From reporting burden to real-time insight

Quality improvement in healthcare has reached an inflection point, as organizations are asked to prepare for a broader transition from today’s approaches to digital quality measures built on Fast Healthcare Interoperability Resources (FHIR) and Clinical Quality Language (CQL). This direction promises multiple advantages, like the standardization of both the data and quality measures themselves, improved interoperability across multiple systems and use cases, and the potential to improve the accuracy, timeliness, and usefulness of clinical quality information.

Getting there will require organizations to overcome significant practical challenges. Providers, payers, accountable care organizations (ACOs), quality reporting vendors, and government programs are already operating under significant reporting demands. Many still struggle to extract complete, consistent data from electronic health records for current electronic Clinical Quality Measurement (eCQM) reporting. The transition to FHIR and CQL can sometimes feel like another layer of burden while the benefits are not yet fully visible.

It is important to acknowledge, rather than minimize this friction. Technology is part of the solution, but it can only do so much without improvements in workflow. Leaders can prioritize key areas, including investment, testing, governance, and change management, to start making meaningful progress toward FHIR-based digital quality measurement. This transition will require patience and collaboration, but the many future-state benefits are worth the effort.

The current model is unsustainable long term

Reporting for quality measures today often depends on a patchwork of program-specific specifications, local mappings, custom extracts, manual reconciliation, and repeated data submission. The same clinical fact may be represented differently across systems or interpreted differently across reporting contexts. Organizations spend time and money aggregating records, deduplicating data, matching patients, resolving gaps, and validating results before a measure can be submitted with confidence.

For many providers and ACOs, these activities are downstream from care delivery. Data created at the point of care must be translated into a reporting structure after the fact, often on an annual or periodic cycle. This separation makes measurement slower and can limit its value to the learning health system: the cycle of care, measurement, reporting, research, and evidence-based updates to clinical decision systems that ensure improved outcomes for patients.

This burden is both technical and operational. A technically sound interface does not guarantee a workable quality reporting process. Organizations must understand who owns issues like data quality, exception handling, and how measure logic is tested. They must define how results are explained and how changes are introduced without disrupting existing obligations. These are workflow and governance challenges as much as technology challenges.

FHIR and CQL present possibilities—and risks

FHIR provides a common data model for representing and exchanging healthcare information. CQL provides a computable and deterministic way to express clinical logic. Used together, they create an opportunity to make quality measure specifications more modular, portable, and better aligned with the data exchange patterns the industry is currently adopting for other use cases.

An optimistic view should not obscure the practical challenges organizations face in transitioning to FHIR and CQL. Several concerns deserve direct attention:

• Data availability and quality. FHIR can standardize exchange, but it cannot create data that was never captured, correct inconsistent coding, or resolve unclear provenance. Missing, duplicated, delayed, or variably represented data will affect measure results unless data-quality work is prioritized.
• Ecosystem maturity. As FHIR implementation guides, terminology expectations, testing tools, and operational patterns evolve, early adopters may encounter uneven conformance and differences among systems that all claim to support FHIR-based exchange.
• Workflow disruption. New APIs and measure packages must align with real-world clinical, payer, and vendor workflows. Technology-driven implementations that overlook operational needs can create additional administrative burden, unclear responsibilities, and challenges in handling exceptions.
• Parallel obligations. Some organizations may need to sustain existing reporting workflows while building and validating new digital capabilities during this transition. Without thoughtful sequencing of requirements, this can create duplicate cost and effort.
• Trust and explainability. Measure authors and regulators need confidence that the same logic and data produce consistent, explainable results across different environments.
• Uneven readiness. Large organizations, small practices, vendors, payers, and ACOs do not have the same infrastructure, staffing, or access to high-quality data. A transition that ignores those differences can widen gaps rather than reduce them.

To move toward real-time insights, interoperability is necessary, but so is a quality-measurement workflow that’s more accurate, less burdensome, timelier, and more useful for improving patient care.

A workflow-centered path forward

The strongest transition strategies will begin with the work people must accomplish, not with the availability of a new endpoint. The goal should be to simplify the whole process, not optimize one technical step while leaving the surrounding burden intact.

For each measure and reporting context, stakeholders should trace the full journey of the data: where it originates, how it is captured, and how it is exchanged. We also need to consider where terminology is normalized, how measure logic is evaluated, how exceptions are resolved, and how results return to the people responsible for care and improvement.

That end-to-end view helps separate true automation from hidden manual effort. It also makes it easier to identify where standards can remove repeated mapping and where local governance is still required.

Organizations can move toward this kind of workflow-centered path by:

• Designing measures collaboratively. Government agencies, measure stewards, providers, payers, ACOs, EHR vendors, and quality-reporting vendors should design and validate requirements together. The people who produce, exchange, interpret, and act on the data all see different failure modes.
• Testing with representative data and workflows. Conformance testing is essential, but it should be paired with realistic end-to-end scenarios, including missing data, corrections, attribution changes, terminology updates, and late-arriving information.
• Creating a helpful migration path. Organizations need “easy buttons” that make some of the more difficult transition tasks—data mapping, terminology normalization, and bulk data exchange—trivial to the point where they do not need to pay outside vendors to do the work.
• Treating data quality as shared infrastructure. Common provenance, terminology services, validation rules, and feedback loops can help turn data-quality improvement from a local cleanup exercise into a powerful reusable capability.
• Measuring burden as well as compliance. A transition should be evaluated not only on whether a submission was accepted, but on the manual effort, rework, cost, timeliness, and usability of the resulting information.
• Designing for reuse. The more that data models, mappings, services, and implementation patterns can support multiple programs and payers, the more likely the transition is to produce durable value.

A shared responsibility, not a handoff

Government agencies can provide leadership in this space, but the transition to FHIR and CQL cannot succeed through policy and specification alone. Providers and payers understand the operational realities facing the industry today. Vendors understand product constraints and implementation patterns. Standards communities bring consensus processes and cross-market expertise. Measure developers understand the clinical intent that must survive every technical translation. These organizations need to work hand in hand.

Collaboration should start before FHIR and CQL requirements harden and continue through testing, deployment, and refinement. Feedback must be specific enough to improve the standard and the workflow, not merely document that implementation was difficult. When stakeholders share test cases, clarify edge conditions, and converge on reusable patterns, the maturity of the entire ecosystem increases.

This is also where organizations with one foot in implementation and another in the standards community can add significant value. These organizations can frame digital quality measurement as more than technology replacement. For the agency, they can connect policy intent, measure logic, data architecture, product behavior, clinical workflow, and operational change. For the field, they can bring lessons from implementation back to the standards community to inform future improvements.

Moving forward with ambition and practicality

The case for digital quality measurement is not that the work will be easy, but that continuing to scale the current model is unlikely to deliver the timely, interoperable, and actionable information the healthcare system needs.

Done well, the move to FHIR-based digital quality measures can create more than a modern reporting mechanism. Data can be submitted once and used for multiple measure calculations and multiple use cases. Data exchanged for care coordination, patient access, or population health can also support quality measurement when it is complete, semantically consistent, and fit for purpose. The long-term opportunity goes far beyond annual submission. Gaps and trends become visible closer to the point of care, rather than months after the fact.

But this future won’t arrive simply by replacing one file format with another. A successful transition will preserve what stakeholders have learned from today’s reporting programs while addressing the burdens those programs have accumulated. It will recognize uneven readiness, protect the clinical meaning of measures, and give organizations a practical route from current obligations to future capabilities. It will place data quality and workflow design alongside APIs and computable logic, and it will treat providers and payers as partners in implementation rather than endpoints for new requirements.

The transition is difficult because the problem is difficult, but organizations do not have to wait to make progress. Each of these steps is within their control, and a deliberate, collaborative approach can turn today’s burden into tomorrow’s capability.