Deriving Insurance Status From Enrollment and Demographic Information in Flatiron Data

‍

Motivation

Healthcare analytics teams rely on accurate insurance coverage data to understand care access patterns, treatment adherence, and health outcomes at specific time points. Flatiron's EHR-derived data contains rich clinical information but requires sophisticated processing to determine true insurance coverage status during critical periods like disease onset or treatment initiation. Manual approaches to reconciling fragmented insurance records are time-intensive and inconsistent, creating bottlenecks when teams need to assess whether coverage gaps influenced patient care decisions or health trajectories.

‍

Solution

Plinth developed a systematic methodology to derive patient insurance status from Flatiron data by combining multiple evidence sources and applying hierarchical decision rules. The approach uses intelligent date imputation when coverage periods are incomplete, infers Medicare eligibility from patient age and demographics, and applies configurable business logic to resolve conflicting insurance records. This multi-source evidence framework creates a single, defensible insurance classification for each patient at their index date, replacing lower-fidelity algorithms that may ignore critical patient information during classification. The methodology was implemented as a reusable R package with extensive documentation and testing capabilities.

‍

Impact

Plinth's systematic insurance derivation methodology enabled our client's healthcare analytics team to generate consistent, reliable insurance classifications with higher fidelity than previous approaches. The standardized approach was integrated into a broader analytic tooling ecosystem, ensuring uniform methodology across different research studies and client projects. HEOR teams can now confidently analyze insurance-related research questions with comprehensive patient classifications, accelerating study timelines while improving the reproducibility and defensibility of real-world evidence studies.

‍