By Dana Jacoby
Why incomplete data is one of healthcare’s most costly problems
Every day, patients fall through the cracks—not because providers don’t care, but because the data needed to catch them isn’t complete, connected, or current. That’s the core problem referential data is built to solve.
Gaps in care are the difference between the care a patient should be receiving and the care they’re actually getting—missed screenings, overdue follow-ups, unmanaged chronic conditions, lapsed medications. The scale is significant.
A study published in Health Affairs by researchers at the Agency for Healthcare Research and Quality (AHRQ) found that only 8% of US adults aged 35 and older had received all recommended high-priority preventive care.
In addition to this, a study published in PMC found that one in three patients across 11 countries experienced at least one gap in care coordination—with the US recording the highest rate of poor primary care coordination. The consequences are well documented: delayed diagnoses, preventable hospitalizations, wasted resources, and worse outcomes.
So why do these gaps persist? In large part, because of how care gap management relies on data—and the limits of the data being used.
Where the data falls short
Most care gap management still depends heavily on claims data. As Chief Healthcare Executive reports, there can be up to a three-month lag between when care is delivered and when claims data becomes available.
By the time a gap is identified, it may already be closed—or significantly worse. This is corroborated by Azara Healthcare’s analysis, which found that claims data typically takes around 90 days to reach a level of completeness sufficient for reliable analysis.
Beyond the lag, claims data doesn’t capture social circumstances, behavioral patterns, or care received outside a patient’s primary system. It presents a partial picture—and partial pictures lead to missed gaps.
How referential data closes those gaps
Referential data addresses these blind spots by drawing on external sources—clinical databases, laboratory results, demographic records, and wearable device data—to build a more complete and current picture of each patient. Rather than comparing records only within a single organization, referential matching cross-references patient data against comprehensive external identity databases. As LexisNexis Risk Solutions notes, internal algorithms alone are constrained by the data organizations already hold—external demographic data is what enables significantly higher matching accuracy.
This unlocks several concrete improvements:
✔ More accurate patient identification
When patients see multiple providers or change health plans, records fragment. Errors accumulate. Referential matching uses external identity data to reconcile those records, giving clinicians a reliable baseline from which to identify and act on gaps.
✔ Real-time longitudinal patient profiles
Combining clinical and claims data with social determinants of health—housing stability, employment, access to transportation—allows organizations to build the kind of longitudinal patient view that supports risk scoring and targeted outreach. Personify Health describes this as moving from reactive gap identification to predictive intervention—knowing who is at risk before a gap widens, rather than after.
✔ Actionable insights at the point of care
When referential data is embedded directly into EHR workflows, clinicians receive patient-specific gap alerts during the visit—not days later via a separate payer portal. Veradigm’s EHR alert platform demonstrates this in practice: by surfacing care gap information within the provider’s existing workflow, organizations reduce the need for follow-up appointments and increase the likelihood that gaps are addressed during the encounter itself.
The bottom line
When organizations invest in strong referential data infrastructure, patients receive more timely, personalized, and coordinated care. Providers reduce duplicative testing and unnecessary costs. And health systems move closer to delivering on the promise of value-based care—outcomes, not just activity.
The data already exists. The challenge is connecting it, trusting it, and putting it to work.
At Vector Medical Group, we help healthcare organizations build the data foundations needed to close care gaps and improve patient outcomes. Get in touch to find out how we can support your team.