This post is part of our Identity First. Everything Follows. blog series, in which we explore our response to CMS’s 2025 RFI in partnership with Snowflake.
In healthcare, we don’t have a data quantity problem—we have a data trust problem.
Organizations are collecting more health data than ever before. From clinical records and claims data to remote monitoring, social determinants of health, and patient-reported outcomes, the sheer volume is staggering. Yet, despite this abundance, many healthcare entities still struggle to generate reliable, actionable insights.
That’s because insights are only as strong as the identity they’re built on.
Clinical teams depend on accurate longitudinal histories to guide treatment. Operations leaders rely on data to optimize performance and allocate resources. Public health agencies need complete views to track trends and shape policy. But when the same person shows up as two, three, or even four different individuals in the data, everything from care quality to risk scoring suffers., everything from care quality to risk scoring suffers.
This isn’t a back-end integration issue—it’s a foundational flaw. Without trusted identity, healthcare organizations will continue making decisions based on disconnected, distorted, and incomplete views. The data may look robust, but the insight it produces can be dangerously misleading.
When fragmented identity breaks your data
When dealing with data, the old saying applies: garbage in, garbage out. But the garbage isn’t always obvious. It often hides in duplicate records, mismatched identities, and incomplete patient profiles.
Fragmented identity data doesn’t just create operational headaches. It undermines the entire foundation of insight-driven healthcare:
- Readmission tracking breaks down. A single patient recorded under multiple identities can skew a hospital’s performance, masking preventable readmissions and obscuring care improvement opportunities.
- Care gap reporting becomes unreliable. A system may flag a patient for missing a test that’s already been completed, just stored under a duplicate record. This results in wasted outreach, inaccurate quality reporting, and delayed care.
- Population health initiatives get misdirected. Risk stratification becomes skewed when patients are miscounted or duplicated, leading to blind spots in chronic disease management and inefficient resource allocation.
- AI and predictive models amplify the problem. When identity fragmentation creeps into the training data, the downstream effects include biased algorithms, faulty decision support, and lost trust in advanced analytics.
- Public health surveillance is distorted. In outbreak scenarios, fragmented identity data can inflate or underreport disease prevalence, prompting misinformed response strategies and policy decisions.
These aren’t isolated errors—they’re systemic failures rooted in unresolved identity. And until healthcare organizations address identity resolution as a foundational data strategy, their analytics, quality programs, and AI investments will remain fundamentally compromised.
Why traditional matching falls short
Most healthcare organizations already have some form of record-matching in place—whether embedded in their EHR, layered into an MDM platform, or bolted on as a post-processing tool. But these solutions often rely on outdated logic: deterministic matching (which demands exact matches) or basic probabilistic methods (which struggle with nuance).
Here’s the problem:
- A typo in a date of birth.
- A missing middle initial.
- A new address from a recent move.
Any one of these can derail matching and create a duplicate. And when multiple attributes change simultaneously—as happens during life events like marriage, divorce, or a name change—the matching logic becomes ineffective.
Traditional systems also treat identity as a transactional event rather than a persistent relationship. They miss connections across time, systems, and care settings. As healthcare becomes more distributed and data sources grow more diverse, this fragmentation only increases.
The result? An illusion of completeness built on flawed foundations.
Why healthcare needs MDM built for healthcare
Solving identity fragmentation requires more than a patchwork of rules, scripts, or bolt-on tools. It demands a master data management (MDM) solution that’s purpose-built for the complexity of healthcare.
Unlike generic MDM systems designed for retail or finance, a healthcare-specific MDM must handle vast volumes of sensitive, rapidly changing, and multi-source data from EHRs, claims systems, call centers, mobile apps, HIEs, and social service platforms. It must account for the nuanced and dynamic ways that identity is expressed in clinical and administrative data, including:
- Frequent demographic changes across time and systems
- Varying levels of data completeness or quality
- Interactions between patients, providers, caregivers, and community partners
- Real-time and retrospective data matching needs
- Regulatory demands around auditability, consent, and PHI integrity
A truly effective solution matches records across systems, time, and life events. It supports analytics, operations, care coordination, AI, and public health from a foundation of clean, connected identity.
Verato’s role in delivering insight-ready identity
That’s exactly what Verato delivers.
We offer the only MDM platform specifically designed for healthcare, combining referential matching, real-time capabilities, and a nationwide reference dataset to resolve identities with unmatched precision.
Unlike traditional MDM tools that rely solely on deterministic or probabilistic logic, Verato adds real-world context. Our engine learns from patterns, infers connections, and links fragmented records even when demographic fields are missing, outdated, or inconsistent.
And we do it at the speed of care. As data flows in from any source, Verato dynamically matches it to the correct person, ensuring that every data point, including clinical, claims, social determinants of health (SDOH), or digital, is anchored to a single, trusted identity.
The result is analytics-grade identity resolution that gives healthcare organizations the clarity and confidence to:
- Trust their data pipelines
- Accurately segment patient populations
- Improve quality metrics and regulatory reporting
- Reduce waste from redundant or misdirected care
- Make AI and analytics safer, faster, and more effective
We unify records and restore trust in the insights those records are meant to deliver—and we do it at scale, across the ecosystem.
Looking ahead: Identity is the foundation for the future
With TEFCA accelerating national data exchange and AI redefining care delivery, the stakes for data accuracy have never been higher. Health systems that can’t unify identity at scale risk falling behind—not just technologically, but clinically and competitively. This is why the Centers for Medicare & Medicaid Services (CMS) is “taking bold steps to modernize the nation’s digital health ecosystem [and] advance a seamless, secure, and patient-centered digital health infrastructure.” And your organization should too.
Whether you’re pursuing value-based care, expanding into virtual health, or investing in predictive analytics, your insights are only as strong as the identity data behind them.
Ready to trust your data?
You can’t drive performance, reduce risk, or deliver value-based care if you don’t know who your data is about. The future of digital health depends on identity just as much as it does on infrastructure.
Talk with Verato to see how we deliver insight-ready identity resolution—so your data can finally do what it was meant to: drive meaningful outcomes.
