The Role of Claims Data in Clinical Trial Feasibility: Why Insurance Records Are the New Recruitment Frontier

Patient recruitment is no longer a logistical challenge. It is a multibillion-dollar attrition problem threatening the modern drug pipeline. Over 80% of clinical trials still fail to meet their original enrolment timelines, and nearly 30% of sites enroll zero patients. For decades, the industry has relied on a reactive "search and rescue" model. Sponsors spend exorbitant sums on broad advertising, hoping the right candidates navigate their way into a clinic. That approach is now a clear strategic liability.

The question is no longer whether sponsors can afford to innovate. It is whether they can afford not to. Every day, the healthcare system generates a massive trail of administrative data through insurance claims and pharmacy records. These datasets, often dismissed as back-office noise, map the real-world patient journey with high fidelity.

What if the patients you need for your next breakthrough are already visible, just overlooked?

From Guesswork to Geographic Precision

Modern sponsors are moving beyond intuition. By leveraging real-world data (RWD) from claims databases and electronic health records, they can model clinical trial patient enrollment with surgical accuracy. The fundamental question of feasibility shifts. It is no longer "Will patients come?" It becomes "Where do they already exist?"

This shift lets sponsors estimate eligible population sizes and geographic densities before spending the first dollar on site activation. More importantly, it directly informs site selection — the most critical lever in preventing zero-enrolling sites. Patient recruitment accounts for 32% of all trial costs and patient dropout averages 18%, according to Deloitte figures. When sponsors identify high-density patient clusters first, enrollment becomes a mathematical probability rather than a hopeful projection.

By analyzing data from electronic health records and claims databases, sponsors can assess the likelihood of enrolling enough patients who meet a trial's criteria, reducing the risk of under-enrollment — a common challenge that delays trial completion and increases costs. The Clinical Trials Transformation Initiative (CTTI) has specifically highlighted the potential of real-world evidence (RWE), including claims and EHRs, to optimize eligibility planning and recruitment.

Unmasking the "Hidden Cohort"

Traditional recruitment is inherently biased toward the "research-aware" patient. These are individuals already within the academic ecosystem or those with the resources to respond to digital ads. The "hidden cohort" represents most eligible patients who are invisible to researchers. They receive treatment in community settings. They are not actively seeking a trial.

Using ML to identify patient cohorts in real-world datasets lets sponsors work backward from established care patterns. By analyzing diagnosis histories, treatment trajectories, and healthcare encounters, teams can reach populations that conventional channels miss entirely. Despite the critical role of clinical trials in advancing cancer treatment, patient enrollment remains below 10% because of health system-, provider-, and patient-level barriers.

Beyond efficiency gains, this approach addresses a regulatory imperative. The FDA's June 2024 draft guidance under the Food and Drug Omnibus Reform Act (FDORA) required sponsors to submit Diversity Action Plans with their late-stage trial protocol submissions to improve enrollment of participants from historically underrepresented populations. Reaching hidden cohorts through claims data harmonization helps ensure trial populations reflect the actual disease burden. This improves result generalizability and supports a smoother path to regulatory approval.

Pharmacy Data as a Clinical GPS

Pharmacy data serves as a real-time signal of a patient's clinical trajectory. A prescription fill is not a simple transaction. It marks disease progression or treatment response. These transactional data points are clinical signals. They allow sponsors to intercept a patient at the exact moment they become eligible for a trial.

Several signals define this journey. Medication initiations mark entry into a new treatment phase. Discontinuations indicate that a current therapy may be ineffective or poorly tolerated. Treatment switches signal a shift in clinical status. Combination therapies highlight the complexity and severity of a patient's condition.

When layered with AI-powered analytics, these pharmacy signals become a predictive recruitment engine. Sponsors no longer wait for patients to self-identify. They map the patient journey in near-real time. The result: accelerated screening and enrollment in clinical trials and dramatically reduced cycle times.

GPU-accelerated AI: The Throughput Multiplier

NGS AI is reshaping the computational core of secondary analysis. GPU-accelerated frameworks now compress alignment and variant calling from hours to minutes.

NVIDIA's Parabricks suite — the current benchmark in GPU-accelerated genomics — can process a 30x whole-genome sample through DeepVariant in as little as eight minutes on a DGX station. The same workload takes approximately five hours on a standard CPU instance. Parabricks v4.6, released in late 2025, pairs pangenome-aware DeepVariant with GPU-accelerated Giraffe alignment, reducing runtime from over 9 hours on a CPU to under 40 minutes on 4 GPUs.

For labs processing dozens of whole genomes daily, this is not an incremental improvement. It is a category shift. NGS pipeline automation at this speed makes same-day reporting operationally feasible rather than aspirational.

Not a Crystal Ball, but a Smarter Filter

Viewing claims data as a replacement for clinical screening is a strategic error. Administrative data has limitations, particularly in capturing nuanced clinical context or specific lab-based exclusion criteria. The goal is not 100% certainty. It is to make the funnel smarter and earlier.

Since 2005, protocol procedure quantity has increased by 139%, endpoints have risen 214%, and data points collected have surged 600%. Against this backdrop of rising complexity, predictive analytics becomes essential. Tufts CSDD's 2024 analysis puts the average direct daily cost of a Phase III trial at $55,716. Each week a site remains inactive costs the sponsor nearly $390,000 in direct costs alone.

By layering claims and pharmacy data with EHR, genomic data, and digital engagement signals, sponsors can build a clinical decision support (CDS) framework that identifies the right patients, at the right sites, at the right time. AI in clinical decision support does not replace the investigator. It arms them with data-driven confidence before the first patient is screened.

The Future of Trial Feasibility

This data-driven paradigm reengineers the sponsor's mindset. Feasibility is no longer a post-protocol afterthought. It is a proactive strategy fueled by real-world evidence. Through federated query layers, a sponsor can count eligible patients across dozens of hospitals and biobanks in minutes, without any patient-level data leaving the institutions that hold it.

As clinical development costs continue to rise, automated patient recruitment software for clinical trials is not an innovation luxury. It is a competitive necessity. Those who continue to rely on legacy models will be outpaced by sponsors who can forecast patient locations with high-resolution data.

The industry is moving toward a model where clinical trial matching is predictable, site selection is optimized for ROI, and timelines are protected. In this model, the trial finds the patient. The burden of discovery no longer falls on the person fighting the disease.

Curious to know more about patient recruitment and its role in the care continuum? Get in touch with us!