Over the years, building healthcare data platforms, I've seen the same story repeat itself across Payviders, ACOs, and health systems. Everyone is chasing outcomes, but no one has a single, trusted view of the patient.
Each organization has pieces of the puzzle: clinical data in EHRs, claims data delayed by weeks, labs in a silo, and social data in spreadsheets. When you try to stitch it together, it's messy, inconsistent, and incomplete.
That fragmentation doesn't just slow analytics — it undermines the very foundation of Value-Based Care.
The Silent Saboteur: How Data Silos Break VBC
The Incomplete Patient Story
Imagine trying to treat a patient when every part of their story is held by a different system that doesn't talk to the others. That's the daily reality for most clinicians and care managers today.
We can't manage what we can't see.
Blind Spots in Risk Stratification
Without connecting clinical, claims, and SDOH data, "risk models" are half-blind. You miss key insights, such as repeated ER visits at another hospital or gaps in follow-up care.
Inaccurate Quality Measurement
Value-based contracts depend on accurate HEDIS and performance reporting. If the data is fragmented, you can't prove outcomes — and you lose the reimbursement you've earned.
Inefficient Care Coordination
Care teams spend hours chasing data instead of patients. Duplicated tests, missed follow-ups, outdated records — it all adds up to cost, friction, and frustration.
Breaking Down the Walls: What's Actually Working
The path forward isn't magic — it's engineering discipline + data governance + interoperability commitment.
✅ Invest in interoperability infrastructure
FHIR-first APIs, data normalization pipelines, and longitudinal patient views need to be core architecture, not side projects.
✅ Leverage HIEs and cross-network data exchange
Regional data collaboratives are underrated — they're the fastest way to close data visibility gaps.
✅ Focus on data quality at the source
Don't wait until the data warehouse to fix mapping errors, coding inconsistencies, or missing context. Automate validation early.
✅ Adopt a patient-centric data model
Design around the patient journey — not system boundaries. Capture every clinical, claims, and social touchpoint in one longitudinal record.
✅ Enable intelligent analytics
Once data is unified and trusted, AI and predictive models can finally deliver meaningful, actionable insights for clinicians.
My Take
Data fragmentation is the root cause of most VBC failures — not policy, not incentives, not even culture. Until we treat clean, connected data as the core enabler of care transformation, we'll keep fighting uphill.
This is the space I'm most passionate about — building modern, interoperable data layers that turn scattered data into something healthcare teams can actually use to improve outcomes.
If you're working on similar challenges or exploring ways to modernize your data platform for VBC, I'd love to connect and swap ideas.
Related Resources
Building Modern Healthcare Data Platforms →
Learn how to build a Lakehouse architecture with FHIR-based patient models and Medallion layers for unified patient data.
Data Platform Modernization Services →
Explore our approach to unifying fragmented healthcare data with modern cloud platforms and FHIR standards.
VBC Challenges in Healthcare →
Discover the five major challenges blocking Value-Based Care adoption, including data fragmentation and interoperability.
FHIR Data Platform Project →
See our work building longitudinal patient records using FHIR standards for a telehealth platform.
Need help building a unified patient view for your organization? Let's discuss your data platform strategy.