Healthcare organizations depend on data to understand their markets, patient populations, and competitive position. But even the biggest datasets can’t deliver accuracy when the total population represented in the data, the denominator, is unknown. In claims analytics, this uncertainty is the rule, not the exception.
This denominator problem sits at the center of nearly every strategic question in healthcare: How many patients did we reach? How much demand exists for a service line? What percentage of the market do we serve? If the denominator is unclear, every answer to these questions carries an invisible margin of uncertainty.
Even large datasets double-count encounters, omit payer segments, or redact sensitive categories of care. These gaps create a false sense of completeness that misleads forecasts, skews market share estimates, and weakens strategic planning.
Kythera Labs tackles this problem by reconstructing and measuring the denominator itself, turning unknown completeness into quantifiable fidelity, transforming open claims into a reliable foundation for market intelligence.
Open claims data offers breadth across payers, systems, and geographies, going far beyond what individual payers or providers can see on their own. The conundrum when using claims for analytics is that they were designed for a particular purpose: so providers could get paid. That means participation is uneven across clearinghouses, payers, and regions; no single aggregator captures the full market; submission behavior varies from organization to organization, and structural blind spots exist wherever data is not shared.
The result is a data divide: what open claims seem to show often differs from what is actually happening in the market. Two vendors may each claim “80% coverage,” yet represent entirely different patient populations, payer mixes, or service lines—the similarity in the number masks major differences in what is truly represented. Without a verified denominator, no one knows what’s missing, and uncertainty silently enters every metric built on top of the data.
Open claims create visibility, but not evenly. Certain entities limit external visibility by design. For example, large integrated payer-provider systems, like Kaiser Permanente, often keep data closed, and major commercial insurers may selectively participate in third-party data networks. Blind spots are also created when sensitive care categories are routinely redacted. Under HIPAA expert determination, segments like behavioral health, reproductive health, and HIV treatment may be partially removed. Additionally, Medicare and Medicaid often share data only through formal agreements, leaving inconsistent representation in open feeds.
These types of exclusions result in some populations being richly represented, while others barely exist in the data. Fidelity begins with acknowledging these limitations.
Data fragmentation prevents any dataset from being understood in relation to the total population it attempts to measure. Claims data is structurally fragmented because the healthcare system is fragmented. Across the country, payers, clearinghouses, and provider systems submit claims through different channels, use different coding standards, and record the same encounter differently, or even not at all. This leads to duplicate claims, conflicting records, and missing encounters.
A dataset may look “complete” because it contains millions of claims, but without a consistent structure, no one knows how many represent distinct, verified encounters.
Kythera Labs resolves structural inconsistency through its Common Data Model (CDM), a unifying framework that standardizes, harmonizes, and verifies disparate claims data sources. The CDM aligns identifiers, vocabularies, and event structures so every record reflects a real, singular healthcare encounter, not a fragment of one. Our CDM includes:
1. Remastering & Alignment: Unifying payer, provider, and patient identifiers across sources.
2. Deduplication & Validation: Eliminating redundant claims and verifying that remaining records reflect true encounters.
3. Completeness Scoring: Quantifying confidence levels for every data slice, service line, and market.
4. Structural Mapping: Linking open and closed claims (when available) to rebuild longitudinal patient journeys.
By restoring structure, the CDM reconstructs the denominator, making coverage measurable instead of assumed. Once normalized, open claims data becomes not just accessible, but trustworthy.
Kythera’s CDM establishes a standardized baseline that becomes the foundation for downstream analysis, including calculating market share, leakage, referral behavior, payer mix, service line demand, and market competitiveness. With the denominator defined, every metric becomes reproducible, comparable, and trustworthy.
A regional health system believed its internal and open claims data provided visibility into roughly 75% of patient encounters in its market.
Kythera’s CDM uncovered duplicate encounters hidden across multiple clearinghouses, missing segments associated with specific payers, and uneven participation among major provider groups.
After normalization, verified visibility dropped to 63%, revealing the denominator more accurately. Once identifiers were aligned, duplicates removed, and completeness scoring applied, validated coverage increased to 88%, a number grounded in measurable fidelity, not assumption.
Healthcare leaders cannot make reliable decisions without understanding what population their data actually represents. When the denominator is unknown, every rate, percentage, and forecast rests on unstable ground.
Kythera Labs solves this long-standing challenge through its Common Data Model, transforming fragmented, uneven open claims into a coherent, measurable representation of real healthcare activity. With a verified denominator in place, coverage becomes measurable, analytics become reproducible and comparable, and strategic decisions are grounded in what’s true, not what appears to be complete. When fidelity replaces assumption, healthcare organizations finally gain a clear, accurate view of their markets and populations they serve.
For a more in-depth look at the unknown denominator and how to fix it, check out our whitepaper.
Ryan Leurck, Kythera Labs, Chief Analytics Officer
