This is part one of a three-part series on the essential qualities of claims data for life sciences research. Next up: the role of consistent data in producing reliable and reproducible results.

In modern health economics and outcomes research (HEOR), comprehensive data is the key to reliable, generalizable, and impactful findings. The ability to track longitudinal patient journeys, access diverse payer representation, and maintain robust sample sizes is essential for producing high-quality real-world evidence (RWE). Without a truly comprehensive closed claims dataset, life sciences researchers face data gaps, incomplete patient records, and limited analytical power.

But what defines a comprehensive dataset? And why is comprehensiveness a non-negotiable requirement for closed claims research? This blog explores the necessity of breadth, depth, and payer diversity in claims data and examines how dataset selection impacts the accuracy of HEOR studies, regulatory submissions, and market access strategies.

Why comprehensive data matters in life sciences research

Longitudinal patient journeys enable evidence-based decision-making

A fundamental requirement in HEOR and RWE studies is the ability to consistently follow patients over time. Longitudinal closed claims data allows researchers to assess:

• Disease progression across different treatment lines.
• Long-term medication adherence and real-world efficacy.
• Economic burden shifts due to comorbidities and disease evolution.

A 2023 study from the RAND Corporation found that missing or incomplete longitudinal data introduces bias into patient tracking, cost analysis, and treatment effectiveness evaluations, leading to skewed study outcomes¹. This illustrates that gaps in patient tracking compromise study validity, making longitudinal stability a critical requirement for any closed claims dataset.

Expanding the dataset to reduce data attrition: A dataset that lacks sustained patient follow-up creates severe research limitations. Patients frequently switch insurers, change care settings, or lapse in coverage, causing data attrition. A comprehensive closed claims dataset must include long-term records from commercial, Medicare, and Medicaid sources to prevent breaks in patient histories.

Understanding the real impact of longitudinal data: By leveraging an ever-expanding dataset, researchers can fill the missing gaps in patient journeys, ensuring that RWE studies reflect actual healthcare utilization patterns rather than fragmented snapshots. Studies lacking this comprehensiveness risk misinterpreted efficacy signals, flawed budget impact assessments, and inaccurate economic burden estimates.

Payer diversity eliminates bias in HEOR and market access

A dataset built on a single payer or a limited payer mix is inherently biased. Coverage policies, reimbursement rules, and drug formulary decisions vary widely among payers, meaning that relying on a single-payer dataset can:

• Distort real-world treatment adherence rates.
• Miss critical subpopulations excluded from certain insurance plans.
• Fail to capture the full economic burden across all payer types.

A 2022 report from Medical Economics underscores this issue, stating, “Real-world data are often fragmented—they include only a sector of healthcare…and incomplete—key outcomes are missing,” highlighting why multi-payer datasets are essential for representative real-world research².

Avoiding single-payer data pitfalls: Relying solely on commercial insurance data excludes critical populations, such as Medicare patients with chronic diseases and Medicaid patients in vulnerable populations. A truly comprehensive dataset must include a balanced mix of public and private payers to avoid payer-specific distortions.

Capturing market dynamics in real time: Claims datasets that continuously add new payer sources provide an advantage over static datasets locked to a specific period. By ensuring a widening pool of payers, researchers can gain real-time insights into evolving reimbursement patterns, treatment adoption rates, and payer policy changes.

Comprehensive datasets improve rare disease research

Studying rare diseases is particularly dependent on comprehensive data, as small patient populations require large-scale datasets to reach statistical significance. A 2023 study published in BMJ Public Health confirmed that national insurance claims data are vital in rare disease research, stating:

*“The study capitalised on national insurance claims data to gather information on patient characteristics and associated costs to better understand the diagnosis and treatment of rare diseases.”*³

Enabling deep research into rare and complex conditions: Rare disease research relies on broad patient representation to ensure sufficient cases for meaningful analysis. Without a comprehensive closed claims dataset, researchers may struggle to:

• Identify eligible patient populations for observational studies.
• Track long-term treatment effects in low-prevalence conditions.
• Conduct comparative effectiveness research (CER) across different care settings.

Supporting faster identification of rare disease populations: By leveraging expansive payer sources, researchers can detect early diagnostic patterns and flag undiagnosed rare disease cohorts. This capability is essential for improving time-to-diagnosis, optimizing trial recruitment, and assessing real-world treatment impact.

Closing the loop: the most comprehensive closed claims dataset available

Without comprehensive closed claims data, research validity suffers. For HEOR and RWE professionals, longitudinal stability, payer diversity, and continuous expansion are no longer optional, they are the foundation for generating accurate, reproducible, and elevated insights that withstand regulatory, clinical and payer scrutiny.

HealthVerity taXonomy℠ is the only closed claims dataset designed to meet these rigorous standards. Built for real-world research, it combines unmatched longitudinal depth with a diverse, balanced, and ever-growing payer ecosystem to deliver a foundation strong enough for today’s most complex evidence generation needs.

Take Alzheimer’s, a disease in which fragmented data has long stalled progress. In taXonomy, researchers can access over 1.3 million Alzheimer’s patients (2016–2024) with:

• 920,000 EMR-linked patients
• 857,000 with lab results, including biomarkers like APOE status, p-tau and plasma Aβ42/40 ratio
• 830,000 with structured clinical assessments, including MMSE, MoCA, PHQ-9
• 110,000 with EMR notes that capture cognitive testing, behavioral insights, interventions and adverse events
• Imaging access supporting real-world safety surveillance for ARIA-E and ARIA-H

Altogether, this represents:

• 71% of Alzheimer’s patients with EMR overlap
• 66% with lab data
• 64% with structured clinical assessments
• 8.5% with deep phenotyping from physician notes

This is research-grade, longitudinal, multimodal data, ready for analysis now. In conditions where most datasets offer only fragments, HealthVerity taXonomy delivers context at scale. If you’re ready to pursue research with more power, more precision, and more possibility, start with the only dataset that was built to be truly comprehensive.

(Next in this series: The critical role of data consistency in HEOR and regulatory-grade research.)

References:

¹ Kranz, A. M., Dworsky, M., Ryan, J., Heins, S. E., & Bhandarkar, M. (2023). State All Payer Claims Databases: Identifying Challenges and Opportunities for Conducting Patient-Centered Outcomes Research and Multi-State Studies. RAND Corporation.
https://aspe.hhs.gov/sites/default/files/documents/a2add9e2d2e196f240357fee73cf3990/APCD-PCOR-Report-2023.pdf

² Brantley, J. (2022). Overcoming Data Fragmentation is Key to Avoiding Future Health Care Crises. Medical Economics.
https://www.medicaleconomics.com/view/overcoming-data-fragmentation-is-key-to-avoiding-future-health-care-crises

³ Chung, S. C., Toh, S., & Wang, S. V. (2023). Leveraging national insurance claims data for insights on rare diseases: a public health approach. BMJ Public Health, 2(1), e000346.
https://bmjpublichealth.bmj.com/content/2/1/e000346