Leveraging CMS Open Payments Data to Identify Channel Preferences and Gather Competitive Intelligence, Thereby Improving HCP Targeting

Leveraging CMS Open Payments Data to Identify Channel Preferences and Gather Competitive Intelligence, Thereby Improving HCP Targeting
Rahul Anand, Engagement Manager, Mu Sigma; Duggan Collier, Deputy Director, Commercial Analytics, Bayer; Yan Jiang, Director, Business Insights, Rare Diseases, Bayer; Janardhan Vellore, Director, Commercial Analytics, Bayer
Abstract:
Organizations continuously explore means to gain competitive intelligence to understand market potential or improve physician targeting. Some of the predominant ways to gain this advantage has been to leverage traditional third-party data sources such as IMS, Symphony Health, and market research among others. Since 2014, the Centers for Medicare and Medicaid Services has published transparency data which captures transfer of value to physicians from pharmaceutical companies through different interaction channels. This paper discusses examples of how a pharmaceutical company has leveraged this data to identify new targets and further improve targeting by identifying channel preferences for existing and new targets.
 
Keywords: CMS open payments, Physician targeting, Competitive intelligence, Physician interactions, Channel preferences

Introduction
Today, while competition is getting tougher with new product launches and pharmaceutical organizations are struggling to meet their goals, such organizations are exploring different ways to gather competitive intelligence. The objective for gaining this intelligence is to better understand market potential, improve HCP targeting, and potentially determine competitive loyalists. Some of the predominant ways in which organizations try to achieve these objectives are:
  1. Leveraging traditional third-party data such as IMS, HMS and others
  2. Measuring the share of voice of their own products and their competitor’s products through market research
  3. Identifying office accessibility or channel preferences of HCPs through licensed data sources such as AccessMonitorTM and AffinityMonitorTM
Organizations leverage this information to either refine/expand their target universe or make targeting more personalized, which aids sales and marketing teams. However, there are challenges that the approaches mentioned above pose—content participation limits and associated cost. (Figure 1)

Figure 1: Currently Available Data Sources for Targeting
Targeting Data Sources
Pros
Cons
Commercial Claims Data (i.e. QuintilesIMS1)
  • Rich patient and provider transaction data
  • Ideal for determining treatment pathways and provider behaviors
  • Visibility to competitive products
  • Incomplete geographic coverage
  • Not projected. Complete only for reporting services
  • Associated cost
  • Projected Prescription Data (i.e. QuintilesIMS1, Symphony Health2)
    • Rx projected to national coverage
    • Provides most complete prescription coverage
    • Visibility to competitive products
    • Projection limitations
    • Effectively limited to Retail/Mail Order channels
    • Specialty pharmacy distribution generally not available
    • Associated cost
    CMS Claims Data (i.e. LDS files3,4)
    • Complete Medicare A, B, D inpatient and outpatient claims regardless of therapy class
    • Low cost
    • Data Latency – 9-12 months after close of period
    • Cumbersome process for acquisition
    CMS NPI Registry5
    • Complete national listing of practitioner and facility healthcare providers
    • No utilization metric
    • Providers limited to those receiving/ requesting reimbursement


    This paper will review examples of how sales and marketing teams can use publicly-available CMS data to identify new targets and gather competitive intelligence. It also showcases how this data can be leveraged to improve HCP targeting by identifying channel preferences for existing and potential new targets.
    Overview of CMS Open Payments Data
    As a result of the Physician Payments Sunshine Act and collection of relevant data associated with the act, additional data resources became available for healthcare analytics to inform targeting decisions. The Act was passed in 2010 to increase transparency of financial relationships between health care providers and pharmaceutical manufacturers. The Centers for Medicare and Medicaid Services (CMS) is managing the compliance data collection activity with the first data published in 2014 for H2’13. Since then, CMS has published transparency data6 for FY 2014 and 2015 which is publicly available. It captures transfer of value to physicians through various Nature of Payments7 (Consulting fees, Honoraria, Gifts, Entertainment, Food and beverage, etc.). CMS has mandated reporting of ToV greater than $10 per activity or greater than $100 per year. Hence capture rate for low-expense channels can sometimes be as low as 60%. The data is reported at Company – Brand – HCP – Nature of Payment1 level.
    Approach
    The first stage of the analysis is to create a database which links CMS Open Payments data6 with internal sales/call activity data. This helps in identifying overlap with the existing target list. Certain keys (combination of First Name, Last Name and Zip) can be used to pull NPI#8 on the CMS data6 which is then mapped to internal sales/call activity data through the data mastering process. Once the database is created, the market and competitors are identified. The next step is to establish business rules to map “Nature of Payments”6 to “Contact Channels” which defines the channel to which the transfer of value is made (Figure 2). A combination of Nature of Payments7 across a certain time period is defined as the Contact channel.

    Figure 2: Defining Contact Channel
    Time Period Nature of Payment(s) Contact Channel
    One Date Consulting Fee Consulting/Ad-Board
    One Date Honararia R&D—Product Development/Improvements
    One Date Research R&D—Clinical Trial
    One Date F&B (<$25) In-Service Activities/Others*
    Date+-1 F&B (>$25) Speaker Program—Attendee
    Date+-1 F&B + T&L —Compensation for services Speaker Program—Speaker
    Date+-1 T&L Speaker Program Training
    Date+-1 F&B + T&L Speaker Program Training
    One Date Education Textbook/Educational Materials


    At this stage, validation of the business rules becomes critical to get buy-in from sales/marketing teams. Scenarios with varying Nature of Payments7 and time period definition are generated to arrive at an acceptable capture rate (Figure 3).
    Figure 3: Validation Table
    Metric Actual Analytical Data* % Capture Rate
    # Speaker Programs 358 269 75%
    # Unique Speakers 86 68 80%
    Calls 48,761 4,019
    (includes ToV for In-Service Activities/Others)
    8%
    (Low capture rate for calls is attributed to >$10 reporting cut-off of CMS data)

    * Based on business rules

    Limitations of the Approach
    The data mastering process might not accurately link physicians in CMS Open Payments data2 to internal sales/call activity data due to lack of, or too constraining, mastering rules. This will lead to low capture rates during validation or capturing false positive physicians.

    Additionally, the combination of the nature of the payments to define Contact Channels might vary across organizations. It should be analyzed for different scenarios to arrive at an acceptable capture rate in partnership with the sales teams.
    Case Study 1: Gathering Competitive Intelligence
    For sales and marketing teams, having a deep understanding of competitors’ level of interactions with HCPs across different channels of influence gives a huge competitive advantage. In this case study, the analysis identified a physician pool and measured level of contact through different channels (Figure 4).
    Figure 4: HCPs Contacted and Touchpoints by Product and Contact Channels

    Comparing ratio of #touchpoint to #HCPs helped identify opportunity to boost targeting effort. For example, Product A has lower ration than Product C, which means that there is an opportunity to increase targeting effort.

    Distribution across states was also studied to understand anomalies in resource allocation (Figure 5). It helped the teams optimize its resources across channels by providing visibility to the targeting strategy deployed by competitors.
    Figure 5: Distribution of Touchpoints (Including All Contact Channels) by States

    Comparison of Company 1’s touchpoints against other companies’ touchpoints helped identify anomaly in targeting and potential to improve and optimize resource allocation. For example, Company 1 has the potential to reallocate a high concentration of touchpoint in Florida to California, where other companies have higher concentration.

    Case Study 2: Identifying New Targets for Call Plan
    For 2016, the sales team of the cardiopulmonary business unit of the company wanted to expand its sales force target list. The existing call plan was created using latest claims and internal prescription data. This posed a challenge to the analytics team to explore other data sources.

    In this case study, CMS Open Payments data6 was explored to identify new potential targets for the sales force. The HCP universe for the market (including the company and defined competitors) was established as per the business rules identified earlier. Their level of contact across commercial channels (Speaker Program – Speaker, Speaker Program – Attendee/Trainings, Speaker Training and In-Service Activities/Others) was studied to funnel down to HCPs who could be targeted by reps. This was overlaid on the existing call plan to exclude HCPs who were already present in the plan. HCPs who participated in competitor clinical trials, or were called historically, were also excluded from the analysis (Figure 6).
    Figure 6: Flow to Identify Potential Target List


    The existing sales force target list and their planned annual touchpoints were validated. 3,108 additional physicians were identified for the cardiopulmonary sales and marketing team. A recommendation was made to add 450 of them as potential targets (8% of the existing targets) to the call plan. Since there were no historical calls made to additional targets to measure promotional responsiveness, tier status was assigned based on comparison of targets per HCP/claims per HCP against current target list. (Figure 7)
    Figure 7: Targets in the Current Target List
    Call Class # HCPs # Touchpoints Touchpoint per HCP Claims per HCP Patients per HCP
    Tier 1 842 6,496 7.7 13 1.5
    Tier 2 925 6,122 6.6 6 0.8
    Tier 3 315 1,716 5.4 1 0.3
    TOTAL 2,082 14,334 6.9 80 10


    Decile
    (Touch Points)
    # HCPs # Touchpoints Touchpoint per HCP Claims per HCP Patients per HCP
    10 50 618 12 2.6 0.48

    Recommended additional 450 targets
    9 91 621 7 2.2 0.36
    8 132 628 5 2.4 0.42
    7 176 621 4 1.9 0.41
    6 208 624 3 1.6 0.33
    5 293 622 2 1.2 0.26
    4 311 622 2 1.3 0.26
    3 602 623 1 0.8 0.18
    2 622 622 1 0.9 0.19
    1 623 623 1 1.8 0.28
    Total 3,108 6,224 2 1.4 0.26

    * Only commercial channels (Speaker Program – Speaker, Speaker Program – Attendee/Trainings, Speaker Training and In-Service Activities/Others) are included for deciling

    Figure 8: Exploring the Data Shows the Potential to Solve a Variety of Interconnected Problems

    Conclusion
    This paper covered examples of how sales and marketing teams have used CMS data6 to improve HCP targeting by identifying channel preferences for existing and potential new targets. There is more untapped potential in the CMS Open Payments data6 to answer questions in sales and marketing space as represented in the universe of the problems (Figure 8). Over time, the richness and accuracy of data will further improve, providing companies the opportunity to monitor existing and newly acquired targets over time. Given that there is no cost associated with the data, if analytical rigor is appropriately applied, pharmaceutical companies can only derive more consumable insights, or at the least, directional insights. It’s still a win-win situation.
    About the Authors
    Rahul Anand, Engagement Manager, Mu Sigma has more than 5 years of experience in driving data-driven decision making in sales & marketing analytics for major pharmaceutical companies across multiple therapeutic areas.

    Duggan Collier, Deputy Director, Commercial Analytics, Bayer is an accomplished business analytics executive with 20+ years of proven experience supporting global pharmaceutical sales operations through data analytics, business analysis, business intelligence and reporting.

    Yan Jiang, Director, Business Insights, Rare Diseases, Bayer is an experienced market analytics, insight, and strategy professional with focus on commercialization of innovative biomedicine, medical devices, and pharmaceuticals for small biotechnology and large, multinational pharmaceutical companies.

    Janardhan Vellore, Director, Commercial Analytics, Bayer is an analytics leader experienced in advanced sales, marketing and payer analytics and commercial ROI analyses leading forecasting efforts, brand planning, driving productivity initiatives, to mention a few of his responsibilities for supporting specialty drugs across the various stages of the product lifecycle.
    References

    1 Quintiles. Empowering healthcare research with realworld data [Internet]. Available from: http://www.quintiles.com/ library/brochures/empowering-healthcare-research-with-realworld-data#pdf

    2 Symphony Health. PHAST Prescription Sales Sheet [Internet]. Available from: http://symphonyhealth.com/wpcontent/ uploads/2017/01/phast_prescription_sales_sheet.pdf

    3 Centers for Medicare & Medicaid Services. DUA – Limited Data Sets [Internet]. Available from: https://www.cms.gov/ Research-Statistics-Data-and-Systems/Computer-Data-and-Systems/Privacy/DUA_-_LDS.html

    4 Centers for Medicare & Medicaid Services. DUA – Requesting CMS’s Limited Data Set (LDS) Files [Internet]. Available from: https://www.cms.gov/Research-Statistics-Data-and-Systems/Files-for-Order/LimitedDataSets/downloads/ RevisedLDSInstructions.pdf

    5 Centers for Medicare & Medicaid Services. NPI Registry [Internet]. Available from: https://npiregistry.cms.hhs.gov/

    6 Centers for Medicare & Medicaid Services. Open Payments Data in Context [Internet]. Available from: https://www. cms.gov/OpenPayments/About/Open-Payments-Data-in-Context.html

    7 Centers for Medicare & Medicaid Services. Natures of Payment [Internet]. Available from: https://www.cms.gov/ OpenPayments/About/Natures-of-Payment.html

    8 Centers for Medicare & Medicaid Services. NPI Files download [Internet]. Available from: http://download.cms.gov/ nppes/NPI_Files.html