The Data Silos Killing Your Star Ratings
Fragmented data systems increase payer operational costs 15–20% — and most of it is avoidable. Here’s where the leak starts.
Every October, health plan executives brace for the same ritual: the release of CMS Star Ratings. For Medicare Advantage plans, a single half-star improvement can unlock hundreds of millions of dollars in quality bonus payments. A half-star decline can trigger member attrition, reduce benchmark payments, and invite regulatory scrutiny. The stakes, in other words, could not be higher. So why are so many plans stuck?
The answer rarely has anything to do with the quality of care being delivered. Most MA members are receiving excellent care from committed clinical teams. The problem is that the data documenting that care never fully makes it back to the plan — at least not in time, not in a usable form, and not at the scale needed to move the needle on HEDIS measures.
The culprit is healthcare data silos, often driven by broader data fragmentation. And it is costing health plans far more than most realize.
What Data Fragmentation Actually Means (and Why It Persists)
Data silos in healthcare, often referred to as data fragmentation, are not a new problem. But the transition to value-based care has made it more consequential than ever. Under fee-for-service, a missed HEDIS measure was an administrative inconvenience. Under value-based contracts, it is a direct financial loss — and a quality signal that follows a plan into the following plan year.
The root cause is structural. Patient data lives in dozens of different systems: EHRs, specialty EMRs, lab systems, pharmacy systems, claims platforms, and care management tools. These systems were not designed to talk to each other. They use different data standards, different patient identifiers, and different update schedules. The result is that no single actor — not the provider, not the payer, not even the patient — has a complete, real-time view of what is happening in a care episode.
For health plans, this creates a specific and very expensive set of problems.
Where the Leak Starts: Three Points of Failure
1. The Chart Retrieval Problem
When a health plan needs medical records to close a HEDIS gap or validate an HCC code, the traditional process looks like this: a coordinator identifies the gap, generates a chart request, and sends it to the provider — usually by fax or through a portal the practice staff checks irregularly. The practice receives the request, locates the record, and responds manually. Turnaround time: days to weeks. Return rate: inconsistent at best. This is not a process problem that can be solved with better outreach. It is a structural problem. Manual chart retrieval scales poorly, creates friction in provider relationships, and produces data that arrives too late to influence the measurement window it was meant to close.
2. The Alert Delivery Gap
Health plans invest significant resources in generating quality alerts — overdue preventative screenings, missed annual visits, and chronic condition management. The problem is delivery. Most of these alerts are distributed through payer-operated portals that providers must log into separately, or through point-of-care tools that are not integrated with the EHR the clinical team is already using. The result is predictable. Providers do not check the portal. Alerts sit unread. Gaps go unclosed. And the plan’s quality score reflects a care gap that, in many cases, was actually addressed in the exam room — just never documented in a way the plan could capture.
3. The Closed-Loop Gap
Most quality programs are built on outbound effort: alerts pushed to providers, reminders sent to members, coordinators calling to schedule preventive visits. The missing piece is the return signal. When a provider completes a HEDIS-qualifying service, that documentation lives in the EHR and rarely flows back to the plan automatically, so quality teams end up running the same outreach on members whose gaps may have closed weeks ago. Plans waste resources chasing gaps that are already closed, generate friction with provider offices fielding duplicate requests, and lose the ability to prioritize members who actually need another touchpoint. Effective quality management requires a feedback loop, and most plans are running without one.
The Cost of the Status Quo
Healthcare data silos, or persistent data silos in healthcare, do not just create inconvenience — they generate measurable financial loss. Research consistently shows that data fragmentation increases healthcare payer operational costs by 15 to 20 percent. In a landscape where every administrative dollar matters, that is a number worth taking seriously.
The $4.9 trillion in projected U.S. healthcare spending is not an abstraction. A significant portion of that number is driven by unnecessary administrative overhead — the back-and-forth of chart retrieval, the rework from denied claims, the labor cost of manual quality management programs that produce inconsistent results. Most of it traces, directly or indirectly, back to the same root cause: systems that do not share data efficiently.
For health plans specifically, the downstream effects hit where it hurts most: Star Ratings that do not reflect actual quality performance and provider relationships that feel adversarial rather than collaborative — because the plan keeps asking for data the practice has already generated but cannot easily share.
“Interoperability isn’t just about data exchange — it’s about making sure payers and providers have a complete, real-time clinical picture of their mutual patients. When systems connect seamlessly, decision-makers can act faster and smarter.” — Megan Zakrewsky, Vice President, Clinical Data Exchange Payer Solutions, Veradigm
What Proactive Payers Are Doing Differently
The plans that are improving their Star Ratings are not working harder at the same broken processes. They are changing the infrastructure underneath those processes.
The shift looks like this: instead of sending chart requests that require manual provider response, they automate the data exchange at the point of care — so records flow to the plan as part of the normal clinical workflow, not as a separate administrative task. Instead of distributing quality alerts through portals no one logs into, they deliver those alerts inside the EHR, at the exact moment a provider is documenting a relevant encounter.
And critically, they don’t treat quality and risk gap closure as separate programs. The strongest plans build provider strategies that serve both — so the same outreach, the same relationship, and the same point-of-care touchpoint that closes a HEDIS gap is also surfacing the clinical context that supports accurate risk documentation. One coordinated approach, not two competing ones. The common thread is eliminating healthcare data silos through better data infrastructure. Not bigger quality teams. Not more outreach calls. Infrastructure that closes the loop between the care delivered and the data captured — automatically, at scale, without adding friction to provider workflows.