How Claims Data is Transforming Consultancies

Real-World Data: The Cornerstone of Life Science Consulting

Life sciences are at an inflection point. As pressure mounts to develop therapies faster, prove real-world value to payers, and personalize interventions, traditional data sources are hitting their limits. Real-world data (RWD), particularly claims data, has emerged as a critical lever across the product lifecycle.

For consultancies, this opens new terrain. Clients are no longer just asking for strategy decks. They want evidence that helps identify which patients to target, where care pathways break down, and how treatments perform beyond the trial setting. 

That evidence increasingly lives in messy, fragmented datasets that require not just access, but interpretation at speed.

As the global RWD market is projected to grow from $1.88 billion in 2025 to over $6.3 billion by 2034, consultancies that can operationalize claims data will have a clear edge. But doing so requires a new kind of infrastructure. One that combines scale, structure, and speed.

The Expanding Role of RWD in Consulting

RWD refers to health-related data collected outside of traditional clinical trials, encompassing insurance claims, electronic health records, registries, wearables, and patient-reported outcomes. 

Its value lies in offering a more representative, real-world view of patient journeys, treatment effectiveness, and care patterns. These insights may not always be captured through randomized controlled trials (RCTs). 

For life science consultancies, this has opened new opportunities. Claims-based analyses enable more precise market sizing, richer health economic studies, and targeted provider or patient segmentation strategies. 

As healthcare becomes more data-driven, consultancies must go beyond access to claims data. They need structured, de-identified, analysis-ready formats and the tools to explore them without bottlenecks.

Regulatory Momentum and Market Forces

The FDA is increasingly incorporating RWD and real-world evidence (RWE) into decision-making, both for accelerating approvals and for post-market surveillance. This shift gives life sciences firms an incentive to build RWD into their development and launch strategies earlier than ever.

The pharmaceutical industry, in turn, is elevating RWD as a complement to traditional clinical trials, seeking greater efficiency and real-world relevance in their research.

Meanwhile, advances in data infrastructure, particularly the digitization of health records, the rise of mobile health platforms, and the growth of claims databases, are expanding the scope and resolution of available data. 

By leveraging analytical tools like AI and machine learning, consultancies can identify patterns within real-world data at scale, leading to more tailored and actionable insights for their clients. This capability directly supports advancements in predictive modeling, personalized medicine, and optimized care pathways.

The current environment creates demand for platforms that combine regulatory-grade data integrity with rapid exploration capabilities, especially for teams working under tight timelines.

Consulting Use Cases Across the Product Lifecycle 

Consultancies now use RWD to support a wide range of use cases. In early R&D, claims data can identify patient cohorts, refine inclusion/exclusion criteria, and inform clinical trial site selection, helping sponsors reduce recruitment times and costs.

For rare diseases or highly segmented populations, this accelerates feasibility assessments and improves targeting precision.

In later stages, RWD plays a critical role in commercialization. Treatment patterns, adherence trends, and regional variations help shape go-to-market strategies. Health economic and outcomes research (HEOR), based on claims or EHR data, supports pricing and reimbursement discussions by demonstrating real-world value to payers.

Other applications include referral mapping, patient flow modeling, and demand forecasting. In recent projects, consultancies have used claims analytics to guide salesforce sizing, target provider deciles, and analyze regional growth dynamics. 

In therapeutic areas such as oncology, cardiology, and immunology, RWD is helping to track outcomes, personalize interventions, and identify unmet needs at scale.

Challenges and Considerations

Despite its value, RWD is not without challenges. Ensuring data privacy, security, and interoperability remains complex, especially as datasets grow larger and more heterogeneous. Standardizing across different data sources and maintaining quality controls are critical to producing reliable insights.

Consultancies must also navigate regulatory uncertainty as frameworks evolve, particularly around how RWE is evaluated by agencies and payers. Moreover, clients often require not just the data, but the expertise to interpret it, creating a growing demand for hybrid teams with both domain and technical fluency.
 

Looking Ahead: RWD as a Strategic Differentiator

As value-based care becomes the norm and personalized medicine accelerates, the demand for RWD-driven insights will continue to rise. For consultancies, this means developing stronger capabilities in data engineering, advanced analytics, and strategy translation.
 
As consultancies take on increasingly data-intensive projects, the challenge isn’t just accessing RWD. it’s translating it into timely, credible insight. This requires infrastructure that combines scale, structure, and usability. PurpleLab is built around that premise.
 

PurpleLab’s Approach to Enabling RWD Insights

At PurpleLab’s core is an integrated platform that unites open and closed claims data to deliver a longitudinal view of over 330 million de-identified patients across all care settings. With more than 8 billion claims processed annually, it offers the coverage needed to model treatment patterns, patient journeys, and provider networks with high fidelity.
 
To make this data usable, PurpleLab applies automated enrichment via its CLEAR™ engine, which transforms raw claims into structured, analysis-ready datasets. This dramatically reduces the data wrangling burden for consultants, cutting weeks of cleaning, validation, and coding from project timelines.
 
For teams without dedicated engineering support, the HealthNexus™ platform enables no-code analytics on top of this enriched data. Whether the goal is mapping referral flows, segmenting patient populations, or assessing payer dynamics, consultants can move from hypothesis to insight without deep technical lift. Integrated SDOH overlays add further context, supporting projects focused on health equity or population risk stratification.
 
PurpleLab’s delivery model is also built for consulting workflows. Short-term access windows (as brief as 12 weeks) allow for leaner, time-bound engagements at lower cost, ideal for pitch development, rapid prototyping, or project-specific analyses. As a CMS Qualified Entity, PurpleLab also provides access to Medicare Parts A, B, and D, expanding its utility for benchmarking, quality metrics, and payer strategy.
 
All of this sits atop a foundation of rigorous data governance. With HIPAA-compliant protocols, standardization pipelines, and secure access controls, teams can work confidently with sensitive data while maintaining privacy and compliance.
 
As the role of real-world data expands, consultancies equipped with partners like PurpleLab will be better positioned to deliver precision insights at scale, accelerating innovation, optimizing strategies, and ultimately driving better patient outcomes.
 

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