Lessons in Specialty Data Integration: Best Practices for Small and Emerging Biopharma Companies

download Lessons in Specialty Data Integration: Best Practices for Small and Emerging Biopharma Companies

of 12

  • date post

    18-Jul-2015
  • Category

    Healthcare

  • view

    133
  • download

    2

Embed Size (px)

Transcript of Lessons in Specialty Data Integration: Best Practices for Small and Emerging Biopharma Companies

  • LESSONS IN SPECIALTY DATA INTEGRATION 1

    Lessons in Specialty Data Integration: Best Practices for Small and Emerging Biopharma Companies

    WHITE PAPERSPECIALTY PHARMA DATA INTEGRATION

  • Sir Isaac Newton modestly said, If I have seen further than others, it is by standing upon the shoulders of giants. This same principlethat ones own success is founded on the work of those who have paved the wayis as applicable in specialty data integration as it is in the sciences. Large pharmaceutical companies have, for the most part, already dealt with the complexities of collecting, integrating and analyzing data on specialty products. Their experiences (both good and bad) are informative for any smaller and emerging companies that are beginning to deal with market data on their own specialty products. Here we share best practices for coping with the diversity and fragmentation that characterizes the data available on specialty pharmaceuticals.

  • LESSONS IN SPECIALTY DATA INTEGRATION 3

    A Growing Market, a Hunger for Data All indications are that the specialty biopharmaceutical market represents much of the industrys future. Today, the U.S. specialty drug market is valued at $106B and accounts for about 30 percent of the countrys pharmaceutical sales. The sector is growing at a rate of 18.8 percent (based on list price sales)outpacing traditional pharma products. Interest in specialty products will undoubtedly continue as 60 percent of all medications in the pipeline are specialty products, and spending in the sector is expected to quadruple by 2020. [ Figure 1 ]

    Because many specialty products are designated as orphan or ultra orphan drugs, companies data needs focus on tracking products used by very small patient populationsa job that may sound deceptively simple but is not at all easy. In reality, specialty products are distributed through so many channelsnot all of which are covered by syndicated data sources. And the need for precise measures is particularly acute because each prescription is very valuable. It is not uncommon for the annual cost of treatment for a single patient to reach $250K.

    BY JOHN GIANNOURIS

    [ FIGURE 1 ]

    U.S. SPECIALTY MARKET EXPECTED TO QUADRUPLE BY 2020ANTICIPATED OVERALL SPECIALTY SPEND

    2012 2016E 2020E

    $200.9B

    $200.9B

    $39.2B

    $200.9B

    $99.4B

    $47.9B

    $401.7B

    $192.2B

    $87.1B

    CVS Caremark projection (2013) based on internal data and forecasts; McKinsey MPACT6 Model, Kaiser Family Foundation: Medicare at a glance, 2012; National Health Expenditure Projections 2011-2021 CMS; Avalere 2013 Medicaid Opt-Out Model, Milliman Specialty Medication Benchmark Study developed using the 2010 and 2011 Truven Health MarketScan Research Database for a commercial population (used for 2012 estimate).

    PHARMACY

    MEDICAL

    Specialty spend is expected to more than quadruple by 2020.

    Nearly half of specialty spend occurs under the medical benefit, where it can be harder to see and manage.

  • 4

    To thrive in the specialty market, companies must almost always contract for incremental databeyond whats available from syndicated sourcesfrom a multitude of disparate sources. This requirement brings with it a set of challenges that could be daunting for smaller companies and result costly inefficiencies and lost business opportunities.

    Challenges in Understanding the Specialty Market Just as with traditional products, companies operating in the specialty market need a broad base of evidence on prescribing practices, sales transactions, treatment pathways and patient outcomes in order to make effective commercial decisions. Yet, collecting such a broad base of evidence is particularly difficult because:

    The data are fragmented and the channels diverse. Data for any given specialty product might be sourced from some combination of a syndicated data provider, a boutique pharmacy network, infusion centers, wholesalers/specialty pharmacy distributors, specialty pharmacy hubs, individual specialty pharmacies, physicians, co-pay partners and/or call centers.

    Nearly 50 percent of all specialty spend occurs under the medical benefit of insurance plans where it can be harder to see and manage details on the patient and provider. Most products covered under the medical benefit typically fall under the buy and bill model, and the available data are, therefore, not as robust or complete.

    Data on competitors products are more limited and potentially cost-prohibitive to collect.

    Multiple entities interact with patients, making it challenging to appreciate the totality of their experience.

    Having to work with multiple data sources makes data integration, manipulation, validation and investigation more complex, and typically companies spend time on these functions rather than on data analysis. It is not uncommon for data analysts to spend 50% or more of their time on data validation, aggregation and integration.

    These difficulties have a direct bearing on companies ability to perform mission-critical commercial functions. A lack of a comprehensive view into market dynamics can compromise patient adherence, measuring promotional effectiveness, targeting, compensation, salesforce sizing and evaluating contract performance.

    It is not uncommon for data analysts to spend 50 percent or more of their

    time on data validation, aggregation and integration.

  • LESSONS IN SPECIALTY DATA INTEGRATION 5

    DATA AND CHANNEL STRATEGY CONSIDERATIONS

    As biopharmaceutical companies plan their approach to specialty data integration, they should begin by identifying the data elements theyll need. Every internal stakeholder group should provide input, including contracting, commercial data/IT operations, brand marketing, commercial analytics, sales operations and managed markets.

    As a general rule, companies should aim to sort and examine data by prescriber, ZIP code, account, payer/plan, and patient. And companies should also collect details on referral prescription status, dispensed prescription sales dollars, new and total prescription volumes, payer/plan, International Classification of Diseases (ICD) 9/10 and dispensing location. The data layout, or exhibit, should be designed before negotiations for data begin, and it should be broad enough to accommodate future data needs from other providers on other brands. Not all fields need to be populated in any given case, but the full array of what might be needed should be anticipated.

    Syndicated data at the national and sub-national levels can provide broad insights into the brand and competitive market. The data can be used to support commercialization planning and as an early indicator of launch performance. This can be especially helpful as contracting for specialty data gets underway.

    Which channels will be involved in distributing the product and, therefore, should provide data is tied closely to:

    Disease state, which determines the sites of care;

    Patient population, which influences the services required and the channels selected; and

    Companys capabilities and the need for supplemental service providers.

    In general, different products in different therapeutic classes rely on different distribution channels. For example, in the hepatitis market, 28 percent of the market goes through the retail channel and 51 percent through mail services. This is a dramatically different picture from the multiple sclerosis market, where 8 percent is retail and 73 percent mail services.

    Data for any given specialty product might be sourced from some

    combination of a syndicated data provider, a boutique pharmacy network,

    infusion centers, wholesalers/specialty pharmacy distributors, specialty

    pharmacy hubs, individual specialty pharmacies, physicians, co-pay partners

    and call centers.

  • 6

    Best Practice Approaches

    DATA CONTRACTING

    Contracting for data should run in parallel with contracting for distributiontypically beginning 8-12 months prior to a products launch. At the very least, companies should ensure that theyll have a network of data suppliers across the necessary channels to capture patient-level transaction data as well as 867 and 852 data (used to verify shipment trends). This network may need to include patient hub/support providers, co-pay programs and marketing vendors. All contracts should include key performance indicators for data quality and timeliness with penalty or reward clauses. A data aggregator/integrator brought into the process at this early stage will be able to help with all of these steps.

    QUALITY CONTROL AND VALIDATION

    Ensuring the quality of incoming data requires a multi-level approach across all data providers to address timeliness, accuracy, completeness and consistency at the data element-, transaction- and file-levels. Companies should be able to perform a comparative analysis of purchases and inventory available to dispense, by location. They should also produce report cards by supplier on timeliness and data quality as well as overall specialty pharmacy performance on patient adherence, time to fill and medical possession ratio, to name a few metrics.

    MASTER DATA MANAGEMENT

    Achieving excellence in Master Data Management (which includes data governance and stewardship) is a topic for an in-depth discussion beyond the scope of this paper. The general goal is to maintain a single authoritative source of product insights across the enterprise that contain timely and accurate data, thus eliminating conflicts and discrepancies. This allows all commercial activities, such as targeting, segmentation, incentive compensation and forecasting, for example, t