Rare disease research depends on better ways to identify patients across fragmented healthcare records

Rare diseases present a data challenge for researchers and pharmaceutical companies. Individual conditions affect relatively small populations, and patients may receive multiple diagnoses or treatments across different providers before their condition is accurately identified. This makes it difficult to estimate disease prevalence, understand treatment patterns and reconstruct patient journeys using isolated healthcare records.

A 2024 study published in BMJ Public Health used commercial insurance claims data from the Healthcare Cost Institute to estimate the prevalence and costs associated with 14 rare diseases in the United States. The researchers identified 170,472 patients with one of the conditions across a database covering nearly 118 million individuals between 2016 and 2020. The study also highlighted differences in prevalence, costs and patient characteristics across diseases and geographic areas.

Claims data can therefore provide an important source for studying healthcare utilization and treatment patterns. However, researchers must account for limitations such as incomplete diagnosis coding, differences in insurance coverage and the fact that claims primarily document billed healthcare services rather than every clinical detail of a patient’s condition.

Best CLEAR Claims Data Analytics Companies for Rare Disease Research

PurpleLab offers CLEAR Claims, a claims data solution designed to make healthcare utilization and market research easier to conduct. The platform combines medical and pharmacy claims with methodologies that help researchers work with structured, analysis-ready datasets.

One of its relevant features is the Qualified Diagnosis Indicator. This methodology helps users identify claims where the diagnosis code supports the identification of a patient’s condition. For rare disease research, this can help refine patient cohorts and reduce the risk of including claims that do not provide sufficient diagnostic support. It does not independently confirm a clinical diagnosis, but it provides an additional layer for population analysis.

CLEAR Claims also includes encounter logic, which links related claims into inpatient admissions and separates bundled claims into visits in other care settings. This can help researchers reconstruct healthcare utilization more consistently, particularly when patients receive services across multiple providers and facilities.

Another component is Final Claim Status, which identifies resubmissions and helps distinguish the most recent version of a claim from earlier records. This reduces the risk of counting duplicate or restated claims as separate healthcare services. Primary HCP/HCO Identification also helps researchers identify the most relevant healthcare professional or organization associated with each claim.

PurpleLab’s Life Sciences solutions extend these capabilities to research applications such as disease and market landscaping, patient journey analysis, prevalence and incidence estimates, trial feasibility and treatment pattern mapping. Researchers can examine patient characteristics, comorbidities, healthcare encounters and events before and after diagnosis or treatment.

For rare disease studies, these capabilities can help answer questions about how patients interact with the healthcare system, which providers manage their care, what treatments they receive and how healthcare utilization changes over time. Such analyses can inform epidemiological research, clinical development planning and assessments of unmet medical needs.

There are important methodological considerations. Rare diseases may lack specific or consistently applied diagnosis codes, and some patients may remain undiagnosed or receive different codes throughout their care journey. A 2021 study published in Orphanet Journal of Rare Diseases found that identifying rare disease populations through healthcare databases can be difficult because ICD coding criteria may underestimate the number of affected patients. Researchers should therefore validate cohort definitions and, where possible, combine claims with clinical records, registries or other data sources.

For organizations evaluating CLEAR Claims data analytics companies for rare disease research, the relevant criteria include diagnostic specificity, longitudinal data, encounter consolidation, provider attribution and the ability to support patient cohort analysis. These methodologies can improve the consistency of claims-based research, while clinical validation remains essential when establishing disease status.