Bridging the 20% Invisible Gap in Healthcare Data Reliability and Success

by Leo Pak Most pilots begin with a sense of reassurance.

Data is flowing. Interfaces are live. Dashboards show steady volume. From the outside, everything looks healthy. The system appears to be doing exactly what it was built to do. For the first few weeks, no alarms go off.

Then we start to slow the conversation down. Instead of asking how much data is coming in, we ask a quieter question. How much of this data can you actually rely on? That question changes everything.

Across multiple three month data quality pilots with healthcare organizations, we see the same pattern repeat. Between 15 and 25 percent of messages never make it through cleanly enough to be trusted. They are quarantined, filtered out, or silently dropped somewhere along the way. That is not a rounding error. That is one out of every five messages.

What is striking is not the number itself, but the reaction to it. Most organizations have no idea this is happening. Not because they are careless, but because their systems were never designed to surface these failures. The platform reports success. The data appears to move. The gaps remain invisible.

When we trace the failures back, they rarely point to anything exotic. The issues are familiar to anyone who has worked in healthcare data long enough. A missing demographic field that breaks patient identity. A local lab code that was never mapped to a standard. Duplicate records that belong to the same person but tell different stories. Messages that technically arrive but fail HL7 or FHIR validation. Events that violate basic clinical logic, like a discharge recorded before an admission.

None of these problems feels catastrophic on its own. Together, they quietly erode trust.

Around the sixth week of a pilot, we share something simple with our clients. A quarantine report. No drama. No blame. Just a clear view into what has been happening beneath the surface. That is usually when the room goes still. Eventually someone says it. “We had no idea.”

Most teams assume their analytics are off because of lag, workflow issues, or reporting complexity. What they do not realize is that a significant portion of their data never qualified as usable in the first place. You cannot analyze what never truly arrived. What happens next is the most important part of the story.

Once the problems are visible, they stop being mysterious. Through systematic curation, mapping codes, resolving identity conflicts, enforcing validation rules, and working directly with data contributors at the source, we consistently see quarantine rates fall. In many cases, organizations move from around 22 percent down to under 3 percent in roughly 90 days.

Nothing magical occurs. No data is invented. No black box AI fills in the gaps. The data was always there. It simply needed refinement. This matters now more than ever.

As healthcare accelerates toward AI, advanced analytics, value based care, and real time decision support, incomplete data is not just inefficient. It is misleading. Models trained on partial information will still produce answers. They will just be confidently wrong. This is where Lynqsys plays its role.

Our job is not to shame systems or point fingers. Our job is to reveal what is hidden. To show what is being dropped, why it is happening, and where the breakdowns begin. Then we help fix those issues in a way that is measurable, durable, and grounded in operational reality.

Think of it less as cleaning data and more as refining it. Raw inputs become usable intelligence.

If you are curious what your own data flows are hiding, a three to four month pilot will show you clearly.

By following this lifecycle through a pilot with our platform Lynqsys, organizations move from "Data Exhaust" to visible outcomes with telemetry. We consistently see quarantine rates fall from approximately 22% to under 3% in roughly 90 days. This is not achieved by "inventing" data or using black-box AI to fill gaps; it is a systematic reduction in risk through the refinement of data that was already there but remained unusable.

Seeing the Full Picture

In the age of advanced analytics and value-based care, "cleaning" data is no longer a sufficient strategy. Organizations must shift toward "refining" data into intelligent assets that provide a transparent, auditable trail.

Models operated on partial information will still produce answers, but those answers will be confidently wrong. In a clinical environment, that is a risk no leader should accept. Operating without seeing the full picture of your data flow is no longer a viable strategy for healthcare leadership.

What are your own data flows hiding? DM me if you want to discuss what a pilot could reveal for your organization. by Leo Pak

Chief Executive Officer

leo.pak@interstella.us