Patent Document

RELATED APPLICATION  
       [0001]     This application claims the benefit of U.S. Provisional Patent Application Ser. No. 60/404,451 filed Aug. 19, 2002, entitled “RXISIGHT SECURITY PROCEDURES”. The entire disclosure of which is incorporated herein by reference. 
     
    
     BACKGROUND OF THE INVENTION  
       [0002]     1. Technical Field  
         [0003]     The present invention relates to security techniques for customer accessible databases.  
         [0004]     2. Background Art  
         [0005]     Pharmacies and wholesale pharmaceutical outlets have engaged various tools to evaluate their market share and monitor other key performance indicators in their national or local operating areas. One approach is to access company data, e.g., dispensed prescriptions, cash pricing, managed care contract rates, and generic dispensing ratios, and to compare that to industry standard market measures. For example, a pharmacy chain might determine their own company&#39;s prescription dispensing growth is 3% in the Philadelphia market, while the overall prescription volume is growing at 8%, and therefore determine that the chain is losing market share.  
         [0006]     In general, such pharmaceutical outlets treat their own market performance information to be a highly protected trade secret. Accordingly, there exists a need for a technique which provides a secure environment to provide information services to these chains, in a cost-effective manner, while protecting the confidentiality of these data, and reducing opportunity for error or corruption in the data.  
       SUMMARY OF THE INVENTION  
       [0007]     An object of the present invention is to provide a technique for permitting pharmaceutical outlets to evaluate their market share in a secure environment.  
         [0008]     Another object of the present invention is to provide information services to pharmaceutical outlets in a cost-effective, confidential accurate manner.  
         [0009]     In order to meet these and other objects of the present invention which will become apparent with reference to further disclosure set forth below, the present invention provides a database security access methodology, combined with a software application and sophisticated reference files and/or database files which enable pharmacy outlets to access confidential pharmaceutical information related to their respective companies, from one single database which houses all chains&#39; data.  
         [0010]     The technique is cost-effective since the cost of populating, updating, and maintaining this information, housed on a central platform, can be spread across all pharmacy chains subscribing to the database. The chains do not have to incur the costs of purchasing a hardware platform and managing it themselves. There is no time delay from when the current month&#39;s information is available, and the chains&#39; receipt of the data, and they can log on and access it immediately upon completion of the data load at the data warehouse. There is no delay in transmitting or shipping the data to a chain&#39;s data center and then waiting for the data load to occur there. Additionally, the data warehouse does not have to generate multiple large data marts for each chain that desires access to these data, thus resulting in additional cost savings, minimizing risk of data corruption in reproduction, and facilitating maintenance and updates to the data by only having one data warehouse to update versus many.  
         [0011]     A system and method for evaluating the performance of a first at least one of a plurality of outlets is provided. The system includes a computer system configured to execute a data access application, wherein the data access application includes a plurality of user accounts, wherein each of the plurality of user accounts includes a user group, and wherein a second at least one of the plurailty of outlets is associated with the user group. The system also including a data storage device configured to store market measures from a portion of the plurality of outlets and industry-standard market measures, wherein the data access application allows a user to access the industry-standard market measures and data associated with the outlets associated with the user group of user&#39;s user account.  
         [0012]     In another advantageous embodiment of the present invention, a system and method for evaluating the performance of a first at least one of a plurality of outlets is provided. The system includes a computer system configured to execute a data access application, wherein the data access application includes a plurality of user accounts, wherein each of the plurality of user accounts includes a user group, and wherein a second at least one of the plurailty of outlets is associated with the user group. The system also including a data storage device configured to store market measures from a portion of the plurality of outlets and industry-standard market measures, wherein the data access application allows a user to access the industry-standard market measures and data associated with the outlets associated with the user group of user&#39;s user account.  
         [0013]     The accompanying drawings, which are incorporated and constitute part of this disclosure, illustrate preferred embodiments of the invention and serve to explain the principles of the invention. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0014]      FIG. 1  is a functional diagram of an exemplary method in accordance with the present invention;  
         [0015]      FIG. 2  is a block diagram showing an arrangement of sales outlets and a processing station illustrative of an embodiment of the invention;  
         [0016]      FIG. 3  is a block diagram of a processing system that may be used as the central station of  FIG. 2 ;  
         [0017]      FIG. 4  is a flow chart showing the estimation of prescription sales at an outlet that is illustrative of the invention;  
         [0018]      FIG. 5  is a flow chart showing the determination of sampled and unsampled outlets of  FIG. 4  in greater detail;  
         [0019]      FIG. 6  is a flow chart showing the selection of the group of sampled outlets for each unsampled outlet in greater detail;  
         [0020]      FIG. 7  is a flow chart showing one arrangement for the estimation of prescriptions sales for a prescribing physician at a plurality of pharmacies illustrative of the invention;  
         [0021]      FIG. 8  is a flow chart showing the confidence signal operation of  FIG. 7  in greater detail; and  
         [0022]      FIG. 9  is a flow chart showing another arrangement for the estimation of prescription sales for a prescribing physician at a plurality of pharmacies illustrative of the invention.  
     
    
       [0023]     Throughout the drawings, the same reference numerals and characters, unless otherwise stated, are used to denote like features, elements, components, or portions of the illustrated embodiments. Moreover, while the present invention will now be described in detail with reference to the FIGS., it is done so in connection with the illustrative embodiments.  
       DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS  
       [0024]      FIG. 1  illustrates an exemplary embodiment of a system and method for permitting retail outlets or a chain of retail outlets to evaluate their performance in a particular market in a secure environment. Generally, the exemplary system and method deliver proprietary sales data and market sales data to a user, thus allowing the user to appreciate the performance of the user&#39;s retail outlet or chain of retail outlets. Specifically, the exemplary methods and systems deliver proprietary pharmaceutical chain data and pharmaceutical market data to a user, thus allowing the user to appreciate the performance of the user&#39;s pharmaceutical retail outlet or chain of pharmaceutical retail outlets.  
         [0025]      FIG. 1  illustrates a block diagram of a pharmaceutical data analysis system  10 . The data analysis system  10  includes a communications network  16 , the central server  120 , a chain database  30 , a projected database  40  and a store master database  50 . The pharmaceutical data analysis systme  10  allows a user  12 ,  14  to upload market measures for a current month and receive information pertaining to pharmaceutical retail outlet, a chain of pharmaceutical retail outlets, and/or a portion of the chain of pharmaceutical retail outlets.  
         [0026]     In a preferred embodiment, the communications network  16  is the Internet. In another preferred embodiment, the communications network  16  is a private network.  
         [0027]     The user  12  utilizes the central station  120  by using his or her user account and logging into a data access application  22  through the communications network  16 . The central station  120  is a server, which includes a transceiver  26 . Data may be transmitted or received by the central station  120  through the transceiver  26 . The data access application  22  and a database security application  24  run on the central station  120 . The user may use the data access application  22  and indirectly the database security application  24  to upload market measures for a current month, receive information, generate reports, and the like.  
         [0028]     In a preferred embodiment, multiple transceivers may be utilized. In another preferred embodiment, the transceiver  26  is a network interface card.  
         [0029]     The users  12 ,  14  upload market measures for a particular month on a monthly basis. The data access application  22  and the database security application  24  receive the market measures from the users  12 ,  14  and populate the chain database  30 , the projected database  40  and the store master database  50  with market measures for a current month. Each of the chain database  30 , the projected database  40  and the store master database  50  are Oracle-based relational databases. The chain database  30  houses market measures including all pertinent transactions submitted to the system  10  from a particular retail establishment&#39;s retail transaction systems, and are stored in a manner which enables each retail establishment&#39;s data to be identifiable and retrievable. For example, information identifying the particular pharmacy may be included. The retail establishment can be a single establishment or a chain of establishments.  
         [0030]     The projected database  40  houses aggregated, projected, industry-standard market measures. The technique for storing and generating the aggregated, projected, industry-standard market measures is described hereinbelow with reference to  FIGS. 2-9 . The projected database  40  provides the benchmark market data for retail establishments, chains of retail establishments or portions of chains of retail establishments to compare their performance to that which is occurring in an overall relevant marketplace.  
         [0031]     The store master database  50  is also updated on a monthly basis. The store master database  50  is a proprietary reference database, also stored in an Oracle-based relational data table, which contains the relationships between each retail store in a geographic area, its local operating company name called the Organization Name, and its Parent company. Preferably, the geographic area is a country, for example: the United States. The store master database  50  contains information describing the many diverse relationships that exist as large corporate entities continue to acquire local retail chains and independent stores offering the product or products being tracked, while maintaining the local operating business name. An example of data stored in the store master database  50  follows as Table I:  
                       TABLE I                       Store Name/Address   Organization Name   Parent Name                   ABC DRUGS   Cutter Supply, Inc.   Diamond Valu Worldwide       Jewel&#39;s Crystal Mart   Colorful Savings Co.   Diamond Valu Worldwide       Lucky&#39;s Save-a-Lot   Carrot-Top Enterprises   Diamond Valu Worldwide       Outlet A   Discount Supplies   Corporation A       Outlet B   Discount Supplies   Corporation A       Sunny&#39;s Selections   Clarity Surplus   Diamond Valu Worldwide                  
 
         [0032]     In order to provide secure access to the databases  30 ,  40 ,  50 , they are accessed by users  12 ,  14  at the retail chain headquarters via a commercially available web-based decision support tool: the data access application  22 . Since it is critical that each user  12 ,  14  have access to only a portion of the data contained in the database  30  and complete access to the data contained in the projected database  40 , several layers of security are required.  
         [0033]     In a preferred embodiment, the data access application  22  is provided by Business Objects.  
         [0034]     Each user  12 ,  14  has an account including a username and password. Each account is also associated with at least one parent name, for example a user account may be associated with the parent name “Diamond Valu Worldwide.” Upon establishing the new user account, the user is given access to all data corresponding to a particular parent entity by default. If a user&#39;s account is associated with Diamond Valu Worldwide, the user may access data associated with each of the stores that are also associated with Diamond Valu Worldwide, i.e., ABC Drugs, Jewel&#39;s Crystal Mart, Lucky&#39;s Save-A-Lot, and Sunny&#39;s Selections. The user would not be able to access data corresponding to Outlet A and Outlet B, because that data is associated with the parent “Corporation A.” The user&#39;s access may be restricted to a portion of the data corresponding to the parent entity. A user account may be given access to data corresponding to all stores associated with a parent entity, all stores associated with a particular organization of a parent entity, a particular store or another group of stores. Using the example above, Diamond Valu Worldwide may create a new user account for Jewel&#39;s Crystal Mart&#39;s manager to see only data pertaining to Jewel&#39;s Crystal Mart.  
         [0035]     In order to realize these differing levels of security, the database security application  24  utilizes Oracle security procedures. A “user group” is developed, associating specific rows in the database to a given parent name, organization name or other subset of retail establishments. A user account associated with a particular user group is only able to access the rows associated with the user group. If the store master database  50  included the data as described by Table I, a Diamond user group could be developed based upon the parent name “Diamond Valu Worldwide.” A user account associated with the Diamond user group would be able to access the rows associated with ABC Drugs, Jewel&#39;s Crystal Mart, Lucky&#39;s Save-a-lot and Sunny&#39;s Selections, but not the rows associated with Outlet A and Outlet B. It should be noted that all user groups are provided access to the entire projected database  40 .  
         [0036]     In a preferred embodiment, a user group may be associated with more than one parent name.  
         [0037]     A level of security is realized using the data access application  22 . Using security procedures of the data access application  22 , a new user account may be established which enables access to the data access application  22  interface. The new user account is associated with a user group, which is developed as described above. In the example above, a new user account for “John Smith” could be associated with the “Diamond” user group.  
         [0038]     A further level of security is established at the data element level. Each parent entity or organization, may submit a chain attribute data file. The chain attribute data file is merged into the chain database  30  and/or the projected database  40 . This file contains chain-specific attributes, from which customized reports and analytic measures may be created. For example, a parent entity or organization could send data to the store master database  50 , which would group stores into districts or regions, in order to generate relevant reports for their district or regional managers. The chain-specific attributes are also linked into the database via the parent name and the user group security noted above. These data are not visible to any user outside of the retail chain&#39;s user group.  
         [0039]      FIG. 2  depicts an arrangement illustrating a first embodiment of the invention in which product sales at unsampled sales outlets are estimated. In  FIG. 2 , there are shown an area  100 , sampled sales outlets  110 - 1 ,  110 - 2 ,  110 - 3 ,  110 -N- 1  and  110 -N and unsampled sales outlets  1104  and  110 - 5  in the area  100  and a central station  120 . Each of sampled outlets  110 - 1 ,  110 - 2 ,  110 - 3 ,  110 -N- 1  and  110 -N may preferably be coupled via a line of lines  130 - 1  through  130 -N to the central station  120 .  
         [0040]     In  FIG. 2 , the outlets may be pharmacies or other type of retail stores or distribution establishments all of which distribute a particular product. The outlets are at various locations in the area  100 . While there are 7 outlets shown in  FIG. 2  for purposes of illustration, it is to be understood that there are generally hundreds or thousands of outlets which are not restricted to a given area. The location of each outlet is generally known in terms of latitude and longitude from available census data or in terms of zip code centroids from Post Office data. Accordingly, the distances between pharmacies can be determined. Product sales data generated at each outlet S n  (e.g.,  110 - 1 ) is preferably transferred to the central station  120  via a line (e.g.,  130 - 1 ). Unsampled outlets U n  (e.g.,  110 - 4  or  110 - 5 ) in the area  100  are not coupled to the central station or, if coupled, do not supply valid sales data so that only an estimate of the sales volume of the particular product can be made.  
         [0041]     A single area  100  is shown in  FIG. 2  for purposes of illustration only. According to the invention, the estimation of sales activity at an unsampled outlet U is formed on the basis of the sales activity at the sampled outlets S in a neighborhood of the unsampled outlet U. The neighborhood of an unsampled outlet U may be defined as the N closest sampled outlets S which is different for each unsampled outlet and not as a predefined geographic area. In an urban area, the neighborhood of closest sampled outlets may all be located within a short distance of the unsampled outlet. In a rural area, the neighborhood of N closest sampled outlets nay spread over distances. Consequently, each unsampled outlet has its own neighborhood area which varies according to the distances to the nearest N sampled outlets. Advantageously, the correlation of sales activity data is not restricted to a predetermined geographic area as in the prior art.  
         [0042]      FIG. 3  depicts a block diagram of the central station  120  of  FIG. 2  which includes an input/output unit  201 , a characteristics and location store  205 , a sales data store  210 , a processor  215 , a program store  220 , a bus  225 , a transfer store  228  and work station processors  230 - 1  through  230 -N. Input/output  201  is coupled to bus  225  and is also coupled to the sampled outlet lines  130 - 1  to  130 -N and to output line  245 . The characteristics and location store  205 , the prescription data store  210  and the program store  225  are coupled to the bus  225 . The Processor  215  is coupled to the bus  220 , the transfer store  228  and is also coupled to work station processors via a control line  240 . The transfer store  228  is coupled to work station processors  230 - 1  to  230 -N and the work station processors are coupled together through a network  250 . Characteristics signals stored in store  205  may include signals representing the type of outlet or the total sales of all products at the outlet. This type of data is available from sources such as Drug Distribution Data (DDD®) available from IMS America, Plymouth Meeting, Pa. DDD® is a trademark of IMS America. Program store  220  stores instruction signals that control the operation of the processor  215  and provide parameter signals to determine the operation of work station processors  230 - 1  through  230 -N through the processor  215  and control line  240 .  
         [0043]     The sales outlet data received by input/output  201  from sales outlets which may exceed 2×10 9  records each having between 88 and 1000 bytes is transferred to data store  210 . In view of the large amount of data to be processed, the processing is divided between the processor  215  and the work station processors  230 - 1  through  230 -N. Information signal arrays produced in the processor  215  are transferred to work station processors  230 - 1  through  230 -N through the transfer store  228 . Each information signal array from the processor  215  placed in the transfer store  228  is divided into N portions. A preassigned portion of the information signal array in the transfer store is supplied to each of work station processors  230 - 1  through  230 -N and the processing of the portions in the work stations  230 - 1  through  230 -N is controlled by signals from the processor  215  via the control line  240 . After the processing of the information signal array portions in the work station processors, the processed information signal array portions are merged into one processed information signal array which is returned to transfer store  228  from the work station processors. The returned information signal array in the transfer store  228  is then further processed in the processor  215  to produce estimate sales volume results. The operation of the system of  FIG. 3  will be further described in connection with the estimation arrangements shown in  FIGS. 4-8 .  
         [0044]      FIG. 4  depicts a flow chart illustrating the operation of the central station  120  of  FIG. 2  in estimating the volume of sales of a particular product at an unsampled outlet such as outlet O 4  or outlet O 5  in  FIG. 2 . The operations depicted in the flow chart of  FIG. 4  are performed by processor  215  and work station processors  230 - 1  to  230 -N of  FIG. 3  under control of instruction signals from in the program store  220 . In the flow chart of  FIG. 4 , product data from outlets O 1 , O 2 , O 3 , ON- 1  and ON are transferred to the input/output unit  201  preferably via corresponding lines  130 - 1  through  130 -N in step  301 . Transferred data is stored in outlet sales data store  210 . A data transfer from an outlet may occur for each sales transaction or may include a number of transactions for a prescribed period of time. At preset intervals, the sales data is sent to processor  215  and therein is evaluated in step  310  to determine the sampled outlets Si and the unsampled outlets Up in the processor  215 . Unsampled outlets may include outlets transferring data evaluated as invalid.  
         [0045]      FIG. 5  shows a method of determining sampled and unsampled outlets in greater detail. Referring to  FIG. 5 , an outlet index n for the outlets O 1 , O 2 , . . . , ON is set to 1 in step  401 . A sampled outlet index i and an unsampled outlet index p are set to 1 in steps  405  and  410 . The sales data for the particular product from each outlet On is checked in decision step  415  to determine if the data is valid (i.e., meets predetermined criteria). If the data is judged to be valid in step  415 , the outlet On is classified as a sampled outlet Si in step  420  and the index i is incremented in step  425 . When no data is available for the outlet On or the data is not accepted as valid in step  415 , the outlet On is classified as an unsampled outlet Up in step  422  and the index p is incremented in step  427 . The index n is then incremented in step  430 . Until index n is greater than N for the last outlet ON, step  415  is reentered from decision step  435 . When all of the outlets  01  through ON have been classified as sampled outlets and unsampled outlets, the last value of index p (pmax) and the last value of index i (imax) representing the number of unsampled outlets and the number of sampled outlets are stored in data store  210  (step  440 ).  
         [0046]     As shown in  FIG. 2 , there are five sampled outlets  110 - 1  (O 1 ),  110 - 2  (O 2 ),  110 - 3  (O 3 ),  110 -N-1 (ON- 1 ) and  110 -N (ON) which are designated S 1 , S 2 , S 3 , S 4  and S 5  from the processing of  FIG. 5  and two unsampled outlets  110 - 4  (O 4 ) and  110 - 5  (O 5 ) which are designated as U 1  and U 2  from processing of  FIG. 5 . Unsampled outlet U 1  is located in the central portion of the area  100  and is surrounded by sampled outlets S 1  through S 5 . Unsampled outlet U 2  is located at one edge of the area  100 , is closest to sampled outlet S 5  and most remote from sampled outlet S 1 . Priorly known techniques based an estimate of the sales volume of a product at an unsampled outlet on the sales volume of the product for the geographic area. Since the sales outlets have different characteristics (e.g., size and location) and have sales related to outlets outside a particular area, estimates based on the overall sales volume in a particular area as in the prior art are biased. In accordance with the invention, an estimate of sales volume of a particular product at a sales outlet is based on the known sales volume of other outlets according to the distances between the sales outlet and the other outlets and the particular characteristics of the outlets independent of any geographic area. By using the outlet characteristics and the distances, an unbiased and more accurate estimate may be determined.  
         [0047]     Signals corresponding to the distances between unsampled outlet U 1  and sampled outlets S 1  through S 5  and the distances between outlet U 2  and sampled outlets S 1  through S 5  are then formed in step  330 . In step  335 , the mmax closest sampled outlets to unsampled outlet p are selected. The selection is performed in the processor  215 . mmax may be chosen according to the total number of sampled outlets. The selection of sampled outlets associated with each unsampled outlet is shown in greater detail in  FIG. 6 .  
         [0048]     With reference to  FIG. 6 , a set of distance signals dip′ for sorting is generated in step  501  corresponding to the distance signals dip generated in step  330 . The unsampled outlet index p is set to one in step  505 . A selected outlet index m is set to one in step  510  and the sampled outlet index i is set to one in step  515 . In step  520 , a signal D is set to LPN (largest possible number) and the loop including steps  525 ,  530 ,  535  and  540  is entered to find the smallest distance of the distances dip′.  
         [0049]     In decision step  525 , the signal dip′ representing the distance from sampled outlet Si and unsampled outlet Up is compared to D. When dip′ is less than D, D is set to dip′, Rm representing a tentative selected outlet is set to Si, the index i* is set to i and a tentative selected distance signal dmp is set to dip′ in step  530 . Step  535  is then entered in which sampled outlet index i is incremented. Where dip′ is not less than D, step  535  is entered directly from decision step  525 . Decision step  540  is then entered. Until sampled outlet index i exceeds imax in step  540 , step  525  is reentered to compare the next distance signal dip′ to the last determined minimum distance signal. When i exceeds imax, the minimum of the selected sampled outlets is chosen as Rm. The minimum distance signal dip′ is then set to LPN in step  545  to exclude Si* from comparison in step  525  and the selected outlet index m is incremented in step  550 .  
         [0050]     Step  515  is reentered from step  555  until mmax closest outlets for unsampled outlet p are selected and another outlet Rm is chosen in the loop from step  525  through  540 . Upon selection of mmax sampled outlets, the unsampled outlet index p is incremented in step  560  and step  510  is reentered via decision step  565  so that a set of m sampled outlets may be selected for the next unsampled outlet Up in  FIG. 6  via decision step  565 . When p is greater than pmax, control is passed to step  340  in  FIG. 4 .  
         [0051]     In step  340 , index p is set to one. A weighting factor w m  is then determined for each selected sampled outlet Rm of unsampled outlet Up in step  345 . Weighting factor generation is performed in the processor  215 . The weighting factor is an inverse function of the distance between the sampled outlet Rm and the unsampled outlet Up and the characteristics of the sampled and unsampled outlets according to 
 
 w   m ={(1 /d   RmUp   q )/(Σ( T   m   /d   RmUp   q )}* T   Up   (1) 
 
         [0052]     where d RmUp  is the distance between sampled outlet S m  and unsampled outlet Up, the summation is over all sampled outlets for m=1 to mmax, T up  is the unsampled outlet characteristic (e.g., total sales volume for all products), T m  is the sampled outlet characteristic and q is greater than zero. q may, for example, be 2. Index p is incremented in step  348  and control is passed to step  345  until p is greater than pmax in decision step  350 .  
         [0053]     The weighting factor signals for unsampled outlets Up and the product data for the outlets are read into the transfer store  228  as a data array which is divided therein into N data array portions. The processor  215  sends control signals to work station processors  230 - 1  through  230 -N to initiate processing of the data file portions in the work station processor. Each work station processor then proceeds to form a product estimate signal for the data file portion assigned to it as indicated with respect to the entire data file in steps  355  through  370  in  FIG. 4 . In step  355 - 1 , a starting value of the unsampled outlet index p=1 is set. The loop from step  360 - 1  to step  370 - 1  is then entered. The estimated sales of the particular product is then generated for a range of unsampled stores Up in step  360  according to 
 
 Est ( V   Up )=Σ w   m   V   m   (2) 
 
         [0054]     where V m  is the sales volume of the particular product at sampled outlet m and the summation is over the sampled outlets from m=1 to m=mmax. The unsampled outlet index p is incremented in step  365 - 1  and control is passed back to step  360 - 1  via decision step  370 - 1  until p is greater than the maximum of the range processed in work station processor  230 - 1  and an estimate of sales volume for all unsampled outlets in the range has been formed. The processing of the other work station processors  230 - 2  through  230 -N is the same as described with respect to the work station processor  230 - 1  except that the range is determined by the portion of the data file sent to the work station. The processing in the work station processor  230 -N is shown in the steps  355 -N through  370 -N.  
         [0055]     For purposes of illustration with respect to  FIG. 2 , the number of selected sampled outlets mmax is chosen as 3. It is to be understood, however, that other values may be chosen. For example, if there are 50 or more sampled outlets, mmax=10 is a suitable value. In  FIG. 2 , sampled outlets O 1 , O 2  and O 3  are selected as the three closest sampled outlets R 1 , R 2  and R 3  to unsampled outlet U 1 . To illustrate the invention, assume that the distance d R1U1  from sampled outlet R 1  to unsampled outlet U 1  is 0.4 miles, the distance d R2U1  between sampled outlet R 2  and unsampled outlet U 1  is 0.2 miles and the distance d R3u1  between sampled outlet R 3  and unsampled outlet U 1  is 0.6 miles. Further assume that the total sales volume for all products at sampled outlets R 1 , R 2 , R 3  and U 1  are $3,000, $2,000, $5,000 and $4,000, respectively. The weighting factor for sampled outlet R 2  is then 
 
 w   2 ={(1/0.2) 2 /(2000/(0.2) 2 +3000/(0.4) 2 +5000/(0.6) 2 )}*4000 
 
w 2 =1.210084  (3) 
 
 Similarly, w 1 =0.302521 and w 3 =0.13445377. For a sales volume of the particular product at R 1 , R 2  and R 3  of 5, 20 and 4, respectively, the estimated sales volume of the particular product at unsampled outlet U 1  is 
 
 Est ( V   U1 )= w   1   *v   R1   +w   2   V   R2   +w   3   v   R3  
 
 Est ( V   u1 )=26.252  (4) 
 
 The product volume signals for the sampled outlets Si is then formed in step  372 - 1  through  372 -N and the total estimated sales volume of the product for unsampled and the sampled outlets is then formed for each range in the work station processors  230 - 1  through  230 -N in steps  375 . The resulting unsampled outlet estimate and total volume estimate signals of the processing in the work station processors is then merged in and totaled step  380  into a result data file. The result data file is transferred to transfer store  228  and therefrom to data store  210 . The results are then sent to output line  245  of the input/output  201 . 
 
         [0056]      FIG. 7  shows a flow chart illustrating estimation of the distribution of a controlled product by a control authority. More particularly,  FIG. 7  shows the operation of the arrangement of  FIGS. 2 and 3  in estimating sales of a prescription product for a prescribing physician at the pharmacies. The operations in the flow chart of  FIG. 7  are performed by processor  215  and work station processors  230 - 1  through  230 -N of  FIG. 3  under control of corresponding instruction signals stored in the program store  220 . Referring to  FIG. 7 , location data, data of type characteristics of pharmacy outlets  01  through ON in  FIG. 2  and physician identification data are stored in the characteristics and locations store  205  of  FIG. 3  in step  601 . Prescription data is transferred from pharmacies O 1 , O 2 , O 3 , ON- 1  and ON and is stored in pharmacy outlet data store  210  according to the prescribing physicians (step  603 ).  
         [0057]     At prescribed intervals, the total sales for the prescribing physician j of a particular prescription product is estimated in steps  605  through  670  of  FIG. 7 . In step  605 , the processor  215  operates to determine the sampled pharmacy outlets Sij and the unsampled pharmacy outlets Up for a particular prescription product according to the validity and volume of the transferred prescription data of the prescribing physician j. The arrangement shown in  FIG. 5  may be used in the determination of step  605 . As described with respect to  FIGS. 2 and 3 , pharmacy outlets O 1 , O 2 , O 3 , ON- 1  and ON can be determined as sampled outlets S 1   j , S 2   j , S 3   j , S 4   j  and S 5   j  where the sampled data is validated. Outlets O 4  and O 5  are classified as unsampled outlets U 1  and U 2 .  
         [0058]     Step  610  is entered from step  605  and signals representative of the distances dip between each sampled outlet Sij and each unsampled outlet Up are generated in the processor  215 . After the distance determination of step  610 , the set of nearest sampled pharmacy outlets Rmj for each unsampled pharmacy outlet Up is selected by processor  215  according to step  615 . Selection of sampled pharmacies may be performed as described with respect to  FIG. 6 . Then, the unsampled pharmacy outlet index p is set to 1 in step  620  and a weighting factor signal wm for the each sampled pharmacy outlet Rm (m—1 to mmax) is generated in loop from step  625  to step  634 . Each weighting factor signal is formed according to 
 
 w   m ={(1 /d   RmUp   q )/(Σ( T   Rm   /d   RmUp   q )}* T   Up   (5) 
 
 where q is greater than 0, T Up  is the total sales volume of all products at pharmacy outlet Up, T Rm  is the total sales volume of all products at pharmacy outlet Rm and the summation Σis from m=1 to mmax. 
 
         [0059]     After the weighting factor signals have been formed for the last unsampled pharmacy in the loop from step  625  to  634 , a data array including the weighting information and the sales data from store  210  is formed by the processor  215  and sent to transfer store  228 . The data array is divided into N portions each of which is processed by one of work station processors  230 - 1  through  230 -N to form a signal representing an estimate of the total prescription product sales volume for physician j. The operations of the work station processors are controlled by the processor  215  through the control line  245 . Each work station processor operates to process a predetermined range of the data array in the transfer store  228 .  
         [0060]     The work station processor  230 - 1  operates according to steps  638 - 1  through  655 - 1  to form the volume product signals for physician j in the range p=1 to p=p 1 . In step  640 - 1 , the prescription estimate signal for unsampled outlets Upj is formed for each unsampled pharmacy in the range from p=1 to p=p 1  and the sales volume signal for the sampled pharmacies in this range is determined by the work station processor  230 - 1  according to step  655 - 1 . Work station processor  230 -N operates in similar manner for physician j over the range p=pN to p=pmax as indicated in  FIG. 7  according to steps  638 -N through  655 -N. Signals are transferred from one work station processor to another as required for the operation of the one work station processor through the network  255 . The results of the operation of work station processors  230 - 1  through  230 -N are merged in step  660  and an estimate of the prescription product volume 
 
 V   T =Σ 1   V   Sij +Σ 2   V   jUp   (6) 
 
 is generated in step  665  where 
 
 V   jUp =Σ 3 {{(1 /d   RmUp   q )/(Σ 3 ( T   Rm   /d   RmUp   q )}* T   Up   {V   Rmj   (7) 
 
 Σ 1  is the summation over all sampled outlets, Σ 2  is the summation over all unsampled outlets, Σ 3  is the summation over all sampled outlets in the neighborhood of unsampled outlet Up. At this time, a confidence signal that estimates the degree of possible error of the total volume V Tj  of the product prescriptions of the prescribing physician j is then generated in step  670 . 
 
         [0061]      FIG. 8  shows the confidence signal generation operation of step  670  in greater detail. Referring to  FIG. 8 , a mean squared error signal MSE is first generated by bootstrapping on the basis of the sales data from the sampled pharmacy outlets S 1  through Simax in step  701 . The bootstrapping method is well known in the art and is described in “The Jacknife, The Bootstrap, and Other Resampling Plans” by B. Efron, Society for Industrial and Applied Mathematics (SIAM) Publications, Philadelphia 1982.  
         [0062]     In the bootstrapping, subsets of pharmacy outlets are selected and the prescribing physician&#39;s prescription volume is estimated therefrom. The variances of the “bootstrapped” estimates closely approximates the true variance. A generalized variance function (GVF) is derived from the MSEs generated in step  701  of the form 
 
log( SQRT ( MSE ))= a+b  log( T   j )+ c  log( N   j )+ d  log( N   Sj )  (8) 
 
 where SQRT is the square root, a, b, c and d are regression coefficients, T j  is the estimated total of the prescription product prescribed by physician j, N j  is the total of prescription products prescribed by physician j and dispensed at the sampled pharmacy outlets and N Sj  is the number of sampled pharmacy outlets with prescription product sales for physician j. The generalized variance function is described in “Introduction to Variance Estimation” by K. M. Wolter, Springer-Verlag, New York 1985. 
 
         [0063]     The values of T j , N j  and NS j  are determined in steps  710 ,  715  and  720  from the prescription data in store  210  of  FIG. 3  in processor  215 . Regression coefficient signals a, b, c and d are generated by multiple regression techniques well known in the art and a log(SQRT(MSE)) value for the physician j is determined in step  720 . Decision step  725  is then entered in which the value log(SQRT(MSE)) is compared to K1. If log(SQRT(MSE)) is less than K1, a low estimated error signal is produced in step  730 . The value log(SQRT(MSE)) is then compared to K2&gt;K1 in step  735  to produce a medium estimated error signal in step  740  if log(SQRT(MSE)) is less than K2. Where log(SQRT(MSE)) is not less than K2, a high estimated error signal is generated in step  745 . While three values for the estimated error signal are determined in the flow chart of  FIG. 8 , it is to be understood that any number of values such of 5 may be used.  
         [0064]     The flow chart of  FIG. 9  illustrates another arrangement for estimating the prescription product sales volume of a prescribing physician. According to the arrangement of  FIG. 9 , a group of sampled pharmacies is selected for each unsampled pharmacy and a weighting factor for each sampled pharmacy in the neighborhood of one of the unsampled pharmacies is generated as in  FIG. 7 . The weighting factors for the sampled pharmacies are combined with the actual sales data for the sampled pharmacies according to 
 
 V   Tj =Σ 1   V   Sij +Σ 2 Σ 3   w   sp   V   Sij   (9) 
 
 which corresponds to 
 
 V   Tj =ΣΣ 1   V   Sij [1+Σ 4   w   ip ]  (10) 
 
 where V Sij  is the prescription product sales volume for physician j at pharmacy i, w ip  is the weighting factor for a sampled pharmacy i in the selected neighborhood of unsampled pharmacy p, Σ 1  is the summation over all sampled pharmacies and Σ 2  is the summation over all unsampled pharmacies, Σ 3  is the summation of all sampled pharmacies in the neighborhood of unsampled pharmacy p and Σ 4  is the summation of weighting factors associated with sampled pharmacy i. The resulting estimate of sales volume for the prescribing physician is similar to that described with respect to  FIG. 7  but the efficiency of the estimate generation is improved. 
 
         [0065]     In the method of  FIG. 9 , the sampled pharmacies in the neighborhood of each sampled pharmacy are first determined and the weighting factor signals w ip  for the neighborhood sampled pharmacies are formed in the processor  215  of  FIG. 3  on the basis of the pharmacy location, the characteristics data and the physician identification data in store  205 . The sales data from the sampled pharmacies and the weighting factor signals w ip  are transferred to the transfer store  228 , divided therein into prescribed ranges and each range of data is supplied to an assigned one of the work station processor  230 - 1  through  230 -N. In each work station processor, the estimated sales volume for each sampled pharmacy outlet in range processed by the work station processor is generated from the sales data and the projection factor for the sampled pharmacy outlet. The total estimated sales volume for the sampled pharmacy outlets over range of the work station processor is then generated. When the work station processing is completed the resulting estimated sales volumes from the work station processors are merged and transferred via the transfer store  228  to the processor  215 .  
         [0066]     Referring to  FIG. 9 , the locations, type characteristics of pharmacy outlets  01  through ON in  FIG. 2  and physician identification data are stored in the characteristics and locations store  205  of  FIG. 3  in step  801 . Prescription data is received from the pharmacies O 1 , O 2 , O 3 , ON- 1  and ON and is stored in data store  210  according to the prescribing physicians (step  803 ).  
         [0067]     The total sales for the prescribing physician j of a particular prescription product is estimated in steps  805  through  870 . In step  805 , the processor  215  operates to identify the sampled pharmacy outlets Sij and the unsampled pharmacy outlets Up for a particular prescription product according to the validity and volume of the transferred prescription data of the prescribing physician j. The arrangement shown in  FIG. 5  may be used. As described with respect to  FIGS. 2 and 4 , pharmacy outlets O 1 , O 2 , O 3 , ON- 1  and ON can be determined as sampled outlets S 1   j , S 2   j , S 3   j , S 4   j  and S 5   j  where the sampled data is validated. Outlets O 4  and O 5  are classified as unsampled outlets U 1  and U 2 .  
         [0068]     Step  810  is entered from step  805  and signals representative of the distances dip between each sampled outlet Sij and each unsampled outlet Up are generated in the processor  215 . After the distance determination of step  810 , the set of nearest sampled pharmacy outlets Rmj for each unsampled pharmacy outlet Up is selected by processor  215  according to step  815 . The selection of sampled pharmacies Rmj may be performed as previously described with respect to  FIG. 6 .  
         [0069]     The unsampled pharmacy index p is reset to one in step  820  and weighting signals wmp are formed for the selected sampled pharmacies Rmi in the loop from step  825  to  834 . In step  825 , the weighting signal 
 
 w   mp ={(1 /d   RmUp   q )/(Σ( T   m   /d   RmUp   q )}*T Up   (11) 
 
 is formed for each sampled pharmacy m selected as in the neighborhood of sampled pharmacy Up. After the weighting signals for all unsampled pharmacies are formed in the processor  215 , the sales data stored in store  210  and the weighting signals are transferred to transfer store  228  wherein the data and weighting signal array is divided into N portions and each portion is transferred to one of work station processors  230 - 1  through  230 -N. The processing of the portion transferred to work station processor  230 - 1  is shown from step  838 - 1  through  855 - 1  in  FIG. 9  and the processing the portion transferred to work station processor  230 -N is indicated from step  838 - 1  to  838 -N. 
 
         [0070]     With respect to the processing of the range of sampled pharmacies in work station processor  230 - 1 , the sampled pharmacy index i is set to one in step  838 - 1  and the loop from step  840 - 1  through  850 - 1  is iterated to form the estimated prescription volume V′ ij  for the sampled pharmacies in the range from i=1 to i=i1. In each iteration, an estimated volume signal is generated for sampled pharmacy i in step  840  according to 
 
 VT   Sij   =V   Sij [1+Σ w   mp ]  (12) 
 
 where V Sij  is the actual prescription product sales volume for physician j at pharmacy i, w mp  are the weighting factor for sampled pharmacy Sij and Σ is the summation over all weighting factors associated with the pharmacy Sij. [1+Σw mp ] is a projection factor for a physician&#39;s prescription at the sampled pharmacy V Sij . 
 
         [0071]     After the estimated volume signal is formed for the range i=1 to i1 in the work station processor  230 - 1 , step  855 - 1  is entered from step  850 - 1  wherein the total volume for the range from i=1 to i=i1 by summing the estimated volumes for the pharmacies Sij. The work station processor  230 -N operates in similar manner from step  838 -N to  855 -N to generate a total estimated volume for the range from iN to imax. Work station processors are interconnected by the network  250  such as an ethernet or token ring arrangement so that signals from one work station processor that are required for the formation of the VT TSij  signal in another work station are transferred. The total volume signals for the ranges are merged in step  860  and the merged signals are transferred to the processor  215  via the transfer store  228 . The resulting estimated total volume signal V Tj  for the physician j is then formed in the processor  215  (step  865 ) and a confidence signal for the estimated total volume V Tj  is generated in step  870  as described with respect to the flow chart of  FIG. 8 .  
         [0072]     The foregoing merely illustrates the principles of the invention. Various modifications and alterations to the described embodiments will be apparent to those skilled in the art in view of the teachings herein. It will thus be appreciated that those skilled in the art will be able to devise numerous techniques which, although not explicitly described herein, embody the principles of the invention and are thus within the spirit and scope of the invention.

Technology Category: 3