based on the reciprocal of the distance from the pharmacy, and determines the apportionment ratio by normalizing the sum of these scores. The sales apportionment means (170) automatically distributes the sales amount recorded at the pharmacy to multiple medical institutions based on the aforementioned ratio. This makes it possible to estimate the sales composition for each medical institution. The output and model linkage means (180) outputs the apportioned sales information as data with composition ratios for each medical institution, making it available for use in a format that can be linked to external prediction models (e.g., prescription probability models, sales activity optimization models, etc.).

[0022] Figure 2 Processing flowchart configuration Figure 2 is a flowchart showing the main processing flow of the present invention. This process takes address data of pharmacies and medical institutions as input and sequentially executes a series of steps including address normalization, latitude and longitude acquisition, city classification determination, distance calculation, extraction and filtering of medical institutions, score calculation and normalization, sales allocation, and data output.

[0023] Figure 3: Explanation of Address Normalization Process Figure 3 is an example illustrating the processing content of the address normalization means (110) according to the present invention. In this diagram, the "Address before normalization (input)" shown in the left column exhibits a mixture of full-width and half-width characters, a mix of Chinese numerals and Arabic numerals, abbreviated block, house number, and address designation, use of abbreviation symbols ("-" and "ー"), and address notation in katakana.

[0024] In contrast, the "Normalized Address (Output)" shown in the right column has each element converted to a standardized address format. Specifically, this includes explicit notation of block, house number, and street number, conversion of Chinese numerals to Arabic numerals, normalization of symbols, addition of prefecture and city / ward names, and removal of unnecessary spaces.

[0025] This address normalization process improves the accuracy of geographic information in the subsequent latitude and longitude acquisition process (120), resulting in increased reliability of the geographical distance calculation to medical institutions (140). In addition, it improves the accuracy of matching identical addresses, contributing to the stability and reproducibility of the sales source estimation process.

[0026] Figure 4: Explanation of the latitude and longitude acquisition process Figure 4 shows an example of the process of obtaining latitude and longitude information from a normalized address. In this figure, a standardized address such as "6-35-3 Jingumae, Shibuya-ku, Tokyo" shown on the left is processed via a geographic information API as shown in the center, and as a result, the latitude (e.g., 35.665498) and longitude (e.g., 139.712891) shown on the right are obtained.

[0027] This process is essential for converting addresses into geographic coordinates and provides foundational data for performing highly accurate geometric calculations in the subsequent distance calculation process (140). Furthermore, obtaining accurate latitude and longitude allows for quantitative evaluation of spatial proximity to medical institutions, improving the accuracy of estimating drug sales sources.

[0028] Figure 5: Explanation of urban division and distance thresholding. Figure 5 shows an example of urban classification determination processing and the corresponding switching of distance thresholds. In this embodiment, the urban classification is determined according to the location of the pharmacy, and a process is implemented to dynamically switch the threshold used for calculating the distance to medical institutions accordingly. As shown in the figure, in urban areas such as government-designated cities, a distance threshold of 300 to 500m is adopted considering that the range of movement of patients is narrow, while in regional cities and suburbs, a longer threshold of 1,000 to 10,000m is set because the range of activity is wider. However, this distance setting may vary depending on the region.

[0029] By adopting distance thresholds that correspond to the attributes of cities in this way, it becomes possible to estimate sales sources that are in line with regional characteristics, and to realize sales allocation processing that is in line with actual patient behavior. Furthermore, since a unified and fair estimation logic can be applied on a nationwide scale regardless of geographical conditions, the present invention is superior in terms of both implementability and scalability.

[0030] Figure 6: Schematic explanation of distance measurement process Figure 6 is a schematic diagram of the process for measuring the geographical distance between a pharmacy and a medical institution. In this invention, based on the latitude and longitude information of each facility obtained in Figure 4, a method is employed to calculate the distance between the pharmacy and the medical institution by calculating the straight-line distance (great circle distance) between two points on a sphere. For this distance calculation, mathematical algorithms that take into account the curvature of the Earth, such as the Haversine Formula or the Vincenty Formula, are used.

[0031] The diagram shows a situation where a pharmacy is at the center, and multiple medical facilities exist within a certain radius distance from it. These distances are all calculated from latitude and longitude and do not differ from actual road distances. However, because geographical proximity can be measured mechanically and fairly, it has high reliability as a premise for sales source estimation processing. Furthermore, since this process is performed automatically and in batches for a large number of pharmacy-medical facility pairs, it has advantages in both processing time and accuracy compared to conventional manual mapping and visual judgment.

[0032] Figure 7 Schematic of distance filtering process Figure 7 is a schematic diagram of the process for extracting medical institutions located within a predetermined threshold distance, starting from a pharmacy. In the diagram, a concentric threshold range (for example, 300-500m in urban areas, and 1,000-10,000m in rural areas) is drawn with the pharmacy at the center, and multiple medical institutions located within this range are selected. These medical institutions are automatically extracted based on the distance calculation results in Figure 6, as they fall below the threshold distance.

[0033] This process eliminates medical institutions that are too far from pharmacies to realistically serve as prescription starting points, enabling more accurate estimation of sales sources. Furthermore, since the filtering process dynamically changes thresholds according to urban classifications, it functions as the foundation for a flexible sales allocation model that takes regional characteristics into account. The extracted information on medical institutions is then passed on to the next stage of scoring and sales allocation processing (Figure 8 onwards).

[0034] Figure 8: Explanation of filtering process based on clinical department and facility size. Figure 8 shows an example of a process for selecting medical institutions that can actually serve as the starting point for prescribing pharmaceuticals, based on factors such as the type of medical department and the size of the facility, from among the medical institutions extracted by distance filtering (Figure 7). In this process, filtering conditions are set using information such as whether the medical institution has medical departments such as "internal medicine," "cardiology," and "surgery," or facility size information such as the number of beds, the number of medical departments, and the number of doctors.

[0035] By performing this filtering process, it becomes possible to exclude facilities that are merely geographically close (e.g., dental clinics, small clinics, health checkup centers, etc.) and narrow down the selection to only medical institutions that are actually relevant to drug prescriptions. This improves the accuracy of the subsequent distance score calculation process (Figure 9) and sales allocation process (Figure 10), resulting in a practically meaningful estimation of drug sales sources.

[0036] Figure 9: Explanation of the distance score calculation process Figure 9 shows a series of processes and an example of calculations for calculating a score based on the geographical distance between a pharmacy and a medical institution, normalizing it, and converting it into a sales allocation ratio. In this invention, when multiple medical institutions exist within a certain distance, a score is assigned to each inversely proportional to the distance. For example, the score is calculated as 1 / d or 1 / (d+ε) (where ε is a small constant to avoid division by zero) for a distance d (m), and it is designed so that medical institutions that are closer receive a higher score. Therefore, for example, if the distance from the pharmacy to Hospital A is 20m and to Hospital B is 100m, the scores will be 1 / 20 (=0.05) and 1 / 100 (=0.01) respectively, with the closer Hospital A receiving a higher score.

[0037] The distance score calculated for each medical institution is normalized based on the sum of the scores for all candidate medical institutions and converted into a ratio in the range of 0 to 1. Specifically, the ratio is obtained by dividing the score of each medical institution (1 / d) by the sum of scores S = Σ(1 / d) in the denominator. In the example above, the score for Hospital A is 0.05 / (0.05+0.01)=0.833 (approximately 83.3%), and for Hospital B it is 0.01 / (0.05+0.01)=0.167 (approximately 16.7%), giving a higher allocation rate to closer facilities. The normalized scores derived in this way serve as important basic data for revenue allocation and model linkage (see Figure 12).

[0038] Conventional allocation methods involve subjective judgments and visual facility mapping, leading to challenges in fairness and reproducibility. In contrast, this invention uses objective geographical distance information between pharmacies and medical institutions to quantitatively derive scores through reciprocal calculation and normalization, enabling fair and reproducible sales distribution free from subjective judgments. Furthermore, this score calculation process can be easily adapted to batch processing and system automation, and is highly practical as it can be scalably applied to nationwide pharmacy and medical institution data. As for the weighting method based on distance, for example, "the reciprocal of the distance" or "the reciprocal of a weight value proportional to the distance" can be used, and in either case, medical institutions that are closer are given a relatively larger weight.

[0039] Figure 10: Outline of sales allocation process using normalized scores (reciprocal weighting and distance weighting allocation) Figure 10 shows an example of a process for allocating the amount of pharmaceutical sales obtained from a pharmacy to each medical institution based on a normalized score. In this invention, the normalized score calculated in Figure 9 is used as the allocation ratio to rationally distribute the pharmacy's sales to each medical institution according to their "degree of involvement as a prescribing source". For example, if the pharmacy's sales are 1 million yen, and the normalized scores are A Hospital: 0.44, B Hospital: 0.27, C Clinic: 0.18, and D Medical Clinic: 0.11, then the sales would be allocated as follows: 440,000 yen, 270,000 yen, 180,000 yen, and 110,000 yen, respectively.

[0040] This sales allocation process is performed mathematically based on the calculation and normalization of distance scores, eliminating the need for subjective judgments such as discretion at the branch level or visual facility selection, as was done in the past. The system is structured to automatically complete the process based on sales data (per pharmacy), making it possible to obtain fair and highly reproducible allocation results.

[0041] This processing method is applicable to sales data from pharmacies and medical institutions nationwide, and is versatile, not limited to specific regions or products. Furthermore, the allocation results can be used for a variety of purposes, including reflecting them in sales evaluations, analyzing sales performance, and linking them to demand forecasting models. In addition, by combining this with period-based sales aggregation and product-specific analysis, it becomes possible to support the development of more precise and high-resolution marketing strategies.

[0042] Figure 11: Example of output of sales breakdown by medical institution Figure 11 shows an example of outputting the results of allocating pharmacy sales to each medical institution based on the normalized score calculated in Figure 10. In this figure, the amount allocated to each medical institution is visually displayed in bar graphs, pie charts, or tables, allowing stakeholders to intuitively understand the contribution of the medical institution that initiated the prescription. Furthermore, by adding the name, address, ID, etc., of each medical institution, it becomes easier to apply this to sales strategies and promotional activities.

[0043] This output format is not simply for recording internal processing results, but can be widely used in sales and marketing departments for strategy planning and evaluation metrics. For example, if it becomes clear that a large portion of a pharmacy's sales come from a specific medical institution, it can lead to practical applications such as adjusting the frequency of visits to that institution, focusing explanatory materials, and reflecting this in the performance metrics of the person in charge. This output can be provided in various formats depending on the application, including system screens, PDF reports, CSV files, and BI tool integration.

[0044] In the medical industry, there are many situations where legitimacy and transparency in sales activities are required. A configuration like the present invention, which allows for the quantitative and visual output of which medical institution each pharmacy's sales originate from, is useful not only for internal accountability but also as disclosure material to external contractors. In particular, this output format offers high reliability and effectiveness in designing incentives at the sales office or individual employee level, and in linking with fair sales evaluation systems.

[0045] Figure 12: Example of integration with external models Figure 12 shows an example of linking the sales allocation results for each medical institution obtained by the present invention to an external information processing model or business support system. The output data of the present invention can be automatically linked to other statistical models and sales support tools by means of a CSV file, database format, or via an API. This makes it possible to consistently perform decision-making and analysis based on the sales contribution of individual medical institutions in other applications.

[0046] The sales data by medical institution obtained by this invention is highly compatible with, for example, the invention relating to a probability model (a model for estimating prescription probabilities at the physician or facility level) for which an international patent application was filed under "Patent No. 7636838". Sales information at the medical institution level is essential for constructing this probability model, but conventional methods have limited model accuracy because the starting point of sales for outpatient prescriptions is unknown. By using this invention to allocate pharmacy sales to medical institutions in a geographically rational manner, consistency with the dependent variable in the probability model is ensured, and prediction accuracy is greatly improved.

[0047] Under the conventional system, determining which medical institution pharmacy sales should be attributed to relied on the visual inspection and subjective judgment of sales representatives and on-site managers, leading to issues such as inconsistencies in processing, lack of fairness, and reduced reproducibility.

[0048] In conventional manual methods, the allocation of sales revenue often relied on the experience and personal knowledge of the decision-maker, leading to difficulties in reaching agreements among stakeholders and raising questions about the validity of the evaluation criteria for sales representatives. In contrast, this invention uses objective distance information based on latitude and longitude to perform scoring and normalization processing, providing mathematically based allocation results and facilitating accountability both within and outside the organization. This is particularly useful in achieving high transparency and acceptance in corporate systems that are closely linked to sales evaluation and personnel systems.

[0049] This invention enables high-speed, batch processing of sales allocation decisions, which were previously made for each individual pharmacy and medical institution, using standardized logic, thereby significantly reducing human resources. As a result, allocation work that previously took several days can now be completed in minutes, and inefficiencies such as inter-departmental verification and rework are greatly reduced. Consequently, it improves the overall operational efficiency of the sales and marketing departments and contributes to the promotion of company-wide DX (Digital Transformation).

[0050] Configuration as an information processing device Figure 13 is a block diagram showing the basic hardware configuration of a typical computer 90 that constitutes an information processing device. Each process of the present invention is executed by an information processing device, i.e., a computer 90. The information processing device has a configuration that includes at least a CPU (Central Processing Unit), main memory (RAM), secondary memory (HDD or SSD, etc.), and, if necessary, a network interface and input / output devices. The CPU controls the various processes according to a program stored in the memory, realizing functions such as distance calculation, score calculation, sales allocation, and output processing. This automates sales estimation based on the geographical relationship between pharmacies and medical institutions. The basic hardware configuration of Computer 90 will be described later.

[0051] Implementation form and system form This invention can be implemented not only on standalone PCs and server devices, but also in cloud environments and as a SaaS-type web application. Specifically, geographical and sales data for each pharmacy and medical institution are stored on a cloud server, and nationwide batch processing, score calculation, and report generation are performed via API or batch processing. Furthermore, these processing results can be integrated with the pharmaceutical company's existing sales support tools (CRM, SFA, etc.) and BI dashboards.

[0052] Provided as a program / recording medium. The information processing function of the present invention can be configured as a software program, and this program can be provided by recording it on a computer-readable recording medium such as an optical disc (CD-ROM, etc.), flash memory, or DVD. Furthermore, the program can be distributed via a network, and a configuration in which the user downloads it from a server and installs it in a local or cloud environment for use is also included. As a result, the present invention is independent of hardware configuration and can flexibly adapt to various system environments.

[0053] Possibility of modifying the embodiment The embodiments of the present invention are not limited to the examples described above, and the processing order, distance score calculation method, threshold setting criteria, medical institution filtering conditions, output format, etc., can be appropriately changed according to the purpose and operating environment. For example, in addition to 1 / d, 1 / (d) can also be used as the distance score. 2 It is also possible to use decay functions such as ) or exp(-d), and the threshold distance in urban divisions can be flexibly redefined based on field surveys and statistical data. Furthermore, in the filtering process, it is possible to add conditions such as the prescription frequency of specific drug effects and DPC codes, in addition to the medical department and facility size.

[0054] Assumptions for Sales Data in Pharmaceutical Companies In the practical application of this invention, sales data by medical institution and sales data by pharmacy held by pharmaceutical companies form the basis of the input information. Normally, in-hospital prescriptions are recorded as sales history by medical institution, but for outpatient prescriptions, only pharmacy delivery information is available, and the relationship with the medical institution that initiated the prescription was unclear. This invention clarifies the overall prescription structure by rationally allocating pharmacy sales for such outpatient prescriptions to medical institutions based on geographical distance and patient activity area.

[0055] Data input and address normalization processing Users input the names and addresses of pharmacies and medical institutions into the information processing system of the present invention in file format such as CSV or Excel. The input addresses are normalized to the prefecture, city / ward / town / village, block / house number level to eliminate variations in notation and differences in expression, and postal code mapping and notation standardization processing are performed (see Figure 3). Through normalization processing, different notations (e.g., "1-2-3" and "1-chome 2-ban 3-go") even for the same facility are integrated, ensuring consistency in subsequent coordinate acquisition processing.

[0056] Latitude and longitude acquisition process and caching support The normalized address information is converted into latitude and longitude information via an external geographic information API (e.g., Google Maps API). To avoid re-acquiring data for the same address, previously acquired coordinate data is cached and used for verification and reuse (see Figure 4). The acquired coordinate information is stored in a database for each pharmacy and medical institution and used as input for the subsequent distance calculation process.

[0057] Urban classification determination and application of threshold distance After obtaining the latitude and longitude, the present invention determines the urban classification of the area where the pharmacy is located and automatically switches the threshold distance according to the geographical characteristics. For example, areas corresponding to the special wards of Tokyo and government-designated cities are classified as "urban type" with a radius of 300 to 600 meters, while other areas are classified as "rural type" with a radius of 3 to 10 kilometers. This urban classification determination process reflects the realistic distance relationship between medical institutions and pharmacies in each region, enabling the extraction of candidate facilities without excess or deficiency (see Figure 5).

[0058] Distance measurement and scoring to medical facilities Next, the straight-line distance (H-sine distance) between the pharmacy and all medical institutions located within a set radius centered on the pharmacy is calculated. For each of the selected medical institutions, a score (1 / d) is calculated inversely proportional to the distance, and the sum of all scores is normalized. This yields an apportionment ratio indicating how much of the pharmacy's sales are attributable to each medical institution, with closer facilities being reflected as having a higher contribution (see Figures 6 to 9).

[0059] Sales allocation and output processing Based on the scores, pharmacy sales are distributed to each medical institution according to the apportionment ratio. For example, if Hospital A receives 45% of 1 million yen in sales and Hospital B receives 30%, the amounts will be processed as 450,000 yen and 300,000 yen respectively (see Figure 10). This result is output as sales composition data by medical institution in CSV, PDF, and graph formats, and is used by sales and marketing departments for strategy planning, evaluation of facility-specific KPIs, and integration with CRM tools (see Figure 12).

[0060] Model integration and practical application Furthermore, these output results can be linked as important explanatory variables and evaluation axes to prescription probability models for individual medical institutions and optimal sales activity promotion models (Patent No. 7636838). This makes it possible to quantitatively reproduce the flow of outpatient prescriptions, which was previously a black box, and enables precise behavioral prediction and targeting based on the actual contributions of doctors and medical institutions. This invention is useful on its own, and when connected with other advanced SaaS technologies, it will strongly support the digital transformation (DX) of sales and marketing in the pharmaceutical industry.

[0061] <Basic Computer Hardware Configuration> Figure 13 is a block diagram showing the basic hardware configuration of a typical computer 90. The computer 90 includes at least a processor 901, main memory 902, auxiliary memory 903, and a communication interface 991. These are electrically connected to each other by a communication bus 921.

[0062] The processor 901 is hardware for executing the instruction set written in a program. The processor 901 consists of an arithmetic unit, registers, peripheral circuits, etc.

[0063] Main memory 902 is used to temporarily store programs and data processed by programs, etc. For example, it is a volatile memory such as DRAM (Dynamic Random Access Memory).

[0064] Auxiliary storage device 903 is a storage device for saving data and programs. Examples include flash memory, HDD (Hard Disc Drive), magneto-optical disk, CD-ROM, DVD-ROM, and semiconductor memory.

[0065] The IF991 communication interface is an interface for inputting and outputting signals for communication with other computers via a network using wired or wireless communication standards. A network consists of various mobile communication systems, such as the internet, LANs, and wireless base stations. For example, a network includes 3G, 4G, and 5G mobile communication systems, LTE (Long Term Evolution), and wireless networks (e.g., Wi-Fi®) that can connect to the internet via designated access points. When connecting wirelessly, communication protocols include, for example, Z-Wave®, ZigBee®, and Bluetooth®. When connecting via a wired connection, the network also includes connections made directly via USB (Universal Serial Bus) cables and other means.

[0066] Furthermore, it is possible to virtually realize a computer 90 by distributing all or part of each hardware configuration across multiple computers 90 and connecting them to each other via a network. Thus, the concept of computer 90 includes not only a computer 90 housed in a single enclosure, but also a virtualized computer system.

[0067] <Basic Functional Configuration of Computer 90> The functional configuration of the computer realized by the basic hardware configuration of computer 90 (Figure 13) is described below. The computer comprises at least one functional unit: a control unit, a memory unit, and a communication unit.

[0068] Furthermore, the functional units of computer 90 can also be realized by distributing all or part of each functional unit across multiple computers 90 interconnected via a network. The concept of computer 90 includes not only a single computer 90 but also a virtualized computer system.

[0069] The control unit reads various programs stored in the auxiliary storage device 903 by the processor 901, loads them into the main memory 902, and executes processing according to the program. The control unit can realize the functions of various information processing units depending on the type of program. In this way, the computer becomes an information processing device that performs information processing.

[0070] The memory unit is implemented by the main memory 902 and the auxiliary memory 903. The memory unit stores data, various programs, and various databases. The processor 901 can also reserve memory areas corresponding to the memory unit in the main memory 902 or the auxiliary memory 903 according to the program. The control unit can also cause the processor 901 to perform operations such as adding, updating, and deleting data stored in the memory unit according to the various programs.

[0071] A database, specifically a relational database, is a system for managing and relating tabular data sets called masters, which are structurally defined by rows and columns. In a database, tables are called tables, masters are called masters, the columns of tables are called columns, and the rows of tables are called records. In a relational database, relationships can be established and linked between tables and masters. Typically, each table and master has a primary key column to uniquely identify records, but setting a primary key column is not mandatory. The control unit can instruct the processor 901 to add, delete, or update records in specific tables and masters stored in the memory unit, according to various programs.

[0072] Furthermore, each table, database, and master in this invention may include any data structure (list, dictionary, associative array, object, etc.) in which information is structurally defined. The data structure also includes data that can be considered a data structure by combining data with functions, classes, methods, etc., written in any programming language.

[0073] The communication unit is implemented by the communication IF991. The communication unit provides the functionality to communicate with other computers 90 via the network. The communication unit can receive information transmitted from other computers 90 and input it to the control unit. The control unit can cause the processor 901 to perform information processing on the received information according to various programs. The communication unit can also transmit information output from the control unit to other computers 90. [Examples]

[0074] Embodiments 1 and 2 are described below as specific examples of implementation. It should be noted that the present invention is not limited to a specific pharmacy or region, but is applicable to a large number of pharmacies and medical institutions located throughout Japan. The following example illustrates a part of the processing using a single pharmacy in a specific region as an example, and assumes a system structure that allows for batch and continuous processing for multiple pharmacies and multiple medical institutions. Furthermore, by applying the processing logic of the present invention to each pharmacy, the system calculates and integrates the sales composition by medical institution on a nationwide scale, providing quantitative support for the overall sales and marketing activities of pharmaceutical companies.

[0075] <Example 1 for chronic diseases> Pharmacy Y is located in an urban area and has recorded sales of 1.5 million yen for drug X (an obesity treatment drug manufactured and sold by Alpha Pharmaceuticals). Within a 600m radius of Pharmacy Y, there are hospitals A (100m), B (300m), C (500m), and D (800m), all of which are medical institutions that have been confirmed to prescribe medication to obese patients.

[0076] Based on the processing logic of the present invention, the reciprocal of the distance from Pharmacy Y to each medical institution (1 / d) was used as the score, and the apportionment ratio was calculated by normalizing this score. As a result, approximately 904,000 yen (0.60) was allocated to Hospital A, approximately 301,000 yen (0.20) to Hospital B, approximately 180,000 yen (0.12) to Clinic C, and approximately 113,000 yen (0.08) to Medical Clinic D.

[0077] These results were incorporated into Alpha Pharmaceutical's sales analysis system and linked to the CRM as sales breakdown by facility. Furthermore, this allocation data was also used as the dependent variable in a separately constructed prescription probability model by medical institution (Patent No. 7636838).

[0078] Previously, the starting point of pharmacy sales was unclear, making it difficult to properly evaluate the contributions of sales representatives. However, by using the processing method of this invention, the contribution rate of each medical institution that makes up pharmacy sales has been clarified, and the quality of feedback on sales activities has been greatly improved.

[0079] At Alpha Pharmaceuticals, the sales performance of assigned facilities was previously the main KPI for evaluating sales representatives. However, now that sales, including outpatient prescriptions, can be quantitatively tracked, it has become possible to implement a fair and transparent personnel evaluation system.

[0080] The allocation results were used as foundational data for multifaceted marketing initiatives, including area-specific prescription trend analysis, disease-specific prescription share analysis, and the development of target strategies for each medical institution.

[0081] Decisions that previously relied on intuition, such as selecting venues for online lectures, scheduling visits by medical representatives (MRs), and selecting advertising placement areas, can now be made quantitatively based on allocated data.

[0082] It was used not only in the sales department, but also in the corporate planning, product strategy, and supply chain departments to understand the net sales composition by region, and was also utilized for product lifecycle management.

[0083] By comparing drug X with its competitor drug Y, it became possible to analyze regional market share and adoption rates by medical institution based on allocated sales data, thereby facilitating the visualization of competitive advantages.

[0084] Currently, it is being tested as a treatment for obesity, but there are plans to expand it to the cardiovascular, diabetes, and cancer fields in the future, and it can be applied to various dosage forms and distribution channels.

[0085] <Example 2 for Rare Diseases> The present invention is also extremely useful in pharmaceuticals for rare diseases. Below, we show an example of its actual application using drug Y for rare diseases sold by Gamma Pharmaceuticals.

[0086] Drug Y targets a rare disease affecting approximately 1,000 people annually, and initial diagnosis and treatment are performed at a limited number of medical institutions, such as university hospitals and specialized research facilities. However, subsequent prescriptions are often made at medical institutions in the patient's local area, and patients are re-evaluated at specialized institutions approximately every six months.

[0087] In rural areas, when sales are generated at pharmacies through outpatient prescriptions, it is difficult to identify the medical institution that initiated the prescription. As a result, the sales and marketing departments were unable to track the actual doctors and facilities that issued the prescriptions. In particular, Gamma Pharmaceuticals was operating under a system that linked medical institutions only within a 600m radius of the pharmacy, meaning that important prescription records were overlooked when specialized institutions were outside this radius.

[0088] As a result, medical representatives (MRs) were unable to provide appropriate information or conduct follow-up visits to the medical institutions that actually contributed to the prescriptions, leading to disadvantages in their sales evaluations. Furthermore, chronic disputes arose between MRs responsible for areas surrounding pharmacies and those responsible for the medical institutions that actually made the prescriptions, regarding the attribution of sales performance.

[0089] By introducing this invention, pharmacy sales are now allocated based on geographical scores, and scores are distributed to medical institutions located outside the geographical area. As a result, it has become possible to rationally extract medical institutions that could be the starting point for prescribing drug Y from a wider range, and the contribution of specialized facilities that were previously overlooked has become visible.

[0090] By quantifying the scores of healthcare institutions involved in prescribing, the sales department was able to accurately identify potential clients, enabling them to develop more realistic sales plans and provide more relevant information. Furthermore, this approach has been applied to analyzing inter-healthcare network collaborations, reflecting the realities of community-based integrated care and inter-clinic cooperation, leading to the development of strategies for disease awareness and support for continued prescriptions.

[0091] As a result, sales performance is now determined by objective numerical values ​​(distance score and sales allocation), significantly reducing the sense of unfairness in evaluations among medical representatives (MRs). Field staff have commented that "we now have a fair evaluation system" and "our efforts towards doctors who actually prescribe medication are now reflected in our evaluations," which has greatly contributed to improved morale.

[0092] The improved accuracy of visits and increased motivation among medical representatives (MRs) ultimately had a positive impact on sales of drug Y, resulting in increased prescription retention rates and a rise in the number of new prescriptions. In particular, the ability to provide appropriate information to medical institutions in regions that had not been reached before led to a 15% increase in sales year-on-year within six months of implementation.

[0093] Because rare diseases affect a limited number of patients, understanding and utilizing each prescription opportunity is extremely important. This invention is particularly useful in the rare disease market because it visualizes prescription records that are often overlooked, and directly leads to strengthening company-wide responses.

[0094] Thus, as demonstrated by the implementation at Gamma Pharmaceuticals, the present invention is not merely limited to sales analysis, but possesses versatility that extends to all aspects of sales strategy, personnel systems, and marketing measures, and serves as a technological foundation that directly contributes to improving the overall performance of the company.

[0095] <Effects and Effects> The present invention provides the following effects and effects. <Standardization of sales attribution based on objectivity and reproducibility>

[0096] This invention transforms the traditional sales attribution process, which relied on subjective judgment and human intervention, into an objective and reproducible process by automatically allocating pharmaceutical sales from pharmacies based on distance scores derived from latitude and longitude. This enables the standardization of evaluation criteria in sales activities, eliminates variations in judgment among different locations and personnel, and realizes a fair sales evaluation system. <Improving the accuracy and productivity of sales and marketing activities>

[0097] The introduction of this invention enables precise estimation of medical institutions based on pharmacy sales, allowing for quantitative targeting of facilities and more efficient sales visits and information provision activities. Furthermore, it is useful for the marketing department in understanding prescription trends by facility and adoption rates by region, contributing to the optimization of resource allocation and strategy design, and leading to improved company-wide productivity. <High connectivity and expandability with other models and systems>

[0098] The revenue allocation data by medical institution obtained through this invention can be linked with other mathematical models, such as prescription probability models and optimal sales activity promotion models, to further improve analytical accuracy and enhance predictive capabilities. Furthermore, because this system uses a general-purpose data structure such as geographic information, sales information, and facility attributes, it can be easily expanded to various fields such as other disease areas, sales channels, and medical department-specific strategies, and has excellent future expandability. [Industrial applicability]

[0099] While the present invention is particularly useful in supporting sales and marketing activities in pharmaceutical sales, it is not limited thereto. For example, it can be applied to analyzing patient behavior and optimizing visit routes in medical devices, testing services, home healthcare, and community-based integrated care, as well as to store opening strategies for drugstore and pharmacy chains. Furthermore, the sales allocation data of the present invention, when combined with probability models, demand forecasting models, and optimal sales activity promotion models (separate application filed), can significantly contribute to supporting decision-making across the entire healthcare industry, including digital transformation (DX).

[0100] <Note> The following is an addendum to the matters explained above.

[0101] (Note 1) Medical institutions and pharmacies The fluctuations in address data have been corrected and normalized. Stores master data including location information. do Memory means and Based on the aforementioned normalized address data, it is possible to automatically retrieve it using an API. The aforementioned medical institutions and The aforementioned Based on the latitude and longitude of the pharmacy, The aforementioned Starting from a pharmacy The calculation method was performed using a mathematical algorithm that includes the Hersine distance or Vincent distance. A processing means for automatically extracting correspondence relationships with the aforementioned medical institutions located within a predetermined distance, A correspondence database that records the extracted correspondences, The aforementioned correspondences are linked to an analysis support platform for analyzing drug prescription trends and optimizing sales activities. also A correspondence relationship extraction system characterized by comprising a means of collaboration that provides information in a format usable for evaluating the cost-effectiveness of sales and marketing activities.

[0102] (Note 2) The processing means calculates the geographical distance from the pharmacy to the medical institution when extracting the correspondence relationship, and sets a predetermined distance. This can be set separately for urban and rural areas. The correspondence relationship extraction system according to Appendix 1, characterized in that it has a determination logic that establishes the correspondence relationship only when it is below a threshold.

[0103] (Note 3) A correspondence extraction system as described in Appendix 1 or 2, which, in order to reduce the number of API calls in automatic acquisition using the aforementioned API, can store the automatically acquired latitude and longitude data as a cache and perform matching and reuse.

[0104] (Note 4) The correspondence relationship extraction system according to Appendix 1 or 2, characterized in that the analysis support platform has a function to allocate or add the sales data of the pharmacy to the medical institutions linked to the pharmacy based on the correspondence relationship, and visualize the prescription contribution of each medical institution.

[0105] (Note 5) The correspondence extraction system according to Appendix 1 or 2, characterized in that the processing means automatically recalculates and updates the correspondence without requiring manual operation by the user.

[0106] (Note 6) The aforementioned analysis support platform, in allocating the sales data of the pharmacy based on the correspondence, uses a weighted allocation method based on the distance between the pharmacy and the medical institution, using the reciprocal of the distance. also The correspondence relationship extraction system according to Appendix 1 or 2 is characterized in that it calculates weights based on the reciprocal of a weighted value proportional to the distance, and can allocate sales to relatively nearby medical institutions at a higher rate.

[0107] (Note 7) Correspondence relationship extraction system as described in Appendix 1 or 2 Using (1) Extract the medical institutions located within a predetermined distance from the pharmacy, (2) Based on the geographical distance between the pharmacy and each of the medical institutions, The aforementioned geographical The reciprocal of the distance, or The aforementioned geographical The weight is calculated by taking the reciprocal of the weight value proportional to the distance. (3) The sales amount of the pharmacy shall be allocated to each of the medical institutions based on the weights, (4) Output the allocation results as data for analysis support. A method for estimating sales by medical institution.

[0108] (Note 8) A program that causes a computer to perform the processing described in Appendix 1 or 2.

[0109] (Note 9) The correspondence relationship extraction system described in Appendix 1 is characterized in that the allocation results are used for area-specific prescription trend analysis, targeting by medical institution, sales representative evaluation indicators, or sales share comparison with competing products.

[0110] (Note 10) The apportionment results are characterized by their use in area-specific prescription trend analysis, targeting by medical institution, sales representative evaluation metrics, or sales share comparisons with competing products. 8 The program described above.

[0111] (Note 11) A correspondence relationship extraction system according to Appendix 1, comprising: display means for showing the aforementioned medical institutions and pharmacies on a map according to their respective geographical locations; display means for displaying a screen that visually shows the correspondence relationship between the medical institutions and pharmacies; acquisition means for acquiring a specific combination of the medical institutions and pharmacies; and means for setting, updating, or deleting the correspondence relationship between the medical institutions and pharmacies.

[0112] (Note 12) The appendix includes a display step of showing the aforementioned medical institutions and pharmacies on a map according to their respective geographical locations, a display step of displaying a screen that visually shows the correspondence between the medical institutions and pharmacies, an acquisition step of acquiring a specific combination of the medical institutions and pharmacies, and a step of setting, updating, or deleting the aforementioned correspondence between the medical institutions and pharmacies. 7 The sales estimation processing method for each medical institution as described above.

[0113] (Note 13) Display means for showing the aforementioned medical institutions and pharmacies on a map according to their respective geographical locations; display means for displaying a screen that visually shows the correspondence between the medical institutions and pharmacies; acquisition means for acquiring a specific combination of the medical institutions and pharmacies; and annotations for executing the processing of means for setting, updating, or deleting the correspondence between the medical institutions and pharmacies. 8 The program described above.

[0114] Although embodiments of the present invention have been disclosed above, the present invention is not limited thereto and can be modified as appropriate without departing from the technical spirit of the invention. [Explanation of Symbols]

[0115] 100 Information Processing Devices 110 Address normalization means 120 Latitude and Longitude Acquisition Methods 130 City classification determination means 140 Distance calculation means 150 Methods for extracting and filtering groups of medical institutions 160 Distance Score Calculation and Normalization Methods 170 Sales Allocation Method 180 Output / Model Linkage Method

Claims

1. A storage means for storing master data including location information that has been corrected and normalized to correct fluctuations in the address data of medical institutions and pharmacies, A processing means for automatically extracting correspondence relationships with the medical institution located within a predetermined distance calculated using a calculation method that allows calculation using a mathematical algorithm including Hersine distance or Vincent distance, starting from the pharmacy, based on the latitude and longitude of the medical institution and the pharmacy which can be automatically obtained using an API based on the normalized address data, and A correspondence database that records the extracted correspondences, A correspondence extraction system characterized by comprising a means for linking the aforementioned correspondences to an analysis support platform and providing them in a format usable for drug prescription trend analysis, optimization of sales activities, or cost-effectiveness evaluation of sales and marketing activities.

2. The correspondence relationship extraction system according to claim 1, characterized in that the processing means has a determination logic that calculates the geographical distance from the pharmacy to the medical institution when extracting the correspondence relationship, and establishes the correspondence relationship only when it is below a predetermined threshold that can be set separately for urban areas and rural areas.

3. The correspondence relationship extraction system according to claim 1 or 2, which, in order to reduce the number of API calls in automatic acquisition using the API, can store the automatically acquired latitude and longitude data as a cache and compare and reuse it.

4. The correspondence relationship extraction system according to claim 1 or 2, characterized in that the analysis support platform has a function to allocate or add the sales data of the pharmacy to the medical institutions linked to the pharmacy based on the correspondence relationship, and visualize the prescription contribution of each medical institution.

5. The correspondence extraction system according to claim 1 or 2, characterized in that the processing means automatically recalculates and updates the correspondence without requiring manual operation by the user.

6. The analysis support platform, in allocating the sales data of the pharmacy based on the correspondence relationship, uses a weighted allocation method based on the distance between the pharmacy and the medical institution, calculates weights based on the reciprocal of the distance or the reciprocal of a weight value proportional to the distance, and is characterized in that it can allocate sales to the medical institution that is relatively close at a higher rate. This is the correspondence relationship extraction system according to claim 1 or 2.

7. Using the correspondence relationship extraction system described in Claim 1 or 2, (1) Extract the medical institutions located within a predetermined distance from the pharmacy, (2) Based on the geographical distance between the pharmacy and each of the medical institutions, the weight is calculated by the reciprocal of the geographical distance or the reciprocal of a weighted value proportional to the geographical distance. (3) The sales amount of the pharmacy shall be allocated to each of the medical institutions based on the weights, (4) Output the apportionment results as data for analysis support. A method for estimating sales by medical institution.

8. A program for causing a computer to perform the processing described in claim 1 or 2.

9. The correspondence relationship extraction system according to claim 1, characterized in that the allocation results are used for area-specific prescription trend analysis, targeting by medical institution, sales representative evaluation indicators, or sales share comparison with competing products.

10. The program according to claim 8, characterized in that the allocation results are used for area-specific prescription trend analysis, targeting by medical institution, sales representative evaluation indicators, or sales share comparison with competing products.

11. A correspondence relationship extraction system according to claim 1, comprising: display means for showing the aforementioned medical institutions and pharmacies on a map according to their respective geographical locations; display means for displaying a screen that visually shows the correspondence relationship between the medical institutions and pharmacies; acquisition means for acquiring a specific combination of the medical institutions and pharmacies; and means for setting, updating, or deleting the correspondence relationship between the medical institutions and pharmacies.

12. A method for estimating sales by medical institution according to claim 7, comprising: a display step of showing the medical institution and the pharmacy on a map according to their respective geographical locations; a display step of displaying a screen that visually shows the correspondence between the medical institution and the pharmacy; an acquisition step of acquiring a specific combination of the medical institution and the pharmacy; and a step of setting, updating, or deleting the correspondence between the medical institution and the pharmacy.

13. The program according to claim 8 for executing the following processes: display means for showing the medical institution and the pharmacy on a map according to their respective geographical locations; display means for displaying a screen that visually shows the correspondence between the medical institution and the pharmacy; acquisition means for acquiring a specific combination of the medical institution and the pharmacy; and means for setting, updating, or deleting the correspondence between the medical institution and the pharmacy.

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