Abstract:
A system and methods for generating a profile concerning the prescription therapy practices of physicians in a therapeutic area of interest. A plurality of historical de-identified patient prescription records are analyzed, and the records are categorized based on changes in the dosage or the prescription product between prescriptions for a particular de-identified patient. The prescriptions may be categorized based on the duration between the due date of a prescription and the date the prescription was filled. This information may be summarized for each physician for each product in the therapy area of interest.

Description:
CROSS-REFERENCE TO RELATED APPLICATION  
       [0001]    This application claims priority on U.S. Provisional Patent Application Serial No. 60/273,714, entitled “Physician Profiling for Enhanced Sales Force Targeting Using Longitudinal Rx Data (LRx),” which was filed on Mar. 5, 2001, and which is incorporated by reference in its entirety herein.  
     
    
     
       BACKGROUND OF THE INVENTION  
         [0002]    1. Field of the Invention  
           [0003]    The invention relates to systems and methods for analyzing prescription claim histories for physicians, and determining statistics about the prescription therapies of such physicians.  
           [0004]    2. Related Art  
           [0005]    Pharmaceutical sales representatives typically determine a territory call plan based on information about physicians in their respective coverage areas, and the range of pharmaceutical products that such physicians typically prescribe. This information may include the specialty of the physician, the physician&#39;s response to promotional efforts, the physician&#39;s ranking in the pharmaceutical product&#39;s market share, the physician&#39;s ranking in total market volume, and the physician&#39;s ranking in the pharmaceutical product&#39;s prescription volume. Based on observed patterns with respect to this information, further qualities about physicians have been successfully modeled such as “new product early adopter,” which refers to physician who tends to prescribe a new product soon after it becomes available, or “brand loyalist,” which refers to a physician who continues to prescribe a specific branded drug, even in the face of competitive drug availability.  
           [0006]    While the above information is derived from prescriptions written by the physicians, the information does not provide insight into either physicians treatment practices over a given period of time, or such practices as applied to different patient types. Such targeting would require a more detailed understanding of a physician&#39;s treatment practices within his patient population, e.g., through the formation of a database of prescription activity for each physician where de-identified patients can be tracked to understand how a physician prescribes in a particular therapeutic area.  
           [0007]    Although algorithms have been developed for use on longitudinal medical records, these applications have been for market research or summarized levels of analysis. Outcome research and market research on longitudinal prescription data is currently being offered through several vendors - all utilizing single sources of data for their applications.  
           [0008]    Accordingly, there exists a need in the art for a technique which can analyze the long term prescribing practices of physicians, including the ability to provide information about changes in both dosages and products prescribed by the physicians and the ability to provide information about the tendencies of a physician&#39;s patient population to follow prescribed regimens.  
         SUMMARY OF THE INVENTION  
         [0009]    An object of the present invention is to provide a technique for analyzing the prescription practices of multiple physicians over a given period of time.  
           [0010]    Another object of the present invention is to provide prescription activity analysis tools which can assist pharmaceutical sales representatives in understanding the prescription practices of physicians.  
           [0011]    A further object of the present invention is to provide a technique which can track changes in prescription dosages prescribed by a physician.  
           [0012]    A still further object of the present invention is to provide a technique for monitoring patient population tendencies to follow prescriptions regimens.  
           [0013]    These and other objects of the invention, which will become apparent with reference to the disclosure herein, are accomplished by a system and method for generating a longitudinal physician profile concerning the prescription therapies for de-identified patients issued by one or more physicians in a particular therapeutic area of interest. A physician profile may be generated on a computer system including a mass storage device containing de-identified historical patient prescription records categorized by de-identified patient, physician, prescription product and dosage.  
           [0014]    The historical de-identified patient prescription records may be retrieved from the mass storage device by a dynamic memory, and user-specified information may be provided to define a subset of the historical de-identified patient prescription records. Particular historical prescription data within the subset of data defined by the user-specified information may be extracted to provide a set of data including de-identified patients that meet certain eligibility requirements.  
           [0015]    In one preferred arrangement, the categorization process of each prescription may include comparing the dosage and prescription product for a particular prescription for each patient with the dosage and prescription product of another prescription for the patient. The categorization of the particular prescription is based on a change in the dosage or in the prescription product between the particular prescription and the other prescription. Particular categories of prescriptions may include a new therapy start, a therapy switch, an add-on therapy, a titration increase or decrease, or a continued therapy.  
           [0016]    Advantageously, persistence may be determined both for each particular patient, as well as for all patients for each physician. The patient prescription data may be extracted with a second subset of data defined by a categorizing step. For example, prescriptions that are a therapy switch or a titration may be excluded from the compliance/persistence analysis. The duration between a due date of a particular prescription and the date the prescription was filled may be determined. The particular prescription may be categorized based on the duration between the due date and the date the prescription was filled.  
           [0017]    In accordance with the invention, the objects as described above have been met, and the need in the art for a technique which can analyze the long term prescription practices of a group of physicians, including the ability to provide information about changes in both dosages and products prescribed by the physicians and the ability to provide information about the tendencies of a physician&#39;s patient population to follow prescribed regimens, has been satisfied. Further features of the invention, its nature and various advantages will be more apparent from the accompanying drawings and the following detailed description of illustrative embodiments. 
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0018]    [0018]FIG. 1 is a block diagram of an exemplary system in accordance with the invention.  
         [0019]    [0019]FIG. 2 is a flowchart illustrating an exemplary procedure in accordance with the invention.  
         [0020]    [0020]FIG. 3 is a flowchart illustrating a portion of the procedure of FIG. 2 in greater detail in accordance with the invention.  
         [0021]    [0021]FIG. 4 is an exemplary summary report in accordance with the invention.  
         [0022]    [0022]FIG. 5 is flowchart illustrating another portion of the procedure of FIG. 2 in greater detail in accordance with the invention.  
         [0023]    [0023]FIG. 6 is a flowchart illustrating a portion of the procedure of FIG. 5 in greater detail in accordance with the invention.  
         [0024]    [0024]FIG. 7 is a flowchart illustrating another portion of the procedure of FIG. 5 in greater detail in accordance with the invention.  
         [0025]    [0025]FIG. 8 is a flowchart illustrating a further portion of the procedure of FIG. 5 in greater detail in accordance with the invention  
         [0026]    [0026]FIG. 9 is a summary of physician prescription information.  
         [0027]    [0027]FIG. 10 is an exemplary summary report in accordance with the invention. 
     
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS  
       [0028]    Referring now to FIG. 1, an illustrative embodiment of a system for processing prescription data is depicted and generally referred to as system  10 . The system  10  may utilize several sources of information for processing. The user supplies information on a particular therapeutic area or market of interest  12 , such as an anti-depressant therapy or blood pressure control therapy. The user may also supply information on certain prescription products which are to be included in the study  14 . Time period information  15 , i.e., an “observation period,” is selected by the user to specify the period of time in which to monitor the dispensing of prescriptions. Information on the specific prescriptions is included in prescription data, i.e., Retail Pharmacy Prescription data  16 , which includes historical de-identified patient prescription data and is typically stored on a mass storage device. This input information in received by the system at the input device  18 .  
         [0029]    The physician targeting system  10  uses longitudinal prescription data from retail pharmacies. Retail Pharmacy Prescription data  16  supplies information such as the prescription product dispensed, the dosage, refill information, i.e., an indication of whether or not a refill is authorized, the day supply, i.e., the number of days until the patient will need a refill, and the date dispensed. Retail Pharmacy Prescription data  16  groups the above information for one patient under a “de-identified” patient identification number. The de-identified patient identification number is an identifier that replaces a patient&#39;s name and protects patient confidentiality since it provides no personal information about the patient. This information allows the system to track prescription therapy over time for one specific, although unknown, patient. Thus whenever a “patient” or “patient data” is described herein, it is understood that the patient&#39;s identity and personal information are excluded (i.e., the patient is “de-identified”) in order to maintain confidentiality of patient records.  
         [0030]    While the disclosure herein is described with use of Retail Pharmacy Prescription data, other data structures could readily be employed, such as Pharmacy Benefit Manager (PBM) prescription claims data, mail order prescription data, or a combination of data sources.  
         [0031]    A prescription categorizer  20  and compliance and persistence calculator  22  perform a series of data processing operations by the central processing unit of a computer, executing software programs in COBOL, which are stored in dynamic computer memory, such as RAM (not shown). The input data is received by the prescription categorizer  20  which first considers whether each de-identified patient is “track-able” to be included into the prescription categorization process. Once track-ability is confirmed, then the prescription categorizer  20  compares the dosage and prescription product for a particular prescription for a each de-identified patient with the dosage and prescription product of another prescription for that de-identified patient identification number and categorizes the particular prescription based on a change in the dosage or the prescribed medication between the particular prescription and the other prescription. Each prescription may be categorized by the prescription categorizer  20  into the following categories: ( 1 ) New Therapy Start, ( 2 ) Therapy Switch, ( 3 ) Add-on Therapy (concomitant), ( 4 ) Titration Decrease, ( 5 ) Titration Increase, or ( 6 ) Continued Therapy. Those skilled in the art will understand that other categories could be added.  
         [0032]    The persistence calculator  22  compares the due date of a particular prescription for a de-identified patient with the date the subsequent prescription was actually filled. The persistence calculator  22  also categorizes the de-identified patient based on the number of days between the due date and the date the prescription was filled. The above calculations performed by the software program. Persistence in the prescription data refers to a de-identified patient&#39;s diligence in following their physician&#39;s prescription therapy. The persistence calculator may also summarize the categories of de-identified patients for each physician. Compliance is a measure attributed to the physician derived from the percentage of days that persistent de-identified patients were on a prescription therapy when compared with the number of days that the de-identified patients were supposed to be on that therapy. These two measures are determined by the compliance and persistence calculator  22 . This information is organized into a summary which is stored as output data  26 . This is described in greater detail as noted below with respect to FIGS.  9 - 10 .  
         [0033]    The procedures implemented by the present invention are described with respect to FIGS.  2 - 3  and  5 - 8 . The foregoing procedures may be implemented on a general purpose computer (not shown) including a suitable input device  18  and mass storage  26 . With specific reference to FIG. 2, information on the therapeutic area of interest, the prescription products included in the study, and the observation period is received at step  100 . The Retail Pharmacy Prescription data for the transactions to be included in the study are received at step  102 . Note that these steps may proceed consecutively or concurrently. Subsequently, each of the prescriptions are categorized into one of several predetermined categories, as listed above, in step  104 . Compliance and persistence information is determined at step  106 . A summary of information determined in steps  104  and  106  is generated and stored at step  108 .  
         [0034]    Further details of step  104  are illustrated in FIG. 3. In order for a de-identified patient&#39;s prescription to be included into the longitudinal prescription categorization process, patient eligibility files are used to determine if a de-identified patient has been prescription benefit eligible for a specified number of weeks, i.e., the observation period. This minimum period of time is useful to get a full view of a de-identified patient&#39;s prescription therapy history. For example, a de-identified patient who is newly enrolled in a health plan would not have sufficient historical information to determine whether a prescription was a continuation or a change in a particular therapy. Thus, if the de-identified patient is not considered eligible, the data for that de-identified patient is discarded.  
         [0035]    Information on the therapeutic area, the prescription products of interest, and observation period received in step  100  and Retail Pharmacy Prescription data received in step  102  are passed on to step  120  where a patient eligibility extract is generated. A filter is used to create a list of de-identified patient identification numbers for patients that are eligible for the prescription plan during the observation period. The patient eligibility extract may also be limited to the specified products in the specified market of interest as defined by the user.  
         [0036]    Steps  122 - 146  may be performed for each prescription in the analysis. The prescription being categorized in steps  122 - 146  is referred to herein as the “current prescription.” The process of categorization for the current prescription begins at step  122 . In accordance with a preferred embodiment of the invention, it is determined whether the current prescription is a “New Therapy Start” for the de-identified patient at step  122 . A New Therapy Start may be defined as the first prescription that a de-identified patient receives in the market of interest. This is performed by searching the records to see if the de-identified patient has any earlier prescriptions in the market of interest during the observation period. If there were no other prescriptions in this market for that de-identified patient and the current prescription has a “new prescription” indicator in the Retail Pharmacy Prescription data, then the current prescription is categorized as a New Therapy Start (step  124 ).  
         [0037]    At step  126 , a prescription that has been categorized as “New Therapy Start” is compared to the patient eligibility extract. If the de-identified patient identification number on the current prescription is not listed in the patient eligibility extract, then the current prescription is discarded from the categorization process for this de-identified patient at step  128 . The process continues with the next prescription as indicated by marker A in FIG. 3  
         [0038]    If there was at least one other “market prescription,” defined as a previous prescription in the market of interest during the observation period, then the analysis proceeds to step  130 . This step determines whether that other market prescription was for the same product as the current prescription. If the other market prescription was for a different product, the analysis proceeds to step  132 . There are two preferable categories when the other market prescription was for a different product: ( 1 ) a “Therapy Switch” which occurs when a de-identified patient receives a prescription for a different product than the last one they received, and ( 2 ) an “Add-on Therapy” occurs when a de-identified patient appears to be taking more than one product at a time, i.e., the prescription is “added-on” to other market prescriptions, as will be described in greater detail herein.  
         [0039]    At step  132 , the dates of the current prescription and the other market prescription are considered. If the other market prescription was not dispensed on the same day as the current prescription, then the current prescription is categorized as a Therapy Switch at step  134  and the process is finished for the current prescription. The current prescription may also store information on the product it was switched from. However, if the current prescription and the other market prescription were dispensed on the same date, then the current prescription is considered to be an Add-On Therapy at step  136 , and the process is finished for the current prescription. After steps  134  and  136 , the process continues with the next prescription as indicated by marker A. Although the embodiment described herein defines a prescription having the same date as an Add-On therapy, the system may be configured to permit a date range or window that would further define the difference between a therapy switch and an add-on.  
         [0040]    If the step  130  determined that the current prescription and the other market prescription are for the same product, then analysis proceeds to step  138 . There are three preferable categories for a current prescription which is for the same product as the other market prescription in the records: ( 1 ) A Continued therapy is a prescription at the same dosage level as the de-identified patient&#39;s last prescription; ( 2 ) A Titration Increase is a prescription for the same product at a greater dosage level than that de-identified patient&#39;s last prescription; and ( 3 ) A Titration Decrease is a prescription for the same product at a smaller dosage level. Other categories of prescriptions could also be utilized.  
         [0041]    At step  138 , the daily dosages for the current prescription and the other market prescription are compared. Dosage is computed as the strength of the product multiplied by the amount of product to be taken each day. For example, product Y may have a strength of 50 mg and require 2 pills per day. The daily dosage for product Y is therefore 100 mg (i.e., 50 mg * 2 pills). Other measures of dosage rate may be utilized as appropriate. If the dosages for the current prescription and the other market prescription are the same, or if either of the dosages is invalid then the current prescription is categorized as a Continued Therapy at step  140 , and the process is finished for the current prescription. At step  142 , if the dosage on the other market prescription is found to be lower than the dosage of the current prescription, the current prescription is categorized as a Titration Increase at step  144 , and the process is finished for the current prescription. If the dosage for the other market prescription is not lower than the dosage for the current prescription, i.e., the other market prescription was greater than the current prescription, then the current prescription is categorized as a Titration Decrease at step  146 , and the process is finished for the current prescription. If there is another prescription, as determined at step  148 , the categorization process continues for that prescription, as indicated by marker A. When the last prescription is reached, the process proceeds to step  160 , as described below.  
         [0042]    With the process described above, each prescription for a track-able de-identified patient will preferably fall into one category. It is noted that if additional prescription categories are utilized, each prescription may fall into more than one such category. Of course, the sequence of steps described above with respect to steps  120 - 146  may be changed in order to determine the category of a prescription. Finally, prescription category counts may be added and summarized for each physician or product for any desired time period.  
         [0043]    Example  
         [0044]    [0044]FIG. 4 provides an example of prescription categorization. In this example, therapeutic area, or market of interest, is the anti-depressant therapy market. The exemplary time frame  300  includes weekly views from the period beginning with the first quarter of 1999 to the third quarter of 1999. The exemplary observation window consists of the 13 weeks prior to any reporting week. Since these prescriptions all contain the same de-identified patient number, they are categorized according to the rules described above with respect to FIG. 3. The particular prescription number, i.e., Prescription #1 to Prescription #16, appears in column  301 . Column  302  indicates the product prescribed, column  304  indicates the dosage, and column  306  has the start date information. Each entry is indicated by a triangle  308  representing the first date of the prescription, and a triangle  310  indicating the last date of the prescription. In this example, Prescription #1 is a New Therapy start. There were no other prescriptions in this market for this de-identified patient number in the prior 13 weeks before Feb. 7, 1999. Based on the cess described above, the remaining prescriptions are categorized as shown in TABLE 1  
                       TABLE 1                       Prescription   Category   Comments                    1   New Therapy start   No previous market prescription        2   Continuing Therapy   Same product, same dosage        3   Titration Increase   40 mg increased from 20 mg        4   Therapy Switch   Drug #1 to Drug #2        5   Titration Increase   225 mg increased from 150 mg        6   Continuing Therapy   Same product, same dosage        7   Continuing Therapy   Same product, same dosage        8   Titration Increase   300 mg increased from 225 mg        9   Therapy Switch   Drug #2 to Drug #3       10a   Therapy Switch   Drug #3 to Drug #2       10b   Add-on Therapy   Drug #2 (75 mg) same day as               Prescription 10a       10c   Add-on Therapy   Drug #4 dispensed same day as               Prescription 10a       11a   Continuing Therapy   Same product, same dosage as               Prescription 10a       11b   Continuing Therapy   Same product, same dosage as               Prescription 10b       11c   Continuing Therapy   Same product, same dosage as               Prescription 10c       12   Titration Increase   30 mg increased from 15 mg       13a   Continuing Therapy   Same product, same dosage as               Prescription 11b       13b   Continuing Therapy   Same product, same dosage as               Prescription 11a       14   Continuing Therapy   Same product, same dosage as               Prescription 12       15   Continuing Therapy   Same product, same dosage as               Prescription 13a       16   Continuing Therapy   Same product, same dosage as               Prescription 13b                  
 
         [0045]    Referring again to FIG. 4, the three types of arrows represent the three unique physicians prescribing during this course of treatment, i.e., Physician A in dashed outline, Physician B in solid outline, and Physician C in solid color. At the end of the categorization, the prescriptions may be aggregated to the physician, product, category level for each weekly reporting time period.  
         [0046]    [0046]FIG. 5 provides further details of step  106  of calculating therapy persistence and compliance. As described above, persistence and compliance are measures of a de-identified patient&#39;s diligence in following their doctor&#39;s prescription therapy. The time period for evaluation was selected by the user at step  100  (See FIG. 2). In order to perform the persistence evaluation, the analysis according to the invention preferably uses one additional month of data beyond the defined measurement time period at step  160  of FIG. 5, although other time periods could be utilized. For example, to report compliance and persistence for a time period (month or quarter) ending in January, then February data is used. Selecting de-identified patients who continued therapy is determined at step  160 . Persistence data is determined for each de-identified patient at step  162 . A de-identified patient is labeled “persistent” if they refilled their prescription on time. If they were late in refilling a prescription they are considered “non-persistent.” A further stage of the process is to calculate the persistence and compliance data for each physician at step  164 . As will be described in greater detail herein, compliance is a physician measure based on the percentage of days that their persistent de-identified patients were supposed to be on a drug therapy divided by the total number of days those de-identified patients were actually on that therapy. These applications are suitable on a weekly, monthly or quarterly basis.  
         [0047]    [0047]FIG. 6 provides further details with respect to step  160 . Step  170  is to determine whether de-identified patients that have at least one market prescription due for refill within the selected time period. If the de-identified patient does not have a prescription due for refill within the selected time period, the data is discarded at step  172 , and the next de-identified patient data is evaluated at step  170 . In order to calculate the refill due date, the day supply is added to the prescription dispense date on the prescription. Using this due date, step  170  selects all de-identified patients who have a refill due in the observation period. For example, if a January observation period has been chosen, all de-identified patients who have a prescription with a refill due date within the month of January are selected. Patient data is discarded at step  172  for de-identified patients who do not have prescription with a refill due date during the time period.  
         [0048]    The next step, step  174 , is to select all prescriptions for the de-identified patients remaining from step  170  during of the time period and extending to a follow-up period. For example, for a monthly report, the observation period may be the month of January and the follow-up period is the month of February. Using the prescription categories previously determined in steps  126 - 146  as described above with respect to FIG. 3, steps  178 - 184  discard data for any de-identified patient who had either a switch or a titration in therapy. More particularly, at step  178 , if the prescription category is determined to be a Therapy Switch, then the data is discarded at step  172  (as illustrated by Marker C). If the prescription is not a Therapy Switch, then the process continues to step  180 , in which the data is discarded if the prescription is a Titration Increase. If the prescription is not a Titration Increase, the prescription category is determined at step  182 . Here the data is discarded if the prescription is a Titration Decrease. Otherwise, the process determines whether the de-identified patient had more than one physician prescribing prescriptions at step  184 . Data is discarded for de-identified patients who had more than one physician at step  172 . The remaining data is for de-identified patients having continuing therapy with one doctor, which proceeds to step  190  (FIG. 7).  
         [0049]    Further details of step  162 , i.e., calculating the persistence data for each de-identified patient is illustrated in FIG. 7. A stage in the process is to compare the date of the last prescription within the observation period (e.g., January) and the date of a subsequent prescription for that product in the follow-up period (e.g., February). More particularly, the date of a prescription in the follow-up period is determined at step  190 . If it is determined at step  192  that the prescription was filled less than 30 days after the due date, that de-identified patient is labeled “persistent” at step  194 . Otherwise, the de-identified patient is labeled “non-persistent” at step  196 . Other time periods could readily be utilized, as could a multi-tiered persistence categorization.  
         [0050]    For the persistent de-identified patients, the total number of days supplied for each de-identified patient is determined at step  198 . In the embodiment described herein, this data may be obtained from the Retail Pharmacy Prescription data. The total number of days on therapy is determined at step  200 . More particularly, the number days on therapy may be calculated by counting the number of days between the dispensed date on the initial prescription in the observation period (e.g., the January prescription) and the dispensed date of the subsequent prescription during the follow-up period (e.g., the February prescription). At step  202 , if the next de-identified patient data is to be analyzed, the analysis returns to step  170  for the next de-identified patient, as described with respect to FIG. 6. If there is no additional patient data, the analysis proceeds to step  204  (FIG. 8).  
         [0051]    In FIG. 8, further details are described with respect to the calculation of compliance and persistence data for a physician (i.e., step  164  of FIG. 5). At step  204 , the total number of non-persistent de-identified patients for a physician is calculated. The total number of persistent de-identified patients is determined at step  206 . Next, the total days of therapy for all persistent de-identified patients for this physician are calculated at step  208 . The total days supply for all persistent de-identified patients for this physician are calculated at step  210 . In the following steps  204 ,  206 ,  208 , and  210 , data for the total number of persistent de-identified patients, the total number of non-persistent de-identified patients, the total number of de-identified patients, i.e., the total number of persistent and non-persistent de-identified patients, the total days supply for persistent de-identified patients, and the total days of therapy for persistent de-identified patients are merged into a single doctor/product level file at step  212 .  
         [0052]    At step  214 , the compliance data for the physician is computed as follows:  
         Compliance=total days supply/total days on therapy   (1)  
         [0053]    Persistence data for the physician/product level is calculated at step  216 :  
         Persistence=Number of persistent patients/Total number of patients   (2)  
         [0054]    As illustrated in FIG. 2, a summary report for each physician is generated at step  108 . After the prescriptions are categorized and aggregated to a physician/product/category level, the data is summarized in a report. An example of a summarized record  350  is illustrated in FIG. 9, and stored as output data  26 . The level of summarization is to the physician and prescription product. As described above with respect to steps  122 - 146  of FIG. 3, the categorization of prescriptions occurs at the individual prescription level. Within the time period of interest, the categorized transactions are summed up in each of the categories. In summarized record  350 , the data has been summarized for physician  352  and for prescription product  354 . The record  350  therefore provides the total number of new therapy starts  356 , continued therapy  358 , therapy switches  360 , titration increases  362  and titration decreases  364  for physician  352  and prescription product  354 . The persistence and compliance data calculated in steps  160 ,  162 , and  164  FIG. 5) are also summarized in report  350 . More particularly, summarized record  350  contains data for the persistence  366  and compliance  368  for each physician  352  and prescription product  364 . Filters may be applied to enable the system to extract only the information that is actionable by sales representatives. Such filters can be based upon either analytical procedures or business rules or a combination of both. For example, a filter is used to select the top 20 doctors in a territory based on market prescription volume.  
         [0055]    In practice, a sales representative may only want to be notified when doctors in his territory start a statistically significant number of patients on a competitor&#39;s product for second line therapy. Armed with this knowledge, the sales representative may use a very specific selling message to inform these doctors about the clinical benefits of using the company&#39;s brand for second line therapy. Thus, the system described herein could be adapted to extract and deliver exception-based alerts and cycle appropriate information insight to the sales representatives with the right message at the right time.  
         [0056]    [0056]FIG. 10 illustrates an exemplary report  400  that may be delivered to a sales representative. The territory level report reformat is generated by software code in a back-end reporting system. The system accepts parameters that control what data and how much data are sent to the report. The system customizes the report to the customer&#39;s needs based on the parameters selected. Exemplary report  400  has been selected to display statistics for products DRUG #1 and DRUG #2 in the “chronic pain” market of interest  402 . Other parameters selected are “sales force”  404  and “territory”  406 . A summary portion  410  of the report  400  contains the reported products&#39; shares  414  and market volume  416  by week, which is currently available in today&#39;s environment. The report  400  may also provide information about physician practice in a prescriber portion  412 . For example, prescriber portion  412  of FIG. 10 illustrates the top four physicians in sections  420   a ,  420   b ,  420   c , and  420   d . In addition, a text message  422   a ,  422   b ,  422   c ,  422   d , further describing an observed trend in prescribing practice, is attached to each physician. Statistically significant trends based on the categorized prescription data are determined by applying statistical modeling and trending tests. Each physician is given a score from the statistical trend tests. If the score falls within a user defined range, then that physician is eligible for an alert. For example, Dr. John Doe, once ranked at #21 on market volume, is now ranked at #1 based on longitudinal prescription activity. A sales representative viewing this report now has a clearer picture of Dr. Doe&#39;s behavior and can tailor his message appropriately and quickly.  
         [0057]    One skilled in the art will appreciate that the present invention can be practiced by other than the described embodiments, which are presented here for purposes of illustration and not of limitation, and the present invention is limited only by the claims that follow.