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Achieving Commercial Excellence: A Strategic Framework for Patient Data Assessment

The evolving commercial imperative in life sciences

The life sciences commercial landscape faces unprecedented transformation, driven by a confluence of factors. These include intensifying competition from highly specialized therapies, managing specialty drugs with intricate distribution and access hurdles, and shifting to value-based care with reimbursement tied directly to patient outcomes. Persistent pressure on revenue forecasts and the need to balance commercial performance with rising R&D costs and lengthy market access timelines also add complexity. To help navigate these market dynamics, commercial teams critically rely on anonymous patient longitudinal data (APLD) to drive commercial growth through precise, proactive targeting.

As biopharma companies are inundated with information, many struggle with the dilemma of data overload versus
actionable insight. Traditional commercial approaches often fall short due to common pitfalls like fragmented data silos, questionable data quality, and a lack of real-time visibility. These issues create significant operational inefficiencies, forcing teams to cobble together disparate data sets or manage multiple vendor purchases for a comprehensive view. This consumes significant time and resources, often leading to ineffective marketing campaigns, misallocated sales resources, and missed revenue forecasts. Assessing claims data has moved beyond simply measuring volume. It’s now a sophisticated process driven by organizational and therapeutic area demands, the complexities of drug benefits, and persistent data fragmentation in specialty channels.

With such vast amounts of data available, how do you ensure insights are actionable?

Your Strategic Framework for APLD Assessment

Use this guide to develop a more holistic approach to data assessment and ensure the
delivery of actionable insights to drive commercialization. Areas of focus will include:

  • The four core pillars of strategic data assessment
  • Fundamental characteristics of APLD

The four core pillars of data assessment

Commercial success hinges on far more than a product’s value proposition; it demands a data-driven approach
to commercialization that forms the foundation of every strategic decision marketers make. The ability to understand your market, patient journeys, and the competition with precision and speed are core differentiators.

To achieve this, companies must take a holistic approach to their data and partner evaluation, focusing on the following pillars:

These pillars are designed to elevate your evaluation framework from tactical scrutiny to a strategic approach. We’ll now turn our attention to the fundamental characteristics that transform raw claims information into a truly strategic asset.

The fundamentals of patient data: Building the foundation of actionable insights

When evaluating a claims data set, there are five fundamental characteristics that enable commercial teams to drive efficiency, make proactive decisions, and ultimately, meet their business objectives.

Fundamental Description Benefit
A more complete journey
  • Depth of data over time for each patient
  • The ability to track an individual patient’s healthcare
    interactions over an extended, continuous period
    (multiple years)
  • Having a consistent view of a patient’s diagnoses,
    medical and prescription treatments, and procedures,
    as they move through the healthcare system and
    potentially across different providers
    and settings of care
  • Understand disease progression,
    treatment pathways, adherence,
    and long-term outcomes
Data breadth and depth
  • A sizable sample from which to derive statistically
    significant findings when studied
  • The data set should accurately reflect the
    demographic and clinical diversity of the target
    patient population or the broader U.S. population
  • A balanced mix of payers (e.g., Commercial,
    Medicare, Medicaid), geographic regions, and
    provider types (i.e., specialists, settings of care)
  • Visibility into all treatments, including medications
    and procedures where data blocking may be in play
  • Ensure that insights derived from
    the data are generalizable to the
    population

  • Support accurate market sizing,
    forecasting, and resource allocation
    across diverse regions/patient types
Accuracy and consistency
  • Clean, valid, and reliable data
  • Data with minimal errors, consistent coding
    practices, robust de-duplication processes
    (especially for patient records and encounters),
    and clear definitions for all fields
  • Strong data governance and curation by the
    data vendor
  • Standardized data to ensure ease of
    implementation and consistent, ongoing insights
  • Drive efficiency by reducing
    time spent on data cleaning and
    validation
  • Avoid inaccurate or inconsistent
    data leading to flawed analyses,
    misleading insights, and poor
    business decisions
  • Mitigate compliance risks
    associated with using unreliable
    data
Patient and
transaction-level detail
  • Access to data at the patient and service-line level
  • Inclusion of all health-related events and their
    associated codes (diagnoses, products and
    procedures) along with key dimensions, such as
    product quantity and days’ supply, dates of service,
    detailed cost components, etc.
  • Identifiers for all entities for analysis (i.e., patients,
    providers, account, and payers)
  • Develop more precise cohort
    definitions for your populations
    of interest
  • Support a broad range of use cases
    and deep patient journey insights
  • Enable linking across data sets
    for provider and account-based
    analyses
Data recency and speed
  • The degree to which data is current and available
    for use at the required time
  • Account for data lag (healthcare event occurrence
    to data in the network) and data delivery frequency
    (how often updated data is provided)
  • Enable proactive decision-making,
    allowing your team to quickly
    identify new opportunities, adapt
    sales and marketing strategies, and
    respond swiftly to market shifts
  • Shorten time from observation to
    insight
  • Improve accuracy with more recent
    leading indicators of performance

Positioning your organization for data-driven commercial success

Focusing on these fundamentals, supported by the right strategic partner, transforms APLD from a raw commodity into a powerful strategic asset. Adopting this comprehensive data assessment framework isn’t just about managing information. It’s the key for life sciences companies to truly drive innovation, optimize commercial performance, and achieve sustainable growth in today’s increasingly competitive and data-driven future.

Preview Data from Veeva Compass

Explore data from Compass to ensure that your patient data reflects the fundamentals
outlined above for your therapeutic areas of interest, including:

  • Patient activity in network over time
  • Key data attributes
  • Market trends and competitive insights
  • Patient journey and claims data samples
  • Data lag and completeness over time

Get Started

Helpful Resources

Assessment Checklist

I. Starting your assessment

Use this checklist to ensure you’ve gathered all necessary internal information and can articulate your needs effectively to potential partners.

Going into an assessment: What you should know

Ensure internal stakeholder alignment and data that is fit for use

  • Will this be our primary data source?
  • Who will be using this data?
  • What questions are we trying to answer? What’s the priority of each question?
  • Do we expect to have additional questions in the future that are unknown today? Does the data set facilitate this or would we need to get an additional data pull once we have new questions?
  • What are we trying to accomplish and by when?
  • Do we have current data gaps? What are they and what is the urgency around these gaps?
  • Are we fully leveraging data today? How could we do more?
  • Do we have the right resources (people, systems, tools) to work with big data? Do we need additional support?

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Information to provide to your prospective partner(s)

Ensure consistency in responses from all partners

  • Full market definitions (diagnoses, products, and procedures of interest)
  • Clear metrics to be measured (patients, HCPs, claims, and records)
  • Clear metric definitions (e.g., When reporting Rx claims, should lifecycle data be included? If yes, should final
    and interim claims be included?)
  • Detailed business logic, data filters, and time periods to define patient cohorts of interest and key metrics
  • Use cases to be supported using APLD
  • Timeline to make a decision

II. Asking the right questions

Use this list of questions when evaluating prospective APLD partners and their data offerings to ensure your investment yields maximum strategic advantage and fuels your commercial success.

Pillar 1: Data integrity and completeness
Ensure that data is
longitudinally complete
  • Is access to data history included (e.g., 5+ years)?
  • On average, how long are patients typically present in the network?
  • Will I have access to all patient records/the full journey?
  • Does it provide a continuous view across patients, providers, and settings of care?
  • Does the data set include lifecycle data for prescription claims tracking (e.g., dispensed,
    rejected, reversed, interim)? And are these included in the coverage estimates/reporting
    provided?
Confirm that data is a
representative sample
  • Do I get access to the full network or am I restricted to a pre-defined market basket?
  • Does your data set include BOTH open and closed claims?
  • Does the data set accurately reflect your target patient population’s demographics?
  • Does it include a balanced mix of payers (Commercial, Medicare, Medicaid)?
  • Does it cover relevant geographic regions and provider types?
Assess ongoing data
quality and delivery
  • Are robust de-duplication processes in place for patient records and encounters?
  • How strong is the product pipeline used to curate and manage the data?
  • Is the data clean, valid, and reliable (e.g., minimal errors, consistent coding)?
  • Is the data delivered in a standard format?
  • Is all data actual? Is any data imputed or inferred?
Determine how granular
you can get
  • Is all data accessible at the individual patient and service-line level?
  • Does it include detailed information like specific diagnoses (ICD-10-CM), procedures
    (CPT/HCPCS/ICD procedure codes), product identifiers (NDCs), and other attributes needed
    for analysis?
  • Are identifiers available for all key entities and can I easily cross-walk to other data sources,
    like reference data, deep scientific data, and industry access data?
Understand how often the data is refreshed
  • What is the typical data latency (lag) between a healthcare event and its availability?
  • Does data refresh in real-time (i.e., daily)?
  • Can data be accessed in real-time (e.g., daily feeds)?
  • Does the refresh frequency enable proactive decision-making and rapid response?
Pillar 2: Compliance and security
Protect sensitive data and mitigate risk
  • Does the partner adhere closely to all relevant privacy regulations (e.g., HIPAA)?
  • Are their de-identification processes precise, robust, and regularly validated?
  • Do they adhere strictly to the contractual terms from all their sources?
  • Is their security infrastructure robust and regularly audited?
Pillar 3: Actionability and interoperability
Turn data into actionable insights
  • Can you seamlessly transfer the data into your existing analytics environment?
  • Can the data easily be integrated with your internal analytical tools (e.g., Python, SQL,
    BI platforms)?
  • Does the data’s structure and format facilitate relevant key use cases
    (e.g., patient journey mapping, market sizing, targeting)?
  • Can your data be integrated with other data sources in a privacy-safe way?
Pillar 4: The partner’s strategic value and stability
Choose a reliable and forward-thinking collaborator
  • Does the partner demonstrate deep life sciences domain expertise and understanding of
    your commercial challenges?
  • Do they offer strategic consultation and thought leadership?
  • Are they financially stable, indicating long-term commitment and investment in their data
    and platforms?
  • Do they have a proven commitment to innovation and what is their strategy of continued
    investment in the data network?
  • What is their track record for client support, responsiveness, and problem resolution?
  • Do their values align with your organization’s ethical and business standards?

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