Patent Publication Number: US-2010131284-A1

Title: Methods and apparatus for analysis of healthcare markets

Description:
RELATED APPLICATIONS 
     This patent claims the benefit of U.S. provisional application Ser. No. 61/118,163, filed on Nov. 26, 2008, which is hereby incorporated by reference herein in its entirety. 
    
    
     FIELD OF THE DISCLOSURE 
     The present disclosure relates generally to market research and, more particularly, to methods and apparatus for analysis of healthcare markets. 
     BACKGROUND 
     Market research companies have developed numerous techniques to measure consumer behavior, retailer/wholesaler characteristics, and/or marketplace demands. For example, ACNielsen® has long marketed consumer behavior data collected under its Homescan® system. The Homescan® system employs a panelist based methodology to measure consumer behavior and identify sales trends. In the Homescan® system, households, which are statistically representative of the demographic composition of a population to be measured, are retained as panelists. These panelists are provided with scanning equipment and agree to use that equipment to identify and/or otherwise scan the Universal Product Code (UPC) of every product they purchase and to note the identity of the retailer or wholesaler (collectively or individually “merchant”) from which the corresponding purchase was made. The data collected via this scanning process is periodically exported to ACNielsen®, where it is compiled into one or more databases. The data in the databases is analyzed using one or more statistical techniques and methodologies to create reports of interest to manufacturers, retailers/wholesalers, and/or other business entities. These reports provide business entities with insight into one or more trends in consumer purchasing behavior with respect to products available in the marketplace. 
     Market research companies also monitor and/or analyze marketplace demands and demographic information related to one or more products in different geographic boundaries. For example, ACNielsen® has long compiled reliable marketing research demographic data and market segmentation data via its Claritas™ and Spectra® services. These services provide this data related to, for example, geographic regions of interest and, thus, enable a customer to, for instance, determine optimum site locations and/or customer advertisement targeting based on, in part, demographics of a particular region. For example, southern demographic indicators may suggest that barbecue sauce sells particularly well during the winter months while similar products do not sell as well in northern markets until the summer months. 
     Market research companies also monitor and/or analyze point of sale data with respect to one or more merchants in different market segments. For example, ACNielsen® has long compiled data via its Scantrack® system. In the Scantrack® system, merchants install equipment at the point of sale that records the UPC code of every sold product, the quantity sold, the sale price, and the date that the sale occurred. The point of sale (POS) data collected at the one or more stores is periodically exported to ACNielsen® where it is compiled into one or more databases. The POS data in the databases is analyzed using one or more statistical techniques and/or methodologies to create reports of interest to manufacturers, wholesalers, retailers, and/or other business entities. These reports provide insight to manufacturers and/or merchants into one or more sales trends associated with products available in the marketplace. For example, the reports reflect the sales volumes of one or more products at one or more merchants. 
     Market research companies may also provide market research information focused on specialized markets. Wolters Kluwer Health developed the Source® brand products to provide information that enables healthcare product manufacturers to design marketing and sales strategies for a healthcare product. To provide the market research information, the Source® products gather information related to healthcare providers (e.g., a location, a recommendation, a prescribing history, etc.), healthcare consumers (e.g., a treatment history, a claim, a payment, etc.), products (e.g., a dispensing location, a price, a prescription, etc.) and payors (e.g., a claim, an affiliation, etc.). For example, Wolters Kluwer Health surveys healthcare providers for their recommendations for over-the-counter (OTC) products and provides the information via Source® Consumers Product Group to assist in identifying marketing opportunities or providing a basis for marketing claims. Another product, Source® Dynamic Claims, provides data associated with actual copay amounts paid at pharmacies by healthcare consumers. This data then is analyzed to provide market information for a healthcare product over a particular region such as, for example, identifying the payors with the most market share within a region. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  depicts an example consumer healthcare market. 
         FIG. 2  is a block diagram depicting an example market analysis system for analyzing the consumer healthcare market illustrated in  FIG. 1 . 
         FIG. 3  is a block diagram representation of an example healthcare market analyzer implemented in the example market analysis system of  FIG. 2 . 
         FIG. 4  is a block diagram representation of an example data set generator implemented in the example healthcare market analyzer of  FIG. 3 . 
         FIG. 5  is a block diagram representation of the example availability analyzer implemented in the example data set generator of  FIG. 4 . 
         FIG. 6  is an illustration of an example matrix containing market availability data determined by the availability analyzer depicted in  FIGS. 4 and 5 . 
         FIG. 7  is a block diagram representation of the example demand analyzer implemented in the example data set determiner of  FIG. 4 . 
         FIG. 8  is a block diagram representation of the example consumption analyzer implemented in the example data set determiner of  FIG. 4 . 
         FIG. 9  is a block diagram representation of the example market opportunity identifier implemented in the example healthcare market analyzer of  FIG. 3 . 
         FIG. 10  is an illustration of example market opportunities that may be identified by the example market opportunity identifier of  FIGS. 3 and 9 . 
         FIG. 11  is a flow diagram representative of an example process that may be performed to implement the market analyzer illustrated in  FIGS. 2-9 . 
         FIG. 12  is a flow diagram flow diagram representative of an example process that may be performed to implement the demand analyzer of  FIGS. 4-7 . 
         FIG. 13  is a flow diagram flow diagram representative an example process that may be performed to implement the availability analyzer of  FIGS. 4 and 5 . 
         FIG. 14  is a flow diagram flow diagram representative of an example process that may be performed to implement the consumption analyzer of  FIGS. 4 and 8 . 
         FIG. 15  is a flow diagram flow diagram representative of an example process that may be performed to implement the market opportunity identifier of  FIGS. 3 and 9 . 
         FIGS. 16A-16B  are representations of example drug index data available from the market availability data set. 
         FIGS. 17A-17C  are representations of example provider and payor data available from the market availability data set. 
         FIG. 18  is a representation of example price and cost data for a healthcare product available from the market availability data set. 
         FIG. 19  is a representation of an example of a disease prevalence distribution map. 
         FIG. 20  is a representation of a map depicting an example illustrating mortality rates due to a disease. 
         FIG. 21  is a representation of an example utilization ratio distribution illustrating a market opportunity for a healthcare product. 
         FIG. 22  is a representation of example access restrictions of a healthcare product in terms of prescriptions lost. 
         FIG. 23  is a block diagram of an example processor system that may be used to implement the methods and/or apparatus described herein. 
     
    
    
     DETAILED DESCRIPTION 
     Certain examples are shown in the above-identified figures and described in detail below. In describing these examples, like or identical reference numbers may be used to identify common or similar elements. Although the example systems described herein include, among other components, software executed on hardware, such apparatus is merely illustrative and should not be considered as limiting. Any or all of the disclosed components could be embodied exclusively in hardware, exclusively in software, exclusively in firmware or in some combination of hardware, firmware or software. 
     Product manufacturers turn to market research companies to gain insight into consumer purchasing behavior and market trends to improve the performance of their brands in the marketplace. Pharmaceutical companies and other manufacturers of healthcare products are no different in that respect, but face some special challenges because healthcare products are often marketed simultaneously to distinct, but interrelated groups such as healthcare providers, payors (e.g., health insurers), retailers, and healthcare consumers. Each of these groups presents particular challenges to the efforts of healthcare product manufacturers to maximize the return on their marketing investment. 
     Consumer medical conditions and the resulting or corresponding healthcare needs drive the demand for a product in the healthcare market, but the price and availability of healthcare products contribute greatly to the choices made by consumers. Further, the complex relationships among healthcare consumers, medical providers, payors and retailers influence the pricing and availability of a healthcare product and may vary greatly between geographic locations. Therefore, for healthcare product manufacturers to maximize their return on marketing investments, the manufacturers need information providing insight into a demand for a healthcare product, a consumer&#39;s access to the healthcare product and a consumer consumption pattern of the healthcare product for geographic areas of interest, as well as the manner in which such different types of information interrelate. 
     Generally, the example methods and apparatus described herein may be used to identify a market opportunity (e.g., a formulary development strategy, a return on investment strategy, a communication strategy, a healthcare market identification strategy, etc.) for a healthcare product by analyzing data compiled into at least one of a market demand data set (e.g., a likelihood of diagnosis of a healthcare need), a market availability data set (e.g., a provider affiliation estimated from claims data), or a market consumption data set (e.g., a predicted or actual use of a healthcare product) for a geographic location or a consumer segment. 
     More specifically, the example methods and apparatus described herein obtain the market availability data set corresponding to the healthcare consumer, the healthcare provider, the retailer, the payor and/or the government organization via (1) data collected from a plurality of sources (e.g., a consumer panel, a healthcare claims data set, a market research demographics data set and/or a government statistics data set) and (2) characteristics estimated from the collected data. The estimated characteristics include a provider characteristic (e.g., an affiliation characteristic based on claims data or a coverage characteristic based on formulary data) and a payor characteristic (e.g., a coverage characteristic based on dispensing records). Further, the payor characteristic and the dispensing records are analyzed to link at least two of a provider, a payor, a retailer or a prescription. 
     The example methods and apparatus described herein also obtain the market demand data set corresponding to the healthcare consumer, the healthcare provider, the retailer, the payor and/or the government organization via (1) data collected from the plurality of sources (e.g., a consumer panel, a healthcare claims data set, a market research demographics data set and/or a government statistics data set) and (2) an estimated healthcare need characteristic (e.g., a likelihood of a healthcare need). Further, the estimated healthcare need characteristic is linked to at least one of a provider, a retailer or payor for a geographic area. 
     Additionally, the example methods and apparatus described herein obtain the market consumption data set corresponding to the healthcare consumer, the healthcare provider, the retailer, the payor and/or the government organization via (1) the data collected from the plurality of sources (e.g., a consumer panel, a healthcare claims data set, a market research demographics data set and/or a government statistics data set) and (2) estimated characteristics associated with patient use and market performance of a healthcare product. The patient usage characteristic is then linked to the market performance of the healthcare product, a provider, a payor and/or a retailer for a geographic location. Further, the patient usage characteristic is used to predict a healthcare product utilization characteristic upon a population represented by a panel of consumers (e.g., the Homescan® panelists). The predicted characteristic is then used to project the utilization characteristic upon a larger population (e.g., the population of a county, state, country, etc.). 
     In one example implementation, a healthcare market analyzer collects data related to the healthcare market from a plurality of sources (e.g., a market research product such as Homescan®, Claritas™, Spectra®, Source®, and/or government records) and determines an example market availability data set, an example market demand data set and an example market consumption data set. An example market opportunity identifier then identifies market opportunities to increase the return on investment of marketing efforts for a healthcare product and outputs the identified market opportunity to a user. The example healthcare market analyzer obtains a healthcare data set from data collected via the market research products mentioned above. The example healthcare data set is then analyzed by a demand analyzer, an availability analyzer and/or a consumption analyzer to determine at least one of the market demand data set, the market availability data set or the market consumption data set, respectively. 
     In determining the market availability data set, an availability analyzer of the example implementation analyzes the healthcare data set to estimate at least one of payor characteristics and/or provider characteristics. The provider characteristics are estimated from claims data and/or formulary data. The payor characteristic is estimated from dispensing records and is used to link a provider to a prescription, a prescription to a retail dispensing location, and/or the retail dispensing location to a payor. A compiler then compiles the estimated characteristics, the linked data and the healthcare data set into the market availability data set. 
     The demand analyzer of the example implementation estimates a healthcare need characteristic (e.g., a likelihood of a specific healthcare need) based on a healthcare prevalence metric and a healthcare consumer metric from the healthcare data set (e.g., a demographics metric, an economic metric, an ethnicity metric, a lifestyle metric, a spending metric, a media consumption metric, etc.) for a geographic location and/or a consumer segment. A linker links the healthcare need characteristic with a provider characteristic, retailer characteristic, and/or a payor characteristic over the geographic area. A compiler then compiles the estimated healthcare need characteristic, the linked characteristic(s) and/or the healthcare data set into a market demand data set. 
     The consumption analyzer of the example implementation first collects a sufferer panel data set related to the characteristics of persons with a healthcare need and/or using a healthcare product. Alternatively or additionally, the consumption analyzer may derive similar characteristics based on data collected from panelist data within the healthcare data set. The consumption analyzer next determines a characteristic related to the use of the healthcare product (e.g., a patient usage characteristic or a market performance characteristic) from prescription claims data and/or OTC sales data. The patient usage characteristic is linked by a linker to at least one of the market performance characteristic, a provider characteristic, a retailer characteristic and/or a payor characteristic for a geographic location. Additionally, a predictor predicts a behavior characteristic from an analysis of a consumer panel characteristic and/or a sufferer panel characteristic. A projector projects the predicted characteristic to the population defined by a geographic area (e.g., a zip code, a health service area, a state, etc.). A generator then generates a market consumption data set from the healthcare data set, the determined characteristics, the predicted behavior characteristic and the projected behavior characteristic. 
       FIG. 1  depicts an example representation of a healthcare market  100  (e.g., a market for a healthcare product) of interest to a healthcare product manufacturer  102 . The example healthcare market  100  includes a population of healthcare consumers represented by a healthcare consumer  104 , a medical provider  106  (e.g., a doctor, a dentist, etc.), a retailer  108  (e.g., a pharmacy, a grocery store, etc.), a payor  110  (e.g., an insurance company) and/or a government organization  112  (e.g., the Centers for Medicare and Medicaid services, The Centers for Disease Control, etc.). A healthcare consumer  104  faces many choices in making healthcare decisions that further involve decisions made by the providers  106 , the payors  110  and the retailers  108 . Therefore, manufacturers of healthcare products  102  turn to market research corporations to provide insight into market-by-market segmentation. 
     Generally, the market  100  for the healthcare product corresponds to a healthcare need (e.g., a medical condition) of healthcare consumers  104 , a treatment prescribed or recommended by the healthcare provider  106 , a cost for the treatment defined by the payor  110  or government organization  112  and/or an availability of the healthcare product at the retailer  108 . More specifically, healthcare needs and treatment opportunities vary between geographic locations due to differences in the healthcare needs of local populations and the individual relationships between the consumers  104 , the providers  106 , the retailers  108 , the payors  110  and/or the government organizations  112  within the geographic areas. Price and availability contribute greatly to the purchasing decisions of the healthcare consumers  104 . For example, the healthcare consumer  104  may decline to purchase a healthcare product if the cost is too high and/or the healthcare product is not locally available. Further, the availability of the healthcare product depends on recommendations and/or prescriptions for the product by the provider  106 , the inclusion of the healthcare product on a formulary list, and/or the healthcare product being offered for sale by the retailer  108 . 
     The relationship between the healthcare consumer  104  and the payor  110  depends on a number of factors such as the healthcare consumer  104  (1) may not be insured, (2) may have a choice of health insurance plans from one or more payors  110  (e.g., an employment health insurance benefit or a privately purchased health plan) or (3) may be eligible for a medical program offered through a government organization  112  (e.g., Medicare and/or Medicaid). Additionally, other factors affecting choices of the healthcare consumer  104  include the monthly cost of the healthcare plan, the healthcare providers  106  within the healthcare plan network, and/or the copay costs associated with treatment. 
     Similarly, a number of factors affect the relationship between the healthcare consumer  104 , the healthcare provider  106  and/or the retailer  108 . For example, the healthcare consumer  104  may choose to seek treatment from a healthcare provider  106  or choose an OTC treatment purchased from the retailer  108  for their symptoms. The healthcare consumer  104  may choose a retailer  108  in the same geographic area, an online retailer  108 , and/or through a mail-order option offered through a healthcare plan. If the healthcare consumer  104  chooses an OTC product, the healthcare consumer  104  may choose the OTC product based on recommendations, research about competing products, advertising and/or price. 
     Additionally or alternatively, the healthcare consumer  104  may seek treatment from the healthcare provider  106 . Depending on the healthcare need, the healthcare provider  106  may have a variety of healthcare products to prescribe or recommend to the healthcare consumer  104 . The healthcare provider  106  may base the recommendation on the effectiveness of a healthcare product to treat a particular healthcare need, personal preference for particular healthcare products, and/or knowledge of the products included on the formulary of the payor  110  utilized by the healthcare consumer  104 . 
     If the healthcare provider  106  prescribes a healthcare product, the healthcare consumer  104  may still not purchase that particular product. For example, a healthcare consumer  104  may choose to not fill a prescription for a variety of reasons including the cessation of symptoms, the price associated with the prescription (e.g., because the prescribed product is not included on the payor  110  formulary) and/or because the healthcare consumer  104  does not have healthcare coverage for prescription products. Further, the retailer  108  may not offer the healthcare product due to an agreement with a pharmacy benefit manager to substitute a generic or competing product. 
       FIG. 2  is an example representation of a market analysis system  200  that may be used by a market research company to analyze the consumer healthcare market  100  of  FIG. 1 . The example system  200  includes a consumer behavior data set  202 , a consumer profiles data set  206 , a healthcare products data set  208 , a healthcare statistics data set  210 , a healthcare market analyzer  212  and an identified market opportunity or opportunities  214 . The data stored in the consumer behavior data set  202 , the consumer profiles data set  206 , the healthcare products data set  208 , and the healthcare statistics data set  210  may be stored in any format (e.g., an American Standard Code for Information Interchange (ASCII) format, a binary format, etc.) for storing data on an electronic medium (e.g., a memory or a mass storage device). The electronic medium may be a non-volatile memory (e.g., flash memory), a mass storage device (e.g., a disk drive), a volatile memory (e.g., static or dynamic random access memory) and/or any combination of the memory types. For example, the consumer behavior data set  202  may be stored in binary format on a random access memory  2308  ( FIG. 23 ) communicatively coupled to a processor  2302  within a processor system  2300 , such as a processor system  2300  described in detail below in conjunction with  FIG. 23 . 
     Generally, a market research company may use the healthcare market analyzer  212  to analyze the healthcare market  100  discussed in conjunction with  FIG. 1 . The consumer behavior data set  202  is collected from healthcare consumers  104  via, for example, UPC scanning equipment and contains data associated with purchases made by the consumer  104  and includes data such as a purchase price and/or a purchase location. The consumer profiles data set  206  is collected from participating households  204  via, for example, surveys and may contain, for example, consumer demographic information and/or market segmentation information arranged by geographic location. The healthcare products data set  208  may be collected from healthcare product manufacturers  102 , healthcare providers  106 , retailers  108  and/or payors  110  and may contain data corresponding to the use of healthcare products in the healthcare market  100 . For example, the healthcare products data set  208  may contain information corresponding to healthcare claims submitted to a payor  110  from healthcare providers  106  and/or retailers  108 , corresponding to sales of healthcare products at retailers  108  including over the counter sales and/or prescription sales. The healthcare statistics data set  210  is collected from governmental agencies  112  and may contain statistical information corresponding to, for example, the health status of a population (e.g., residents of a state) or subgroup of a population (e.g., residents of a state over the age of 65). The healthcare market analyzer  212  is then used by the market research company to identify the market opportunities  214  based on information from one or more of the above-mentioned data sets  202 ,  206 ,  208 , and  210 . 
     For example, ACNielsen® has long collected consumer behavior data via its Homescan® system from panelists retained to be representatives of a population. Data of this type is stored for analysis in accessible data sets, such as the example consumer behavior data set  202 . Additionally, ACNielsen® has also collected demographic and market segmentation data via its Claritas™ and Spectra® services. These services store collected demographic and segmentation data for use in creating consumer profiles, such as the example consumer profiles data set  206 . Other market research companies collect market information targeting specific markets, such as the Source® products from Wolters Kluwer Health that collect information associated with the healthcare market as stored in the example healthcare products data set  206 . Government organizations  112  (e.g., the National Center for Disease Statistics) collect statistical data associated with the health and welfare of citizens (e.g., mortality rates from specific health conditions, disease prevalence, etc.) and provide that information to the public via the example healthcare statistics data set  210 . 
     The example healthcare market analyzer  212  is configured to analyze the data provided from market research corporations via the consumer behavior data set  202 , the consumer profiles data set  206 , the healthcare products data set  208  and the healthcare statistics data set  210  to determine the marketing opportunities  214 , which may be used by healthcare product manufactures  102  to focus marketing efforts for one or more healthcare products. The example healthcare market analyzer  212  analyzes the data sets  202 ,  206 ,  208 , and/or  210  to identify a healthcare need associated with a healthcare product, calculate an availability of the healthcare product and determine a consumption behavior for consumers of the healthcare product as described in more detail below in conjunction with  FIGS. 3-15 . The market research company may then provide the identified marketing opportunities  214  (e.g., a location of a potential healthcare market, a formulary development strategy, a product success forecast, etc.) to healthcare product manufacturers  102  in any manner and/or type of video, audio and/or print format to facilitate a marketing effort for the healthcare product. 
       FIG. 3  depicts a block diagram of an example implementation of the healthcare market analyzer  212  of  FIG. 2 . The example healthcare market analyzer  212  includes a data set generator  302 , a market demand data set  304 , a market availability data set  306 , a market consumption data set  308  and a market opportunity identifier  310 . The example data set generator  302  first analyzes data provided by market research companies via the consumer behavior data set  202 , the consumer profiles data set  206 , the healthcare products data set  208  and the healthcare statistics data set  210  to determine market segmentation data (e.g., a market demand, a market availability, and/or a market consumption pattern) corresponding to the use of healthcare products within the healthcare market  100  by healthcare consumers  104 . The data stored in the market demand data set  304 , the market availability data set  306 , and/or the market consumption data set  308  may be stored in any format similar to those discussed above in conjunction with the example data sets  202  and  206 - 210  of  FIG. 2 . 
     The data set generator  302  generates the market demand data set  304  by analyzing data from the plurality of sources (e.g., the consumer behavior data set  202 , the consumer profiles data set  206 , the healthcare products data set  208 , and/or the healthcare statistics data set  210 ) with an estimated healthcare need characteristic (e.g., a likelihood of a healthcare need for a consumer demographic). The market demand data set  304  defines the healthcare market  100  in terms of consumer characteristics, (e.g., demographics, geographic location, treatment preferences, and/or media consumption history) and/or a likelihood of a healthcare need by location and/or consumer segment. Further, the data set generator  302  generates the market availability data set  306  by analyzing the consumer behavior data set  202 , the consumer profiles data set  206 , the healthcare products data set  208  and/or the healthcare statistics data set  210 , estimating provider and/or payor characteristics (e.g., a provider affiliation characteristic, a provider coverage characteristic and/or a payor coverage characteristic), and integrating the analyzed data and the estimates into a data set organized by geographic location and/or consumer demographics. The market availability data set  306  defines the healthcare market  100  in terms of a healthcare consumer&#39;s access to healthcare in terms of price across provider affiliations and payor formularies for a geographic area (e.g., a health service area, a manufacturer sales area and/or a zip code) or a specific consumer group (e.g., a subscriber employee territory or a demographic segment). Additionally, the data set generator  302  generates the market consumption data set  308  to define the healthcare market  100  in terms of healthcare product utilization by examining actual healthcare consumer consumption patterns through aggregate retailer sales data and individual prescriptions. 
     The market opportunity identifier  310  identifies marketing opportunities  214  by analyzing the market demand data set  304 , the market availability data set  306  and the market consumption data set  308  and outputs the identified marketing opportunities to a user in any manner and/or type(s) of formats, such as a textual format and/or a graphical format. For example, the market opportunity identifier  310  may analyze the market demand data set  304 , the market availability data set  306  and the market consumption data set  308  to determine the marketing opportunities  214  to improve the return on investment of marketing activities and to forecast further opportunities for healthcare products and generate a written report containing the marketing opportunities  214 . 
       FIG. 4  is a block diagram representation of the data set generator  302  of  FIG. 3 . The example implementation of the data set generator  302  includes a scheduler  402 , a collector  404 , an assembler  406 , a healthcare data set  408 , a demand analyzer  410 , an availability analyzer  412 , and a consumption analyzer  414 . The example data set generator  302  may be implemented using any desired combination of hardware, firmware and/or software. For example, one or more integrated circuits, processing devices, discrete semiconductor components and/or passive electronic components may be used to implement the example data set generator  302 . 
     Generally, the data set generator  302  is configured to determine the market demand data set  304 , the market availability data set  306  and/or the market consumption data set  308 , all of which may be used to identify the marketing opportunities  214  within a healthcare market  100 . More specifically, the example scheduler  402  determines when the collector  404  collects data from the data sets collected by the market research companies (e.g., the consumer behavior data set  202 , the consumer profiles data set  206 , the healthcare products data set  208  and/or the healthcare statistics data set  210 ). The scheduled collections triggered by the scheduler  402  may occur periodically (e.g., at 11:00 p.m., every Friday), or aperiodically (e.g., when the scheduler  402  detects that the consumer behavior data set  202  has been updated). 
     Once triggered by the scheduler  402 , the example collector  404  collects the data from the consumer behavior data set  202 , the consumer profiles data set  206 , the healthcare products data set  208  and/or the healthcare statistics data set  210 . For example, the collector  404  loads a market research data set (e.g., the healthcare products data set  206 ) into a computer readable medium (e.g., a memory and/or a mass storage device) for further processing within the example data set generator  302 . The computer readable medium may be a non-volatile memory (e.g., a flash memory), a mass storage device (e.g., a disk drive), a volatile memory (e.g., static or dynamic random access memory) and/or any combination of the memory types. 
     The example collector  404  may then collect the complete consumer behavior data set  202 , the consumer profiles data set  206 , the healthcare products data set  208  and the healthcare statistics data set  210  or only a portion of a data set (e.g., an updated selection of data within one or more of the market research data sets). Once the market research data sets are collected by the collector  404 , the assembler  406  generates the healthcare data set  408  organized by healthcare consumer  104 , provider  106 , retailer  108 , payor  110 , and government organization  112  according to geographic locations (e.g., an area defined by an extended zip code). 
     The example demand analyzer  410  then analyzes the healthcare data set  408  to determine the market demand data set  304  in terms of characteristics of the healthcare consumer  104  (e.g., demographics, economics, ethnicity, spending, etc.) as discussed in detail below in conjunction with  FIG. 7 . For example, the demand analyzer  410  first analyzes the data integrated from the consumer behavior data set  202 , the consumer profiles data set  206 , the healthcare products data set  208  and/or the healthcare statistics data set  210 . The example demand analyzer  410  then estimates a likelihood of a healthcare need for healthcare consumers  104  within a geographic area (e.g., a region defined by a postal zip code) or market segment (e.g., a segment defined by age, gender and/or ethnicity) based on the analysis. Further the example demand analyzer  410  links the likelihood of the healthcare need to the characteristics of the healthcare provider  106 , retailer  108  and/or payor  110 . 
     The example availability analyzer  412  analyzes the data in the example healthcare data set  408  to determine the market availability data set  306  in terms of the availability of a healthcare product by price across provider affiliations (e.g., a healthcare consumer relationship or membership in healthcare networks), payor formularies, geographic locations and/or consumer segments. The example availability analyzer  412  further derives a provider affiliation estimate, a provider coverage estimate and a payor coverage estimate from the healthcare data set  408  by analyzing claims data, formulary data and dispensing records, respectively. The example availability analyzer  412  is discussed in detail below in conjunction with  FIG. 5 . 
     The example consumption analyzer  414  analyzes the data in the example healthcare data set  408  to determine the market consumption data set  308  in terms of behavior of healthcare consumers  104  by examining aggregate retail sales (e.g., OTC product sales and/or prescription product sales) and the use of healthcare products by individual healthcare consumers  104 . The example consumption analyzer  414  predicts a treatment characteristic (e.g., a prescription usage characteristic) for a population of healthcare consumers  104  (e.g., a Homescan® panel) based on the analysis of characteristics of a sufferer panel (e.g., a panel of healthcare consumers  104  with a particular healthcare need), the characteristics of the syndicated panel, and a healthcare product characteristic (e.g., a prescription product usage characteristic). Further, the predicted treatment characteristic is projected to a population of healthcare consumers (e.g., a national population, a regional population, etc.). The consumption analyzer  414  is discussed in detail below in conjunction with  FIG. 8   
     A block diagram depicting an example implementation of the example availability analyzer  412  of  FIG. 4  is illustrated in  FIG. 5 . The example availability analyzer  412  includes an assembler  502 , an organizer  504 , an estimator  506  and a generator  508 . The example availability analyzer  412  may be implemented using any desired combination of hardware, firmware and/or software. For example, one or more integrated circuits, processing devices, discrete semiconductor components and/or passive electronic components may be used to implement the example availability analyzer  412 . 
     Generally, the availability analyzer  412  analyzes the example healthcare data set  408  to generate the market availability data set  306 , which at least partially defines the healthcare market  100  of a healthcare product. More specifically, the availability analyzer  412  of the illustrated example estimates provider and/or payor characteristics from the various data sources (e.g., the example consumer behavior data set  202 , the example consumer profiles data set  206 , the example healthcare products data set  208 , and/or the example healthcare statistics data set  210  of  FIG. 2 ). The example availability analyzer  412  then generates the example market availability data set  306  based on the estimated characteristics, formulary data, price and copay data, product substitution data, and/or population coverage characteristics. The market availability data set  306  generated by the availability analyzer  412  of the illustrated example provides data useful to healthcare product manufacturers  102  to define the healthcare market  100  in terms of treatment availability based on price points of healthcare products across provider affiliations, payor formularies, geographic locations and/or consumer segments. 
     The example assembler  502  of the illustrated example gathers data corresponding to the use of a healthcare product from the healthcare data set  408  for a geographic area of interest. For example, the assembler  502  may gather information including a government (e.g., Medicare) data record, a syndicated formulary database, a syndicated claims data record (e.g., claims submitted by a healthcare provider  106  and/or a retailer  108 ), and/or retailer characteristics. Next, the example organizer  504  organizes the data gathered from the healthcare data set  408  into a multidimensional matrix represented by an example data structure  600  ( FIG. 6 ). For example, the organizer  504  organizes the data into a healthcare product (e.g., a drug) dimension, a provider dimension, a payor dimension, a retailer dimension (e.g., a pharmacy), and a price dimension. Further, the dimensions are referenced to a geographic location, such as an extended zip code, in a universe dimension. The multidimensional matrix represented by the example data structure  600  is described in detail below in conjunction with  FIG. 6 . 
     Next, the estimator  506  of the illustrated example estimates a provider affiliation characteristic, a provider coverage characteristic and/or a payor coverage characteristic from claims data, formulary data and/or dispensing records gathered from the healthcare data set  408 . The provider characteristics and/or payor characteristics estimated by the estimator  506  are then stored, for example, in the example data structure  600  of  FIG. 6  at local zip code granularity to provide insight into factors (e.g., a payor plan participation rate, a formulary penetration characteristic, and a price and/or copay cost of healthcare product, etc.) affecting the treatment availability for a geographic area. Further, the availability of healthcare products to healthcare consumers  104  is affected by factors associated with the healthcare consumer&#39;s  104  choice of a healthcare provider  106  and/or retailer  108 . 
     Healthcare consumers  104  choose healthcare providers  106  based on a variety of reasons, which include payor affiliations and/or location. For example, a healthcare consumer  104  typically has a limited choice of payor healthcare plans offered through an employer and/or government organization. Further, healthcare providers  106 , and/or their associated medical group, accept a subset of health insurance plans offered by the payors  110 . Additionally, a healthcare consumer  104  may also be limited in choosing a healthcare provider  106  because the payor  110  may offer reduced coverage for treatment by providers  106  outside a payor approved network of healthcare providers  106 . Therefore, the provider coverage characteristic, provider affiliation characteristic and payor coverage characteristic estimated by the example estimator  506  are useful to healthcare product manufacturers  102  in identifying the marketing opportunities  214  for a healthcare product. 
     The estimator  506  of the illustrated example estimates the provider affiliation characteristic (e.g., the affiliation with the payor  110 ) from claims data (e.g., a prescription claim or a service claim) associated with a geographic area (e.g., a healthcare provider coverage area or a hospital service area) gathered from the healthcare data set  408 . The provider affiliation characteristic provides useful information associated with the provider choices available to a healthcare consumer  104  within a chosen geographic area. For example, a healthcare consumer  104  may choose a healthcare provider  106  located within a geographic area close to the consumer&#39;s home. The choices of providers  106  available to consumers  104  are limited by the affiliations with payors that provide healthcare insurance to the healthcare consumer  104 . Due to the cost of healthcare services, healthcare consumers  104  typically choose a healthcare provider  106  affiliated with their payor  110 . Further, a healthcare consumer  104  typically purchases healthcare products from retailers  108  located within the same geographic area associated with both the healthcare consumer  104  and their chosen healthcare provider  106 . 
     The estimator  506  of the illustrated example estimates the provider affiliation characteristic by first determining a geographic area associated with a healthcare provider  106  (e.g., the provider coverage area) and/or a retailer  108  within that same geographic area. Next, the example estimator  506  analyzes the claims data associated with the healthcare provider  106  and the retailers  108  to determine affiliations between the healthcare provider  106  and the payor  110  for the segment of the population sampled by the market research company. The example estimator  506  then analyzes the determined affiliations along with consumer demographic information for the population within the provider coverage area to estimate payors  110  affiliated with a healthcare provider  106  within the geographic area of interest by analyzing the consumer characteristics of the sampled population and the consumer demographic data. 
     Further, the estimator  506  of the illustrated example estimates the provider coverage characteristic based on formulary data associated with a payor  110 . The formulary is a list of healthcare products approved for coverage under a healthcare plan provided by a payor  110  and may include tiers (e.g., generic, preferred brand, non-preferred brand, or specialty) determining a copay amount responsibility of the healthcare consumer  104 . The estimator  506  analyzes the formulary lists, formulary tiers and provider affiliations to estimate a provider coverage characteristic that represents, for example, a percentage of time that a healthcare provider  106  may prescribe a particular healthcare product. For example, the estimator  506  may estimate a provider coverage characteristic for a healthcare provider  106  that is affiliated with a number of payors  110  utilizing three different formularies. The example estimator  506  analyzes the number of plans offered by a payor  110 , the number of healthcare consumers  104  enrolled in the plans to determine a percentage of the population within the geographic area covered by the affiliated plans. Additionally, the example estimator  506  analyzes the formulary tiers to determine a coverage area for a healthcare product of interest along with the percentage of the healthcare consumers  104  covered by the payor plans affiliated with each formulary. The estimator  506  of this example may then determine a payor coverage characteristic where the healthcare provider  106  can prescribe a particular drug (e.g., drug X thirty five percent of the time). 
     A third characteristic estimated by the estimator  506  of the illustrated example is the payor coverage characteristic estimated through, for example, an analysis of dispensing records. A dispensing record is a record kept by the retailer  108  (e.g., a pharmacy) for each prescription filled and contains information associated with the healthcare product and the associated healthcare consumer  104 . Further, the payor coverage characteristic is used to link the provider  106  to a prescription for a healthcare product, the prescription to retail dispensing and retail dispensing to a specific payor  110 . For example, the estimator  506  of the illustrated example may use the dispensing records from a retailer  108  in to create a record illustrating the prescription usage for consumers  104  within the geographic area serviced by the retailer  108  (e.g., a retail coverage area). The example estimator  506  then associates the dispensing records with a payor  110  through claims data submitted to the payor  110  for prescriptions from the same retail coverage area. Next, the example estimator  506  estimates the payor coverage characteristic by analyzing the dispensing records from the retailer  108 , the claims data corresponding to the retail coverage area and/or healthcare consumer segmentation data. An example payor coverage characteristic is the percentage of healthcare consumers  104  within a geographic area receiving coverage from a payor  110 . 
     Next, the generator  508  creates the market availability data set  306  by compiling the fact-based data (e.g., a formulary list, a prescription price, a prescription copay, a prescription substitution record, etc.) assembled from the healthcare data set  408  along with the estimates (e.g., the provider affiliation estimate, the provider coverage estimate, and the payor coverage estimate) generated by the estimator  506  into the multidimensional matrix or data structure  600  as represented by the data structure  602  of  FIG. 6 . The generator  508  stores the data (e.g., the fact-based data and the estimated data) within the multidimensional matrix  600  according to a relatively small geographic location (e.g., an area represented by an extended zip code) to enable analysis to be accomplished over larger geographic area (e.g., a health service area, manufacturer sales territory, demographic segments, etc.). 
     As mentioned above,  FIG. 6  is an example representation of the multidimensional matrix or data structure  600  that may be used to store a data set (e.g., the market availability data set  306 ) to facilitate the analysis of healthcare markets. For clarity, the multidimensional matrix  600  is represented as a data cube  602  and contains data representative of the consumer healthcare market. Such data (e.g., corresponding to the dimensions of the data structure or cube) include healthcare products (e.g., drugs  604 ), healthcare providers  106  (e.g., a provider  606 ), healthcare costs (e.g., a price  608 ) and geographic location (e.g., universe  610 ), healthcare consumers  104  (e.g., consumer demographics), and/or retailers  108  (e.g., pharmacy characteristics). The multidimensional matrix  600  generated by the generator  508  may be stored in a data file in any format similar to those discussed above in conjunction with the example data sets  202  and  206 - 210  of  FIG. 2 . 
     The generator  508  generates the market availability data set  306  by organizing data assembled from the healthcare data set  408  along with estimates generated via the estimator  506  into dimensions within the matrix  600  by geographic location. To facilitate further analysis of the availability of healthcare products within a healthcare market for the multidimensional matrix  600 , the data is referenced to the geographic location. 
     As mentioned above, healthcare product manufacturers  102  simultaneously market healthcare products to distinct, but interrelated groups (e.g., the healthcare providers  106 , the retailers  108 , the payors  108  and the consumers  104 ). To facilitate the marketing efforts to these distinct groups, the data stored within the market availability data set  306  is stored within the multidimensional matrix  600  according to an extended zip code. By storing the data referenced to a small geographic area, the universe dimension  610  retains a high level of granularity, thereby enabling flexibility in defining the scope of the universe  610  useful in targeting marketing efforts to specific groups. Examples of the targeted marketing efforts include, but are not limited to, efforts to target markets with price advantage to grow market share, prioritizing promotional spending in markets with a likelihood of increasing market share, targeting payors  110  by a local market in formulary and tier negotiations, and realigning sales forces to increase the product availability in markets with a low market share. 
     For example, the universe dimension  610  may be defined in terms of direct observations such as health service areas, manufacturer sales territories or coverage areas for healthcare providers  106  or retailers  108 . Additionally, the universe dimension  610  may be defined in terms of imputed groupings such as subscriber employee territories or demographic segments. A healthcare market may be defined as a geographic region bounded by the coverage area of a healthcare provider  106  and the data within the market availability data set  306  may be analyzed to determine characteristics of the market to facilitate the marketing efforts within that geographic area. For example, the data within the market availability data set  306  may indicate that in the region  612 , the provider coverage area for the healthcare provider (e.g., Doctor G), is affiliated with three payors  110  covering 34% of the population within the region  612  and can prescribe a healthcare product (e.g., drug D) by brand 37% of the time. In another example, in a region  614  (e.g., a health service area), a specific healthcare product (e.g., drug X) is priced higher (e.g., has higher copays for healthcare consumers  104 ) than a competing product (e.g., drug Y) for 85% of the potential patients. 
       FIG. 7  is a block diagram representation of an example implementation of the example demand analyzer  410  of  FIG. 4 . The example demand analyzer  410  includes an assembler  702 , an estimator  704 , a geographic linker  706  and a generator  708  to generate the market demand data set  304  of  FIG. 3 . The example demand analyzer  410  may be implemented using any desired combination of hardware, firmware and/or software. For example, one or more integrated circuits, processing devices, discrete semiconductor components and/or passive electronic components may be used to implement the example demand analyzer  410 . 
     Generally, the example methods and apparatus described herein analyze a study corresponding to a healthcare need (e.g., a study describing mortality rates for a disease) and data describing the healthcare market  100  of  FIG. 1 , including data related to the consumer  104 , the provider  106 , the retailer  108  and the payor  110  data (e.g., demographics, location, treatment preferences, claims information, formulary data, etc.) to generate the market demand data set  304 . More specifically, the assembler  702  of the illustrated example assembles data associated with a healthcare need (e.g., a disease prevalence study, a mortality rate study, healthcare consumer demographic data, healthcare product usage data, etc.) and stores the data in a memory according to a geographic location and/or consumer segment. The example assembler  702  may be integrated within the assembler  406  of  FIG. 4  or implemented as an independent set of machine-readable instructions executed by the processor  2302  of the processor system  2300 . The data assembled by the example assembler  702  may be stored, for example, in a data file in any format similar to those discussed above in conjunction with the example data sets  202  and  206 - 210  of  FIG. 2 . 
     Once the example assembler  702  assembles the healthcare need data, the estimator  704  of the illustrated example analyzes the assembled data. The example estimator  704  then estimates a likelihood of a specific healthcare need for healthcare consumers  104  residing within a geographic area and/or belonging to a consumer segment. More specifically, the example estimator  704  analyzes two or more studies correlating to a healthcare need collected from a government organization  112  (e.g., the National Center for Health Statistics) or another source (e.g., a university sponsored study) and determines a correlation between the studies. Further, the example estimator  704  analyzes the determined correlation with information associated with the healthcare consumer  104 , the healthcare provider  106 , the payor  110 , and the retailer  108  (e.g., consumer demographic data, claims data, etc.) to identify differences in availability of healthcare products and treatment opportunities. The example estimator  704  then estimates a likelihood of a specific healthcare need by location and/or consumer segment from the study data and the marketplace data. 
     In an example illustrating the operation of the estimator  704 , the estimator  704  examines a study of hypertension prevalence rates for people over the age of 65 (e.g., the example study illustrated in  FIG. 19 ), analyzes the study data along with a hypertension mortality rate study over the same geographic area (e.g., the example study illustrated in  FIG. 20 ), and determines that the hypertension mortality rates inversely correlate to the disease prevalence. Then, the example estimator  704  analyzes the correlation determined from the studies and the healthcare consumer  104  characteristics (e.g., demographics, ethnicity, lifestyles, spending, media consumption habits, etc.) to estimate a likelihood of specific healthcare needs, such as treatment for hypertension, for a geographic location and/or consumer segment. 
     Once the estimator  704  estimates the likelihood of specific healthcare needs, the example geographic linker  706  links the likelihoods to characteristics of the healthcare providers  106 , the retailers  108  and the payors  110 , such as payor coverage areas, formulary coverage statistics, and provider coverage areas. Next, the generator  708  generates the estimated likelihood and the linked characteristics into the market demand data set  304 . The market demand data set  304  defines the market demand for a healthcare product in terms of an identified need, an awareness of the identified healthcare need, treatment opportunities and/or treatment affordability within a specified geographic area. 
     A block diagram depicting an example implementation of the consumption analyzer  414  of  FIG. 4  is illustrated in  FIG. 8 . The example implementation of the consumption analyzer  414  includes a collector  802 , an analyzer  804 , a geographic linker  806 , a predictor  808 , a projector  812  and a generator  814  to generate the example market consumption data set  308 . The example consumption analyzer  414  may be implemented using any desired combination of hardware, firmware and/or software. For example, one or more integrated circuits, processing devices, discrete semiconductor components and/or passive electronic components may be used to implement the example consumption analyzer  414 . 
     In general, the example consumption analyzer  414  stores a measure of healthcare consumption in terms of healthcare product usage for individuals and market level performance (e.g., prescription usage and/or OTC sales) in the market consumption data set  308 . More specifically, the example methods and apparatus described herein utilize the example collector  802  to collect information associated with the use of a healthcare product by healthcare consumers  104  having a healthcare need further associated with the healthcare product. The collector  802  collects the information associated with the use of the healthcare product via inference from panelist data collected from a consumer behavior panel such as Homescan®. Alternatively or additionally, the example collector  802  gathers the healthcare product usage information from a sufferer panel. The sufferer panel information may be gathered by a market research company from healthcare consumers  104  that suffer from a specific healthcare need (e.g., hypertension, diabetes, allergies, etc.) to provide insight into the consumers  104  attitudes and behaviors driving their purchasing decisions corresponding to their healthcare needs. 
     Next, the analyzer  804  of the illustrated example assembles the collected data from sufferer panels and/or syndicated surveys, prescription claims data, and sales data from the retailers  108  (e.g., prescription sales and/or OTC sales). The example analyzer  804  analyzes the assembled data to determine a patient usage characteristic and/or a market performance characteristic associated with a healthcare product. For example, the example analyzer  804  determines a patient usage characteristic from the assembled data indicating that a consumer  104  with a healthcare need showing that the healthcare consumers  104  chose to purchase a specific OTC product in addition to a prescription product 30% of the time. An example market performance characteristic determined by the analyzer  804  may include the statistics showing that a prescription product (e.g., a drug brand X), was purchased over a competing product (e.g., brand Y) by 65% of consumers  104  with the healthcare need within the specified geographic location. 
     The example geographic linker  806  links the patient usage characteristic to the market performance characteristic and/or characteristics of the provider  106 , the payor  110  and/or the retailer  108  (e.g., a payor coverage area, a formulary coverage statistics, a provider coverage area, etc.). The geographic linker  806  links the characteristics by associating the patient usage characteristic and/or the market performance characteristic to the geographic location corresponding to the healthcare consumer  104  and/or retailer  108 . 
     Next, the example predictor  808  analyzes the patient and/or market performance characteristics with actual prescription and/or claims data to predict prescription behavior for a known population. For example, the analyzer  804  of the illustrated example determines a patient usage characteristic and a market usage characteristic based on data obtained from a sufferer panel. The predictor  808  then predicts prescription behavior for a syndicated panel (e.g., the Homescan® panel) based on a matched sample overlap between the syndicated panel and the sufferer panel. The example projector  812  then projects the predicted prescription behavior of the syndicated panel to a larger population of healthcare consumers  104 . For example, syndicated panels are designed so that the panelists are representative of healthcare consumers  104  as a whole so that a behavior predicted for a syndicated panel may be projected to a population in a regional area (e.g., a zip code, city, county, etc.), a national population and/or consumer segment. 
     Once the example projector  812  projects the predicted behavior to a larger population, the generator  814  generates the market consumption data set  308 . The market consumption data set  308  provides four levels of coverage and/or precision useful in generating the marketing opportunities  214 . The predictions and projections stored in the example market consumption data set  308  may be analyzed by matching actual measured panel behavior and attitudes with predicted treatment, actual healthcare product usage during treatment and actual measured panel behavior and attitudes, actual healthcare product usage during treatment and predicted panel behavior and attitudes, and/or projected healthcare product usage during treatment and projected panel behavior and attitudes. 
     A block diagram depicting an example implementation of the example market opportunity identifier  310  of  FIG. 3  is illustrated in  FIG. 9 . The example implementation of the market opportunity identifier  310  includes a requestor  902 , a determiner  904 , a calculator  906  and an opportunity generator  908 . The example market opportunity identifier  310  may be implemented using any desired combination of hardware, firmware and/or software. For example, one or more integrated circuits, processing devices, discrete semiconductor components and/or passive electronic components may be used to implement the example market opportunity identifier  310 . 
     Generally, the market opportunity identifier  310  of the illustrated example analyzes the market demand data set  304 , the market availability data set  306 , and/or the market consumption data set  308  to identify the marketing opportunities  214 . More specifically, a user via the requester  902  of the example market opportunity identifier  310  may request one or more marketing opportunities. Additionally or alternatively, the requester  902  may be configured to generate a marketing opportunity based on pre-determined criteria and the example implementation should not be construed as limiting. An example marketing opportunity request may include a market location opportunity request configured to identify markets with price advantage so targeted marketing efforts may be implemented to grow market share. 
     Once a marketing opportunity request is obtained via the example requester  902 , the example determiner  904  determines one or more of the market demand data set  304 , the market availability data set  306 , and/or the market consumption data set  308  containing data useful in determining the requested marketing opportunity. Example marketing requests include a strategy to target formulary development (e.g., including a healthcare product on a formulary and/or changing a formulary tier associated with the healthcare product) or a request to prioritize marketing investments by location and/or provider. 
     For example, if the request identified by the requestor  902  is the aforementioned strategy to target formulary development for a geographic region (e.g., a state), the determiner  904  determines that the example marketing opportunity identifier  214  determines the marketing opportunity from the market demand data set  304  and the market availability data set  306 . Alternatively, if the requested marketing opportunity is the aforementioned request to prioritize the marketing investments of a healthcare manufacturer  102  for a location, the determiner  904  determines that the marketing opportunity is generated from the market availability data set  306 . 
     Next, the example opportunity calculator  906  analyzes data within the data sets identified (e.g., the market demand data set  304 , the market availability data set  306 , and/or the market consumption data set  308 ) to determine the marketing opportunities  214 . For example, to determine a marketing opportunity from a request to target counties with a price advantage to improve market share, the calculator  906  analyzes data in the market demand data set  304 . An example method utilized by the example calculator  906  may calculate average consumer copays for competing healthcare products for each county in a state, an example of which is discussed below in conjunction with  FIGS. 16A and 16B . This example method only represents one method of determining or calculating a marketing opportunity  214  from the example market demand data set  304  and should not be considered limiting. The example opportunity calculator  906  may output a list of copay costs useful in determining the marketing opportunity  214  by the example opportunity generator  908 . 
     The opportunity generator  908  of the illustrated example generates and/or outputs the marketing opportunities  214  from the data calculated by the opportunity calculator  906  in response to a marketing opportunity request processed by the requestor  902 . The example opportunity generator  908  may generate and/or output the marketing opportunities  214  in a graphical and/or textual format in any manner and/or type(s) of reporting methods including, but not limited to, displaying on a video terminal or generating a hardcopy using a printer or plotter. For example, the opportunity generator  908  may analyze the copay data from the opportunity calculator  906  example to determine a marketing opportunities  214  to target marketing efforts in the counties with the lowest copay cost in order to increase market share and generate a printed report. 
     While an example manner of implementing the healthcare market analyzer  212  of  FIG. 2  has been illustrated in  FIGS. 2-5  and  7 - 9 , one or more of the elements, processes and/or devices illustrated in  FIGS. 2-5  and  7 - 9  may be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way. Further, the data set generator  302 , the market demand data set  304 , the market availability analyzer  306 , the market consumption data set  308 , the market opportunity identifier  310 , the scheduler  402 , the collector  404 , the assembler  406 , the healthcare data set  408 , the demand analyzer  410 , the availability analyzer  412 , the consumption the analyzer  414 , the assembler  502 , the organizer  504 , the estimator  506 , the generator  508  the assembler  702 , the estimator  704 , the geographic linker  706 , the generator  708 , the collector  802 , the analyzer  804 , the geographic linker  806 , the predictor  808 , the projector  812 , the generator  814 , the requester  902 , the determiner  904 , the opportunity calculator  906 , the opportunity generator  908 , and/or, more generally, the healthcare market analyzer  212  of  FIGS. 2-5  and  7 - 9  may be implemented by hardware, software, firmware and/or any combination of hardware, software and/or firmware. Thus, for example, any of the data set generator  302 , the market demand data set  304 , the market availability analyzer  306 , the market consumption data set  308  and the market opportunity identifier  310 , the scheduler  402 , the collector  404 , the assembler  406 , the healthcare data set  408 , the demand analyzer  410 , the availability analyzer  412 , the consumption analyzer  414 , the assembler  502 , the organizer  504 , the estimator  506 , the generator  508  the assembler  702 , the estimator  704 , the geographic linker  706 , the generator  708 , the collector  802 , the analyzer  804 , the geographic linker  806 , the predictor  808 , the projector  812 , the generator  814 , the requester  902 , the determiner  904 , the opportunity calculator  906 , the opportunity generator  908 , and/or, more generally, the healthcare market analyzer  212  of  FIGS. 2-5  and  7 - 9  could be implemented by one or more circuit(s), programmable processor(s), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)), etc. When any of the appended claims are read to cover a purely software and/or firmware implementation, at least one of, the data set generator  302 , the market demand data set  304 , the market availability analyzer  306 , the market consumption data set  308  and the market opportunity identifier  310 , the scheduler  402 , the collector  404 , the assembler  406 , the healthcare data set  408 , the demand analyzer  410 , the availability analyzer  412 , the consumption analyzer  414 , the assembler  502 , the organizer  504 , the estimator  506 , the generator  508  the assembler  702 , the estimator  704 , the geographic linker  706 , the generator  708 , the collector  802 , the analyzer  804 , the geographic linker  806 , the predictor  808 , the projector  812 , the generator  814 , the requester  902 , the determiner  904 , the opportunity calculator  906 , the opportunity generator  908  are hereby expressly defined to include a tangible medium such as a memory, DVD, CD, etc. storing the software and/or firmware. Further still, the healthcare market analyzer  212  of  FIG. 2  may include one or more elements, processes and/or devices in addition to, or instead of, those illustrated in  FIGS. 2-5  and  7 - 9 , and/or may include more than one of any or all of the illustrated elements, processes and devices. 
       FIG. 10  depicts an example block diagram representation of a marketing opportunity generation system  1000  utilizing one or more of the market demand data set  304 , the market availability data set  306 , and/or the market consumption data set  308  to generate the example marketing opportunities  214  of  FIG. 2 . The example marketing opportunities  214  include a patient behavior opportunity  1002 , an investment prioritization opportunity  1004 , a market location opportunity  1006 , a communication opportunity  1008 , a forecasting opportunity  1010 , a formulary development opportunity  1012  and/or a return on investment opportunity  1014 . The marketing opportunities  1002 ,  1004 ,  1006 ,  1008 ,  1010 , and  1014  are merely example marking opportunities that may be determined from the data contained in the example market demand data set  304 , the market availability data set  306  and/or the market consumption data set  308  and should not be construed as limiting. The marketing opportunities may be stored electronically on an electronic medium (e.g., a mass storage device, a memory, a CD, a DVD, etc), displayed on a display (e.g., an LCD monitor, a CRT monitor, etc.) provided via printed media (e.g., a printed report, a letter, etc.) and/or any other method for disseminating information to an individual. 
     As discussed above in conjunction with  FIG. 9 , the example market opportunity identifier  310  determines the marketing opportunity  214  by analyzing at least one of the market demand data set  304 , the market availability data set  306 , or the market consumption data set  308 . For example, the patient behavior opportunity  1002 , the investment prioritization opportunity  1004  and/or the market location opportunity  1006  may be determined by examining, respectively, the market consumption data set  308 , the market availability data set  306  and the market demand data set  304 . The example marketing opportunity identifier  310  may identify the example patient behavior opportunity  1002  by analyzing patient behavior related to a healthcare need and a healthcare product, tracking competitive product usage and/or OTC and prescription healthcare product interaction. The example investment prioritization opportunity  1004  may be determined from the market availability data set  306  by analyzing this data to determine marketing opportunities identified by a geographic location and/or healthcare provider  106 . The market opportunity identifier  310  may identify the example market location opportunity  1006  by analyzing the market demand data set  304  to determine, for example, a geographic location with favorable price points for a healthcare product as compared to competing products. 
     The example market opportunity identifier  310  of  FIGS. 3 and 9  may examine two or more of the market demand data set  304 , the market availability data set  306  or the market consumption data set  308  to determine the marketing opportunity (e.g., the communication opportunity  1008 , the forecasting opportunity  1010 , the formulary development opportunity  1012 , etc.). The example communication opportunity  1008  may be determined by the example market opportunity identifier  310  from the example market demand data set  304  and the example market consumption data set  308 . For example, by analyzing data associated with a market demand (e.g., copay cost, list price, etc) with data associated with the consumption of a healthcare product (e.g., actual patient consumption, predicted patient consumption, etc.), the market opportunity identifier  310  may generate a communication opportunity  1008  quantifying communicating strategies and/or targeting delivery to increase patient consumption and demand for the healthcare product. 
     The example market opportunity identifier  310  may generate the example forecasting opportunity  1010  by analyzing the market availability data set  306  and the market consumption data set  308 . For example, the example market opportunity identifier  310  may generate the example forecasting opportunity  1010  by analyzing the patient behavior and consumption patterns along with the availability data for a healthcare product to identify geographic locations to launch a new product and forecast a likelihood of success for a new product launch. The example market opportunity identifier  310  may identify the example formulary development opportunity  1012  by analyzing the market demand data set  304  and the market availability data set  306 . For example, the market opportunity identifier  310  of the illustrated example may analyze the market demand data set  304  and the market availability data set  306  to determine that the top two payors  110  in the state of Florida have a 56% market share and eighty-two other plans share the remaining market. Further, a healthcare demand for the healthcare product of interest exists, but competing products may have a similar and/or a more favorable copay cost. The example market opportunity identifier  310  then generates a formulary development opportunity  1012  detailing local markets that are favorable for formulary pricing negotiations to improve the market share of the healthcare product. 
     The example market opportunity identifier  310  may analyze the market demand data set  304 , the market availability data set  306  and the market consumption data set  308  to determine the market opportunity  214  such as the example return on investment opportunity  1014 . For example, the market opportunity identifier  310  of the illustrated example analyzes the market demand data set  304  to determine geographic locations with the highest demand for the specified healthcare product of interest. Further, the example market opportunity identifier  310  analyzes the market availability data set  306  and the market consumption data set  308  to determine the geographic locations with the lowest availability and highest potential for consumption of the healthcare product. The example return on investment opportunity  1014  identifies the matched geographic locations from the analysis of each of the data sets  304 ,  306  and  308  as the geographic locations with the highest potential return on the marketing investment dollars. 
     Flowcharts representative of example processes that may be executed to implement the healthcare market analyzer  212  of  FIGS. 2-5 ,  7  and  8  are shown in  FIGS. 11 through 15 . In these examples, the operations represented by each flowchart may comprise one or more programs for execution by: (a) a processor, such as the processor  2302  shown in the example processor system  2300  discussed below in connection with  FIG. 23 , (b) a controller, and/or (c) any other suitable device. The one or more programs may be embodied in software stored on a tangible medium such as, for example, a flash memory, a CD-ROM, a floppy disk, a hard drive, a DVD, or a memory associated with the processor  2302 , but the entire program or programs and/or portions thereof could alternatively be executed by a device other than the processor  2302  and/or embodied in firmware or dedicated hardware (e.g., implemented by an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable logic device (FPLD), discrete logic, etc.). For example, any or all of the data set generator  302 , the market demand data set  304 , the market availability data set  306 , the market consumption data set  308 , the market opportunity identifier  310 , the scheduler  402 , the collector  404 , the assembler  406 , the healthcare data set  408 , the market demand analyzer  410 , the market availability analyzer  412 , the market consumption analyzer  414 , the assembler  502 , the organizer  504 , the estimator  506 , the generator  508 , the assembler  702 , the estimator  704 , the geographic linker  706 , the generator  708 , the collector  802 , the analyzer  804 , the geographic linker  806 , the predictor  808 , the projector  812 , the generator  814 , the requester  902 , the determiner  904 , the opportunity calculator  906 , and the opportunity generator  908  could be implemented by any combination of software, hardware, and/or firmware. In addition, one or more of the operations represented by the flowcharts of  FIGS. 11 through 15  may be implemented manually. 
     Further, although the example processes are described with reference to the flowcharts illustrated in  FIGS. 11 through 15 , many other techniques for implementing the example methods and apparatus described herein may alternatively be used. For example, with reference to the flowcharts illustrated in  FIGS. 11 through 15 , the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, combined and/or subdivided into multiple blocks. 
     An example process  1100  that may be executed to implement healthcare market analyzer  212  of  FIGS. 2-5  and  7 - 9  is represented by the flowchart shown in  FIG. 11 . In this example process, the operations analyze data associated with the use of healthcare products within a healthcare market  100  ( FIG. 1 ) gathered from a plurality of sources (e.g., the healthcare consumer  104 , the healthcare provider  106 , the retailer  108  and/or the payor  110 ) to generate one or more data sets (e.g., a market demand data set  304 , a market availability data set  306  and/or a market consumption data set  308  of  FIG. 3 ) and/or to generate one or more marketing opportunities  214  ( FIG. 2 ) (e.g., the patient behavior opportunity  1002 , the investment prioritization opportunity  1004 , the market location opportunity  1006 , the communication opportunity  1008 , the forecasting opportunity  1010 , the formulary development opportunity  1012  and/or the return on investment opportunity  1014  of  FIG. 10 ). While the example process  1100  is shown to be by the example healthcare market analyzer  212  of  FIG. 2 , the process may be performed anywhere the collected data associated with the healthcare market  100  may be accessed. For example, the example process  1100  may also be implemented where one or more of the components of the example healthcare market analyzer  212  represented in  FIG. 3  (e.g., the data set generator  302 , the market demand data set  304 , the market availability data set  306 , the market consumption data set  308  and/or market opportunity identifier  310 ) are implemented on different processor systems  2300  ( FIG. 23 ). The example operations may be executed at periodic or aperiodic intervals, based on an occurrence of a predetermined event, etc., or any combination thereof. 
     The example process  1100  of  FIG. 11  initially causes the example scheduler  402  ( FIG. 4 ) of the healthcare market analyzer  212  to determine whether a scheduled update event (e.g., an update scheduled for every Friday evening at 11:00 p.m.) and/or an aperiodic update event (e.g., an update of data within one or more of the example consumer behavior data set  202 , the consumer profiles data set  206 , the healthcare products data set  208 , and/or the healthcare statistic data set  210 ) has occurred (block  1102 ). If the scheduler  402  determines that an update event has not occurred (block  1102 ), then the healthcare market analyzer  212  examines whether the market demand data set  304 , the market availability data set  306  and the market consumption data set  308  have been determined by the demand analyzer  410 , the availability analyzer  412  and the consumption analyzer  414 , respectively (block  1114 ). If the scheduler  402  determines that an update event has occurred (block  1102 ), then the scheduler  402  triggers the collector  404  to collect data from one or more of the example consumer behavior data set  202 , the example consumer profiles data set  206 , the example healthcare products data set  208 , and/or the example healthcare statistic data set  210  (block  1104 ). Once the data is collected by the collector  404 , the assembler  406  generates the healthcare data set  408  (block  11106 ). 
     Next, the example demand analyzer  410  of the healthcare market analyzer  212  determines the market demand data set  304  (block  1108 ). Next, the example availability analyzer  412  analyzes the healthcare data set  408  to determine the example market availability data set  306  (block  1110 ). Further, the example consumption analyzer  414  analyzes the healthcare data set  408  to determine the example market consumption data set  308  (block  1112 ). Next, the market opportunity identifier  310  checks whether the data set analyzers  410 - 414  have completed the scheduled update of the market demand data set  304 , market availability data set  306  and/or the market consumption data set  308  or if no update was scheduled (block  1102 ), the market opportunity identifier  310  checks if the data sets  304 - 308  are complete (block  1114 ). If the market opportunity identifier  310  determines the data sets  304 - 308  are not complete (block  1114 ), the example process  1100  terminates. If the market opportunity identifier  310  determines that one or more of the data sets  304 - 308  are complete (block  1114 ), the market opportunity identifier  310  identifies the marketing opportunity  214  based on data contained in one or more of the market demand data set  304 , the market availability data set  306  and the market consumption data set  308  (block  1116 ) and outputs the identified marketing opportunity to a user (block  1118 ). 
     An example process  1200  that may be used to implement the example demand analyzer  410  of  FIGS. 4 and 7  and/or used to implement block  1106  of  FIG. 11  to determine the example market demand data set  304  is represented by the flowchart depicted in  FIG. 12 . In the example process  1200 , the demand analyzer  410  analyzes a study corresponding to a healthcare need (e.g., a study describing mortality rates for a disease) and data describing the healthcare market  100  of  FIG. 1  including data related to the consumer  104 , the provider  106 , the retailer  108  and the payor  110  (e.g., demographics, location, treatment preferences, claims information, formulary data, etc.) to generate the market demand data set  304 . While the example operations are shown to be implemented within the example data set generator  302 , the operations may be implemented anywhere the healthcare data set  408  may be accessed. Further, the example operations may be executed at periodic or aperiodic intervals, based on an occurrence of a predetermined event, etc., or any combination thereof. 
     The example process  1200  of  FIG. 12  begins when the example assembler  702  of the demand analyzer  410  assembles statistical data corresponding to one or more healthcare studies (block  1202 ). For example, the assembler  702  may gather data from one study corresponding to hypertension prevalence for consumers  104  over the age of 65 for residents of a state and data corresponding to hypertension mortality rates for the same state. Next, the assembler  702  gathers consumer demographic data from the healthcare data set  408  of  FIG. 4  (block  1204 ). Additionally, the example assembler  702  collects provider data, payor data and/or retailer data from the healthcare data set  408  (block  1206 ). Once the data is assembled, the data (e.g., the statistics data, the consumer demographic data, the provider data, the payor data and/or the retailer data) is organized according to relatively small geographic locations (e.g., a local zip code) to facilitate operations performed by the estimator  704  (block  1208 ). 
     Next, in this example implementation, the estimator  704  estimates a percentage of consumers  104  diagnosed with a medical condition for a geographic location by analyzing the healthcare statistics data along with the consumer demographic data (block  1210 ). For example, the estimator  704  may analyze the consumer demographic data with the above-mentioned hypertension prevalence and mortality studies to determine a first healthcare need characteristic (e.g., a percentage of consumers  104  within the state that are diagnosed having the healthcare need). Next, the example estimator  704  analyzes the first estimated healthcare need characteristic, the provider data, retailer data and/or payor data to estimate a second healthcare need characteristic (e.g., a percentage of consumers  104  receiving treatment for the healthcare need) (block  1212 ). For example, the estimator  704  may analyze data associated with healthcare claims for prescriptions for healthcare products used in hypertension treatments to estimate a percentage of consumers  104  that are receiving treatment for hypertension. The geographic linker  706  analyzes the first and second estimated healthcare need characteristics along with the consumer demographics data, the payor data, provider data and retailer data to link actual patient usage of a healthcare product to the estimated healthcare need characteristics (block  1214 ). The generator  708  then compiles the estimated and linked characteristics along with the data within the healthcare data set  408  into the market demand data set  304  (block  1216 ). 
     An example process  1300  that may be used to implement the example availability analyzer  412  of  FIGS. 4 and 5  and/or used to implement block  1108  of  FIG. 11  to determine the example market availability data set  306  is represented by the flowchart depicted in  FIG. 13 . In the example process  1300 , the availability analyzer  412  analyzes the data in the example healthcare data set  408  to derive estimated provider characteristics and/or payor characteristics and determine the market availability data set  306  in terms of the availability of a healthcare product by price across provider affiliations (e.g., a healthcare consumer relationship or membership in healthcare networks), payor formularies, geographic locations and/or consumer segments. While the example operations are shown to be implemented within the example data set generator  302 , the operations may be implemented anywhere the healthcare data set  408  may be accessed. Further, the example operations may be executed at periodic or aperiodic intervals, based on an occurrence of a predetermined event, etc., or any combination thereof. 
     The example process  1300  of  FIG. 13  begins when the example assembler  502  of the availability analyzer  412  gathers data corresponding to the use of a healthcare product from the healthcare data set  408  for a geographic area of interest, including, for example, formulary data, claims data, retailer location data and/or provider location data (block  1302 ). For example, the example operations may cause the example assembler  502  to gather a government data record, a syndicated formulary database, a syndicated claims data record, and/or retailer characteristics. Next, the organizer  504  organizes the data gathered by the assembler  502  by geographic location, for example, the data is organized according to region defined by an extended zip code (block  1304 ). The organizer  504  organizes the data in a multidimensional matrix (e.g., the example data structure  600  of  FIG. 6 ) into dimensions such as, a healthcare product dimension, a provider dimension, a payor dimension a retailer dimension and a price dimension (block  1306 ). 
     Next, the estimator  506  of the illustrated example estimates a provider affiliation characteristic by analyzing the claims data over a geographic area (block  1308 ). For example, the provider affiliation characteristic may be determined by analyzing claims data corresponding to claims submitted to a payor  110  associated with healthcare treatments and/or prescriptions for healthcare products within a geographic area. The example estimator  506  then estimates a provider coverage characteristic based on formulary data associated with a payor  110  (block  1310 ). For example, an estimated payor coverage characteristic may correspond to a percentage of time that a healthcare provider  106  may prescribe a particular healthcare product and be determined by analyzing formulary lists, formulary tiers and provider affiliations. Next, the example estimator  506  estimates a payor coverage characteristic by analyzing dispensing records (block  1312 ). For example, the example estimator  506  may analyze the dispensing records from a retailer  108  to determine a usage pattern for healthcare consumers  104  and then associate the dispensing records to a payor  110  through an analysis of claims data for a geographic area associated with the retailer  108 . 
     Next the generator  508  links the provider  106  to prescriptions, prescriptions to a retail dispensing record and the retail dispensing record to a payor  110  by analyzing the payor coverage characteristic (block  1314 ). For example, the generator  508  may analyze the prescription claims data and the payor coverage characteristic to determine, for example, a percentage of dispensed healthcare products that is associated with a payor  110 . Further, the dispensing records and the prescription claim data submitted to a payor  110  may further be analyzed to link the retail dispensing record to a prescription (block  1314 ). Additionally, the prescription claim record and the provider claim records may then be analyzed to link a healthcare provider  106  to a prescription (block  1314 ). Finally, the example generator  508  generates the market availability data set  306  by compiling the fact-based data within the healthcare data set  408  with the estimates generated by the estimator  506  into the multidimensional matrix represented by the data structure  600  of  FIG. 6  (block  1316 ). 
     An example process  1400  that may be used to implement the example consumption analyzer  414  of  FIGS. 4 and 8  and/or used to implement block  1110  of  FIG. 11  to determine the example market consumption data set  308  is represented by the flowchart depicted in  FIG. 14 . In the example process  1400 , the consumption analyzer  414  analyzes the data in the example healthcare data set  408  to determine the market consumption data set  308  in terms of behavior of healthcare consumers  104  by examining aggregate retail sales (e.g., OTC product sales and/or prescription product sales) and the use of healthcare products by individual healthcare consumers  104 . While the example operations are described as being implemented within the example data set generator  302 , the operations may be implemented anywhere the healthcare data set  408  may be accessed. Further, the example operations may be executed at periodic or aperiodic intervals, based on an occurrence of a predetermined event, etc., or any combination thereof. 
     The example process  1400  of  FIG. 14  begins when the example collector  802  of  FIG. 8  assembles data associated with the use of a healthcare product by healthcare consumers  104  having a healthcare need from the healthcare data set  408  (block  1402 ). For example, the collector  802  collects data associated with the use of a healthcare product that was generated by a consumer panelist, generated from payor claims data and/or retail data associated with sales of OTC and prescription healthcare products. Next, the example collector  802  determines whether a sufferer panel associated with a healthcare need is to be collected by, for example, prompting a user to enter the data (block  1404 ). If the collector  802  determines that no sufferer panel data is to be collected (block  1404 ), then control advances to block  1408 . If the collector  802  determines that sufferer panel data is available for collection (block  1404 ), the sufferer panel data is collected (block  1406 ). The example analyzer  804  of the example consumption analyzer  414  then analyzes the data collected by the collector  802  to determine a patient usage characteristic and a market performance characteristic associated with a healthcare product (block  1408 ). For example, the analyzer  804  may analyze the claims and purchasing records associated with a healthcare product to determine a patient usage characteristic corresponding to the consumer&#39;s  104  usage of the prescription healthcare product. Additionally, the market usage characteristic determined by the analyzer  804  may be prescription loss characteristic corresponding to the number of prescriptions for the healthcare product written by healthcare providers  106  that are not filled, not refilled or substituted for a competing product. 
     Once the example analyzer  804  determines one or more patient usage and/or market performance characteristics associated with the healthcare product of interest, the example geographic linker  806  links the patient usage characteristic(s) to the market performance characteristic(s) and/or characteristics of the provider  106 , retailer  108  and/or payor  110  (e.g., a payor coverage area, a formulary coverage statistic, a provider coverage area, etc.) (block  1410 ). For example, the geographic linker  806  links the characteristics by associating the patient usage characteristic and/or market performance characteristic to the geographic location corresponding to the healthcare consumer  104  and/or retailer  108 . 
     Next, the example predictor  808  analyzes the patient and/or market performance characteristics with actual prescription dispensing and/or claims data to predict prescription behavior for a known population (block  1412 ). For example, the predictor  808  uses a patient usage characteristic and/or market performance characteristic based on data collected from a sufferer panel. Then the example predictor  808  predicts prescription behavior for a population represented by a syndicated panel (e.g., a Homescan® panel) based on a matched sample overlap with the sufferer panel. Once the predictor  808  predicts a characteristic associated with the syndicated panel, such as a prescription behavior, the example projector  812  projects the predicted characteristic to a larger population (block  1414 ). For example, a syndicated panel may be chosen so that the panelists are representative of healthcare consumers  104  as a whole to enable data determined from the panel to be projected to a larger population. Next, the generator  814  of the example consumption analyzer  414  then generates the market consumption data set  308  by compiling the fact based data collected from the healthcare data set  408 , the determined characteristic(s) from the analyzer  804 , the predictions from the example predictor  808  and projections from the projector  812  (block  1416 ). 
     An example process  1500  that may be used to implement the example market opportunity identifier  310  of  FIGS. 3 and 9  and/or used to implement block  1114  of  FIG. 11  to determine the example marketing opportunities  214  ( FIG. 2 ) is represented by the flowchart depicted in  FIG. 15 . In the example process  1500 , the market opportunity identifier  310  analyzes the data in the example market demand data set  304 , the market availability data set  306  and/or the market consumption data set  308  to identify the marketing opportunities  214  for a healthcare product. While the example operations are shown to be implemented within the example healthcare market analyzer  212  ( FIG. 2 ), the operations may be implemented anywhere one or more of the example market demand data set  304 , the market availability data set  306  and the market consumption data set  308  may be accessed. Further, the example operations may be executed at periodic or aperiodic intervals, based on an occurrence of a predetermined event, etc., or any combination thereof. 
     The example process  1500  of  FIG. 15  begins when the requestor  902  of the example market opportunity identifier  310  of  FIGS. 3 and 9  receives a request for a marketing opportunity  214  (block  1502 ). The example request may be entered by a user via an input device  2318  of  FIG. 23  (e.g., a keyboard, touch screen, etc.) and/or from predetermined criteria stored in a memory (e.g., the example random access memory  2308 , the example read only memory  2310 , etc.). Next, the example determiner  904  determines the geographic region and/or consumer segment corresponding to the requested marketing opportunity  214  (block  1504 ). For example, the marketing opportunity  214  may be requested for a sales territory of a healthcare product manufacturer  102  or for a particular consumer demographic segment in a geographic area (e.g., healthcare consumers  104  over the age of 40 living in Cook County, Ill.). Next, the example determiner  904  determines one or more of the example market demand data set  304 , the market availability data set  306  and/or the market consumption data set  308  to analyze to create the requested marketing opportunity  214  (block  1506 ). 
     Next, the opportunity calculator  906  of the illustrated example analyzes data contained in the data sets that were identified by the determiner  904  and extracts data useful in identifying the requested marketing opportunity  214  (block  1508 ). The example opportunity calculator  906  then calculates metrics from the identified data sets that are useful in creating the marketing opportunity  214  (block  1510 ). For example, the opportunity calculator  906  may calculate an average copay cost for a healthcare product. The opportunity calculator  906  then determines whether another metric is to be calculated to generate the requested marketing opportunity (block  1512 ). If another metric is to be calculated (block  1512 ), then control returns to block  1508  to extract further data from the data sets  304 ,  306  and/or  308  to use in determining another metric. If no further metric is needed to calculate the marketing opportunity (block  1512 ), the example opportunity generator  908  generates the marketing opportunity (block  1514 ) and outputs the marketing opportunity to a user (block  1516 ). For example, the example opportunity generator  908  may analyze copay costs associated with competing healthcare products within a state (e.g., Florida) that were calculated by the opportunity calculator  906  in identifying and/or outputting the marketing opportunity  214  that targets marketing efforts in counties where the healthcare product of interest has a pricing advantage. 
       FIGS. 16A and 16B  depict data contained in and/or calculated from, for example, the market availability data set  306  to illustrate an example availability metric for a healthcare product based on average consumer copay costs.  FIG. 16A  is an example table arranged counties of Florida (e.g., Broward, Palm Beach, Liberty, etc.) and containing data reflecting the weighted average copay costs for two competing healthcare products (e.g., drug A  1602  and drug B  1604 ). The table also contains a calculated metric (e.g., the drug index  1606 ) useful in comparing the copay costs for two or more competing healthcare products, in this example drug A  1602  and drug B  1604 . 
     The drug index  1606  of the illustrated example is shown to be calculated based on the average copay costs of two healthcare products of interest (e.g., drug index=(average copay cost of a first healthcare product)/(the average copay cost of a second healthcare product)). For example, the drug index of  FIGS. 16A-B  are calculated by dividing the average copay cost of drug A  1602  by the average copay cost for drug B  1604  for each county in Florida. The average copay costs of this example represent a weighted average of copay costs within the county based at least, in part, on the formulary data, payor data and copay cost data, an example of which is illustrated below in  FIGS. 17A-17C . 
     The example drug index  1606  represents a metric useful in comparing healthcare product costs of healthcare consumers  104  reflecting the availability of the healthcare product. The lower drug index  1606  values reflect a greater availability based on copay cost because consumers  104  are more likely to purchase the lowest cost healthcare product among competing products. For example, in Broward county  1614 , the average copay cost of drug A  1602  is $40.99 as compared to the average copay cost of $20.77 in Liberty county  1616 . Further, the calculated drug index  1606  in Broward county  1614  is 1.48 (e.g., drug index=$40.99/$27.71) and in Liberty county  1616 , the drug index is 0.76 (e.g., drug index=$29.77/$39.15). Therefore, drug A  1602  may be said to have a greater availability in Liberty county  1616  and drug B  1604  has a greater availability in Broward county  1614 . 
       FIGS. 17A ,  17 B and  17 C are representations of example data that may be contained in the example market availability data set  306  and useful to determine the marketing opportunity  214 , for example, based on price competitiveness in a given market  100 . Factors useful in generating the price competitiveness marketing opportunity  214  may include plan participation ( FIG. 17A ), formulary penetration ( FIG. 17B ), and/or formulary copay tier ( FIG. 17C ). As shown below in conjunction with  FIG. 18 , costs associated with healthcare products correspond to many factors in addition to manufacturer list price. 
       FIG. 17A  is a table representative of healthcare consumer participation in healthcare plans available in a state such as Florida. The table in  FIG. 17A  contains data representing market share  1702  (e.g., the percentage of total enrolled healthcare consumers  104  participating in plans offered by the payor  110 ) for the top five payors  110  providing healthcare coverage in Florida. The market share  1702  associated with the payors  110  is useful in creating an example marketing opportunity  214  because healthcare consumers  104  purchase healthcare products based not only on a need, but also by the price and/or copay cost determined by the healthcare consumer&#39;s  104  healthcare plan. 
     In the illustrated example, one hundred and sixteen healthcare plans are available to healthcare consumers  104  in the state of Florida. Twenty-three of the healthcare plans are offered through the top five payors  1704  (e.g., healthcare provider  1 , healthcare provider  2 , etc.) and account for 74% of the market share  1706 . Further, the top two plans, provider  1  and provider  2 , collectively cover 56% of the healthcare consumers  104  in the market for health insurance. Therefore, for example, the marketing opportunity identifier  310  may analyze the market availability data set  306  to identify a marketing opportunity  214  for a healthcare product by analyzing data associated with one or more of the top five healthcare plans  1704  for the state of Florida. 
     In  FIG. 17B , an example formulary metric  1710  (e.g., a formulary penetration metric for competing healthcare products) useful in determining the availability of a healthcare product to consumers  104  is shown. As noted above, price is an important factor influencing purchasing decisions of the healthcare consumer  104 , which in turn depends on the copay costs of healthcare products determined by the payor  110  through formulary lists. In  FIG. 17B , the number of healthcare plans utilizing formulary lists containing two competing products, drug A  1712  and drug B  1714 , are compared to determine the example formulary metric  1710 . Drug A  1712  is shown to be on formularies utilized by 79% of the available healthcare plans, whereas drug B  1714  is on formularies utilized by 66% of the healthcare plans in Florida. For example, the example formulary penetration metric data may be extracted from the market availability data set  306  ( FIG. 3 ) by the market opportunity identifier  310  to determine a marketing opportunity  214  targeting marketing efforts to increase formulary penetration for a product. 
       FIG. 17C  demonstrates the effect that formulary tiers have on the price competitiveness of healthcare products through a formulary tier metric. For example, a formulary may categorize healthcare products according to tiers to determine the copay responsibility of the healthcare consumer  104 . A tier  1  may include preferred healthcare products, such as generic drugs, and have the lowest copay level. A mid-range formulary tier, tier  2 , may contain preferred brand-name healthcare products that have higher copays than tier  1  products. A third formulary tier may include other healthcare products that are not included on the preferred product list of tier  2 , and have copay costs higher that either tier  1  or tier  2  products. Some healthcare plans also include a tier for specialty drugs where the copay costs may be up to 33% of the retail list price. 
     In  FIG. 17C , the formulary copay tiers for example healthcare products may be inferred from the data in the chart for two example payors  110 , provider  1   1718  and provider  2   1720 . For example, provider  1   1718  plans have copay costs for the drug A family of products  1722  that are lower than the copay costs for the drug B family of products  1724  (e.g., $24.01 versus $40.42, respectively). Drug A may therefore be inferred to be on a lower formulary tier than drug B for the healthcare plans offered through provider  1 . The pricing structure is reversed for provider  2   1720 , where drug B may be inferred to be on a lower formulary tier than drug A for the plans offered through provider  2 . The determined formulary tier metric may be used by the example market opportunity identifier  310  to identify a marketing opportunity  214  for formulary development. 
       FIG. 18  is an example representation of a price model that may be used by the market opportunity identifier  310  to identify pricing elements from data in the example market availability data set  306 . The pricing elements may be calculated for geographic locations (e.g., a local zip code, a sales territory, etc.) or consumer segments (e.g., consumers  104  over the age of 65) to facilitate the generation of marketing opportunities  214 . 
     The pricing elements may include a selling component  1802 , a paying component  1804  associated with purchased healthcare product and/or a discount component  1806  (e.g., a discount, rebate, etc.). The selling  1802 , paying  1804  and/or discount  1806  components provide a more accurate representation of healthcare product costs than manufacturer list price which, in turn, allows the market opportunity identifier  310  to identify the marketing opportunities  214  more accurately. 
     The healthcare product manufacturer  102  ( FIG. 1 ) determines a list price  1810  for a healthcare product and discounts and/or rebates  1812  to encourage a wholesaler, the retailer  108  and/or the pharmacy benefit manager to promote the use of the healthcare product. A wholesale price  1814  includes the manufacturer list price  1810  and a first added margin  1816 . Similarly, the retail list  1818  price includes the wholesale price  1814  plus a second margin  1820 . A healthcare product manufacturer  102  may provide the wholesaler a discount and/or rebate to allow the wholesaler to reduce the wholesale price  1814  of the product which, in turn, reduces the retail price  1818 . 
     In general, the pharmacy benefit manager negotiates with a retailer  108  to set the cost of the healthcare product  1822 . Then, the payor  110  and/or the pharmacy benefit manager determine the formulary tier associated with the healthcare product. The formulary tier then determines a payor cost portion  1824  and a consumer copay cost  1826 . The healthcare product manufacturer  102  has a direct of indirect influence on the pricing and/or cost negotiations by providing discounts and/or rebates to any one or more of the wholesaler, retailer  108 , and/or pharmacy benefit manager. The pharmacy benefit manager may use any discount and/or rebate received from the healthcare manufacturer  102  in the pricing negotiations with the retailer  108 . The healthcare product manufacturer  102  may further provide a discount to a healthcare consumer  104 , for example in a coupon for the healthcare product (not illustrated). 
       FIGS. 19 and 20  are example healthcare studies that may be provided by a government  112  agency and utilized by the demand analyzer  410  ( FIGS. 4 and 7 ) in determining the example market demand data set  304  of  FIG. 3 .  FIG. 19  represents the hypertension prevalence rates of a demographic group (e.g., people age 65 and over) in Florida and reported by county. As can be seen in  FIG. 19 , the hypertension prevalence rates vary significantly by geographic regions (e.g., counties) and are driven by the underlying demographics of the population. For example, Collier county  1902  has one of the top-ten hypertension prevalence rates for the state of Florida at 19.1%. Conversely, Leon county  1904  has one of the lowest ten-hypertension prevalence rates in the state at 7.1%. Previously, market opportunities  214  may have been generated by marketing research companies only based on studies substantially similar to the one illustrated in  FIG. 19 . 
       FIG. 20  is an example hypertension mortality rate study representing a mortality rate in terms of population (e.g., the mortality rate per 100,000 people). For example, Collier county  2002  has one of the lowest hypertension mortality rates of the state at 58.1 per 100,000 persons. Conversely, Leon county  2004  has one of the highest hypertension mortality rates at 193.2 per 100,000 persons. 
     In the illustrated example, the demand analyzer  410  analyzes the data in one or more healthcare studies (e.g., the studies shown in  FIGS. 19 and 20 ) over a geographic area, such as Florida, to determine characteristics associated with the population and useful in determining the market demand data set  304  and/or marketing opportunities  214 . For example, by comparing the data in the studies of  FIGS. 19 and 20 , the demand analyzer  410  may determine a characteristic of the population showing that the hypertension mortality rates inversely correlate to hypertension prevalence. The determined characteristic further indicates differences in access to healthcare products and treatment opportunities for the underlying population. Further, the market opportunity identifier  310  may utilize this data to identify a marketing opportunity  214  useful to a healthcare manufacturer  102  in focusing marketing efforts specifically designed for a geographic area. For example, the studies of  FIGS. 19 and 20  may be analyzed to show that, in one geographic area, 45% of the population with the healthcare condition is not diagnosed and 42% is diagnosed but not treated. Further, the data may show that, for the treated healthcare consumers, 70% are treated but not at goal and/or only 9% are treated optimally. 
       FIG. 21  is a representation of example data illustrating a marketing opportunity  214  ( FIG. 2 ) created by the example market opportunity identifier  310  ( FIG. 3 ) by analyzing data in the market demand data set  304 , the market availability data set  306  and market consumption data set  308 . The market opportunity identifier  310  may create a metric useful in determining a marketing opportunity for a healthcare product (e.g., a drug utilization ratio), for example, by analyzing data from the market demand data set  304  and the market consumption data set  308 . For example, the market opportunity identifier  310  may extract the actual number of prescriptions filled for a healthcare product from the market consumption data set  308  and a value representative of an estimated potential for prescriptions for the healthcare product from the market demand data set  304 . 
     The example drug utilization ratio may be calculated for a geographic area (e.g., a county, a state, a sales territory, etc.) based on the formula: drug utilization ratio=(actual prescriptions)÷(estimated prescription potential). For example, an estimated prescription potential for a state may be calculated based on (1) the number of enrolled healthcare consumers  104  having a condition (e.g., calculated from prevalence and access data from the market demand data set  304 ), (2) the average number of prescriptions written per member of a health plan and (3) from a market share associated with a healthcare product. For example, an estimated prescription potential for drug A in Florida may be calculated to be 1,068,323, and the actual number of prescriptions written were 604,319. The resulting utilization ratio for drug A is 0.57 (604,319÷.1,068,323). The unrealized market potential of this example in Florida (e.g., estimated potential prescriptions−actual prescriptions) is 464,004 prescriptions having a market value of $30.7 million. 
     Further, the data may further analyzed for smaller geographic locations (e.g., a county) to better identify marketing opportunities  214  to target the unrealized potential market. For example, in Okaloosa county  2102 , the utilization ratio is 0.85, representing 85% of the estimated potential scripts were written for drug A. However, in Broward county  2104 , only 22% of the estimated prescriptions were written. 
       FIG. 22  is a representation of example data illustrating another marketing opportunity  214  that may be created by the market opportunity identifier  310  by analyzing data in the market demand data set  304 , the market availability data set  306  and market consumption data set  308 . A healthcare consumer&#39;s access to a healthcare product through prescriptions may be limited by formulary restrictions such as, a healthcare product belonging to the highest tier or not included on the formulary. The healthcare consumer&#39;s limited access to the product results in, for example, lost prescriptions and a corresponding loss in sales for the product. The example market opportunity identifier  310  may use this information to generate an example market opportunity  214  for formulary development to remove the restrictions on the consumer&#39;s access to the product, thus increasing sales. In the illustrated example, counties with little or no identified restrictions (e.g., Bay county  2202 , Monroe county  2204 ) can be separated from the counties with more identified lost opportunities (e.g., Miami-Dade  2206 , Pinellas  2208 , etc.). The market opportunity identifier  310  may utilize this information in identifying an opportunity to target formulary development in the counties with large numbers of lost opportunities, such as Pinellas  2208 . 
       FIG. 23  is a schematic diagram of an example processor platform  2300  that may be used and/or programmed to implement all or a portion of any or all of the example operations of  FIGS. 11-15 . For example, one or more general-purpose processors, microcontrollers, etc can implement the processor platform  2300 . The example processor platform  2300  or a platform similar thereto, may be used to implement the example market segmentation system  200 . The processor platform  2300  of the example of  FIG. 23  includes at least one general-purpose programmable processor  2300 . The processor  2302  executes coded instructions  2304  and/or  2306  present in main memory of the processor  2302  (e.g., within a RAM  2308  and/or a ROM  2310 ). The processor  2302  may be any type of processing unit, such as a processor or a microcontroller. The processor  2302  may execute, among other things, the example methods and apparatus described herein. 
     The processor  2302  is in communication with the main memory (including a RAM  2308  and/or a ROM  2310 ) via a bus  2312 . The RAM  2308  may be implemented by dynamic random-access memory (DRAM), synchronous dynamic random-access memory (SDRAM), and/or any other type of RAM device, and the ROM  2310  may be implemented by flash memory and/or any other desired type of memory device. A memory controller  2314  may control access to the memory  2308  and the memory  2310 . 
     The processor platform  2302  also includes an interface circuit  2316 . The interface circuit  2316  may be implemented by any type of interface standard, such as an external memory interface, serial port, general purpose input/output, etc. One or more input devices  2318  and one or more output devices  2320  are connected to the interface circuit  2316 . 
     Although certain example methods, apparatus and articles of manufacture have been described herein, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all methods, apparatus and articles of manufacture fairly falling within the scope of the appended claims either literally or under the doctrine of equivalents.