WO2004079538A2 - System and method for outcome-based management of medical science liaisons - Google Patents

System and method for outcome-based management of medical science liaisons Download PDF

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Publication number
WO2004079538A2
WO2004079538A2 PCT/US2004/006573 US2004006573W WO2004079538A2 WO 2004079538 A2 WO2004079538 A2 WO 2004079538A2 US 2004006573 W US2004006573 W US 2004006573W WO 2004079538 A2 WO2004079538 A2 WO 2004079538A2
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msl
data
customer
investigator
business
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PCT/US2004/006573
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French (fr)
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WO2004079538A3 (en
Inventor
Bill W. Massey
Daniel J. Cushing
Daniel J. Leonard
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Scientific Commercialization, Llc
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Priority to EP04717393A priority Critical patent/EP1606744A4/en
Publication of WO2004079538A2 publication Critical patent/WO2004079538A2/en
Publication of WO2004079538A3 publication Critical patent/WO2004079538A3/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • This invention relates to a management system for the efficient management and evaluation of medical support groups in the pharmaceutical, bio-pharmaceutical and medical device industries.
  • the present invention is a system that provides a means to generate business metrics that enable the MSL team to plan for and manage their activities, effectively allocate resources, and measure their accomplishments.
  • the assignment of specific business outcomes toward a targeted MTL allows for the MSL team's efforts to be incorporated into the sponsor company's overall business planning process and business objectives.
  • the methods of the present invention may be used by pharmaceutical company in determining the appropriate use of access channels to the customer.
  • the metrics derived from the methods of the present invention enable executive management to optimally allocate resources across customer-interfacing groups within the organization in order to achieve vital business objectives.
  • the methods of the present invention are organized into a cyclic process consisting of three phases: Planning, Executing, and Evaluating.
  • the Planning phase provides methods for determining "real world" MSL capacity, MTL targeting and selection, inco ⁇ orating MSL business objectives in support of the sponsor company's overall business strategy, and defining performance metrics.
  • the system provides for the assessment of performance and documentation of MSL activities. This information is summarized to produce the targeted customer lists (TCL) and to efficiently focus the resources of the sponsor company.
  • TCL targeted customer lists
  • the Evaluating phase involves assessment of MSL impact through analysis of achieved business outcomes, MSL-specific surveys of targeted MTLs, impact on prescribing behavior of targeted MTLs and their influence network, and analysis of the value provided by the MSL's internal activities (training sales, reviewing protocols, etc.).
  • the outputs of the Executing phase's activity assessment and Evaluation phase allow for refinement of future planning and execution, thereby providing a cyclic system for continuous business improvement.
  • a system and method for managing customer interaction activities of medical liaison personnel of a sponsor organization with health professional customers to achieve one or more desired business outcomes is disclosed.
  • the system uses a customer relation database to record data regarding customer interaction activity of the medical liaison personnel and data regarding the business outcomes achieved or not achieved during the predetermined time period.
  • the system correlates the customer interaction activity data - and the business outcome data so that it can be used to conduct capacity and tactical assessments for future medical liaison activities.
  • a method for targeting medical thought leaders or other health professionals who are most likely to achieve the business outcomes is also disclosed.
  • the system also provides a method for surveying the health professional customers to determine their level of satisfaction with medical liaison personnel and sponsor organization.
  • a distributed customer relation management system for distributed MSL data communications comprising a master computer system.
  • the master computer system is preferably centralized and comprises a memory system, a network interface system and a processing system.
  • the memory system preferably includes at least one at least temporary memory device
  • the network interface system preferably includes at least one network interface device
  • the processing system preferably includes at least one electronic controller.
  • the memory system preferably comprises a master customer relation management database with master data stored therein, the master data comprising at least one of desired business outcome attributes, activity attributes and preferred MTL data.
  • the network interface system is preferably for receiving first MSL data from a first MSL computer system via a first network architecture and for receiving second MSL data from a second MSL computer system via a second network architecture.
  • the processing system is preferably for analyzing the master data in conjunction with at least one of the first MSL data and the second MSL data to produce resultant data.
  • Some embodiments of the customer relation management system comprise the first MSL computer system and/or the second MSL computer system.
  • the first MSL computer system and/or the second MSL computer system preferably comprise a notebook computer, a desktop computer, a personal digital assistant, a wireless telephone, a workstation and or another network-compatible device.
  • the master computer system is adapted to communicate the resultant data to the first MSL computer system via the first network architecture and to the second MSL computer system via the second network architecture.
  • the first network architecture and/or the second network architecture comprise wired networks (e.g. guided networks) and or wireless networks (e.g. unguided networks).
  • the first and/or second network architectures comprise a local networking architecture (e.g. via token ring, Ethernet, other LAN technologies).
  • the first and/or second MSL data is preferably MSL-captured data, and in some embodiments, the first and/or second MSL data comprise at least one of MTL interaction activity data and business outcome achieved data.
  • the master data includes a first attribute value for each of a plurality of MTLs
  • the master data includes a second attribute value for each of the plurality of MTLs
  • the resultant data comprises a weighted score for each of the plurality of MTLs calculated by the processing system and based at least in part on the first attribute value, a first attribute weight, the second attribute value, and a second attribute weight.
  • the processing system preferably orders the MTLs in accordance with the weighted score of each of the plurality of MTLs.
  • the master computer system prioritizes and selects MTLs to be targeted for MSL interaction to achieve desired business outcomes.
  • the processing system defines a plurality of business outcome attributes corresponding to desired business outcomes, the first and/or second MSL data comprise an attribute value for each identified business outcome attribute for each of a plurality of individual MTLs, and the processing system assigns a relative weight to each of the business outcome attributes.
  • the at least one of the business outcome attributes is preferably selected from the group consisting of a magnitude of clinical investigations, a magnitude of commercial potential, a frequency of publications, and a frequency of presentations.
  • the processing system manages customer interaction activities of MTLs with MSLs.
  • the processing system preferably identifies one or more desired business outcomes, the processing system preferably identifies at least one activity attributes of customer interaction activity to be performed by the MTL, the first and/or second MSL data preferably comprise data regarding customer interaction activity of the MSL for a predetermined time period, the first and/or second MSL data preferably comprise data regarding the business outcomes achieved or not achieved during the predetermined time period, and the processing system preferably correlates the customer interaction activity data and the business outcome data.
  • Figure 1 is a schematic of the relationship of the data structures, execution phase and evaluation output.
  • Figure 2 is a schematic of the planning, execution and evaluation phases.
  • Figure 3 is a flow chart diagram of a preferred embodiment of the method of the present invention.
  • Figure 4 is a network topology diagram showing an embodiment of the present system.
  • FIG. 1 shows an illustration of data structures, execution sub-processes and evaluation output.
  • Block 10 shows examples of data types to be tracked in a customer relation database table from MSL timesheets.
  • Block 12 is a sample data structure for and MSL Activity/Business Outcome table and block 14 is a sample data structure for data relating to Outcome Details. The data may be recorded in a relational database as is well known in the art.
  • Circle 16 illustrates an overview of the sub-processes executed by the sponsor organization (or the consultants or outside advisers) of the present system.
  • Data is collected regarding the MTLs, the activities of the MSLs, the business outcomes achieved or not achieved, and MTK satisfaction. This data is recorded in a database or databases and may be used for planning or evaluation of the impact of the MSL activities on the sponsor organization business objectives.
  • Block 18 illustrates types of output from the databases that may be used by the management of the sponsor organization to analyze the results of MSL activities.
  • FIG. 2 illustrates the iterative nature of the system.
  • Block 22 lists sample factors for assessing the capacity of an MSL team for a predetermined time period such as a month, calendar quarter or year. Once capacity has been determined, it is correlated to desired business outcomes such as those set forth in block 24. After the plan has been executed, the sponsor organization management can evaluate the impact of the MSL activity on the business outcomes as illustrated in block 26. The measures of business outcome correlated with activity data can then be assessed and used by management as shown in block 28 and used to establish plans for future capacity allocation and tactical planning.
  • step 30 the sponsor organization's business objectives are established. Typically, these objectives would conform to generally accepted industry objectives. Desired business outcomes of the MSL activity such as those set forth in detail below are defined in step 31.
  • the types or attributes of MTL interaction activities to be carried out by the MSLs are defined in step 32.
  • management assesses the capacity of the MSL team to accomplish the desired business outcomes.
  • specific MTLs are targeted for achieving the business outcomes in step 34. More detail regarding a preferred method of targeting MTLs is set forth below.
  • the MTL interaction activities of the MSL team and the business outcomes achieved or not achieved are recorded in the database for a given time period as shown in steps 35 and 36.
  • step 37 The activity and business outcome data are correlated in step 37.
  • step 38 the business outcomes are evaluated relative to the activities performed.
  • the targeted MTLs are surveyed preferably using the survey method set forth below in step 39 to determine MTL satisfaction with the MSL activities and other factors such as educational support or product.
  • step 40 the impact of the business outcomes and/or the interaction activities are evaluated relative to the planned business objectives. This evaluation may be used to re-start the overall process as illustrated by arrow 41.
  • step 32 may be omitted in subsequent iterations as indicated by arrow 42.
  • the system of the present invention begins with a planning and initialization phase wherein the desired objective of the sponsor company initiates an assessment method for a desired outcome.
  • Time tracking is accomplished by implementing a system that allows time spent in a set of time categories to be documented.
  • a set of time categories is established and each is assigned an activity attribute also known as an Activity Type.
  • Examples of Activity Types and a corresponding activity code are set forth in Table 1.
  • the available categories are not limited to those listed in Table 1, but can be expanded or deleted as necessary to obtain a desired business objective. If an internal tracking system is not available or unable to incorporate the MSL-specific time tracking categories, a computer-based system utilizing commercially available customer relation management (CRM) software for time tracking and resource allocation metrics can be modified for utilization.
  • CRM customer relation management
  • Example 1 illustrates a typical capacity calculation for a MSL individual
  • Example 1 MSL Capacity Calculation
  • 0.5 day/week Knowledge Acquisition/Management 0.5 day/week Project management (e.g., list activity, protocol review etc.); 0.5 day/week Administrative activities (e.g., CRM data input, expenses, routing/scheduling; equals 2 days/week away from customers
  • 0.5 day/week Knowledge Acquisition/Management e.g., list activity, protocol review etc.
  • 0.5 day/week Administrative activities e.g., CRM data input, expenses, routing/scheduling; equals 2 days/week away from customers
  • an MSL will have an average of three days per week available to interact with customers. If one multiplies the number of days per week by the number of available weeks, the days available per year to interact with customers is obtained, e.g., three days times 33 weeks equals 99 days with customers.
  • the amount of time can be further broken down by the amount of customer interactions that can be conducted per day in the field and, on average, how many times per year each customer should be visited to achieve the sponsor company's objectives.
  • an MSL can have approximately five face-to-face interactions per day on prospective MTLs. Therefore, an MSL could make approximately 500 calls per year (5 calls per day multiplied by the ⁇ 100 available days. If the total number of calls possible by the MSL team per year was divided by the number of times an MSL member should meet with an MTL, for example, 6 meeting per year, that equates to interaction with 83 MTLs.
  • the sponsor company may evaluate the MSL group to ascertain whether its desired objective have been obtained. If the objective has not been obtained, the time spent on the elements noted in the above example can be changed to produce a different outcome which is closer to or meets the initial sponsor company objective based on analysis in the evaluation phase.
  • the desired business outcomes are defined by their attributes.
  • Business outcomes are defined so that they are objective, measurable, and obvious to stakeholders when achieved.
  • the business outcomes are typically chosen to reflect the activities of the customer physicians that the MSL group is able to influence. Typical MTL activities include, for example, publishing medical articles, conducting clinical investigations, attending formulary meetings, and lecturing.
  • each defined business outcome is assigned a business outcome attribute also known as a Business Outcome Type. Examples of Business Outcome Types and a corresponding business outcome code are set forth in Table 2.
  • the available Business Outcome Types are not limited to those listed in Table 2, but can be expanded or deleted as necessary to obtain a desired business objective.
  • the present invention includes a process for selecting and prioritizing MTLs according to a multiple attribute system that can assign specific weight to individual attributes to support the sponsor's customer management strategy to obtain a desired objective.
  • the attributes measured are quantifiable and objective in nature.
  • the MTL attributes can be categorized into measures of "voice" in the marketplace, i.e. publications, presentations, and relevant clinical investigation experience and measures of commercial potential/class prescription volume.
  • the attributes in the market place "voice" category are crucial for increasing product/brand awareness in the relevant medical communities and also reflect the degree of influence that an MTL exerts in these communities. These attributes can be used to prioritize MTLs along the dimension of influence on the practices of physicians in their sphere of influence. Such influence by MTLs has a major impact on acceptance and market uptake of pharmaceuticals.
  • Commercial attributes such as dollar volume of prescription writing, can be used to target MTLs who may have a direct business impact via their prescription writing for FDA approved indications. By assessing these attributes, MTLs are targeted in a manner that supports the sponsor company's business strategy. It is the responsibility of the MSL to develop business plans that outline major goals set for quarterly or annual evaluation, for example, the number of MTL journal publications, presentations, clinical investigations and number in prescription written.
  • ICRRI Individual Component Relative Ranking Index
  • ICRRI value/((highest value - lowest value)/ 10) which will result in an ICRRI with a value between approximately 1 and 10.
  • Each attribute is evaluated based on the same equation:
  • Publications Relative Ranking Index number of publications/((most publications by any MTL in the group - lowest number of publications by any MTL in the group)/ 10)
  • Example 5 ICRRI for evaluation of MTL during different stages of product development and market life
  • the final MTL Relative Ranking Index is obtained by multiplying each ICRRI by a weighting value (making sure all weights sum to 1; e.g., 0.2, 0.4, 0.3, 0.1) and then sum the weight-adjusted component indices for the prioritization.
  • the assignment of the weighting value corresponds to the importance of a particular attribute at a particular time.
  • MTL Attribute weighted component 1.0 value
  • the amount of presentation would be found as the most prevalent attribute of the MTL targeted and should correspond to the goals set by the sponsor company at the particular time for a particular product.
  • the weighting of the index allows for changing the weights based on product lifecycle stage, without having to do major recalculations i.e., commercial can be weighted as zero during product development, or can be weighted heavily i.e., 0.8 for late phases in the product lifecycle. For example using the number achieved above but making evaluating 1 year after FDA approval:
  • the MSL has succeeded in obtaining the required objective.
  • the most predominate attribute may be commercial productivity, i.e. prescription writing, having a value of 16. This value should coincide with the objective of the sponsor company at one year after FDA approval. .
  • Table 3 illustrates how a group of potential MTLs may be prioritized by ranking them according to the ICRRI.
  • the attributes shown in this illustration are publications, presentations, clinical investigations, and commercial value of the individual prescription writing. Table 3.
  • NeoTotal ASH Commercial Pubs Presents Investigtns Commercial [ plasms ; MM One ASCO Oral ESMO Total Clinical Value (S Ranking Ranking Ranking Ranking Prioritization
  • the results obtained by the attribute system may serve as part of the basis for the planning stage of a second cycle in obtaining another business objective defined by the sponsor company.
  • the results of the components in the Evaluation Phase and in the CRM ' discussed below will also serve as the basis.
  • CRM Customer Relation Management System
  • the system of the present invention requires that customer interactions be documented and that certain attributes regarding the nature, duration, costs and date of each interaction be captured for retrospective analysis.
  • a mechanism for tracking MSL activities and their impact is incorporated into a Customer Relation Management System (CRM).
  • MSL-specific activity attributes may be incorporated into an existing CRM (using commercially available software with modifications) for the purposes of providing the data for analyses.
  • CRM allows for the assignment of specific business outcomes (see types and definitions above) to specifically targeted MTLs and preferably will define an end point when an outcome is achieved.
  • Each customer interaction is documented in the CRM and is classified according to an activity type (see types and their definitions below).
  • the CRM is capable of providing queries by MSL, MTL, Business Outcome Type, and Activity Type etc..
  • the present invention allows the information to be evaluated in order to provide for more efficient use of the time of interaction between the MSL and the MTL. This is based on the Customer Relation Management System (described below) which memorializes the interactions between the MSL and the MTL.
  • the data obtained from CRM is available for periodic reporting of activities and outcome achievement.
  • the periodic reporting format may be in the form of a "Scorecard".
  • the Scorecard consists of territory, regional, and national level data (the resolution to be defined by the Sponsor's MSL organizational structure). Information that may be included is the number of activities by type and by duration, funds spent/track to plan, time utilization, and position vacancies. These categories are not limiting and may be modified as needed to meet the predefined business objectives.
  • a scorecard is illustrated summarizing various types of activities and recorded information based on the interaction between the MSL representative, Dr. John Know and various MTLs over a predefined period of time. These particular activities were concentrated for the particular business outcome goal of investigator (as described above). This information is further summarized in Table 5, wherein the time spent is particularly broken down in order to be able to use the information based on whether the business outcome (investigator) had been achieved and what types of activities may need to be done, in terms of changing the activities when interacting with a particular MTL. Table 5 illustrates the activity data for each particular
  • This output allows (a) evaluation by management as to the daily activity of an MSL and (b) a journal for organization and planning of the MSL activity in the future.
  • Blade 1 2 1 1 4 9 60.0 540 Y Investigator
  • Table 6 illustrates yet another view of the exemplary data in which the frequency and duration of customer interaction are set forth by activity type for each MTL having a successful investigator outcome.
  • the data incorporated into the CRM are particularly useful for prompt, accurate and specific "activity to outcome” analysis.
  • the interactions with MTL Adams yielded a desired outcome of investigator based on the activities and time as highlighted in Figure 2.
  • the desired outcome of investigator was not achieved by the activities and time spent on MTL Philbin.
  • the Evaluating phase examines metrics of different categories from a variety of sources. Among these sources are commercial data, i.e., increased prescriptions of particular product, business outcomes analyses, i.e., based on the Scorecard information, internal services provided to the MTL, and survey results. Direct Analysis The impact of MSL activities may be measured in commercial terms.
  • MSL efforts toward a select group of physicians/outcomes the conditions are met to enable comparison of product prescribing between the targeted physicians/institutions and the relevant physician/institution universe.
  • the targeted customer's product utilization uptake can be compared to the appropriate customer universe. More rapid uptake would result in an increase in the slope of the sales curve over the time since launch, compared to the slope of the sales curve of the comparator population. Historically, the rate of market uptake following launch is a major determinant of total sales over the commercial life of the drug. Indirect Analysis
  • the statistical tests detect variables that co-vary (in this case, activity types and business outcome types) with a given outcome status (achieved or non-achieved). This permits objective measurement of the effort required to achieve a targeted business outcome, thereby increasing the accuracy of MSL capacity assessments and commercial planning efforts.
  • the analyses determine which activities and at what frequency/duration resulted in achieved outcomes, versus those activities and frequency/duration that resulted in non-achievement of a targeted outcome. The determination is accomplished through conducting a statistical analysis that provides the aggregate weight of individual activity types for a specific business outcome type differentiated by achievement and non-achievement.
  • Tables 7 and 8 illustrate a statistical analysis of the average frequency of interactions by activity type with respect to achievement and non-achievement of an investigator outcome based on the data in Table 4.
  • Tables 9 and 10 illustrate a sample data and statistical analysis report for a second exemplary set of interactions in which multiple business outcomes were targeted over a predetermined period of time.
  • Prescriber 2 Prescriber Average of BUSSOL
  • Prescriber 2 Prescriber Average of MEDSOL 1.00 0.00 S +
  • Prescriber 2 Prescriber Average of KX 1.89 0.99 S -
  • Prescriber 2 Prescriber Average of RECRUIT
  • Prescriber 2 Prescriber Average of REL 3.22 0.63 NS
  • Prescriber 2 Prescriber Average of NET 1.00 0.00 NS
  • Prescriber 2 Prescriber Average of ASSESS
  • Survey Analysis Another source of performance information is the use of a survey designed to evaluate customer perception of the value of the MSL team.
  • the survey methodology of the present invention measures physician perception along multiple dimensions, allowing the results to be used in operational management, as well as an indicator of the MSL team's progress over time.
  • the data from the surveys, in combination with the quantitative activity data, is useful in identifying adjustments needed to optimize MSL team size, structure, and strategy.
  • the survey method incorporates questions that allow for the identification of the most valued MSL activities. The activities most valued by the targeted customer are likely to be the most effective activities for increasing brand advocacy.
  • Survey Architecture The survey method is a tool for measuring brand advocacy among targeted MTLs and the perceived quality and utility of the MSL role.
  • this method is used to measure brand advocacy and the perceived value of the MSL organization within the MSL customer universe.
  • the results obtained from the MSL customer universe can then be compared to the pharmaceutical company's overall customer universe to assess the value added to pharmaceutical company by the MSL organization.
  • the MSL customer universe is defined by the collective Targeted Customer Lists (TCL) for all MSLs of the company. Although multiple attributes are considered for the inclusion of a physician in a TCL, they can generally be considered MTLs.
  • this survey method is designed to obtain and integrate multidimensional physician perception data into a quantitative index that is a relevant predictor of physician perceptions.
  • the index integrates the perception dimensions of customer satisfaction, product value, MSL value, and customer service into a quantitative value.
  • the sub-group of physicians that respond "very satisfied" to all perception dimensions under a categorical scale are labeled Brand Advocates.
  • Brand Advocates The positive effects of strong brand advocacy on a company's commercial success are a well-established tenet in marketing.
  • the index provides a quantitative measure of a MSL organization's contribution to its parent company's commercial success. Since the questions are categorized according to MSL activity type, the index can be used as a business metric to assess organizational performance and identify areas in need of improvement.
  • the index is used as a rating of the relative perceived importance of categories of MSL activities. These categories are: MSL-Physician Interactions, Educational Funding, and Knowledge Exchange. This ranking function allows the index to be used in tactical business planning.
  • TCL physicians can be surveyed (mailed/paper-based surveys) or a random sample of MSL TCL physicians can be surveyed (telephone surveys).
  • Each survey methodology has its advantages and disadvantages (inconvenience of timing of the call, low return rate, etc.). Given an estimated 5% return rate for a mailed survey, this data gathering methodology will provide a sufficient number of evaluable respondents, provided the customer universe is not unusually small (less than 500 targeted customers). Since most MSL groups interact with more than 500 physicians, even if the return rate is lower than 5%, the mailed survey methodology may still be the most cost-effective and provide a sufficient number of respondents upon which to base the analysis of the data.
  • the questions comprising the survey are designed to assess satisfaction for each of the categories of MSL activities, organized into perception dimensions of Customer Satisfaction (C), Product Value (P), MSL Value (M), and Customer Service (S), and the answers are categorized according to: Very Satisfied (1.00), Satisfied (0.75), Neutral (0.50), and Dissatisfied (0.00); or Strongly Disagree (0.00), Disagree (.50), Agree (.75), Strongly Agree (1.00), depending upon the context of the question.
  • C Customer Satisfaction
  • P Product Value
  • M MSL Value
  • S Customer Service
  • the mean score from all respondents on all perception dimensions comprises the index converted to a decimal. Multiple sub-analyses are performed according to the way the questions are categorized.
  • the questions are preferably designed to fit into each of two categories: MSL Activity Type and Customer Perception Dimension.
  • the questions also focus on attributes that can be acted upon by the MSL organization.
  • the index is used in a number of different analyses, mostly differentiated by predefined criteria for categorizing questions and categorization of respondents based on overall index score. For example, the mean index sub-score for each of the MSL Activity Type categories may be used to identify areas of excellence as well as areas in need of improvement. These analyses may be driven down to the level of an individual question from which a specific activity can be targeted and assessed.
  • the average score of all of the responses is 0.64, obtained by taking the total value of all responses 14.75 and dividing by the number of questions 23. This illustrates the customers evaluation of all the services provided in the example is between neutral (0.5) and satisfied (0.75).
  • each activity may be evaluated to find the strengths and weaknesses of the MSL. Again using the example above, the average score for product satisfaction is 0.85 confirming a high approval rating. Conversely, the average score of MSL-Physician
  • Interactions is 0.39 illustrating a low approval rating.
  • the score may be based on the perception dimension of customer satisfaction. For example, all of the perception dimensions combined will equal 0.64 as calculated for the MSL activities above. However, the score for customer satisfaction is 0.75 corresponding to a satisfactory rating.
  • This survey method and feedback is used to improve and modify the activities of the MSL and to increase customer approval and efficiency of the MSL. Specifically, the survey results may be used to modify other components of the method to obtain the desired business goal of the sponsor company. Eventually, by continuous cyclic repetition of the method, the average score of the entire survey and of particular activity and perception groups will rise to near the 1.0 "very satisfied" rating.
  • the MSL customer universe can be subdivided into those physicians on whom only MSLs call and those physicians on which both MSLs and the company's traditional sales force call. Comparisons of survey scores and business outcomes (script volume and market share) can then be made between these groups and to the entire physician population in order to examine the relationship of index scores to increased brand advocacy. These measures can then be tracked over multiple assessments and the information used to allocate resources among the categories of MSL activities, change MSL practices, and improve the MSL organization's business model through the enabling of continuous business improvements.
  • the system of the present invention permits the user to normalize data to headcount for trend analyses since the anticipated sharp increase in recorded activities resulting from addition of new MSLs may make projections inaccurate.
  • the absolute numbers will also be available, enabling senior management to determine their ROI in the MSL team.
  • Effective implementation of MSL team activities will facilitate the appropriate use of the sponsor company's products.
  • the above-described business system and methods provides the information needed to maximize effectiveness of the MSL team.
  • Business Management Tools/Scorecards Returning to the example in the execution phase of Dr. John Know, a review of the activities and time spent with MTL Adams may illustrate the needed activities and time to achieve the business outcome of investigator with MTL Philbin.
  • a feedback system is established to guide the modification of the activities and time spent in the "subsequent" planning phase with any MTL to obtain the desired business outcome.
  • This method can be applied to any objective discussed above in the attribute system to obtain the desired business outcomes, i.e. more publications, presentations, investigation or higher amount of prescriptions written, depending on the sponsor company's objective.
  • the time/capacity model can be modified based on the information obtained performing the attribute and CRM assessment.
  • the MSLs may be encouraged to input their activities into the CRM tool on a weekly basis (e.g., by Friday 5 PM Pacific Time), and strongly encouraged to input their activities more frequently (2 times per week).
  • a preferred distributed customer relation management system for MSL data communications is shown and designated generally 45.
  • the constituent systems and components of customer relation management system 45 will first be discussed, followed by a discussion of some sample communication schemes between the constituent systems and components.
  • Distributed customer relation management system 45 preferably includes a master computer system 47, comprising a memory system 47a, a processing system 47b and a network interface system 47c.
  • customer relation management system 45 includes a plurality of MSL computer systems designated generally as CS and designated specifically as CSi, CS 2 , CS 3 , CS 4 , ..., ..., and CS n .
  • MSL computer system CSi is shown to include a notebook computer
  • MSL computer systems CS 2 is shown to include a portable digital assistant (e.g. Tablet PC, etc.)
  • MSL computer system CS 3 is shown to include a wireless telephone (e.g.
  • the preferred embodiment of customer relation management system 45 is scalable and includes any desirable number of MSL computer systems CS.
  • MSL computer systems CS include notebook computers, PDAs (e.g. Tablet PC), wireless telephones (e.g. Smart phone), desktop computers and client workstations, any suitable networked data entry system can be utilized.
  • an on-board database is included in and/or with the MSL computer system CS.
  • Each of MSL computer systems CS are networked with master computer system 47 via compatible network architectures designated generally as NA and designated specifically as NA ls NA 2 , NA 3 , NA 4 , ..., ..., and NA n .
  • notebook MSL computer system CSi is networked with master computer system 47 via network architecture NAi
  • PDA MSL computer system CS 2 is networked with master computer system 47 via network architecture NA 2
  • wireless telephone MSL computer system CS 3 is networked with master computer system 47 via network architecture NA 3
  • desktop MSL computer systems CS 4a and CS 4 b are networked with master computer system 47 via network architecture NA 4
  • client workstation MSL computer system CS n is networked with master computer system 47 via network architecture NA n .
  • Any number of MSL computer systems CS can be simultaneously connected a single network architecture NA, so long as network architecture is suitably scaled for the number of connected MSL computer systems.
  • Any suitable network architecture NA can be utilized so long as it is compatible with both master computer system 47 and all MSL computer systems CS networked to master computer system 47 therewith.
  • Each of network architectures NA comprises a system that transmits that data between master computer 47 and a corresponding MSL computer system CS.
  • Network architectures can be wired (e.g. guided) and/or wireless (e.g. unguided) and include all constituent components, including as nonlimiting examples, client and server machines, the cables connecting them and all supporting hardware in between such as bridges, routers and switches.
  • network architectures NA preferably include antennas and towers and/or satellites.
  • Each network architecture NA is governed by one or more data transmission protocols, including as nonlimiting examples, TCP/IP, SPX/IPX, GPRS, GSM, etc.
  • Customer relation management system 45 preferably also includes an alternative data flow channel 49, which networks master computer system and each MSL computer system CSi - CS n with each compatible MSL computer system CSi - CS n .
  • alternative data flow channel 49 provides a redundant back up for communication between any MSL computer system CS and master computer system 47.
  • Alternative flow channel 49 comprises a common network architecture and common protocol for communication between each MSL computer system CS, each other MSL computer system CS, and master computer system 47.
  • Alternative flow channel 49 comprises a plurality of gateway interfaces (not shown) in order to convert the data transmissions of any MSL computer system CS to the common protocol.
  • Each primary communications channel can preferably be integrated with existing infrastructures.
  • Memory system 47a preferably comprises a master customer relation management database with master data stored therein, the master data comprising at least one of desired business outcome attributes, activity attributes and preferred MTL data.
  • Master data preferably comprises a compilation of all data required for MSL management activity and contains the modules and other code required to perform data analysis.
  • the master data is preferably defined and can be pushed through any of network architecmres NA to a MSL computer system CS networked thereto via network interface system 47c.
  • defined master data can include, for example, business objectives, desired business outcomes, activity attributes of MTL interaction activities and preferred MTLs to target for MSL interaction.
  • a distributed data component at any given MSL computer system CS preferably comprises a subset of the data specific to each MSL, such as MSL data at a database local to the given MSL computer system CS.
  • the distributed data component at any given MSL computer system CS may also be an at least temporary access of the master data hosted at master computer system 47.
  • the given MSL computer system CS is able to interact with the master data, record and update data relevant to the MSLs activities and business objectives.
  • examples of MLS date that can be captured at a MSL computer system CS include MTL interaction activities performed by the MSL and business outcomes achieved/not achieved.
  • an MLS computer system CS can conduct some analysis of data, including for example, correlating MTL interaction activity and business outcomes.
  • analysis is preferably conducted at processing system 47b of master computer system 47.
  • examples of analyses that are preferably conducted at processing system 47b include correlating MTL interaction activity and business outcome data, evaluating business outcomes relative to MTL interaction activities, analyzing surveys of MTLs to determine MTL satisfaction of MSLs, and evaluating the impact of MTL interaction activities and business outcomes.
  • Network interface system preferably receives first MSL data from a first MSL computer system CS (e.g. MSL computer system CS 4b ) via a first network architecture NA (e.g. network architecture NA 4 ) and receives second MSL data from a second MSL computer system CS (e.g. MSL computer system CS 3 ) via a second network architecmre (e.g. network architecture NA ).
  • Processing system 47b preferably analyzes the master data in conjunction with at least one of the first MSL data and the second MSL data to produce resultant data (e.g. the analysis).
  • the master data includes a first attribute value for each of a plurality of MTLs
  • the master data includes a second attribute value for each of the plurality of MTLs
  • the resultant data comprises a weighted score for each of the plurality of MTLs calculated by the processing system and based at least in part on the first attribute value, a first attribute weight, the second attribute value, and a second attribute weight.
  • Processing system 47b preferably orders the MTLs in accordance with the weighted score of each of the plurality of MTLs.
  • master computer system 47 prioritizes and selects MTLs to be targeted for MSL interaction to achieve desired business outcomes.
  • processing system 47b defines a plurality of business outcome attributes corresponding to desired business outcomes
  • the first and/or second MSL data comprise an attribute value for each identified business outcome attribute for each of a plurality of individual MTLs
  • processing system 47b assigns a relative weight to each of the business outcome attributes.
  • the at least one of the business outcome attributes is preferably selected from the group consisting of a magnitude of clinical investigations, a magnitade of commercial potential, a frequency of publications, and a frequency of presentations.
  • processing system 47b manages customer interaction activities of MTLs with MSLs.
  • Processing system 47b preferably identifies one or more desired business outcomes
  • processing system 47b preferably identifies at least one activity attributes of customer interaction activity to be performed by the MTL
  • the first and/or second MSL data preferably comprise data regarding customer interaction activity of the MSL for a predetermined time period
  • the first and/or second MSL data preferably comprise data regarding the business outcomes achieved or not achieved during the predetermined time period
  • processing system 47b preferably correlates the customer interaction activity data and the business outcome data.

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Abstract

A system and method for managing customer interaction activities of medical liaison personnel of a sponsor organization with health professional customers to achieve one or more desired business outcomes is disclosed. The system uses a customer relation database to record data regarding customer interaction activity of the medical liaison personnel (26) and data regarding the business outcomes achieved or not achieved during the predetermined time period (24).

Description

SYSTEM AND METHOD FOR OUTCOME-BASED MANAGEMENT OF MEDICAL SCDXNCE LIAISONS
Field of the Invention
This invention relates to a management system for the efficient management and evaluation of medical support groups in the pharmaceutical, bio-pharmaceutical and medical device industries.
Background of the Invention
Virtually all major pharmaceutical companies have deployed field-based medical support programs. Medical liaison personnel have supported a range of customers, including medical thought leaders (MTL), investigators, and health care decision makers. The necessity of support will increase with technological advances, consolidation of decision making, and the increasing complexity of health care decisions.
Field-based medical support programs were established as a result of the necessity for more knowledgeable personnel to support and advise the medical industry. Initially, a small group of technically-oriented sales representatives was formed with the goal of improving the image of the company with researchers, key opinion leaders, and investigators. These medical science liaisons (MSLs), as they were known, utilized face-to-face peer interactions to better understand what their customers needed and to leverage products into ongoing research activities.
Today, professionals having advanced degree's constitute the majority of pharmaceutical company medical personnel. As a result of their advanced education, training, and clinical experience, field-based medical personnel are regarded as more knowledgeable than pharmaceutical company sales representatives and account executives and are favored by some'customer segments in clinical peer discussions. The services of- fered by field-based medical personnel have evolved over time with the increasing complexity of marketed products and customer medical information and education needs.
Due to the changes in patient treatment options today, field-based medical liaisons work with a continually changing mix of opinion leaders and decision makers. Although most health care providers are interested in traditional safety and efficacy information, some seek information on health economic/pharmacoeconomic analyses, outcomes, disease management information, and clinical programs (i.e. treatment algorithms, practice guidelines, and care mapping). Ultimately, they desire this data for their own practice setting or environment in order to reflect the clinical and cost structures unique to their patient mix.
Until now, there has been little or no means available for assessment of the impact of MSL activity on the sponsor company's business objectives. Internal evaluation, if any, has been typically limited to merely recording the activities of the individuals on a MSL team. Consequently, there is a need for a system to optimize the management of an MSL team and establish business metrics (measuring elements) to accurately track the MSL team activities, track the time spent performing various tasks and in customer interaction, and measure the business impact of the MSL team.
Summary of the Invention
The present invention is a system that provides a means to generate business metrics that enable the MSL team to plan for and manage their activities, effectively allocate resources, and measure their accomplishments. The assignment of specific business outcomes toward a targeted MTL allows for the MSL team's efforts to be incorporated into the sponsor company's overall business planning process and business objectives.
The methods of the present invention may be used by pharmaceutical company in determining the appropriate use of access channels to the customer. The metrics derived from the methods of the present invention enable executive management to optimally allocate resources across customer-interfacing groups within the organization in order to achieve vital business objectives.
The methods of the present invention are organized into a cyclic process consisting of three phases: Planning, Executing, and Evaluating.
The Planning phase provides methods for determining "real world" MSL capacity, MTL targeting and selection, incoφorating MSL business objectives in support of the sponsor company's overall business strategy, and defining performance metrics.
During the Executing phase, the system provides for the assessment of performance and documentation of MSL activities. This information is summarized to produce the targeted customer lists (TCL) and to efficiently focus the resources of the sponsor company.
The Evaluating phase involves assessment of MSL impact through analysis of achieved business outcomes, MSL-specific surveys of targeted MTLs, impact on prescribing behavior of targeted MTLs and their influence network, and analysis of the value provided by the MSL's internal activities (training sales, reviewing protocols, etc.). The outputs of the Executing phase's activity assessment and Evaluation phase allow for refinement of future planning and execution, thereby providing a cyclic system for continuous business improvement.
A system and method for managing customer interaction activities of medical liaison personnel of a sponsor organization with health professional customers to achieve one or more desired business outcomes is disclosed. The system uses a customer relation database to record data regarding customer interaction activity of the medical liaison personnel and data regarding the business outcomes achieved or not achieved during the predetermined time period. The system correlates the customer interaction activity data - and the business outcome data so that it can be used to conduct capacity and tactical assessments for future medical liaison activities. A method for targeting medical thought leaders or other health professionals who are most likely to achieve the business outcomes is also disclosed. In one embodiment, the system also provides a method for surveying the health professional customers to determine their level of satisfaction with medical liaison personnel and sponsor organization. Also disclosed herein is a distributed customer relation management system for distributed MSL data communications, comprising a master computer system. The master computer system is preferably centralized and comprises a memory system, a network interface system and a processing system. The memory system preferably includes at least one at least temporary memory device, the network interface system preferably includes at least one network interface device, and the processing system preferably includes at least one electronic controller.
The memory system preferably comprises a master customer relation management database with master data stored therein, the master data comprising at least one of desired business outcome attributes, activity attributes and preferred MTL data. The network interface system is preferably for receiving first MSL data from a first MSL computer system via a first network architecture and for receiving second MSL data from a second MSL computer system via a second network architecture. The processing system is preferably for analyzing the master data in conjunction with at least one of the first MSL data and the second MSL data to produce resultant data. Some embodiments of the customer relation management system comprise the first MSL computer system and/or the second MSL computer system.
The first MSL computer system and/or the second MSL computer system preferably comprise a notebook computer, a desktop computer, a personal digital assistant, a wireless telephone, a workstation and or another network-compatible device. In some aspects, the master computer system is adapted to communicate the resultant data to the first MSL computer system via the first network architecture and to the second MSL computer system via the second network architecture. The first network architecture and/or the second network architecture comprise wired networks (e.g. guided networks) and or wireless networks (e.g. unguided networks). Although preferred embodiments of the master computer system are networked with remote MSL computer systems, in some embodiments, the first and/or second network architectures comprise a local networking architecture (e.g. via token ring, Ethernet, other LAN technologies).
The first and/or second MSL data is preferably MSL-captured data, and in some embodiments, the first and/or second MSL data comprise at least one of MTL interaction activity data and business outcome achieved data.
In preferred embodiments of the distributed customer relation management system, the master data includes a first attribute value for each of a plurality of MTLs, the master data includes a second attribute value for each of the plurality of MTLs, and the resultant data comprises a weighted score for each of the plurality of MTLs calculated by the processing system and based at least in part on the first attribute value, a first attribute weight, the second attribute value, and a second attribute weight. The processing system preferably orders the MTLs in accordance with the weighted score of each of the plurality of MTLs.
In some embodiments of the distributed customer relation management system, the master computer system prioritizes and selects MTLs to be targeted for MSL interaction to achieve desired business outcomes. In some embodiments, the processing system defines a plurality of business outcome attributes corresponding to desired business outcomes, the first and/or second MSL data comprise an attribute value for each identified business outcome attribute for each of a plurality of individual MTLs, and the processing system assigns a relative weight to each of the business outcome attributes. Furthermore, the at least one of the business outcome attributes is preferably selected from the group consisting of a magnitude of clinical investigations, a magnitude of commercial potential, a frequency of publications, and a frequency of presentations.
In some embodiments of the distributed customer relation management system, the processing system manages customer interaction activities of MTLs with MSLs. The processing system preferably identifies one or more desired business outcomes, the processing system preferably identifies at least one activity attributes of customer interaction activity to be performed by the MTL, the first and/or second MSL data preferably comprise data regarding customer interaction activity of the MSL for a predetermined time period, the first and/or second MSL data preferably comprise data regarding the business outcomes achieved or not achieved during the predetermined time period, and the processing system preferably correlates the customer interaction activity data and the business outcome data.
Brief Description of the Drawings
Figure 1 is a schematic of the relationship of the data structures, execution phase and evaluation output.
Figure 2 is a schematic of the planning, execution and evaluation phases. Figure 3 is a flow chart diagram of a preferred embodiment of the method of the present invention. Figure 4 is a network topology diagram showing an embodiment of the present system.
Detailed Description of the Invention
With reference to Figs. 1 through 4, the flow path relationship of the activities of the planning, execution and evaluation phases will be based on the desired information needed to obtain a specific business objective. The activities of the MSLs in each phase and the evaluation of the information obtained by these activities is discussed herein. Figure 1 shows an illustration of data structures, execution sub-processes and evaluation output. Block 10 shows examples of data types to be tracked in a customer relation database table from MSL timesheets. Block 12 is a sample data structure for and MSL Activity/Business Outcome table and block 14 is a sample data structure for data relating to Outcome Details. The data may be recorded in a relational database as is well known in the art. Circle 16 illustrates an overview of the sub-processes executed by the sponsor organization (or the consultants or outside advisers) of the present system. Data is collected regarding the MTLs, the activities of the MSLs, the business outcomes achieved or not achieved, and MTK satisfaction. This data is recorded in a database or databases and may be used for planning or evaluation of the impact of the MSL activities on the sponsor organization business objectives. Block 18 illustrates types of output from the databases that may be used by the management of the sponsor organization to analyze the results of MSL activities.
Figure 2 illustrates the iterative nature of the system. Block 22 lists sample factors for assessing the capacity of an MSL team for a predetermined time period such as a month, calendar quarter or year. Once capacity has been determined, it is correlated to desired business outcomes such as those set forth in block 24. After the plan has been executed, the sponsor organization management can evaluate the impact of the MSL activity on the business outcomes as illustrated in block 26. The measures of business outcome correlated with activity data can then be assessed and used by management as shown in block 28 and used to establish plans for future capacity allocation and tactical planning.
A preferred embodiment of the method of the present invention is illustrated in Figure 3. In step 30, the sponsor organization's business objectives are established. Typically, these objectives would conform to generally accepted industry objectives. Desired business outcomes of the MSL activity such as those set forth in detail below are defined in step 31. The types or attributes of MTL interaction activities to be carried out by the MSLs are defined in step 32. In step 33, management assesses the capacity of the MSL team to accomplish the desired business outcomes. To optimize potential success of the plan, specific MTLs are targeted for achieving the business outcomes in step 34. More detail regarding a preferred method of targeting MTLs is set forth below. The MTL interaction activities of the MSL team and the business outcomes achieved or not achieved are recorded in the database for a given time period as shown in steps 35 and 36. The activity and business outcome data are correlated in step 37. In step 38 the business outcomes are evaluated relative to the activities performed. The targeted MTLs are surveyed preferably using the survey method set forth below in step 39 to determine MTL satisfaction with the MSL activities and other factors such as educational support or product. In step 40, the impact of the business outcomes and/or the interaction activities are evaluated relative to the planned business objectives. This evaluation may be used to re-start the overall process as illustrated by arrow 41. Optionally, if no new activity attributes are defined, step 32 may be omitted in subsequent iterations as indicated by arrow 42. Planning and Executing Phases
The system of the present invention begins with a planning and initialization phase wherein the desired objective of the sponsor company initiates an assessment method for a desired outcome.
Time Tracking/Capacity Assessment and Workload Build-up
Time tracking is accomplished by implementing a system that allows time spent in a set of time categories to be documented. Generally, a set of time categories is established and each is assigned an activity attribute also known as an Activity Type. Examples of Activity Types and a corresponding activity code are set forth in Table 1.
Table 1. Activit Code Activit T e
Figure imgf000010_0001
The available categories are not limited to those listed in Table 1, but can be expanded or deleted as necessary to obtain a desired business objective. If an internal tracking system is not available or unable to incorporate the MSL-specific time tracking categories, a computer-based system utilizing commercially available customer relation management (CRM) software for time tracking and resource allocation metrics can be modified for utilization.
In order to determine the amount of time available for engaging in customer interactions, one must first determine the number of days that a MSL has available to meet with customers. Example 1 illustrates a typical capacity calculation for a MSL individual Example 1 : MSL Capacity Calculation
240 workdays per year minus
15 days Society Meetings (3 mtgs/year);
12 days Team Meetings (quarterly); 4 days Sub-Team Meetings;
4 days Departmental alignment meetings (Quarterly);
10 days ad hoc project meetings with HQ staff;
10 days Advisory Board Meetings (5 mtgs/year);
10 days Professional/career development; and 10 to 15 vacation days equals 165 potential days (i.e. 33 weeks or 69% of their total time) Upon determining the number of available customer days, one must determine the time spent conducting tasks that take away from time spent in customer interactions. Example 2: Time Away From Targeted Customers
0.5 day/week Travel;
0.5 day/week Knowledge Acquisition/Management; 0.5 day/week Project management (e.g., list activity, protocol review etc.); 0.5 day/week Administrative activities (e.g., CRM data input, expenses, routing/scheduling; equals 2 days/week away from customers Thus, by way of illustration, an MSL will have an average of three days per week available to interact with customers. If one multiplies the number of days per week by the number of available weeks, the days available per year to interact with customers is obtained, e.g., three days times 33 weeks equals 99 days with customers.
Thereafter, the amount of time can be further broken down by the amount of customer interactions that can be conducted per day in the field and, on average, how many times per year each customer should be visited to achieve the sponsor company's objectives. Again, by way of illustration, experience in the industry has shown that an MSL can have approximately five face-to-face interactions per day on prospective MTLs. Therefore, an MSL could make approximately 500 calls per year (5 calls per day multiplied by the ~100 available days. If the total number of calls possible by the MSL team per year was divided by the number of times an MSL member should meet with an MTL, for example, 6 meeting per year, that equates to interaction with 83 MTLs.
Based on this information combined with the results of the systems discussed below (i.e. MTL targeting system, CRM, statistical analysis and survey), at certain intervals of time, for example, annually, the sponsor company may evaluate the MSL group to ascertain whether its desired objective have been obtained. If the objective has not been obtained, the time spent on the elements noted in the above example can be changed to produce a different outcome which is closer to or meets the initial sponsor company objective based on analysis in the evaluation phase.
Establishing and Implementing Business Outcomes
In the system of the present invention, the desired business outcomes are defined by their attributes. Business outcomes are defined so that they are objective, measurable, and obvious to stakeholders when achieved. The business outcomes are typically chosen to reflect the activities of the customer physicians that the MSL group is able to influence. Typical MTL activities include, for example, publishing medical articles, conducting clinical investigations, attending formulary meetings, and lecturing. Generally, each defined business outcome is assigned a business outcome attribute also known as a Business Outcome Type. Examples of Business Outcome Types and a corresponding business outcome code are set forth in Table 2.
Table 2.
Business Outcome Code Business Outcome T e
Figure imgf000013_0001
The available Business Outcome Types are not limited to those listed in Table 2, but can be expanded or deleted as necessary to obtain a desired business objective.
Targeting Specific MTLs Using MTL Attributes
The present invention includes a process for selecting and prioritizing MTLs according to a multiple attribute system that can assign specific weight to individual attributes to support the sponsor's customer management strategy to obtain a desired objective. The attributes measured are quantifiable and objective in nature. The MTL attributes can be categorized into measures of "voice" in the marketplace, i.e. publications, presentations, and relevant clinical investigation experience and measures of commercial potential/class prescription volume.
The attributes in the market place "voice" category are crucial for increasing product/brand awareness in the relevant medical communities and also reflect the degree of influence that an MTL exerts in these communities. These attributes can be used to prioritize MTLs along the dimension of influence on the practices of physicians in their sphere of influence. Such influence by MTLs has a major impact on acceptance and market uptake of pharmaceuticals. Commercial attributes, such as dollar volume of prescription writing, can be used to target MTLs who may have a direct business impact via their prescription writing for FDA approved indications. By assessing these attributes, MTLs are targeted in a manner that supports the sponsor company's business strategy. It is the responsibility of the MSL to develop business plans that outline major goals set for quarterly or annual evaluation, for example, the number of MTL journal publications, presentations, clinical investigations and number in prescription written.
Below is an example of an MTL prioritization process in accordance with the present invention. In this framework, quantifiable MTL attributes representative of
"market voice" and commercial importance are identified and assigned a value. The value is then normalized by converting it into an Individual Component Relative Ranking Index (ICRRI) by the following equation:
ICRRI=value/((highest value - lowest value)/ 10) which will result in an ICRRI with a value between approximately 1 and 10. Each attribute is evaluated based on the same equation:
Publications = Value/(highest value - lowest value)/ 10 = ICRRI
Presentations = Value/(highest value - lowest value)/10 = ICRRI
Investigations = Value/(highest value - lowest value)/ 10 = ICRRI Commercial Measure
/Prescriptions = Value/(highest value - lowest value)/ 10 = ICRRI For example, the relative ranking index for publications may be calculated as follows:
Publications Relative Ranking Index = number of publications/((most publications by any MTL in the group - lowest number of publications by any MTL in the group)/ 10)
Example 3: using 10 publications:
Publication Relative Ranking Index = 10/((50-2)/10)
= 2.083
Example 4: using 20 publications:
Publication Relative Ranking Index = 20/((50-2)/10)
= 4.17
This same approach for calculating an ICRRI for the other MTL attributes such as
Presentations, Investigations, and Commercial Measure.
Example 5: ICRRI for evaluation of MTL during different stages of product development and market life
Upon obtaining the index for each attribute as described above during the FDA approval process, e.g.,
Publication RRI = 2.083
Presentation RRI = 2.791 Investigations RRI = 2.622
Commercial RRI = 0 (note: since drug not approved, no prescriptions could be written)
The final MTL Relative Ranking Index is obtained by multiplying each ICRRI by a weighting value (making sure all weights sum to 1; e.g., 0.2, 0.4, 0.3, 0.1) and then sum the weight-adjusted component indices for the prioritization. The assignment of the weighting value corresponds to the importance of a particular attribute at a particular time. MTL Attribute weighted component = 1.0 value
Publication RRI = 2.083 X 0.4 = 0.83 Presentation RRI = 2.791 X 0.5 = 1.40
Investigations RRI = 2.622 X 0.1 = 0.26 Commercial RRI = 0.0 X 0.0 = 0.0
This would then be evaluated by the sponsor company's goals as discussed above. Here, the amount of presentation would be found as the most prevalent attribute of the MTL targeted and should correspond to the goals set by the sponsor company at the particular time for a particular product.
However, the weighting of the index allows for changing the weights based on product lifecycle stage, without having to do major recalculations i.e., commercial can be weighted as zero during product development, or can be weighted heavily i.e., 0.8 for late phases in the product lifecycle. For example using the number achieved above but making evaluating 1 year after FDA approval:
MTL Attribute weighted component = 1.0 value
Publication RRI = 2.083 X 0.1 = 0.2
Presentation RRI = 2.791 X 0.1 = 0.2
Investigations RRI = 2.622 X 0.0 = 0.0 Commercial RRI = 20.0 X 0.8 = 16.0
If the highest ranking attribute coincides with the goal set by the sponsor company, the MSL has succeeded in obtaining the required objective. At a time of one year after FDA approval as illustrated above, the most predominate attribute may be commercial productivity, i.e. prescription writing, having a value of 16. This value should coincide with the objective of the sponsor company at one year after FDA approval. .
Using sample data, Table 3 illustrates how a group of potential MTLs may be prioritized by ranking them according to the ICRRI. The attributes shown in this illustration are publications, presentations, clinical investigations, and commercial value of the individual prescription writing. Table 3.
Illustration of Prioritization of MTLs Using MTL Attributes
NeoTotal ASH Commercial Pubs Presents Investigtns Commercial [ plasms ; MM One ASCO Oral ESMO Total Clinical Value (S Ranking Ranking Ranking Ranking Prioritization
Last Name First Name Pubs Pubs Pubs Presents ; Presents Presents Presents Investgtni s MM scripts) Variable Variable Variable Variable Index Value
Barlogie Bart 264 35 299 17 9 5 31 35 $2,789,369 10.136 13.478 10.938 7.158 11.161
Alexaπian Raymond 145 9 154 15 6 6 27 25 $3,819,765 5.220 11.739 7.813 9.802 8.671
Berenson James 85 24 109 13 8 7 28 19 $3,896,778 3.695 12.174 5.938 10.000 7.766
Blade Joan 96 16 112 18 6 5 29 16 $1,907,222 3.797 12.609 5.000 4.894 7.031
Ahmed Tausee 97 1 98 9 4 5 18 20 $2,689,996 3.322 7.826 6.250 6.903 6.203
Anderson Kenneth 89 42 131 7 2 6 15 17 $2,893,565 4.441 6.522 5.313 7.426 5.712
Attal Michel 19 10 29 4 1 4 9 13 $798,007 0.983 3.913 4.063 2.048 3.200
Akhtar N 5 2 7 4 3 4 11 7 $3,002,298 0.237 4.783 2.188 7.705 3.128
Alsina Melissa 36 5 41 8 2 2 12 6 $978,232 1.390 5.217 1.875 2.510 2.844
Bensiπger William 9 3 12 8 3 5 16 4 $478,563 0.407 6.957 1.250 1.228 2.791
Besa Emmanuel 3 1 4 5 3 3 11 3 $298,786 0.136 4.783 0.938 0.767 1.914
Barrett A 7 2 9 3 1 7 11 3 $0 0.305 4.783 0.938 0.000 1.871.
Ag a M 3 1 4 5 1 2 8 4 $1,000,277 0.136 3.478 1.250 2.567 1.827
Individual Component Relative
Weighting:
Publications 0.2
Presentations 0.3
Investigations 0.4
Commercial 0.1
The results obtained by the attribute system may serve as part of the basis for the planning stage of a second cycle in obtaining another business objective defined by the sponsor company. The results of the components in the Evaluation Phase and in the CRM ' discussed below will also serve as the basis.
Customer Relation Management System (CRM)
The system of the present invention requires that customer interactions be documented and that certain attributes regarding the nature, duration, costs and date of each interaction be captured for retrospective analysis. A mechanism for tracking MSL activities and their impact is incorporated into a Customer Relation Management System (CRM). MSL-specific activity attributes may be incorporated into an existing CRM (using commercially available software with modifications) for the purposes of providing the data for analyses. The CRM allows for the assignment of specific business outcomes (see types and definitions above) to specifically targeted MTLs and preferably will define an end point when an outcome is achieved. Each customer interaction is documented in the CRM and is classified according to an activity type (see types and their definitions below). The CRM is capable of providing queries by MSL, MTL, Business Outcome Type, and Activity Type etc..
The present invention allows the information to be evaluated in order to provide for more efficient use of the time of interaction between the MSL and the MTL. This is based on the Customer Relation Management System (described below) which memorializes the interactions between the MSL and the MTL.
The data obtained from CRM is available for periodic reporting of activities and outcome achievement. As illustrated in Table 4, the periodic reporting format may be in the form of a "Scorecard". The Scorecard consists of territory, regional, and national level data (the resolution to be defined by the Sponsor's MSL organizational structure). Information that may be included is the number of activities by type and by duration, funds spent/track to plan, time utilization, and position vacancies. These categories are not limiting and may be modified as needed to meet the predefined business objectives.
Table 4.
CRM Data for MSL Dr. John Know by MTL for Targeted Investigator Outcome
Targeted Outcome
Interaction MTL Last MTL First Activity Duration of
Business Achieved Date Name Name Type Interaction
Outcome (Y/N)?
1/5/2002 Adams Joan REL 30 Investigator N
2/7/2002 Adams Joan KX 25 Investigator N
3/8/2002 Adams Joan KX 10 Investigator N
4/2/2002 Adams Joan ASSESS 40 Investigator N
5/10/2002 Adams Joan RECRUIT 60 Investigator N
6/3/2002 Adams Joan REL 120 Investigator Y
7/9/2002 Adams Joan KX 50 Investigator Y
8/2/2002 Adams Joan KX 40 Investigator Y
8/28/2002 Adams Joan NET 45 Investigator Y
1/5/2002 Aden A REL 40 Investigator N
2/14/2002 Aden A A CύO 60 Investigator N
3/19/2002 Aden A RECRUIT 120 Investigator N
5/18/2002 Aden A RECRUIT 50 Investigator N
6/24/2002 Aden A REL 20 Investigator N
8/2/2002 Aden A KX 40 Investigator Y
1/5/2002 Benek James REL 120 Investigator N
2/7/2002 Benek James ASSESS 50 Investigator N
3/8/2002 Benek James KX 20 Investigator N
4/2/2002 Benek James RECRUIT 40 Investigator N
5/10/2002 Benek James RECRUIT 60 Investigator N
6/3/2002 Benek James KX 30 Investigator N
7/9/2002 Benek James REL 25 Investigator N
8/2/2002 Benek James KX 10 Investigator N
8/28/2002 Benek James REL 40 Investigator N
1/5/2002 Casey N REL 10 Investigator N
2/7/2002 Casey N KX 40 Investigator N
3/9/2002 Casey N ASSESS 60 Investigator N
4/2/2002 Casey N RECRUIT 120 Investigator N
5/15/2002 Casey N RECRUIT 50 Investigator N
6/24/2002 Casey N REL 20 Investigator N
7/20/2002 Casey N RECRUIT 40 Investigator N
8/2/2002 Casey N KX 60 Investigator N
8/28/2002 Casey N REL 30 Investigator N
1/5/2002 Dodds Kenneth REL 50 Investigator N
2/7/2002 Dodds Kenneth ASSESS 20 Investigator N
3/8/2002 Dodds Kenneth RECRUIT 40 Investigator N
4/2/2002 Dodds Kenneth REL 60 Investigator N
5/15/2002 Dodds Kenneth KX 30 Investigator N
6/24/2002 Dodds Kenneth KX 25 Investigator N
7/20/2002 Dodds Kenneth KX 10 Investigator N
8/2/2002 Dodds Kenneth KX 40 Investigator Y
8/28/2002 Dodds Kenneth REL 60 Investigator Y
1/4/2002 Emrick Michel REL 120 Investigator N
2/7/2002 Emrick Michel ASSESS 50 Investigator N /9/2002 Emrick Michel RECRUIT 20 Investigator N /2/2002 Emrick Michel RECRUIT 40 Investigator N /15/2002 Emrick Michel REL 60 Investigator N /24/2002 Emrick Michel REL 30 Investigator N /20/2002 Emrick Michel RECRUIT 25 Investigator N /2/2002 Emrick Michel KX 10 Investigator N /28/2002 Emrick Michel RECRUIT 40 Investigator N /4/2002 Fitch Raymond REL 25 Investigator N /7/2002 Fitch Raymond KX 10 Investigator N /8/2002 Fitch Raymond ASSESS 40 Investigator N /2/2002 Fitch Raymond RECRUIT 60 Investigator N /10/2002 Fitch Raymond REL 120 Investigator N /3/2002 Fitch Raymond NET 50 Investigator N /9/2002 Fitch Raymond REL 20 Investigator Y /20/2002 Fitch Raymond KX 40 Investigator Y /28/2002 Fitch Raymond KX 60 Investigator Y /5/2002 Gerber M REL 20 Investigator N /14/2002 Gerber M KX 40 Investigator N /19/2002 Gerber M ASSESS 60 Investigator N /18/2002 Gerber M NET 30 Investigator N /24/2002 Gerber M RECRUIT 25 Investigator Y /2/2002 Gerber M REL 10 Investigator Y /5/2002 Hicks Melissa REL 30 Investigator N /19/2002 Hicks Melissa ASSESS 25 Investigator N /18/2002 Hicks Melissa RECRUIT 10 Investigator N /24/2002 Hicks Melissa KX 40 Investigator Y /2/2002 Hicks Melissa BUSSOL 60 Investigator Y /28/2002 Hicks Melissa KX 120 Investigator Y /4/2002 Howe Tausee REL 40 Investigator N /7/2002 Howe Tausee REL 60 Investigator N /8/2002 Howe Tausee ASSESS 120 Investigator N /2/2002 Howe Tausee KX 50 Investigator N /10/2002 Howe Tausee KX 20 Investigator N /8/2002 Howe Tausee RECRUIT 40 Investigator N /9/2002 Howe Tausee NET 60 Investigator N /2/2002 Howe Tausee MEDSOL 30 Investigator Y /28/2002 Howe Tausee REL 25 Investigator Y /4/2002 Keeler Bart REL 60 Investigator N /7/2002 Keeler Bart ASSESS 120 Investigator N /8/2002 Keeler Bart RECRUIT 50 Investigator N /2/2002 Keeler Bart KX 20 Investigator N /10/2002 Keeler Bart REL 40 Investigator N /3/2002 Keeler Bart COACH 60 Investigator N /9/2002 Keeler Bart BUSSOL 30 Investigator N /20/2002 Keeler Bart REL 25 Investigator Y /28/2002 Keeler Bart REL 10 Investigator Y /4/2002 Lucas Emmanuel REL 60 Investigator N /14/2002 Lucas Emmanuel KX 120 Investigator N /19/2002 Lucas Emmanuel ASSESS 50 Investigator N /18/2002 Lucas Emmanuel RECRUIT 20 Investigator N /24/2002 Lucas Emmanuel REL 40 Investigator N /2/2002 Lucas Emmanuel REL 60 Investigator N /4/2002 Markley William REL 60 Investigator N /14/2002 Markley William RECRUIT 30 Investigator N /19/2002 Markley William RECRUIT 25 Investigator N /18/2002 Markley William KX 10 Investigator N /24/2002 Markley William REL 40 Investigator N /2/2002 Markley William REL 60 Investigator N /5/2002 Martin Joan REL 30 Investigator N /7/2002 Martin Joan KX 25 Investigator N /8/2002 Martin Joan MEDSOL 10 Investigator N /2/2002 Martin Joan ASSESS 40 Investigator N /10/2002 Martin Joan RECRUIT 60 Investigator N /3/2002 Martin Joan REL 120 Investigator Y /9/2002 Martin Joan KX 50 Investigator Y /2/2002 Martin Joan KX 40 Investigator Y /28/2002 Martin Joan NET 45 Investigator Y /5/2002 Metzger A REL 40 Investigator N /14/2002 Metzger A KX 60 Investigator N /19/2002 Metzger A ASSESS 120 Investigator N /18/2002 Metzger A RECRUIT 50 Investigator N /24/2002 Metzger A BUSSOL 20 Investigator N /2/2002 Metzger A KX 40 Investigator Y /5/2002 Milnes James REL 120 Investigator N /7/2002 Milnes James REL 50 Investigator N /8/2002 Milnes James ASSESS 20 Investigator N /2/2002 Milnes James RECRUIT 40 Investigator N /10/2002 Milnes James RECRUIT 60 Investigator N /3/2002 Milnes James KX 30 Investigator N /9/2002 Milnes James REL 25 Investigator N /2/2002 Milnes James KX 10 Investigator N /28/2002 Milnes James REL 40 Investigator N /5/2002 Myers N REL 10 Investigator N /7/2002 Myers N KX 40 Investigator N /9/2002 Myers N ASSESS 60 Investigator N /2/2002 Myers N RECRUIT 120 Investigator N /15/2002 Myers N RECRUIT 50 Investigator N /24/2002 Myers N REL 20 Investigator N /20/2002 Myers N RECRUIT 40 Investigator N /2/2002 Myers N KX 60 Investigator N /28/2002 Myers N REL 30 Investigator N /5/2002 Nichols Kenneth REL 50 Investigator N /7/2002 Nichols Kenneth ASSESS 20 Investigator N /8/2002 Nichols Kenneth RECRUIT 40 Investigator N /2/2002 Nichols Kenneth REL 60 Investigator N /15/2002 Nichols Kenneth KX 30 Investigator N /24/2002 Nichols Kenneth KX 25 Investigator N /20/2002 Nichols Kenneth KX 10 Investigator N /2/2002 Nichols Kenneth KX 40 Investigator Y /28/2002 Nichols Kenneth REL 60 Investigator Y /4/2002 Nolan Michel REL 120 Investigator N /7/2002 Nolan Michel KX 50 Investigator N /9/2002 Nolan Michel ASSESS 20 Investigator N /2/2002 Nolan Michel RECRUIT 40 Investigator N /15/2002 Nolan Michel REL 60 Investigator N /24/2002 Nolan Michel REL 30 Investigator N /20/2002 Nolan Michel RECRUIT 25 Investigator N /2/2002 Nolan Michel KX 10 Investigator N /28/2002 Nolan Michel RECRUIT 40 Investigator N /4/2002 Osborne Raymond REL 25 Investigator N /7/2002 Osborne Raymond ASSESS 10 Investigator N /8/2002 Osborne Raymond RECRUIT 40 Investigator N /2/2002 Osborne Raymond KX 60 Investigator N /10/2002 Osborne Raymond REL 120 Investigator N /3/2002 Osborne Raymond NET 50 Investigator N /9/2002 Osborne Raymond REL 20 Investigator Y /20/2002 Osborne Raymond KX 40 Investigator Y /28/2002 Osborne Raymond KX 60 Investigator Y /5/2002 Owens M REL 20 Investigator N /14/2002 Owens M KX 40 Investigator N /19/2002 Owens M REL 60 Investigator N /18/2002 Owens M NET 30 Investigator N /24/2002 Owens M REL 25 Investigator N /2/2002 Owens M REL 10 Investigator N /5/2002 Padva Melissa REL 30 Investigator N /19/2002 Padva Melissa REL 25 Investigator N /18/2002 Padva Melissa REL 10 Investigator N /24/2002 Padva Melissa KX 40 Investigator N /2/2002 Padva Melissa KX 60 Investigator N /28/2002 Padva Melissa KX 120 Investigator N /4/2002 Patterson Tausee REL 40 nvestigator N /7/2002 Patterson Tausee REL 60 nvestigator N /8/2002 Patterson Tausee KX 120 nvestigator N /2/2002 Patterson Tausee ASSESS 50 nvestigator N /10/2002 Patterson Tausee RECRUIT 20 nvestigator N /8/2002 Patterson Tausee REL 40 nvestigator N /9/2002 Patterson Tausee NET 60 nvestigator N /2/2002 Patterson Tausee MEDSOL 30 nvestigator Y /28/2002 Patterson Tausee REL 25 nvestigator Y /4/2002 Petty Bart REL 60 nvestigator N /7/2002 Petty Bart ASSESS 120 nvestigator N /8/2002 Petty Bart RECRUIT 50 nvestigator N /2/2002 Petty Bart KX 20 nvestigator N /10/2002 Petty Bart REL 40 nvestigator N /3/2002 Petty Bart COACH 60 nvestigator N /9/2002 Petty Bart KX 30 nvestigator N /20/2002 Petty Bart REL 25 nvestigator Y /28/2002 Petty Bart REL 10 nvestigator Y /4/2002 Philbin Emmanuel REL 60 nvestigator N /14/2002 Philbin Emmanuel KX 120 nvestigator N /19/2002 Philbin Emmanuel RECRUIT 50 nvestigator N /18/2002 Philbin Emmanuel RECRUIT 20 nvestigator N /24/2002 Philbin Emmanuel REL 40 nvestigator N /2/2002 Philbin Emmanuel REL 60 nvestigator N /4/2002 Pollack William REL 60 nvestigator N /14/2002 Pollack William RECRUIT 30 nvestigator N /19/2002 Pollack William RECRUIT 25 nvestigator N /18/2002 Pollack William KX 10 nvestigator N /24/2002 Pollack William REL 40 nvestigator N /2/2002 Pollack William REL 60 nvestigator N /5/2002 Potter Joan REL 30 nvestigator N /7/2002 Potter Joan KX 25 nvestigator N /8/2002 Potter Joan KX 10 nvestigator N /2/2002 Potter Joan ASSESS 40 nvestigator N /10/2002 Potter Joan RECRUIT 60 nvestigator N /3/2002 Potter Joan REL 120 nvestigator Y /9/2002 Potter Joan KX 50 nvestigator Y /2/2002 Potter Joan KX 40 nvestigator Y /28/2002 Potter Joan NET 45 nvestigator Y /5/2002 Ramsey A REL 40 nvestigator N /14/2002 Ramsey A KX 60 nvestigator N /19/2002 Ramsey A RECRUIT 120 nvestigator N /18/2002 Ramsey A RECRUIT 50 nvestigator N /24/2002 Ramsey A REL 20 nvestigator N /2/2002 Ramsey A KX 40 nvestigator N /5/2002 Reinhart James REL 120 nvestigator N /7/2002 Reinhart James REL 50 nvestigator N /8/2002 Reinhart James KX 20 nvestigator N /2/2002 Reinhart James RECRUIT 40 nvestigator N /10/2002 Reinhart James RECRUIT 60 nvestigator N /3/2002 Reinhart James KX 30 nvestigator N /9/2002 Reinhart James REL 25 nvestigator N /2/2002 Reinhart James KX 10 nvestigator N /28/2002 Reinhart James REL 40 nvestigator N /5/2002 Richards N REL 10 nvestigator N /7/2002 Richards N KX 40 nvestigator N /9/2002 Richards N REL 60 nvestigator N /2/2002 Richards N RECRUIT 120 nvestigator N /15/2002 Richards N RECRUIT 50 nvestigator N /24/2002 Richards N REL 20 nvestigator N /20/2002 Richards N RECRUIT 40 nvestigator N /2/2002 Richards N KX 60 nvestigator N /28/2002 Richards N REL 30 nvestigator N /5/2002 Rosen Kenneth REL 50 nvestigator N /7/2002 Rosen Kenneth ASSESS 20 nvestigator N /8/2002 Rosen Kenneth RECRUIT 40 nvestigator N /2/2002 Rosen Kenneth REL 60 nvestigator N /15/2002 Rosen Kenneth KX 30 nvestigator N /24/2002 Rosen Kenneth KX 25 nvestigator N /20/2002 Rosen Kenneth KX 10 nvestigator N /2/2002 Rosen Kenneth KX 40 nvestigator Y /28/2002 Rosen Kenneth REL 60 nvestigator Y /4/2002 Ryan Michel REL 120 nvestigator N /7/2002 Ryan Michel KX 50 nvestigator N /9/2002 Ryan Michel RECRUIT 20 nvestigator N /2/2002 Ryan Michel RECRUIT 40 nvestigator N /15/2002 Ryan Michel REL 60 nvestigator N /24/2002 Ryan Michel REL 30 nvestigator N /20/2002 Ryan Michel RECRUIT 25 nvestigator N /2/2002 Ryan Michel KX 10 nvestigator N /28/2002 Ryan Michel RECRUIT 40 nvestigator N /4/2002 Saxton Raymond REL 25 nvestigator N /7/2002 Saxton Raymond RECRUIT 10 nvestigator N /8/2002 Saxton Raymond RECRUIT 40 nvestigator N /2/2002 Saxton Raymond KX 60 nvestigator N /10/2002 Saxton Raymond REL 120 nvestigator N /3/2002 Saxton Raymond NET 50 nvestigator N /9/2002 Saxton Raymond REL 20 nvestigator N /20/2002 Saxton Raymond KX 40 nvestigator N /28/2002 Saxton Raymond KX 60 nvestigator N /5/2002 Schmitt M REL 20 nvestigator N /14/2002 Schmitt M KX 40 nvestigator N /19/2002 Schmitt M RECRUIT 60 nvestigator N /18/2002 Schmitt M NET 30 nvestigator N /24/2002 Schmitt M BUSSOL 25 nvestigator N /2/2002 Schmitt M REL 10 nvestigator Y /5/2002 Stewart Melissa REL 30 nvestigator N /19/2002 Stewart Melissa REL 25 nvestigator N /18/2002 Stewart Melissa REL 10 nvestigator N /24/2002 Stewart Melissa KX 40 nvestigator N /2/2002 Stewart Melissa KX 60 nvestigator N /28/2002 Stewart Melissa KX 120 nvestigator N /4/2002 Thompson Tausee REL 40 nvestigator N /7/2002 Thompson Tausee REL 60 nvestigator N /8/2002 Thompson Tausee ASSESS 120 nvestigator N /2/2002 Thompson Tausee KX 50 nvestigator N /10/2002 Thompson Tausee KX 20 nvestigator N /8/2002 Thompson Tausee. REL 40 nvestigator N /9/2002 Thompson Tausee NET 60 nvestigator N /2/2002 Thompson Tausee REL 30 nvestigator N /28/2002 Thompson Tausee REL 25 nvestigator N /4/2002 Ulshafer Bart REL 60 nvestigator N /7/2002 Ulshafer Bart ASSESS 120 nvestigator N /8/2002 Ulshafer Bart RECRUIT 50 nvestigator N /2/2002 Ulshafer Bart KX 20 nvestigator N /10/2002 Ulshafer Bart REL 40 nvestigator N /3/2002 Ulshafer Bart COACH 60 nvestigator N /9/2002 Ulshafer Bart KX 30 nvestigator N /20/2002 Ulshafer Bart REL 25 nvestigator Y /28/2002 Ulshafer Bart REL 10 nvestigator Y /4/2002 Vogel Emmanuel REL 60 nvestigator N /14/2002 Vogel Emmanuel KX 120 nvestigator N /19/2002 Vogel Emmanuel RECRUIT 50 nvestigator N /18/2002 Vogel Emmanuel RECRUIT 20 nvestigator N /24/2002 Vogel Emmanuel REL 40 nvestigator N /2/2002 Vogel Emmanuel REL 60 nvestigator N /4/2002 Wellington William REL 60 nvestigator N /14/2002 Wellington William RECRUIT 30 nvestigator N /19/2002 Wellington William RECRUIT 25 nvestigator N /18/2002 Wellington William KX 10 nvestigator N 6/24/2002 Wellington William REL 40 Investigator N
8/2/2002 Wellington William REL 60 Investigator N
Referring to Table 4, a scorecard is illustrated summarizing various types of activities and recorded information based on the interaction between the MSL representative, Dr. John Know and various MTLs over a predefined period of time. These particular activities were concentrated for the particular business outcome goal of investigator (as described above). This information is further summarized in Table 5, wherein the time spent is particularly broken down in order to be able to use the information based on whether the business outcome (investigator) had been achieved and what types of activities may need to be done, in terms of changing the activities when interacting with a particular MTL. Table 5 illustrates the activity data for each particular
MSL in a certain period of time. This output allows (a) evaluation by management as to the daily activity of an MSL and (b) a journal for organization and planning of the MSL activity in the future.
Table 5.
Frequency by Activity Type and Cumulative Duration by MTL
Total Interactions
Prior to Average Cumulative Outcome
Achieving Interaction Interactions Achieved Business
MTL RECRUIT COACH REL NET ASSESS BUSSOL MEDSOL KX Outcome Duration Duration (Y/N) Outcome
Abptar 3 3 2 9 47.8 430 N Investigator
Agha 2 1 1 1 6 35.0 210 Y Investigator
Ahmed 3 1 1 1 2 9 52.8 475 Y Investigator
A htar 3 2 9 47.8 430 N Investigator
Alexanian 3 1 1 3 9 49.4 445 Y Investigator
Alixandor 3 1 1 3 9 49.4 445 Y Investigator
Alsina 1 1 2 6 54.2 325 Y Investigator
Anderson 3 4 9 41.7 375 Y Investigator
Andersten 3 4 9 41.7 375 Y Investigator
Attal 4 3 1 9 43.9 395 N Investigator
Baholst 1 1 2 6 61.7 370 Y Investigator
Barlogie 1 1 4 1 1 9 48.9 440 Y Investigator
Barrett 2 2 1 6 61.7 370 Y Investigator
Barsot 1 1 4 2 9 48.9 440 Y Investigator
Bensinger 2 3 1 6 37.5 225 N Investigator
Bensoner 2 4 2 9 43.9 395 N Investigator
Bentinger 2 3 1 6 37.5 225 N Investigator
Berenson 2 3 3 9 43.9 395 N Investigator
Besa 1 3 1 6 58.3 350 N Investigator
Besalt 2 3 1 6 58.3 350 N Investigator
Blade 1 2 1 1 4 9 60.0 540 Y Investigator
Burnast 4 1 1 6 29.2 175 N Investigator
Cahmet 1 4 1 1 1 1 9 52.8 475 Y Investigator
Calsina 3 3 6 17.5 105 N Investigator
Table 5. (cont.)
Total Interactions Prior to Average Cumulative Outcome Achieving Interaction Interactions Achieved Business
MTL RECRUIT COACH REL NET ASSESS BUSSOL MEDSOL KX Outcome Duration Duration (YIN) Outcome
Codst 3 3 1 2 9 43.9 395 N Investigator
Dickerson 1 2 1 1 3 9 60.0 540 Y Investigator
Fabptar 3 4 2 9 47.8 430 N Investigator
Fahmet 5 1 1 2 9 49.4 445 N Investigator
Falexan 2 3 1 3 9 47.2 425 N Investigator
Falsina 3 3 6 47.5 285 N Investigator
Feholst 2 2 2 6 55.0 330 N Investigator
Fendersten 1 3 1 4 9 42.8 385 Y Investigator
Fensoner 2 4 3 9 43.9 395 N Investigator
Fersot 1 4 1 2 9 48.9 440 Y Investigator
Fickerson 1 2 1 1 4 9 60.0 540 Y Investigator
Fodsten 4 3 2 9 43.9 395 N Investigator
Funtinger 2 3 1 6 37.5 225 N Investigator
Furnast 1 2 1 1 6 30.8 185 Y Investigator
Fusalt 2 3 1 6 58.3 350 N Investigator
Table 6 below illustrates yet another view of the exemplary data in which the frequency and duration of customer interaction are set forth by activity type for each MTL having a successful investigator outcome.
Table 6.
Summary of Frequency and Duration by Activity Type Resulting in Successful Investigation Outcome
Outcome Achieved (Y/N)? Y
Figure imgf000027_0001
Figure imgf000028_0001
Figure imgf000029_0001
The data incorporated into the CRM are particularly useful for prompt, accurate and specific "activity to outcome" analysis. For example, the interactions with MTL Adams yielded a desired outcome of investigator based on the activities and time as highlighted in Figure 2. In contrast, the desired outcome of investigator was not achieved by the activities and time spent on MTL Philbin.
Evaluating Phase The Evaluating phase examines metrics of different categories from a variety of sources. Among these sources are commercial data, i.e., increased prescriptions of particular product, business outcomes analyses, i.e., based on the Scorecard information, internal services provided to the MTL, and survey results. Direct Analysis The impact of MSL activities may be measured in commercial terms. By targeting
MSL efforts toward a select group of physicians/outcomes, the conditions are met to enable comparison of product prescribing between the targeted physicians/institutions and the relevant physician/institution universe. For example, to examine the impact of MSL activities, the targeted customer's product utilization uptake can be compared to the appropriate customer universe. More rapid uptake would result in an increase in the slope of the sales curve over the time since launch, compared to the slope of the sales curve of the comparator population. Historically, the rate of market uptake following launch is a major determinant of total sales over the commercial life of the drug. Indirect Analysis
The statistical tests (e.g., ANCoVA) detect variables that co-vary (in this case, activity types and business outcome types) with a given outcome status (achieved or non-achieved). This permits objective measurement of the effort required to achieve a targeted business outcome, thereby increasing the accuracy of MSL capacity assessments and commercial planning efforts. During the Evaluating phase, the data pertaining to business outcomes, and activities conducted in the attempt to achieve these outcomes, is analyzed. The analyses determine which activities and at what frequency/duration resulted in achieved outcomes, versus those activities and frequency/duration that resulted in non-achievement of a targeted outcome. The determination is accomplished through conducting a statistical analysis that provides the aggregate weight of individual activity types for a specific business outcome type differentiated by achievement and non-achievement.
Tables 7 and 8 illustrate a statistical analysis of the average frequency of interactions by activity type with respect to achievement and non-achievement of an investigator outcome based on the data in Table 4.
Table 7.
Statistical Analysis of Investigator Outcome Data
Business Outcome Outcome Achieved (Y/N) Data Average Interactions StdDevP
Investigator N Average of RECRUIT 2.412 0.771
Investigator N Average of COACH
Investigator N Average of REL 3.238 0.610
Investigator N Average of NET 1.000 0.000
Investigator N Average of ASSESS 1.000 0.000
Investigator N Average of BUSSOL
Investigator N Average of MEDSOL
Investigator N Average of KX 1.857 0.774
Investigator Y Average of RECRUIT 1.056 0.229
Investigator Y Average of COACH 1.000 0.000
Investigator Y Average of REL 2.667 0.943
Investigator Y Average of NET 1.000 0.000
Investigator Y Average of ASSESS 1.000 0.000
Investigator Y Average of BUSSOL 1.000 0.000
Investigator Y Average of MEDSOL 1.000 0.000
Investigator Y Average of KX 2.444 1.165
Investigator Average of RECRUIT 1.714 0.881
Investigator Average of COACH 1 000 0.000
Investigator Average of REL 2 974 0.832
Investigator Average of NET 1 000 0.000
Investigator Average of ASSESS 1 000 0.000
Investigator Average of BUSSOL 1 000 0.000
Investigator Average of MEDSOL 1 000 0.000
Investigator Average of KX 2 128 1.017
Total Average of RECRUIT 1 714 0.881
Total Average of COACH 1 000 0.000
Total Average of REL 2 974 0.832
Total Average of NET 1 000 0.000
Total Average of ASSESS 1 000 0.000
Total Average of BUSSOL 1 000 0.000
Total Average of MEDSOL 1 000 0.000
Total Average of KX 2 128 1.017
Table 8.
N Data N Data Sum of Average StdDevP Difference StdDevPs Significance
2.412 0.771 -1.356 1.000 S - RECRUIT
1.000 0.000 S + COACH
3.238 0.61 -0.571 1.553 NS REL
1 0 0.000 0.000 NS NET
1 0 0.000 0.000 NS ASSESS
1.000 0.000 S + BUSSOL
1.000 0.000 S + MEDSOL
1.857 0.774 0.587 1.939 NS KX
** Considered Significant if StdDevPs do not overlap
Significance (significant effect defined as the difference between the means is greater than the sum of the combined StdDevPs; if the StdDevP = 0, then use the Combined StdDevP instead):
NS = Non-Significant
S - = Significant Negative Result
S + = Significant Positive Result
Several conclusions may be drawn from the statistical analyses in Tables 7 and 8. For example, the data average for recruiting type activity suggests that if the MSL does not get a commitment after two recruiting interactions, then an investigator outcome is highly unlikely. Also, based on this exemplary data, coaching, medical solutions and business solutions interactions improve the likelihood of a successful investigator outcome. The data suggest that a successful approach to achieve an investigator outcome may be obtained through the following set of interactions:
1.056 RECRUIT interactions
1.000 COACH interactions
2.667 REL interactions
1.000 NET interactions
1.000 ASSESS interactions
1.000 BUSSOL interactions
1.000 MEDSOL interactions
2.444 KX interactions
11.167 Total interactions plus or minus 3.111
A statistical analysis based on duration rather than frequency of interactions and activity types may also be derived from the data in a similar manner. Tables 9 and 10 illustrate a sample data and statistical analysis report for a second exemplary set of interactions in which multiple business outcomes were targeted over a predetermined period of time.
Table 9. CRM Data for All MTL and All Targeted Business Outcomes
Targeted Outcome
Interaction MTL Last MTL First Duration of Business Achieved
Date Name Name Activity Type Interaction Outcome (Y/N)?
1/5/2002 Adams Joan REL 30 Author N
2/7/2002 Adams Joan KX 25 Author N
3/8/2002 Adams Joan KX 10 Author N
4/2/2002 Adams Joan KX 40 Author N
5/10/2002 Adams Joan RECRUIT 60 Author N
6/3/2002 Adams Joan REL 120 Author Y
7/9/2002 Adams Joan KX 50 Author M
8/2/2002 Adams Joan KX 40 Author M
8/28/2002 Adams Joan NET 45 Author M
1/5/2002 Aden A REL 40 Consultant N
2/14/2002 Aden A KX 60 Consultant N
3/19/2002 Aden A RECRUIT 120 Consultant N
5/18/2002 Aden A RECRUIT 50 Consultant N
6/24/2002 Aden A REL 20 Consultant N
8/2/2002 Aden A KX 40 Consultant Y
1/5/2002 Benek James REL 120 Consultant N
2/7/2002 Benek James REL 50 Consultant N
3/8/2002 Benek James KX 20 Consultant N
4/2/2002 Benek James RECRUIT 40 Consultant N
5/10/2002 Benek James RECRUIT 60 Consultant N
6/3/2002 Benek James KX 30 Consultant N
7/9/2002 Benek James REL 25 Consultant N
8/2/2002 Benek James KX 10 Consultant N
8/28/2002 Benek James REL 40 Consultant N
1/5/2002 Casey N REL 10 Consultant N
2/7/2002 Casey N KX 40 Consultant N
3/9/2002 Casey N REL 60 Consultant N
4/2/2002 Casey N RECRUIT 120 Consultant N
5/15/2002 Casey N RECRUIT 50 Consultant N
6/24/2002 Casey N REL 20 Consultant N
7/20/2002 Casey N RECRUIT 40 Consultant N
8/2/2002 Casey N KX 60 Consultant N
8/28/2002 Casey N REL 30 Consultant N
1/5/2002 Dodds Kenneth REL 50 Author N
2/7/2002 Dodds Kenneth KX 20 Author N
3/8/2002 Dodds Kenneth RECRUIT 40 Author N
4/2/2002 Dodds Kenneth REL ' 60 Author N
5/15/2002 Dodds Kenneth KX 30 Author N
6/24/2002 Dodds Kenneth KX 25 Author N
7/20/2002 Dodds Kenneth KX 10 Author N
8/2/2002 Dodds Kenneth KX 40 Author Y
8/28/2002 Dodds Kenneth REL 60 Author M
1/4/2002 Emrick Michel REL 120 Investigator N
2/7/2002 Emrick Michel KX 50 Investigator N
3/9/2002 Emrick Michel RECRUIT 20 Investigator N
4/2/2002 Emrick Michel RECRUIT 40 Investigator N
5/15/2002 Emrick Michel REL 60 Investigator N
6/24/2002 Emrick Michel REL 30 Investigator N /20/2002 Emrick Michel RECRUIT 25 Investigator N /2/2002 Emrick Michel KX 10 Investigator N /28/2002 Emrick Michel RECRUIT 40 Investigator N /4/2002 Fitch Raymond REL 25 Investigator N /7/2002 Fitch Raymond RECRUIT 10 Investigator N /8/2002 Fitch Raymond ASSESS 40 Investigator N /2/2002 Fitch Raymond KX 60 Investigator N /10/2002 Fitch Raymond REL 120 Investigator N /3/2002 Fitch Raymond NET 50 Investigator N /9/2002 Fitch Raymond REL 20 Investigator Y /20/2002 Fitch Raymond KX 40 Investigator M /28/2002 Fitch Raymond KX 60 Investigator M /5/2002 Gerber M REL 20 Prescriber N /14/2002 Gerber M KX 40 Prescriber N /19/2002 Gerber M REL 60 Prescriber N /18/2002 Gerber M NET 30 Prescriber N /24/2002 Gerber M REL 25 Prescriber Y /2/2002 Gerber M REL 10 Prescriber M /5/2002 Hicks Melissa REL 30 Prescriber N /19/2002 Hicks Melissa REL 25 Prescriber N /18/2002 Hicks Melissa REL 10 Prescriber N /24/2002 Hicks Melissa KX 40 Prescriber Y /2/2002 Hicks Melissa KX 60 Prescriber M /28/2002 Hicks Melissa KX 120 Prescriber M /4/2002 Howe Tausee REL 40 Prescriber N /7/2002 Howe Tausee REL 60 Prescriber N /8/2002 Howe Tausee KX 120 Prescriber N /2/2002 Howe Tausee KX 50 Prescriber N /10/2002 Howe Tausee KX 20 Prescriber N /8/2002 Howe Tausee REL 40 Prescriber N /9/2002 Howe Tausee NET 60 Prescriber N /2/2002 Howe Tausee MEDSOL 30 Prescriber Y /28/2002 Howe Tausee REL 25 Prescriber M /4/2002 Keeler Bart REL 60 Speaker N /7/2002 Keeler Bart KX • 120 Speaker N /8/2002 Keeler Bart RECRUIT 50 Speaker N /2/2002 Keeler Bart KX 20 Speaker N /10/2002 Keeler Bart REL 40 Speaker N /3/2002 Keeler Bart COACH 60 Speaker N /9/2002 Keeler Bart KX 30 Speaker N /20/2002 Keeler Bart REL 25 Speaker Y /28/2002 Keeler Bart REL 10 Speaker M /4/2002 Lucas Emmanuel REL 60 Speaker N /14/2002 Lucas Emmanuel KX 120 Speaker N /19/2002 Lucas Emmanuel RECRUIT 50 Speaker N /18/2002 Lucas Emmanuel RECRUIT 20 Speaker N /24/2002 Lucas Emmanuel REL 40 Speaker N /2/2002 Lucas Emmanuel REL 60 Speaker N /4/2002 Markley William REL 60 Speaker N /14/2002 Markley William RECRUIT 30 Speaker N /19/2002 Markley William RECRUIT 25 Speaker N /18/2002 Markley William KX 10 Speaker N /24/2002 Markley William REL 40 Speaker N /2/2002 Markley William REL 60 Speaker N /5/2002 Martin Joan REL 30 Author N /7/2002 Martin Joan KX 25 Author N /8/2002 Martin Joan KX 10 Author N /2/2002 Martin Joan KX 40 Author N /10/2002 Martin Joan RECRUIT 60 Author N /3/2002 Martin Joan REL 120 Author Y /9/2002 Martin Joan KX 50 Author M /2/2002 Martin Joan ' KX 40 Author M /28/2002 Martin Joan NET 45 Author M /5/2002 Metzger A REL 40 Consultant N /14/2002 Metzger A KX 60 Consultant N /19/2002 Metzger A RECRUIT 120 Consultant N /18/2002 Metzger A RECRUIT 50 Consultant N /24/2002 Metzger A REL 20 Consultant N /2/2002 Metzger A KX 40 Consultant Y /5/2002 Milnes James REL 120 Consultant N /7/2002 Milnes James REL 50 Consultant N /8/2002 Milnes James KX 20 Consultant N /2/2002 Milnes James RECRUIT 40 Consultant N /10/2002 Milnes James RECRUIT 60 Consultant N /3/2002 Milnes James KX 30 Consultant N /9/2002 Milnes James REL 25 Consultant N /2/2002 Milnes James KX 10 Consultant N /28/2002 Milnes James REL 40 Consultant N /5/2002 Myers N REL 10 Consultant N /7/2002 Myers N KX 40 Consultant N /9/2002 Myers N REL 60 Consultant N /2/2002 Myers N RECRUIT 120 Consultant N /15/2002 Myers N RECRUIT 50 Consultant N /24/2002 Myers N REL 20 Consultant N /20/2002 Myers N RECRUIT 40 Consultant N /2/2002 Myers N KX 60 Consultant N /28/2002 Myers N REL 30 Consultant N /5/2002 Nichols Kenneth REL 50 Author N /7/2002 Nichols Kenneth KX 20 Author N /8/2002 Nichols Kenneth RECRUIT 40 Author N /2/2002 Nichols Kenneth REL 60 Author N /15/2002 Nichols Kenneth KX 30 Author N /24/2002 Nichols Kenneth KX 25 Author N /20/2002 Nichols Kenneth KX 10 Author N /2/2002 Nichols Kenneth KX 40 Author Y /28/2002 Nichols Kenneth REL 60 Author M /4/2002 Nolan Michel REL 120 Investigato N /7/2002 Nolan Michel KX 50 Investigato N /9/2002 Nolan Michel RECRUIT 20 Investigato N /2/2002 Nolan Michel RECRUIT 40 Investigato N /15/2002 Nolan Michel REL 60 Investigator N /24/2002 Nolan Michel REL 30 Investigatot N /20/2002 Nolan Michel RECRUIT 25 Investigato N /2/2002 Nolan Michel KX 10 Investigato N /28/2002 Nolan Michel RECRUIT 40 Investigato N /4/2002 Osborne Raymond REL 25 Investigato • N /7/2002 Osborne Raymond RECRUIT 10 Investigato • N /8/2002 Osborne Raymond AOvCOu 40 Investigato - N /2/2002 Osborne Raymond KX 60 Investigato - N /10/2002 Osborne Raymond REL 120 Investigato N /3/2002 Osborne Raymond NET 50 Investigato N /9/2002 Osborne Raymond REL 20 Investigato Y /20/2002 Osborne Raymond KX 40 Investigato M /28/2002 Osborne Raymond KX 60 Investigato • M /5/2002 Owens M REL 20 Prescriber N /14/2002 Owens M KX 40 Prescriber N /19/2002 Owens M REL 60 Prescriber N /18/2002 Owens M NET 30 Prescriber N /24/2002 Owens M REL 25 Prescriber Y /2/2002 Owens M REL 10 Prescriber M /5/2002 Padva Melissa REL 30 Prescriber N /19/2002 Padva Melissa REL 25 Prescriber N /18/2002 Padva Melissa REL 10 Prescriber N /24/2002 Padva Melissa KX 40 Prescriber Y /2/2002 Padva Melissa KX 60 Prescriber M /28/2002 Padva Melissa KX 120 Prescriber M /4/2002 Patterson Tausee REL 40 Prescriber N /7/2002 Patterson Tausee REL 60 Prescriber N /8/2002 Patterson Tausee KX 120 Prescriber N /2/2002 Patterson Tausee KX 50 Prescriber N /10/2002 Patterson Tausee KX 20 Prescriber N /8/2002 Patterson Tausee REL 40 Prescriber N /9/2002 Patterson Tausee NET 60 Prescriber N /2/2002 Patterson Tausee MEDSOL 30 Prescriber Y /28/2002 Patterson Tausee REL 25 Prescriber M /4/2002 Petty Bart REL 60 Speaker N /7/2002 Petty Bart KX 120 Speaker N /8/2002 Petty Bart RECRUIT 50 Speaker N /2/2002 Petty Bart KX 20 Speaker N /10/2002 Petty Bart REL 40 Speaker N /3/2002 Petty Bart COACH 60 Speaker N /9/2002 Petty Bart KX 30 Speaker N /20/2002 Petty Bart REL 25 Speaker Y /28/2002 Petty Bart REL 10 Speaker M /4/2002 Philbin Emmanuel REL 60 Speaker N /14/2002 Philbin Emmanuel KX 120 Speaker N /19/2002 Philbin Emmanuel RECRUIT 50 Speaker N /18/2002 Philbin Emmanuel RECRUIT 20 Speaker N /24/2002 Philbin Emmanuel REL 40 Speaker N /2/2002 Philbin Emmanuel REL 60 Speaker N /4/2002 Pollack William REL 60 Speaker N /14/2002 Pollack William RECRUIT 30 Speaker N /19/2002 Pollack William RECRUIT 25 Speaker N /18/2002 Pollack William KX 10 Speaker N /24/2002 Pollack William REL 40 Speaker N /2/2002 Pollack William REL 60 Speaker N
Formulary /5/2002 Potter Joan REL 30 Supporter
Formulary /7/2002 Potter Joan KX 25 Supporter
Formulary /8/2002 Potter Joan KX 10 Supporter
Formulary /2/2002 Potter Joan KX 40 Supporter
Formulary /10/2002 Potter Joan RECRUIT 60 Supporter
Formulary /3/2002 Potter Joan REL 120 Supporter
Formulary /9/2002 Potter Joan KX 50 M
Supporter
Formulary /2/2002 Potter Joan KX 40 Supporter M
Formulary /28/2002 Potter Joan NET 45 M
Supporter /5/2002 Ramsey A REL 40 Consultant N /14/2002 Ramsey A KX 60 Consultant N /19/2002 Ramsey A RECRUIT 120 Consultant N /18/2002 Ramsey A RECRUIT 50 Consultant N /24/2002 Ramsey A REL 20 Consultant N /2/2002 Ramsey A KX 40 Consultant Y
Formulary /5/2002 Reinhart James REL 120 Supporter
Formulary /7/2002 Reinhart James REL 50
Supporter
Formulary /8/2002 Reinhart James KX 20 Supporter
Formulary /2/2002 Reinhart James RECRUIT 40 Supporter
Formulary /10/2002 Reinhart James RECRUIT 60
Supporter
Formulary /3/2002 Reinhart James KX 30 Supporter
Formulary /9/2002 Reinhart James REL 25 Supporter /2/2002 Reinhart James KX 10 Formulary Formulary /28/2002 Reinhart James REL 40 N Supporter /5/2002 Richards N REL 10 Consultant N /7/2002 Richards N KX 40 Consultant N /9/2002 Richards N REL 60 Consultant N /2/2002 Richards N RECRUIT 120 Consultant N /15/2002 Richards N RECRUIT 50 Consultant N /24/2002 Richards N REL 20 Consultant N /20/2002 Richards N RECRUIT 40 Consultant N /2/2002 Richards N KX 60 Consultant N /28/2002 Richards N REL 30 Consultant N /5/2002 Rosen Kenneth REL 50 Author N /7/2002 Rosen Kenneth KX 20 Author N /8/2002 Rosen Kenneth RECRUIT 40 Author N /2/2002 Rosen Kenneth REL 60 Author N /15/2002 Rosen Kenneth KX 30 Author N /24/2002 Rosen Kenneth KX 25 Author N /20/2002 Rosen Kenneth KX 10 Author N /2/2002 Rosen Kenneth KX 40 Author Y /28/2002 Rosen Kenneth REL 60 Author M /4/2002 Ryan Michel REL 120 Investigator N /7/2002 Ryan Michel KX 50 Investigator N /9/2002 Ryan Michel RECRUIT 20 Investigator N /2/2002 Ryan Michel RECRUIT 40 Investigator N /15/2002 Ryan Michel REL 60 Investigator N /24/2002 Ryan Michel REL 30 Investigator N /20/2002 Ryan Michel RECRUIT 25 Investigator N /2/2002 Ryan Michel KX 10 Investigator N /28/2002 Ryan Michel RECRUIT 40 Investigator N /4/2002 Saxton Raymond REL 25 Formulary N Supporter
Formulary /7/2002 Saxton Raymond RECRUIT 10 N Supporter
Formulary /8/2002 Saxton Raymond RECRUIT 40 N
Supporter
Formulary /2/2002 Saxton Raymond KX 60 N Supporter
Formulary /10/2002 Saxton Raymond REL 120 N Supporter
Formulary /3/2002 Saxton Raymond NET 50 N Supporter
Formulary /9/2002 Saxton Raymond REL 20 N
Supporter
Formulary /20/2002 Saxton Raymond KX 40 Supporter N
Formulary /28/2002 Saxton Raymond KX 60 N Supporter /5/2002 Schmitt M REL 20 Prescriber N /14/2002 Schmitt M KX 40 Prescriber N /19/2002 Schmitt M REL 60 Prescriber N /18/2002 Schmitt M NET 30 Prescriber N /24/2002 Schmitt M REL 25 Prescriber Y /2/2002 Schmitt M REL 10 Prescriber M /5/2002 Stewart Melissa REL 30 Prescriber N /19/2002 Stewart Melissa REL 25 Prescriber N /18/2002 Stewart Melissa REL 10 Prescriber N /24/2002 Stewart Melissa KX 40 Prescriber N /2/2002 Stewart Melissa KX 60 Prescriber N /28/2002 Stewart Melissa KX 120 Prescriber N /4/2002 Thompson Tausee REL 40 Prescriber N /7/2002 Thompson Tausee REL 60 Prescriber N /8/2002 Thompson Tausee KX 120 Prescriber N /2/2002 Thompson Tausee KX 50 Prescriber N /10/2002 Thompson Tausee KX 20 Prescriber N 6/8/2002 Thompson Tausee REL 40 Prescriber N
7/9/2002 Thompson Tausee NET 60 Prescriber N
8/2/2002 Thompson Tausee REL 30 Prescriber N
8/28/2002 Thompson Tausee REL 25 Prescriber N
1/4/2002 Ulshafer Bart REL 60 Speaker N
2/7/2002 Ulshafer Bart KX 120 Speaker N
3/8/2002 Ulshafer Bart RECRUIT 50 Speaker N
4/2/2002 Ulshafer Bart KX 20 Speaker N
5/10/2002 Ulshafer Bart REL 40 Speaker N
6/3/2002 Ulshafer Bart COACH 60 Speaker N
7/9/2002 Ulshafer Bart KX 30 Speaker N
7/20/2002 Ulshafer Bart REL 25 Speaker Y
8/28/2002 Ulshafer Bart REL 10 Speaker M
1/4/2002 Vogel Emmanuel REL 60 Speaker N
2/14/2002 Vogel Emmanuel KX 120 Speaker N
3/19/2002 Vogel Emmanuel RECRUIT 50 Speaker N
5/18/2002 Vogel Emmanuel RECRUIT 20 Speaker N
6/24/2002 Vogel Emmanuel REL 40 Speaker N
8/2/2002 Vogel Emmanuel REL 60 Speaker N
1/4/2002 Wellington William REL 60 Speaker N
2/14/2002 Wellington William RECRUIT 30 Speaker N
3/19/2002 Wellington William RECRUIT 25 Speaker N
5/18/2002 Wellington William KX 10 Speaker N
6/24/2002 Wellington William REL 40 Speaker N
8/2/2002 Wellington William REL 60 Speaker N Table 10.
Statistical Analysis for Multiple Targeted Business Outcomes
Outcome
Business Outcome Achieved Data Average StdDevP
Author Avei rage oi BUSSOL
Author Avei rage oi MEDSOL
Author Ave ri age oi KX 4.20 0.98
Author Avei rage o: RECRUIT 1.00 0.00
Author Avei rage o- COACH
Author Ave ri age o: REL 2.00 0.00
Author Ave ri age o- NET
Author Avei rage σ ASSESS
Author Author Avei rage of BUSSOL
Author Author Avei rage of MEDSOL
Author Author Avei rage of KX 4.20 0.98
Author Author Avei rage of RECRUIT 1.00 0.00
Author Author Avei rage of COACH
Author Author Avei rage of REL 2.00 0.00
Author Author Avei rage of NET
Author Author Avei rage of ASSESS
Consultant Average of BUSSOL
Consultant Average of MEDSOL
Consultant Average of KX 2.40 0.49
Consultant Average of RECRUIT 2.60 0.49
Consultant Average of COACH
Consultant Average of REL 4.00 0.00
Consultant Average of NET
Consultant Average of ASSESS
Consultant Average of BUSSOL
Consultant Average of MEDSOL
Consultant Average of KX 2.00 0.00 Consultant Average of RECRUIT 2.00 0.00 Consultant Average of COACH Consultant Average of REL 2.00 0.00 Consultant Average of NET Consultant Average of ASS ESS Consultant 2 Consultant Average of BUSSOL Consultant 2 Consultant Average of MEDSOL Consultant 2 Consultant Average of KX 2.25 0.43 NS Consultant 2 Consultant Average of RECRUIT 2.38 0.48 S - Consultant 2 Consultant Average of COACH Consultant 2 Consultant Average of REL 3.25 0.97 Consultant 2 Consultant Average of NET Consultant 2 Consultant Average of ASSESS Formulary Supporter 0 Average of BUSSOL Formulary Supporter 0 Average of MEDSOL Formulary Supporter 0 Average of KX 3.00 0.00 Formulary Supporter 0 Average of RECRUIT 2.00 0.00 Formulary Supporter 0 Average of COACH Formulary Supporter 0 Average of REL 3.50 0.50 Formulary Supporter 0 Average of NET 1.00 0.00 Formulary Supporter 0 Average of ASSESS Formulary Supporter 1 Average of BUSSOL Formulary Supporter 1 Average of MEDSOL Formulary Supporter 1 Average of KX 3.00 0.00 Formulary Supporter 1 Average of RECRUIT 1.00 0.00 Formulary Supporter 1 Average of COACH Formulary Supporter 1 Average of REL 2.00 0.00 Formulary Supporter 1 Average of NET Formulary Supporter 1 Average of ASSESS
Formulary Supporter Average of
Formulary Supportei 2 BUSSOL Formulary Supportei 2 Formulary Supporter Average of MEDSOL Formulary Supportei 2 Formulary Supporter Average of KX 3.00 0.00 NS
Formulary Supporter Average of
Formulary Supportei 2 RECRUIT 1.67 0.47 S -
Formulary Supporter Average of
Formulary Supportei 2 COACH
Formulary Supporter Average of
Formulary Supportei 2 REL 3.00 0.82 S -
Formulary Supporter Average of
Formulary Supportei 2 NET 1.00 0.00 S -
Formulary Supporter Average of
Formulary Supportei 2 ASSESS
Investigator 0 Average of BUSSOL
Investigator 0 Average of MEDSOL
Investigator 0 Average of KX 2.00 0.00
Investigator 0 Average of RECRUIT 4.00 0.00
Investigator 0 Average of COACH
Investigator 0 Average of REL 3.00 0.00
Investigator 0 Average of NET
Investigator 0 Average of ASSESS
Investigator Average of BUSSOL
Investigator Average of MEDSOL
Investigator Average of KX 1.00 0.00
Investigator Average of RECRUIT 1.00 0.00
Investigator Average of COACH
Investigator Average of REL 3.00 0.00
Investigator Average of NET 1.00 0.00 Investigator 1 Average of ASSESS 1.00 0.00
Investigator 2 Investigator Average of BUSSOL
Investigator 2 Investigator Average of MEDSOL
Investigator 2 Investigator Average of KX 1.60 0.49 S -
Investigator 2 Investigator Average of RECRUIT 2.80 1.47 S -
Investigator 2 Investigator Average of COACH
Investigator 2 Investigator Average of REL 3.00 0.00 NS
Investigator 2 Investigator Average of NET 1.00 0.00 S +
Investigator 2 Investigator Average of ASSESS 1.00 0.00 S +
Prescriber 0 Average of BUSSOL
Prescriber 0 Average of MEDSOL
Prescriber 0 Average of KX 3.00 0.00
Prescriber 0 Average of RECRUIT
Prescriber 0 Average of COACH
Prescriber 0 Average of REL 4.00 1.00
Prescriber 0 Average of NET 1.00 0.00
Prescriber 0 Average of ASSESS
Prescriber Average of BUSSOL
Prescriber Average of MEDSOL 1.00 0.00
Prescriber Average of KX 1.57 0.90
Prescriber Average of RECRUIT
Prescriber Average of COACH
Prescriber Average of REL 3.00 0.00
Prescriber Average of NET 1.00 0.00
Prescriber Average of ASSESS
Prescriber 2 Prescriber Average of BUSSOL
Prescriber 2 Prescriber Average of MEDSOL 1.00 0.00 S +
Prescriber 2 Prescriber Average of KX 1.89 0.99 S -
Prescriber 2 Prescriber Average of RECRUIT
Prescriber 2 Prescriber Average of COACH
Prescriber 2 Prescriber Average of REL 3.22 0.63 NS
Prescriber 2 Prescriber Average of NET 1.00 0.00 NS
Prescriber 2 Prescriber Average of ASSESS
Speaker 0 Average of BUSSOL
Speaker 0 Average of MEDSOL
Speaker 0 Average of KX 1.00 0.00
Speaker 0 Average of RECRUIT 2.00 0.00
Speaker 0 Average of COACH
Speaker 0 Average of REL 2.83 0.37
Speaker 0 Average of NET
Speaker 0 Average of ASSESS
Speaker Average of BUSSOL
Speaker Average of MEDSOL
Speaker Average of KX 3.00 0.00
Speaker Average of RECRUIT 1.00 0.00
Speaker Average of COACH 1.00 0.00
Speaker Average of REL 3.00 0.00
Speaker Average of NET
Speaker Average of ASSESS
Speaker 2 Speaker Average of BUSSOL
Speaker 2 Speaker Average of MEDSOL
Speaker 2 Speaker Average of KX 1.67 0.94 S +
Speaker 2 Speaker Average of RECRUIT 1.67 0.47 s -
Speaker 2 Speaker Average of COACH 1.00 0.00 s +
Speaker 2 Speaker Average of REL 2.89 0.31 NS
Speaker 2 Speaker Average of NET Speaker Speaker Average of ASSESS Total Average of BUSSOL Total Average of MEDSOL 1.00 0.00 Total Average of KX 2.26 1.15 Total Average of RECRUIT 1.93 0.93 Total Average of COACH 1.00 0.00 Total Average of REL 2.95 0.71 Total Average of NET 1.00 0.00 Total Average of ASSESS 1.00 0.00
For Outcome Achieved: No = 0 Yes = 1 Combined Data = 2
Significance (significant effect defined as the difference between the means is greater than the sum of the combined StdDevPs; if the StdDevP = 0, then use the Combined StdDevP instead):
NS = Non-Significant
S - = Significant Negative Result (may be confounded)
S + = Significant Positive Result
Survey Analysis Another source of performance information is the use of a survey designed to evaluate customer perception of the value of the MSL team. The survey methodology of the present invention measures physician perception along multiple dimensions, allowing the results to be used in operational management, as well as an indicator of the MSL team's progress over time. The data from the surveys, in combination with the quantitative activity data, is useful in identifying adjustments needed to optimize MSL team size, structure, and strategy. The survey method incorporates questions that allow for the identification of the most valued MSL activities. The activities most valued by the targeted customer are likely to be the most effective activities for increasing brand advocacy. Survey Architecture The survey method is a tool for measuring brand advocacy among targeted MTLs and the perceived quality and utility of the MSL role. Further, this method is used to measure brand advocacy and the perceived value of the MSL organization within the MSL customer universe. The results obtained from the MSL customer universe can then be compared to the pharmaceutical company's overall customer universe to assess the value added to pharmaceutical company by the MSL organization. The MSL customer universe is defined by the collective Targeted Customer Lists (TCL) for all MSLs of the company. Although multiple attributes are considered for the inclusion of a physician in a TCL, they can generally be considered MTLs.
Specifically, this survey method is designed to obtain and integrate multidimensional physician perception data into a quantitative index that is a relevant predictor of physician perceptions. The index integrates the perception dimensions of customer satisfaction, product value, MSL value, and customer service into a quantitative value. The sub-group of physicians that respond "very satisfied" to all perception dimensions under a categorical scale are labeled Brand Advocates. The positive effects of strong brand advocacy on a company's commercial success are a well-established tenet in marketing. Thus, the index provides a quantitative measure of a MSL organization's contribution to its parent company's commercial success. Since the questions are categorized according to MSL activity type, the index can be used as a business metric to assess organizational performance and identify areas in need of improvement.
The index is used as a rating of the relative perceived importance of categories of MSL activities. These categories are: MSL-Physician Interactions, Educational Funding, and Knowledge Exchange. This ranking function allows the index to be used in tactical business planning.
Survey Methodologies Depending upon resources and/or survey methodologies utilized, all TCL physicians can be surveyed (mailed/paper-based surveys) or a random sample of MSL TCL physicians can be surveyed (telephone surveys). Each survey methodology has its advantages and disadvantages (inconvenience of timing of the call, low return rate, etc.). Given an estimated 5% return rate for a mailed survey, this data gathering methodology will provide a sufficient number of evaluable respondents, provided the customer universe is not unusually small (less than 500 targeted customers). Since most MSL groups interact with more than 500 physicians, even if the return rate is lower than 5%, the mailed survey methodology may still be the most cost-effective and provide a sufficient number of respondents upon which to base the analysis of the data.
The questions comprising the survey are designed to assess satisfaction for each of the categories of MSL activities, organized into perception dimensions of Customer Satisfaction (C), Product Value (P), MSL Value (M), and Customer Service (S), and the answers are categorized according to: Very Satisfied (1.00), Satisfied (0.75), Neutral (0.50), and Dissatisfied (0.00); or Strongly Disagree (0.00), Disagree (.50), Agree (.75), Strongly Agree (1.00), depending upon the context of the question.
The mean score from all respondents on all perception dimensions comprises the index converted to a decimal. Multiple sub-analyses are performed according to the way the questions are categorized. The questions are preferably designed to fit into each of two categories: MSL Activity Type and Customer Perception Dimension. The questions also focus on attributes that can be acted upon by the MSL organization.
Below are listed the exemplary questions categorized according to MSL Activity Type and to their relationship to the identified perception dimension, represented as C, P, M or S as discussed above. In addition, a corresponding response value has been added. Example 6:
MSL-Physician Interactions Questions Question Perce tion Dimension Response Data
Figure imgf000043_0001
Figure imgf000044_0001
Educational Funding Questions
Figure imgf000044_0002
Knowledge Exchange Questions Question Perception Dimension Res onse Data
Figure imgf000044_0003
Product Satisfaction Questions
Figure imgf000044_0004
Figure imgf000045_0001
Analyses
The index is used in a number of different analyses, mostly differentiated by predefined criteria for categorizing questions and categorization of respondents based on overall index score. For example, the mean index sub-score for each of the MSL Activity Type categories may be used to identify areas of excellence as well as areas in need of improvement. These analyses may be driven down to the level of an individual question from which a specific activity can be targeted and assessed.
Using the example above, the average score of all of the responses is 0.64, obtained by taking the total value of all responses 14.75 and dividing by the number of questions 23. This illustrates the customers evaluation of all the services provided in the example is between neutral (0.5) and satisfied (0.75).
Further, each activity may be evaluated to find the strengths and weaknesses of the MSL. Again using the example above, the average score for product satisfaction is 0.85 confirming a high approval rating. Conversely, the average score of MSL-Physician
Interactions is 0.39 illustrating a low approval rating. Moreover, the score may be based on the perception dimension of customer satisfaction. For example, all of the perception dimensions combined will equal 0.64 as calculated for the MSL activities above. However, the score for customer satisfaction is 0.75 corresponding to a satisfactory rating. This survey method and feedback is used to improve and modify the activities of the MSL and to increase customer approval and efficiency of the MSL. Specifically, the survey results may be used to modify other components of the method to obtain the desired business goal of the sponsor company. Eventually, by continuous cyclic repetition of the method, the average score of the entire survey and of particular activity and perception groups will rise to near the 1.0 "very satisfied" rating.
Value Provided
In order to perform analyses of the perceived value added by the MSL organization, the MSL customer universe can be subdivided into those physicians on whom only MSLs call and those physicians on which both MSLs and the company's traditional sales force call. Comparisons of survey scores and business outcomes (script volume and market share) can then be made between these groups and to the entire physician population in order to examine the relationship of index scores to increased brand advocacy. These measures can then be tracked over multiple assessments and the information used to allocate resources among the categories of MSL activities, change MSL practices, and improve the MSL organization's business model through the enabling of continuous business improvements.
The system of the present invention permits the user to normalize data to headcount for trend analyses since the anticipated sharp increase in recorded activities resulting from addition of new MSLs may make projections inaccurate. The absolute numbers will also be available, enabling senior management to determine their ROI in the MSL team. Effective implementation of MSL team activities will facilitate the appropriate use of the sponsor company's products. The above-described business system and methods provides the information needed to maximize effectiveness of the MSL team. Business Management Tools/Scorecards Returning to the example in the execution phase of Dr. John Know, a review of the activities and time spent with MTL Adams may illustrate the needed activities and time to achieve the business outcome of investigator with MTL Philbin. Thus, a feedback system is established to guide the modification of the activities and time spent in the "subsequent" planning phase with any MTL to obtain the desired business outcome. This method can be applied to any objective discussed above in the attribute system to obtain the desired business outcomes, i.e. more publications, presentations, investigation or higher amount of prescriptions written, depending on the sponsor company's objective.
Further, as discussed above the time/capacity model can be modified based on the information obtained performing the attribute and CRM assessment. For example, the MSLs may be encouraged to input their activities into the CRM tool on a weekly basis (e.g., by Friday 5 PM Pacific Time), and strongly encouraged to input their activities more frequently (2 times per week). In addition to the regular weekly reporting, it is also desirable to input activities into the CRM on the last working day of the reporting period (the regular weekly input of activities can substitute for this if performed on the last business day of the reporting period).
Distributed Customer Relation Management System
With principal reference to Figure 4, a preferred distributed customer relation management system for MSL data communications is shown and designated generally 45. The constituent systems and components of customer relation management system 45 will first be discussed, followed by a discussion of some sample communication schemes between the constituent systems and components.
Distributed customer relation management system 45 preferably includes a master computer system 47, comprising a memory system 47a, a processing system 47b and a network interface system 47c. Some embodiments of customer relation management system 45 includes a plurality of MSL computer systems designated generally as CS and designated specifically as CSi, CS2, CS3, CS4, ..., ..., and CSn. MSL computer system CSi is shown to include a notebook computer, MSL computer systems CS2 is shown to include a portable digital assistant (e.g. Tablet PC, etc.), MSL computer system CS3 is shown to include a wireless telephone (e.g. Smart phone), MSL computer systems CS4a and CS4bare shown to include a plurality of desktop computers, and MSL computer system CSn is shown to include a generic workstation. The preferred embodiment of customer relation management system 45 is scalable and includes any desirable number of MSL computer systems CS. Although preferred embodiments of MSL computer systems CS include notebook computers, PDAs (e.g. Tablet PC), wireless telephones (e.g. Smart phone), desktop computers and client workstations, any suitable networked data entry system can be utilized. In some embodiments of MSL computer system CS, an on-board database is included in and/or with the MSL computer system CS.
Each of MSL computer systems CS are networked with master computer system 47 via compatible network architectures designated generally as NA and designated specifically as NAls NA2, NA3, NA4, ..., ..., and NAn. As shown, notebook MSL computer system CSi is networked with master computer system 47 via network architecture NAi, PDA MSL computer system CS2 is networked with master computer system 47 via network architecture NA2, wireless telephone MSL computer system CS3 is networked with master computer system 47 via network architecture NA3, desktop MSL computer systems CS4a and CS4b are networked with master computer system 47 via network architecture NA4, and client workstation MSL computer system CSn is networked with master computer system 47 via network architecture NAn. Any number of MSL computer systems CS can be simultaneously connected a single network architecture NA, so long as network architecture is suitably scaled for the number of connected MSL computer systems. Any suitable network architecture NA can be utilized so long as it is compatible with both master computer system 47 and all MSL computer systems CS networked to master computer system 47 therewith. Each of network architectures NA comprises a system that transmits that data between master computer 47 and a corresponding MSL computer system CS. Network architectures can be wired (e.g. guided) and/or wireless (e.g. unguided) and include all constituent components, including as nonlimiting examples, client and server machines, the cables connecting them and all supporting hardware in between such as bridges, routers and switches. In wireless systems, network architectures NA preferably include antennas and towers and/or satellites. Each network architecture NA is governed by one or more data transmission protocols, including as nonlimiting examples, TCP/IP, SPX/IPX, GPRS, GSM, etc.
Customer relation management system 45 preferably also includes an alternative data flow channel 49, which networks master computer system and each MSL computer system CSi - CSn with each compatible MSL computer system CSi - CSn. In this respect, alternative data flow channel 49 provides a redundant back up for communication between any MSL computer system CS and master computer system 47. Alternative flow channel 49 comprises a common network architecture and common protocol for communication between each MSL computer system CS, each other MSL computer system CS, and master computer system 47. Alternative flow channel 49 comprises a plurality of gateway interfaces (not shown) in order to convert the data transmissions of any MSL computer system CS to the common protocol. Each primary communications channel can preferably be integrated with existing infrastructures.
Continuing with principal reference to Figure 4, the preferred communication scheme between the various systems and components will now be discussed. Memory system 47a preferably comprises a master customer relation management database with master data stored therein, the master data comprising at least one of desired business outcome attributes, activity attributes and preferred MTL data. Master data preferably comprises a compilation of all data required for MSL management activity and contains the modules and other code required to perform data analysis.
The master data is preferably defined and can be pushed through any of network architecmres NA to a MSL computer system CS networked thereto via network interface system 47c. Defined master data can include, for example, business objectives, desired business outcomes, activity attributes of MTL interaction activities and preferred MTLs to target for MSL interaction.
A distributed data component at any given MSL computer system CS preferably comprises a subset of the data specific to each MSL, such as MSL data at a database local to the given MSL computer system CS. The distributed data component at any given MSL computer system CS may also be an at least temporary access of the master data hosted at master computer system 47. Depending on the embodiment of the given MSL computer system CS, the given MSL computer system CS is able to interact with the master data, record and update data relevant to the MSLs activities and business objectives.
For the purpose of illustration and without limitation, examples of MLS date that can be captured at a MSL computer system CS include MTL interaction activities performed by the MSL and business outcomes achieved/not achieved. In some embodiments, an MLS computer system CS can conduct some analysis of data, including for example, correlating MTL interaction activity and business outcomes. However, analysis is preferably conducted at processing system 47b of master computer system 47. For the purpose of illustration and without limitation, examples of analyses that are preferably conducted at processing system 47b include correlating MTL interaction activity and business outcome data, evaluating business outcomes relative to MTL interaction activities, analyzing surveys of MTLs to determine MTL satisfaction of MSLs, and evaluating the impact of MTL interaction activities and business outcomes.
Network interface system preferably receives first MSL data from a first MSL computer system CS (e.g. MSL computer system CS4b) via a first network architecture NA (e.g. network architecture NA4) and receives second MSL data from a second MSL computer system CS (e.g. MSL computer system CS3) via a second network architecmre (e.g. network architecture NA ). Processing system 47b preferably analyzes the master data in conjunction with at least one of the first MSL data and the second MSL data to produce resultant data (e.g. the analysis).
All suitable methods and analyses disclosed herein may be conducted utilizing distributed customer relation management system 45. However, for the purpose of illustration and without limitation, a few examples will now be discussed.
In preferred embodiments of distributed customer relation management system 45, the master data includes a first attribute value for each of a plurality of MTLs, the master data includes a second attribute value for each of the plurality of MTLs, and the resultant data comprises a weighted score for each of the plurality of MTLs calculated by the processing system and based at least in part on the first attribute value, a first attribute weight, the second attribute value, and a second attribute weight. Processing system 47b preferably orders the MTLs in accordance with the weighted score of each of the plurality of MTLs. In some embodiments of distributed customer relation management system 45, master computer system 47 prioritizes and selects MTLs to be targeted for MSL interaction to achieve desired business outcomes. In some embodiments, processing system 47b defines a plurality of business outcome attributes corresponding to desired business outcomes, the first and/or second MSL data comprise an attribute value for each identified business outcome attribute for each of a plurality of individual MTLs, and processing system 47b assigns a relative weight to each of the business outcome attributes. ' Furthermore, the at least one of the business outcome attributes is preferably selected from the group consisting of a magnitude of clinical investigations, a magnitade of commercial potential, a frequency of publications, and a frequency of presentations. In some embodiments of distributed customer relation management system
45, processing system 47b manages customer interaction activities of MTLs with MSLs. Processing system 47b preferably identifies one or more desired business outcomes, processing system 47b preferably identifies at least one activity attributes of customer interaction activity to be performed by the MTL, the first and/or second MSL data preferably comprise data regarding customer interaction activity of the MSL for a predetermined time period, the first and/or second MSL data preferably comprise data regarding the business outcomes achieved or not achieved during the predetermined time period, and processing system 47b preferably correlates the customer interaction activity data and the business outcome data. Although this invention has been illustrated by specific embodiments, it is not intended that the invention be limited to these embodiments. It will be apparent to those skilled in the art that various changes and modifications may be made which clearly fall within the scope of the invention. The invention is intended to be protected broadly within the spirit and scope of the appended claims.

Claims

ClaimsWhat is claimed is:
1. A distributed customer relation management system for MSL data communications, comprising: a master computer system, comprising: a memory system having a master customer relation management database with master data stored therein, said master data comprising at least one of desired business outcome attributes, activity attributes and preferred MTL data; a network interface system for receiving first MSL data from a first MSL computer system via a first network architecture and for receiving second MSL data from a second MSL computer system via a second network architecture; and a processing system for analyzing the master data in conjunction with at least one of the first MSL data and the second MSL data to produce resultant data.
2. The distributed customer relation management system of claim 1, wherein at least one of the first MSL computer system and the second MSL computer system comprise at least one of a notebook computer, a desktop computer, a personal digital assistant, a wireless telephone, a workstation and another network-compatible device.
3. The distributed customer relation management system of claim 1 , wherein the master computer system is adapted to communicate the resultant data to the first MSL computer system via the first network architecture and to the second MSL computer system via the second network architecture.
4. The distributed customer relation management system of claim 1 , wherein the master computer system comprises a centralized master computer system.
5. The distributed customer relation management system of claim 1 , wherein the network interface system comprises at least one network interface device, wherein the processing system comprises at least one electronic controller, and wherein the memory system comprises at least one at least temporary memory device.
6. The distributed customer relation management system of claim 1, wherein at least one of the first network architectare and the second network architecture comprise at least one of a wired network and a wireless network.
7. The distributed customer relation management system of claim 1 , wherein at least one of the first network architectare and the second network architecture comprise a local networking network architecture.
8. The distributed customer relation management system of claim 1 , wherein at least one of the first MSL data and the second MSL data comprise MSL-captared data.
9. The distributed customer relation management system of claim 1, wherein at least one of the first MSL data and the second MSL data comprise at least one of MTL interaction activity data and business outcome achieved data.
10. The distributed customer relation management system of claim 1 , wherein the master data includes a first attribute value for each of a plurality of MTLs, wherein the master data includes a second attribute value for each of the plurality of MTLs, and wherein the resultant data comprises a weighted score for each of the plurality of MTLs calculated by the processing system and based at least in part on the first attribute value, a first attribute weight, the second attribute value, and a second attribute weight.
11. The distributed customer relation management system of claim 10, wherein the processing system orders the MTLs in accordance with the weighted score of each of the plurality of MTLs.
12. The distributed customer relation management system of claim 1, wherein the master computer system prioritizes and selects MTLs to be targeted for MSL interaction to achieve desired business outcomes.
13. The distributed customer relation management system of claim 12, wherein the processing system defines a plurality of business outcome attributes corresponding to desired business outcomes, wherein at least one of the first MSL data and the second MSL data comprise an attribute value for each identified business outcome attribute for each of a plurality of individual MTLs, wherein the processing system assigns a relative weight to each of the business outcome attributes.
14. The distributed customer relation management system of claim 13, wherein at least one of the business outcome attributes is selected from the group consisting of a magnitade of clinical investigations, a magnitade of commercial potential, a frequency of publications, and a frequency of presentations.
15. The distributed customer relation management system of claim 1, wherein the processing system manages customer interaction activities of MTLs with MSLs.
16. The distributed customer relation management system of claim 15, wherein the processing system identifies one or more desired business outcomes, wherein the processing system identifies at least one activity attributes of customer interaction activity to be performed by the MTL, wherein at least one of the first MSL data and the second MSL data comprise data regarding customer interaction activity of the MSL for a predetermined time period, wherein at least one of the first MSL data and the second MSL data comprise data regarding the business outcomes achieved or not achieved during the predetermined time period, and wherein the processing system correlates the customer interaction activity data and the business outcome data.
17. The distributed customer relation management system of claim 1, comprising at least one of the first MSL computer system and the second MSL computer system.
18. The distributed customer relation management system of claim 17 , wherein at least one of the first MSL data and the second MSL data comprise MSL-captared data.
19. The distributed customer relation management system of claim 17, wherein at least one of the first MSL data and the second MSL data comprise at least one of MTL interaction activity data and business outcome achieved data.
20. The distributed customer relation management system of claim 17, wherein the master data includes a first attribute value for each of a plurality of MTLs, wherein the master data includes a second attribute value for each of the plurality of MTLs, wherein the resultant data comprises a weighted score for each of the plurality of MTLs calculated by the processing system and based at least in part on the first attribute value, a first attribute weight, the second attribute value, and a second attribute weight.
21. The distributed customer relation management system of claim 20, wherein the processing system orders the MTLs in accordance with the weighted score of each of the plurality of MTLs.
22. The distributed customer relation management system of claim 17, wherein the master computer system prioritizes and selects MTLs to be targeted for MSL interaction to achieve desired business outcomes.
23. The distributed customer relation management system of claim 22, wherein the processing system defines a plurality of business outcome attributes corresponding to desired business outcomes, wherein at least one of the first MSL data and the second MSL data comprise an attribute value for each identified business outcome attribute for each of a plurality of individual MTLs, wherein the processing system assigns a relative weight to each of the business outcome attributes.
24. The distributed customer relation management system of claim 23, wherein at least one of the business outcome attributes is selected from the group consisting of a magnitude of clinical investigations, a magnitade of commercial potential, a frequency of publications, and a frequency of presentations.
25. The distributed customer relation management system of claim 17, wherein the processing system manages customer interaction activities of MTLs with MSLs.
26. The distributed customer relation management system of claim 25, wherein the processing system identifies one or more desired business outcomes, wherein the processing system identifies at least one activity attributes of customer interaction activity to be performed by the MTL, wherein at least one of the first MSL data and the second MSL data comprise data regarding customer interaction activity of the MSL for a predetermined time period, wherein at least one of the first MSL data and the second MSL data comprise data regarding the business outcomes achieved or not achieved during the predetermined time period, and wherein the processing system correlates the customer interaction activity data and the business outcome data.
27. A system for managing customer interaction activities of medical liaison personnel of a sponsor organization with health professional customers to achieve one or more desired business outcomes, the system comprising: a customer relation database; means for defining one or more activity attributes of customer interaction activity to be performed by the medical liaison personnel associated with the customer relation database; means for recording data regarding customer interaction activity of the medical liaison personnel for a predetermined time period into the customer relation database; means for recording data regarding the business outcomes achieved or not achieved during the predetermined time period into the customer relation database; and means for correlating the customer interaction activity data and the business outcome data.
28. The system of claim 27, wherein at least one of the desired business outcomes is an activity by the customer selected from the group consisting of publishing of a medical article, conducting a clinical investigation, attending a formulary meeting, speaking on a medical topic, and prescribing a pharmaceutical product to a predetermined level.
29. The system of claim 27, wherein at least one of the activity attributes is selected from the group consisting of facilitating the ability of a customer to utilize a sponsor product, improving a customer's disease management practice, exchanging scientific information with a customer, coaching a customer for a presentation, interacting with a customer on a social basis, facilitating interactions between customers, and investigating potential clinical investigation sites.
30. The system of claim 27, wherein the recorded customer interaction data corresponds to time, frequency, duration or sequence data for customer interaction activities.
31. A method of facilitating a desired business outcome of a sponsor organization, comprising: identifying a past business interaction having a past business outcome at least similar to the desired business outcome; and identifying a plurality of customer-relations values each corresponding to one of a plurality of customer-relations attributes associated with the past business interaction.
32. The method of claim 31, comprising defining each of the customer-relations' attributes as one of interaction date, name, activity type, duration of interaction, and business outcome type.
33. The method of claim 31 , wherein identifying the past business interaction comprises querying a database with at least one of MSL, MTL, business outcome type, and activity type.
34. The method of claim 31 , wherein identifying the past business interaction comprises identifying a past interaction having an outcome value representative of at least one of a favorable past business outcome and an unfavorable past business outcome, the past business interaction having a past business outcome equal to the desired business outcome.
35. The method of claim 31 , wherein identifying the past business interaction comprises identifying an interaction having an outcome value representative of a level of favorability within a range.
36. The method of claim 31 , comprising communicating the plurality of customer-relations values to at least one of a user and a computer program.
37. The method of claim 31 , comprising documenting the past business outcome and at least one of the plurality of customer-relations values and the information represented by the plurality of customer-relations values.
38. The method of claim 31 , comprising communicating information represented by the plurality of customer-relations values to at least one of a user and a computer program.
39. The method of claim 38, comprising defining each of the customer-relations attributes as one of interaction date, name, activity type, duration of interaction, and business outcome type.
40. The method of claim 38, wherein identifying the past business interaction comprises querying a database with at least one of MSL, MTL, business outcome type, and activity type.
41. The method of claim 38, wherein identifying the past business interaction comprises identifying a past interaction having an outcome value representative of at least one of a favorable past business outcome and an unfavorable past business outcome, the past business interaction having a past business outcome equal to the desired business outcome.
42. The method of claim 38, wherein identifying the past business interaction comprises identifying an interaction having an outcome value representative of a level of favorability within a range.
43. The method of claim 38, comprising communicating the plurality of customer-relations values to at least one of a user and a computer program.
44. The method of claim 38, comprising communicating information represented by the plurality of customer-relations values to at least one of a user and a computer program.
45. The method of claim 38, comprising documenting the past business outcome and at least one of the plurality of customer-relations values and the information represented by the plurality of customer-relations values.
46. A method for assessing health professional satisfaction with medical liaison and sponsor organization performance, the method comprising: defining one or more medical liaison attributes; defining one or more health professional perception attributes; presenting a survey to a plurality of health professional having one or more survey questions associated with the defined medical liaison attributes and the defined perception attributes; and recording the survey responses of responding health professionals.
47. The method of claim 46, comprising assigning a relative satisfaction value to the survey responses
48. The method of claim 46, comprising evaluating the survey responses relative to the medical liaison attributes, health professional perception attributes, or a combination thereof.
49. The method of claim 48, comprising adjusting plans for future medical liaison activity based on the evaluated survey responses.
50. The method of claim 46, wherein at least one of the medical liaison performance attributes is selected from the group consisting of medical liaison-health professional interaction, educational funding and knowledge exchange, and product satisfaction.
51. The method of claim 46, wherein at least one of the health professional perception attributes is selected from the group consisting of customer satisfaction, product value, medical liaison value and customer service.
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