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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 principle—that one’s own success is founded on the work of those who have paved the way—is 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.


A Growing Market, a Hunger for Data All indications are that the specialty biopharmaceutical market represents much of the industry’s future. Today, the U.S. specialty drug market is valued at $106B and accounts for about 30 percent of the country’s 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 populations—a job that may sound deceptively simple but is not at all easy. In reality, specialty products are distributed through so many channels—not 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.

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To thrive in the specialty market, companies must almost always contract for incremental data—beyond what’s available from syndicated sources—from 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.


DATA AND CHANNEL STRATEGY CONSIDERATIONS

As biopharmaceutical companies plan their approach to specialty data integration, they should begin by identifying the data elements they’ll 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

• Company’s 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.

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Best Practice Approaches

DATA CONTRACTING

Contracting for data should run in parallel with contracting for distribution—typically beginning 8-12 months prior to a product’s launch. At the very least, companies should ensure that they’ll 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, to be based on “one version of the truth.”

Companies need to have both a de-identification engine that is compliant with HIPAA guidelines to anonymize incoming data as well as common master keys to bridge data across healthcare professionals, payers and plans, pharmacies and products. A single, centralized data governance team should oversee quality standards and access rules for data across all brands. Ideally, those responsible for governing specialty pharmacy data will be those responsible for managing syndicated data as well.

A single, centralized data governance team should oversee quality

standards and access rules for data across all brands.


BIO-PHARMA DATA STORES

PATIENT RESEARCH

MART

CONTRACTS MART

SALES REPORTING

MART

INTERACTIVE COMPENSATION

MART

DATA WAREHOUSE

BUSINESS PROCESSES

DATA SUPPLIER

CENTRAL DATA INTEGRATION POINT

• Anonymization

• Data validation & management

• Transformation

• Mastering

• Integration

SYNDICATED GPO / HUB SP1 SP2

?$

BEST-IN-CLASS SPECIALTY PHARMACY DATA PROCESS[ FIGURE 2 ]

Targeting Incentive Compensation

Measuring Promotional

Effectiveness

Salesforce Sizing

Evaluating Contract

Performance

Measuring Patient

Adherence

AGGREGATED DATA TRANSACTIONAL, PATIENT LEVEL DATA

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DATA WAREHOUSING

A single powerful business intelligence tool should be capable of supporting up to 90 percent of the company’s data mining, analytics and commercial reporting needs. (Note: MS Access and Excel are not sufficient to serve as data warehouses or data marts; neither is a data aggregation service a data warehousing service.) When it is time to build a data warehouse or replace an existing one, companies should evaluate the advantages of turning to cloud-based services as a cost-effective alternative.

DATA MINING, ANALYTICS AND REPORTING

The ability to extract value from specialty data depends on having completed all of the above functions successfully. It also requires either hiring or contracting with analysts with deep experience using specialty data. The data mining and analytical tools at their disposal should draw data directly from the data warehouse to maintain “one version of the truth.”

Ideally, the data and tools should support analyses on:

• Patients and prescribers, including analyses on source of business and compliance and persistence

• Clinical care, including analyses of approval and fulfillment and the impact of Patient Assistance Programs

• Product uptake and purchasing vs. dispensing

• Payers, including analyses on reimbursement patterns, contract effectiveness, rejections and reversed claims

• Distribution, including channel switching and benchmarking individual specialty pharmacies

• Data quality, or the completeness and quality of data elements

Reporting should be done in easy-to-digest dashboards as shown in Figure 3.

A common pitfall is to turn to separate entities for data aggregation,

integration, warehousing and reporting/analytics. The more entities

involved, the more complex and time consuming the handoffs between

parties, and the more difficult it is to resolve data issues.


Patient/Prescriber

• Source of business

• Compliance/Persistency

Clinical Care

• PAP impact

• Approval/Fulfillment analysis

Weekly/Monthly Tracking • Product uptake

• Specialty pharmacy dispensing vs. purchasing

Payer

• Reject analysis

• Reimbursement patterns

• Contract effectiveness

Distribution

• Channel switching

• Individual specialty pharmacy persistency and benchmarking

Data Quality

• Data element content completeness and quality

REPORTING AND ANALYTICS

[ FIGURE 3 ]

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Pitfalls to Avoid Companies just now developing their procurement and management strategy for specialty data can avoid the missteps that others have made, including:

• Waiting too long to begin contracting for data, rather than tying data negotiations to your product distribution planning.

• Failing to consult with internal stakeholders about their data needs before beginning contract negotiations. This can lead to paying for more data elements than are needed, or to having to amend contracts later to pick up missing data elements.

• Calling upon the services of a data aggregator after the data exhibit has been established and negotiations with specialty pharmacies have begun. Often, data aggregators can provide helpful tips for setting up the data exhibit, and their expertise is usually required when working out HIPAA terms with suppliers.

• Giving up too easily when specialty pharmacies are reluctant to provide particular data elements (such as BIN and PCN data). A data aggregator will have a good sense of what data specialty pharmacies have and regularly provide to other companies.

• Requiring national account managers or key account managers to manage the data relationship with specialty data providers. This is best handled by those responsible for data governance.

• Allowing the relationship with the specialty pharmacy as a service provider to influence expectations surrounding data provisions. Specialty pharmacies should be held to the same exacting standards for data timeliness and quality as other data vendors.

• Expecting data analysts to perform validation and integration; this is not their area of expertise.

• Relying on commercially available software programs (or a data aggregator) to serve as a data warehouse.

• Turning to separate entities to perform data aggregation, integration, warehousing and reporting/analytics. The more entities involved, the more complex and time consuming the handoffs between parties, and the more difficult it is to resolve data issues.

• Thinking only of short-term needs and not developing a data layout and infrastructure that will expand across data providers and brands.


Case Studies

A SCALABLE SOLUTION FOR INTEGRATING DATA

A mid-sized biopharma company was launching its first specialty product designated as an ultra-orphan drug. The company, which relied on a closed network of a single specialty pharmacy and limited distribution to clinics and physician offices, wanted to ensure that its initial aggregation solution would support a growing network for this product as well as others in the pipeline. The company also wanted the specialty data to be integrated seamlessly into its current data warehouse and to have advanced patient analytics capabilities.

Within 14 weeks, IMS Health assigned a single patient key across data from the specialty pharmacy and large group practices as well as syndicated longitudinal data. All could then be integrated within a data mart. The data were validated, bridged and summarized into syndicated data loads that required minimal extract-transform-load (ETL) changes and eliminated additional master data management processing.

The process increased analysts’ efficiency; they were freed from the laborious tasks of managing data and could devote their time to conducting deep patient analytics. The platform was easily scalable, and the client launched its second specialty product just 13 months later using four specialty pharmacies. Upon launch, analysts were able to access fully-integrated data in half the time needed for its first product.

HELP WITH DATA RECEIPT, VALIDATION AND PROCESSING

A small biopharma company was awaiting approval on an expanded indication for an ultra-orphan product that would increase its patient population exponentially and expand its distribution network from two to eight specialty pharmacies. The company was concerned about:

1. Its team’s ability to receive, validate and process data from the additional pharmacies; and

2. The hubs’ ability to match the triage data from specialty pharmacies to a consistent, longitudinal patient key.

In response, IMS Health created one consistent data layout across the specialty pharmacy network that linked all data with an encrypted, longitudinal patient key. Incoming triage data from the eight specialty pharmacies were validated, de-identified and sent to the hub daily. Every week, the specialty pharmacy and hub data were standardized and bridged (using the mastered keys) and then integrated into a de-identified patient mart.

The solution was implemented in 18 weeks and gave the company confidence that the specialty pharmacy updates could be easily uploaded to the hub every day. The load files required minimal extract-transform-load (ETL) changes, and there was no need for additional master data management processing. The company’s analysts, relieved of the need to perform data management, were able to focus on data analytics.

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ABOUT IMS HEALTH

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ConclusionThere is no indication that the fragmentation of data or diversity of data sources in the specialty market is going to change anytime soon. That puts the onus on companies to cope with disparate data and ensure that their incoming data are handled efficiently, integrated seamlessly into the current data warehouse, properly anonymized and made ready for advanced patient analysis. With the proper strategy, planning, discipline, support and tools, companies can develop a scalable platform that radically reduces both the time needed to generate actionable patient insights and the total cost of ownership.

Downstream commercial activities including targeting, segmentation, incentive compensation and forecasting can thus be based on accurate evidence. Companies new to the specialty market have an opportunity to learn from the experiences of others and avoid making costly mistakes related to contracting for, managing, integrating, reporting on and mining data.