Insights from Heather Crosby, AVP of Clinical Strategy at b.well Connected Health
For years, healthcare quality measurement has promised better outcomes. What’s actually delivered? Administrative burden, delayed insights, and frustration for the people closest to care.
The issue isn’t the intent behind quality programs–it’s how we’ve been executing them. Fragmented systems, disconnected data sources, and inconsistent clinical logic that varies from one system to the next. The result is quality data that’s hard to trust and even harder to act on in time to make a difference.
Meanwhile, clinicians are asked to document the same information repeatedly. Quality teams burn hours manually reconciling reports that don’t match. And patients? They’re getting duplicate recommendations, sometimes contradictory ones, that make them wonder if anyone actually knows what’s going on.
As the industry transitions to Digital Quality Measures (dQMs), we have a rare opportunity to fix the foundation, not just for reporting compliance, but for timely care that meaningfully improves outcomes. dQMs represent more than a new format; they introduce a new operating model for quality, one that centralizes clinical logic, standardizes execution, and enables accuracy at scale.
At b.well, we believe quality should be continuous, transparent, and actionable, embedded into care delivery rather than layered on top of it. That belief has guided nearly a decade of work building interoperable, consumer-driven infrastructure that translates evidence-based guidance into real-time insights across the healthcare ecosystem.
Why Digital Quality Measurement Is a Turning Point
The shift from traditional quality measures to dQMs is an entirely necessary structural reset. For the first time, quality is being designed to run on standardized data, execute consistently across systems, and support multisource input, including clinical data, claims, and consumer-mediated information.
More importantly, dQMs are emerging as the leading mandate, driving alignment, a centralized, computable expression of clinical intent that can be executed consistently everywhere. When implemented correctly, dQMs become the system of record for clinical logic and measurement accuracy across an organization’s ecosystem and network.
Yet dQMs only deliver on that promise when they are built on the right foundation. FHIR and CQL form that foundation, the perfect pairing of open standards, interoperability, and computable clinical logic. Together, they enable digital quality to be repeatable, reusable, and portable—not just for quality reporting in a single system, but as a shared source of truth across environments.
When quality logic is authored and then executed consistently, it can be applied far beyond reporting. The same FHIR-native data and CQL-based logic that power dQMs can drive downstream and cross-system use cases, including care management, AI-driven predictive and proactive analytics, population health monitoring, prior authorization, and consistent, timely clinical decision support. In this model, digital quality becomes a plug-in value layer across the network, delivering consistent, credible insights wherever care decisions are made.
Hard-coded measures, proprietary data models, and siloed implementations create brittle systems that fracture as requirements evolve. Sustainable digital quality is enabled by a standards-native foundation built for scale, adaptability, and reuse. Built to grow alongside regulatory change, clinical innovation, and emerging data sources while continuously extending its value across the healthcare ecosystem.
From Retrospective Reporting to Continuous Quality
Historically, quality measurement has been retrospective. Performance is assessed weeks or months after the measurement period closes, long after opportunities to intervene have passed. dQMs on multisource (clinical, claims, patient-generated health data) data enable a fundamental shift from backward-looking reporting to near-real-time insights.
With standardized data and centralized logic, organizations can identify care gaps while there is still time to act. Quality moves from a year-end obligation to a continuous capability, informing outreach, guiding interventions, and aligning care teams around shared, trusted signals.
This creates a common language across payers, providers, and consumers. Quality becomes measurable, traceable, and actionable, no longer confined to a reporting function, but embedded into how care is delivered and experienced.
Architecture Is the Strategy
One of the most important lessons from digital quality transformation is that success is not driven by measures as standardized content alone; it is driven by architecture.
A future-ready quality system must be built on open standards, able to absorb new data sources, adapt to evolving clinical guidance, and scale across programs without rewriting logic each time. FHIR and CQL enable this by separating clinical evidence from application workflows, allowing logic to be authored once and reused across quality measurement, care management, predictive analytics, prior authorization, population health, and clinical decision support.
This architectural approach reduces implementation complexity, minimizes operational burden, and builds trust. Clinicians, quality leaders, regulators, and consumers can understand how results were calculated and why recommendations are being made. Transparency becomes a feature rather than an afterthought.
Quality Measurement That Works for Clinicians and Consumers
Quality measurement often fails because it is invisible until it becomes punitive. Clinicians rarely see how their actions influence outcomes, and consumers are left uncertain about which recommendations to follow.
Digital quality on multisource data changes that dynamic, especially when measured and shared using standardized frameworks and consistent, computable content.
What this means:
- For consumers, this creates accuracy and trust. Recommendations are clear, timely, and aligned across touchpoints, eliminating duplication and conflicting guidance. They can engage confidently in their care, knowing insights are based on a single, credible source of truth.
- For clinicians, real-time, evidence-based quality insights support better decisions at the point of care, without additional documentation burden.
- For organizations, transparent and auditable logic aligns care delivery with regulatory expectations while reinforcing credibility across the network.
The Next 12–18 Months Matter
The industry’s direction is visible; CMS is advancing digital submission as the default. NCQA is converting its full HEDIS portfolio to dQMs. Quality performance is increasingly tied to reimbursement, with meaningful financial impact accelerating in 2026 and beyond.
Organizations that succeed will be those that treat digital quality as a core capability, not a side project. This means investing in open standards architectures that can evolve as standards, measures, and care models change, while maintaining a centralized, trusted foundation for clinical logic and insight.
Reclaiming the Purpose of Quality
At its best, quality measurement exists to improve care. dQMs give us the tools to finally realize that purpose by centralizing logic, standardizing execution, and enabling accuracy across the ecosystem.
At b.well, we see digital quality not as a reporting requirement, but as a connective layer between evidence, data, and action. When quality is continuous, interoperable, and powered by reusable FHIR and CQL logic across multiple domains, it stops being something organizations chase and starts becoming something they use.
That is how quality moves from obligation to opportunity and how fixing the foundation truly improves outcomes.
