This is part three of a three-part series on the essential qualities of claims data for life sciences research. If you missed the previous parts (part 1, part 2), we explored the importance of comprehensive and consistent data. This final installment focuses on why curation is critical for high-quality research.
In real-world evidence (RWE) research, health economics and outcomes research (HEOR), and regulatory decision-making, curated data is no longer a luxury—it is a necessity. As payer claims datasets (also known as closed claims datasets) grow in size and complexity, the ability to extract relevant, structured, and high-quality insights has become a competitive advantage for life sciences organizations.
Without properly curated data, researchers waste time cleaning, normalizing, and de-duplicating claims records, often leading to:
- Inconsistent findings
- Redundant data processing efforts
- Regulatory non-compliance
But what defines curation in a closed claims dataset? And why does curation matter as much as comprehensiveness and consistency in life sciences research? This blog explores the critical role of data curation in HEOR studies, RWE generation, and regulatory-grade analysis—and why selecting a curated closed claims dataset is an essential choice for pharmaceutical and life sciences researchers.
Why data curation matters in life sciences research
Eliminating redundancy and enhancing research efficiency
Healthcare claims data is inherently messy—it is collected from hundreds of payers, multiple provider networks, and varied reimbursement structures. Duplicate patient records, misclassified diagnoses, and incomplete encounters create inefficiencies that slow down research.
An article published in IRE Journals emphasizes that data fragmentation and lack of proper curation force life sciences researchers to spend substantial time and resources on data management tasks—often representing a significant portion of total research effort—delaying critical insights and inflating study timelines¹.
How data redundancy skews HEOR findings: Without curation, researchers may unknowingly count multiple transactions as separate encounters, leading to:
- Overestimation of disease burden
- Inflated treatment costs
- Incorrect patient cohort analyses
De-duplicated and streamlined patient records reduce analysis time: A curated closed claims dataset should apply automated de-duplication processes to merge multiple records from the same patient, remove redundant transactions, and ensure each medical encounter is accurately represented. This not only improves research efficiency but also reduces bias introduced by overrepresented data points.
Standardization for regulatory-grade data integrity
Regulatory agencies, including the FDA, EMA, and ICER, demand clean, standardized, and validated datasets for market access, HTA evaluations, and pharmacovigilance studies. Claims datasets that lack structured data governance introduce:
- Ambiguities in drug utilization and adherence measurement
- Inconsistencies in disease coding and comorbidity assessment
- Gaps in payer-derived cost calculations
A New England Journal of Medicine review highlights the challenges of using real-world data in regulatory submissions, noting:
*"Combining multiple RWD sources or using non-uniform data challenges the standardization of RWD, leading to increased regulatory scrutiny and approval risks."*²
This issue extends beyond regulatory concerns. Data experts emphasize that structured, high-quality datasets are also essential for AI-driven insights and analytics. A study published in JAMA Network Open found that data usability, governance, and interoperability were critical factors influencing the effectiveness of AI-based analyses, particularly in complex datasets such as healthcare claims (Dwyer-Lindgren et al., 2023)⁴. This reinforces the need for well-curated claims data to ensure AI-driven health economics and real-world evidence studies produce accurate and reproducible results.
The role of data curation in drug safety monitoring: Poorly structured datasets may fail to detect adverse events, medication switches, or polypharmacy risks due to misclassified claims and duplicate prescriptions. A curated closed claims dataset enhances pharmacovigilance by ensuring accurate capture of treatment patterns and patient safety data.
Curated data improves reproducibility and submission success: By ensuring cost standardization, aligned procedural mappings, and uniform coding practices, curated datasets minimize regulatory risk and increase the likelihood of successful market access and reimbursement approvals.
Curation enables seamless data linkage for multimodal research
Life sciences research increasingly relies on integrating claims data with electronic medical records (EMR), genomics, lab results, and social determinants of health (SDOH). However, without proper curation, linking disparate datasets can introduce errors in:
- Patient matching
- Duplications in encounters
- Misalignment in disease coding frameworks
An ISPOR Task Force report on real-world data best practices found that when claims datasets undergo rigorous curation—including patient matching and normalization—the ability to link those claims with other datasets (such as EMRs and registries) improves significantly³.
The impact of poorly curated data on multimodal analysis: When claims data is not curated, integration with EMR or lab data becomes problematic, leading to incomplete patient cohorts, redundant data points, and unreliable treatment-response analyses. Structured curation processes ensure that multimodal research retains analytical integrity.
How curated data enhances real-world evidence generation: Curated datasets align patient records across care settings, standardize cost and utilization metrics, and ensure accurate linkage between claims and clinical outcomes—reducing errors in comparative effectiveness research (CER) and economic burden studies.
Closing the loop: the most curated closed claims dataset available
When it comes to real-world data, volume alone doesn’t deliver value. For life sciences teams pursuing signal-rich, actionable insights, curation—not aggregation—is the defining factor in a dataset’s utility. That means precision over patchwork. Quality over clutter.
HealthVerity taXonomy is the only closed claims dataset built with curation at its core. Through rigorous de-duplication, standardized cost and encounter structures, and deep integration with clinical modalities, it enables life sciences organizations to study patient populations with unmatched clarity and clinical nuance.
Take inflammatory bowel disease (IBD), a set of chronic autoimmune conditions where quality-of-life assessments, inflammatory biomarkers, and comorbid mental health signals are critical to treatment evaluation and long-term outcomes. In taXonomy, researchers can access 1.95 million patients with Crohn’s disease and ulcerative colitis between 2016 and 2024 (Figure 1) with:
- 1.49 million EMR-linked patients with structured clinical observations, enabling capture and assessment of therapies, disease course, comorbidities, symptoms, procedures, and complicationss
- 1.71 million with relevant lab results, including CRP, ESR, calprotectin, and inflammatory markers to track real-world severity
- 248,060 patients with EMR notes, capturing cognitive, behavioral, and disease-specific insights not found in structured fields: Colonoscopy with Biopsy, Sigmoidoscopy, Fecal Calprotectin, Erythrocyte Sedimentation Rate (ESR) & C-Reactive Protein (CRP), Serologic Markers (e.g., p-ANCA), Mayo Clinic Score, Endoscopic Severity (Mucosal Healing), PHQ-2, PHQ-9, IBD-Q
Altogether, this represents:
- 76.2% of IBD patients with EMR overlap and structured clinical observations
- 88% with diagnostic lab signal
- 12.7% with unstructured note detail
Figure 1: Multimodal overlap across 1.95M IBD patients in taXonomy (2016–2024).
This is research-grade, longitudinal, multimodal data—ready for targeted analysis. In a therapeutic area where data fragmentation has long limited signal clarity, HealthVerity taXonomy delivers curated depth at scale.
As autoimmune research pushes into new frontiers such as radiology-confirmed endpoints, adverse event detection and early cancer signals, taXonomy is expanding even further. With imaging integrations from partners like OneMedNet and Gradient, researchers can access real-world scans and reports that align directly with structured claims, lab, and EMR data.
Read the announcement on our OneMedNet imaging integration.
When clarity is the competitive advantage, curated data is the only way forward.
(This concludes our three-part series on HealthVerity taXonomy. We encourage you to apply these insights to your real-world data strategy.)
References:
- IRE Journals. (2023). Addressing Data Fragmentation in Life Sciences: Developing Unified Portals for Improved Research Outcomes.
https://www.irejournals.com/formatedpaper/1706397.pdf - 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 - 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 - Dwyer-Lindgren, L., et al. (2023). Perceptions of Data Set Experts on Important Characteristics of Artificial Intelligence Data Sets: A Qualitative Study. JAMA Network Open.
https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2812417
