This is part two of a three-part series on the essential qualities of claims data for life sciences research. If you missed part one, we covered the importance of comprehensive claims data. Next up: how curated data enhances analytical precision and efficiency.

In health economics and outcomes research (HEOR) and real-world evidence (RWE) studies, data consistency is a foundational pillar of research validity. Without a stable, reliable, and methodologically sound dataset, researchers risk drawing inconsistent conclusions, failing regulatory scrutiny, and generating unreproducible results.

As healthcare data sources fluctuate, payers change, provider networks shift, and claims processing methodologies evolve, data consistency must be a priority when selecting a payer claims dataset (also known as a closed claims dataset). Without it, even the most comprehensive and curated datasets fail to produce meaningful, repeatable insights.

But what defines consistency in a closed claims dataset? And how does it impact regulatory submissions, payer analytics, and clinical research? This blog explores why data consistency is essential in life sciences research, highlighting key factors such as payer stability, data structure uniformity, and standardization.

Why data consistency matters in life sciences research

Payer stability ensures longitudinal data integrity

One of the biggest challenges in RWE and HEOR research is payer churn—the fluctuation in payer participation in claims datasets due to contract terminations, market exits, or acquisitions. This instability leads to gaps in patient records, which can:

• Distort longitudinal treatment adherence trends.
• Disrupt disease progression tracking.
• Create artificial fluctuations in cost models.

A RAND Corporation report on all-payer claims databases confirms that payer churn significantly disrupts longitudinal tracking in claims data, impacting data completeness and introducing bias in utilization and cost analyses¹. Without payer consistency, long-term patient tracking becomes unreliable, leading to missing patient histories that distort treatment adherence patterns and long-term outcome studies.

Why static datasets struggle with research validity: Static datasets, particularly those with high payer turnover, can create false signals in HEOR research. Studies based on unstable datasets risk misinterpreting cost trends, failing to capture true drug adherence patterns, and producing inconsistent regulatory-grade analyses.

A balanced and expanding payer mix mitigates churn risks: Ensuring a stable mix of commercial, Medicare, and Medicaid payers reduces the risk of sudden data attrition. A dataset that actively expands its payer ecosystem prevents coverage gaps and provides a more complete view of patient populations.

Data structure uniformity reduces variability in HEOR studies

Even when claims data is available, variability in cost structures, coding methodologies, and provider reporting standards creates significant challenges for HEOR professionals. Unstructured or non-standardized data leads to:

• Discrepancies in cost-effectiveness models.
• Inaccurate comparisons of payer reimbursement trends.
• Inconsistencies in population-based drug utilization assessments.

An ISPOR Task Force report outlines best practices for standardizing real-world data (RWD) methodologies across payers, emphasizing that data standardization reduces variability and enhances the reproducibility of HEOR research². Without these uniform standards, inconsistencies across datasets create analytical challenges, leading to skewed budget impact models and unreliable patient cohort analyses.

How inconsistent coding creates research ambiguity: Without data structure uniformity, HEOR research risks comparing incompatible reimbursement rates, incorrectly attributing procedure costs, or failing to identify accurate disease cohorts. A consistent dataset must harmonize payer data across time periods to ensure reproducibility.

Standardized cost models improve research reproducibility: Standardizing cost structures, by aligning procedural data across payer types and eliminating outlier pricing variations, is essential for real-world cost assessments. Without this, economic modeling lacks consistency, producing skewed reimbursement insights.

Closing the loop: the most consistent closed claims dataset available

Without consistent closed claims data, research integrity erodes. For HEOR and RWE professionals, payer stability, data uniformity, and methodological continuity are essential to producing insights that are not only reproducible but also defensible in regulatory, clinical, and payer settings.

HealthVerity taXonomy is the only closed claims dataset designed to meet these standards at scale. Built for longitudinal consistency, it supports large-volume, clinically relevant, and structurally harmonized data—year over year, across therapeutic areas.

Take the intersection of obesity and type 2 diabetes (T2DM), two chronic and comorbid conditions that require persistent visibility across care settings and time. In taXonomy, researchers can access over 12.5 million co-diagnosed patients (2016–2024) with:

• 9.6 million EMR-linked patients, offering structured views into medications, diagnoses, vitals, and comorbidities
• 8.7 million with HbA1c results, enabling real-world tracking of glycemic control and disease progression
• 7.6 million with structured clinical observations, including BMI, weight, blood pressure, and height—standardized across time
• 3.9 million patients with GLP-1/GIP or SGLT-2 prescriptions, supporting treatment pathway analysis and safety surveillance
• 1.7 million with EMR notes, capturing adverse events like hypoglycemia, gastrointestinal side effects, and patient-reported symptoms

Altogether, this represents:

• 77.4% of patients with EMR overlap
• 70% with HbA1c lab values
• 61% with structured clinical observations
• 31% with evidence of modern T2DM pharmacotherapy
• 13.8% with physician-authored EMR notes

This population-level consistency is only part of the story. taXonomy also offers a balanced and stable payer mix over time, supporting fair representation across Commercial, Medicare Advantage, and Managed Medicaid populations.

The chart below illustrates that no single payer type dominates the dataset in any year from 2016 to 2024, ensuring that findings derived from taXonomy are both robust and generalizable.

Payer mix across taXonomy’s Obesity + T2DM population (2016–2024). Distribution of medical enrollment across Commercial, Managed Medicaid, and Medicare Advantage remains balanced year-over-year, underscoring stability and reducing systemic payer bias in HEOR and RWE studies.

This is consistent, longitudinal, multimodal data—structured for real-world evidence, not cobbled together from fragmented sources. In chronic conditions where year-over-year stability is essential for valid trend analysis and payer-relevant insight, HealthVerity taXonomy provides the continuity that other datasets cannot.

(Next in this series: The critical role of curated data in achieving research precision and analytical efficiency.)

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 Berger, M.L., Sox, H., Willke, R.J., et al. (2017). Good Practices for Real-World Data Studies of Treatment and Comparative Effectiveness: Recommendations From the Joint ISPOR-ISPE Task Force. Pharmacoepidemiology and Drug Safety, 26(9), 1033–1039.
https://onlinelibrary.wiley.com/doi/full/10.1002/pds.4297

³ Sherman, R.E., Anderson, S.A., Dal Pan, G.J., et al. (2016). Real-World Evidence — What Is It and What Can It Tell Us? New England Journal of Medicine, 375(23), 2293-2297.
https://www.nejm.org/doi/full/10.1056/NEJMsb1609216