Siloed health data: the infrastructure problem nobody has solved yet
The average patient's health data is scattered across 19 different systems, and no integration standard has fixed the problem. Siloed health data costs U.S. healthcare roughly $150 billion per year in redundant testing, care delays, and administrative waste. The issue is not a lack of interoperability standards; it is that the data itself has never been scored for trustworthiness before anyone tries to connect it.
A single patient with a chronic condition generates records across an average of 19 different health IT systems. Primary care, specialists, labs, imaging centers, pharmacies, wearables, behavioral health, social services. Each system stores a fragment. None of them talk to each other in a way that produces a reliable, complete picture.
This is the siloed health data problem. And despite two decades of interoperability mandates, HL7 FHIR adoption pushes, and billions in EHR spending, nobody has solved it.
Why integration keeps failing
The conventional explanation is technical: different formats, different APIs, different vendor incentives. That is true, but incomplete.
The deeper issue is that connecting data across systems only works if the data in each system is trustworthy. When a lab result has no provenance trail, when a consent record is ambiguous, when a clinical note was last updated 14 months ago, merging these records does not create integration. It creates a larger pile of unreliable information.
Every major interoperability initiative, from Meaningful Use to the 21st Century Cures Act's information blocking rules, has focused on moving data between systems. Almost none have focused on whether the data is worth moving.
Why is siloed data a problem for customers building an integrated storage infrastructure?
Organizations building centralized data warehouses or federated data networks face a compounding trust problem. When you pull records from 12 different source systems into a single repository, you inherit every quality issue, every stale record, every consent gap from each source.
Without a trust score on each record, you cannot prioritize. You cannot tell which records are safe to use for AI model training, which meet regulatory thresholds, and which will introduce liability. The result is an integrated storage layer that looks complete but is functionally unreliable.
SuperTruth's Data Trust Index scores every record from 0 to 100 across eight dimensions: Provenance, Consent, Recency, Quality, Concordance, Validation, Breadth, and Stability. When imaware brought 105,000 diagnostic records through the DTI Engine, we reduced standardization time from three weeks to two hours and identified a patient segment driving 20% of their revenue that had been invisible in their fragmented data.
Key statistics
The numbers illustrate why this problem persists and what happens when you address it directly.
How does infrastructure affect health?
Infrastructure here means more than roads and pipes, though those matter too. Data infrastructure determines whether a physician sees your full medication history before prescribing, whether a care coordinator knows you missed three appointments because you lost transportation, whether an AI model trained on population data actually reflects your community.
When data infrastructure is fragmented, patients fall through gaps. A veteran transitioning from VA care to a private system loses continuity. A rural patient whose county health department uses a different reporting system than the nearest hospital becomes invisible to population health models. A child whose pediatric records sit in a system that does not communicate with their school-based health program gets duplicate screenings or, worse, none at all.
The lack of trusted, connected data infrastructure does not just slow down care. It shapes who receives care and who does not.
What rare medical cases remain unsolved because of data silos?
Rare disease diagnosis takes an average of 4.8 years in the United States. A significant factor is that the clinical signals are scattered across specialists who never see each other's notes. A geneticist in one system flags a variant of uncertain significance. A neurologist in another system documents an unusual symptom pattern. A third provider orders labs that, combined with the first two data points, would point to a diagnosis.
But the records never converge. The patient waits. For conditions affecting fewer than 200,000 people, the probability that any single provider has seen the full pattern is low. The only way to accelerate diagnosis is to connect the fragments, and connecting fragments requires knowing which ones are current, validated, and consented for use.
The trust layer that has to come first
FHIR solves the format problem. Information blocking rules address the access problem. But neither solves the trust problem.
Before you integrate, you need to know: Is this record current? Was consent properly obtained? Does it match what other sources say? Has it been validated against a primary source?
This is what SuperTruth built the DTI Engine to answer. Every record gets a score. Every score breaks down across eight weighted dimensions. Organizations can set thresholds: Platinum-grade data for FDA submissions, Gold for AI training, Silver for population analytics. No more guessing which records are safe to use.
As imaware CEO Brodie Flanders put it: "The lab industry has never had a trust standard. DTI created one."
What actually has to change
The industry does not need another interoperability standard. It needs a trust standard that sits underneath every integration effort. Score the data before you move it. Flag the gaps before you build models on top of them. Give patients, providers, and payers a shared language for data reliability.
That is the infrastructure layer nobody built until now.
To see how trust scoring changes integration outcomes for your organization, contact Louis Simeonidis, SVP Commercial Operations, at louis@supertruth.ai or (215) 918-4140.
Further reading:
Jason Alan Snyder
Co-founder of SuperTruth and Artists & Robots, and an inventor on the Data Trust Index patents. Twenty-plus years building technology inside Interpublic Group. He writes here nearly every day on data trust, provenance, and what AI should be allowed to act on, and publishes essays on his Substack.
About SuperTruth · LinkedIn · Substack · jasonalansnyder.com
See it in practice
The FICO score for health data.
8 dimensions. 0–100. Travels with every record permanently.