How should quality measurement evolve as CMS expands value-based care?
As the Centers for Medicare & Medicaid Services (CMS) shifts Medicare toward prospective payment and accountable care, quality measurement must evolve from encounter-based reporting to longitudinal, digital measurement that evaluates patient outcomes across the full care journey. Payment reform and measurement reform must advance together to support value-based care.
Key Takeaways
- CMS is shifting Medicare toward prospective, population-based payment models.
- Traditional encounter-based quality measures were designed for fee-for-service care.
- Digital quality measures should evaluate longitudinal outcomes, prevention, and care coordination.
- Interoperability infrastructure—including Fast Healthcare Interoperability Resources (FHIR) APIs and Qualified Health Information Networks (QHINs)—should enable measurement rather than function solely as a compliance requirement.
- Healthcare organizations need integrated policy, data, analytics, governance, and technology capabilities to succeed in value-based care.
Across the Innovation Center strategy, accountable care initiatives, and the National Quality Strategy,[1] the policy direction is increasingly clear: payment models should reward positive outcomes, seamless care coordination, and high performance across the entire patient journey—not just an isolated activity.
Prospective payment encourages primary prevention, cohesive team-based care, virtual engagement, timely social needs response, and proactive follow-up. However, the quality measurement architecture supporting Merit-based Incentive Payment System (MIPS) Value Pathways (MVPs)[2] and Promoting Interoperability (PI)[3] is not keeping pace. Too much of today’s quality measurement focuses only on encounters, service-oriented events, fixed reporting periods, and discrete measure submissions. As a result, CMS risks building future payment models on measurement logic designed for a traditional transactional healthcare system.
Success in value-based care requires the ability to manage longitudinal data, understand quality measure logic, close care gaps proactively, establish accountable care relationships, and translate digital infrastructure into measurable improvements in quality, outcomes, and cost performance.
The central challenge is not whether quality measurement becomes digital. It is whether digital quality measurement becomes truly aligned with prospective accountability.
CMS Focuses on Outcomes
CMS’s recent strategies point toward coordinated, team-based, accountable care in which clinicians and other entities are responsible for quality, cost, and outcomes over time. This policy shift recognizes that meaningful improvement often happens outside the walls of a triggering encounter: closing a care gap before a visit, preventing deterioration, reconciling medications after a transition, engaging a patient between appointments, or coordinating services across settings.
Prospective payment rewards organizations for managing risk. Quality measurement should therefore follow the same logic. Clinical quality measures used for accountability should evaluate patient-centered outcomes, longitudinal trajectories, prevention and early detection, patient experience, and cross-provider coordination. Technical capability measures matter but should not be confused with patient outcome signals.
A measurement system built for the future must be able to evaluate whether accountable entities are improving patient outcomes over time. It should capture prevention, patient experience, care coordination, and total cost management in ways that reflect real clinical accountability. That is a different task than confirming whether documentation was completed during a denominator-eligible encounter.
dQMs Should Not Simply Digitize Legacy Reporting
Digital quality measures (dQMs) are quality measures expressed as standardized computable specifications, using FHIR and Clinical Quality Language (CQL) to automate measure calculations, reduce manual data abstraction, and provide more timely insights into patient care. Traditional clinical quality measure models were developed when interoperable, electronic clinical data were limited, and manual abstraction was standard practice. Because many still reflect important evidence-based care, those measures should not necessarily be discarded, but technical specifications must be re-evaluated to effectively operate in a healthcare environment in which connected networks, APIs, and broader data liquidity make earlier identification, cross-setting coordination, and proactive outreach are the new normal.
MVPs are an important evolutionary step for MIPS. By grouping measures and activities around specialties, conditions, or episodes of care, MVPs can make quality reporting more coherent and clinically relevant than traditional MIPS. However, MVPs do not automatically transform the core logic of quality measurement, nor do they enable the individual measures to function as a cohesive unit. If the measures inside an MVP remain tied to encounter-triggered denominators, legacy numerator-denominator constructs, and retrospective submissions, the program may become more organized without advancing value-based care. Successful administrative reporting is not the same as patient improvement, and any quality measurement system used for prospective population-based accountability should make that distinction explicit.
CMS’s digital quality measurement agenda[4] offers a major opportunity. Digital quality measures can draw from standardized electronic data, support FHIR-based exchange, reduce manual abstraction, and create more timely feedback loops for quality improvement. In a mature interoperable environment, data from EHRs, claims, registries, health information exchanges, devices, and other relevant sources help organizations identify care gaps before visits occur and effectively track outcomes across settings. However, digitization alone is not modernization. A quality measure can be expressed in FHIR-CQL and packaged as a dQM while still carrying assumptions from the previous era of clinical quality reporting. If the underlying logic remains anchored in payment-coded encounters and retrospective documentation, the industry will simply automate yesterday’s measurement model.
Without modernization across both quality measurement and payment, CMS will create an increasingly digital system that remains fundamentally encounter-based. Organizations may invest in coding, documentation, attestation, and measure optimization while remaining only loosely connected to the outcomes that matter to patients and purchasers. The result would be more burden, more opportunities for gaming, and weaker alignment between quality reporting and the goals of value-based care. While this might reduce some reporting friction, it would not support the prospective accountability CMS is advancing.
Promoting Interoperability Is Necessary Infrastructure
The same concern applies to PI. Interoperability is essential to modern accountability, but when PI operates primarily as a scored compliance category—through Certified Electronic Health Record Technology (CEHRT),[5] attestations, fixed reporting windows, and required measure sets—it can become a parallel administrative layer rather than the infrastructure that enables better outcomes. This creates a subtle but important policy risk: CMS may reward technical compliance even when the measurement system does not reliably demonstrate improvements in longitudinal health.
This argument is not anti-interoperability. In fact, prospective payment absolutely depends upon a reliable interoperability infrastructure. QHINs,[6] FHIR APIs, patient access capabilities, health information exchange, e-prescribing, and electronic public health reporting are all foundational to a digital learning health system and to effective longitudinal care management.
The issue is how interoperability is recognized as an integral part of a prospective quality strategy. PI should function less like an independent scoring domain and more like the infrastructure that allows accountable entities to understand their patient populations healthcare needs, efficiently exchange actionable care plans, identify care gaps, incorporate patient-generated data, and continuously evaluate outcomes over time.
Conclusion: Success in Value-Based Care Requires Operational Transformation
CMS is moving toward prospective payment, accountable care, interoperability, and outcome-based accountability, but payment reform and measurement reform must advance together. If CMS continues to place modern payment models on top of legacy measurement logic, the system may become more digital without becoming more meaningful. The next phase of quality strategy should use interoperability not as an end goal, but as the operating foundation for measuring what prospective payment is intended to reward—better outcomes across the full patient journey. For healthcare organizations, the implications are immediate. Success in value-based care will require more than compliance with reporting requirements. It will require the ability to manage longitudinal data, understand measure logic, close care gaps proactively, establish accountable care relationships, and translate digital infrastructure into measurable improvements in quality, outcomes, and cost performance.
As CMS expands accountable care and prospective payment, organizations will need quality measurement systems that evaluate outcomes across the patient journey—not simply document clinical encounters. Digital quality measurement, interoperability, longitudinal analytics, and proactive care management will increasingly become core capabilities for success in value-based care.
HMA’s perspective: Digital quality measurement should not simply automate legacy quality reporting. It should measure whether accountable organizations improve patient outcomes over time.
HMA’s Digital Healthcare Quality Transformation service brings a unique combination of expertise spanning healthcare policy, value-based care strategy, interoperability, data quality, digital quality measurement, analytics, governance, and operational transformation. We work with health plans, providers, ACOs, states, and healthcare innovators to bridge the gap between regulatory compliance and real-world performance, helping organizations build the data infrastructure, governance frameworks, care delivery capabilities, and measurement strategies needed to succeed in an increasingly digital and outcomes-driven healthcare ecosystem. By connecting strategy, technology, and execution, HMA helps clients move beyond compliance and develop the capabilities necessary to deliver measurable improvements in quality, patient outcomes, operational performance, and value.
Frequently Asked Questions
What are digital quality measures (dQMs)?
Digital quality measures (dQMs) use standardized electronic clinical data to evaluate healthcare quality and outcomes. dQMs leverage standards such as Fast Healthcare Interoperability Resources (FHIR) and Clinical Quality Language (CQL) to automate measure calculations, reduce manual data abstraction, and provide more timely insights into patient care. When implemented effectively, dQMs enable healthcare organizations to identify care gaps, monitor performance, and improve patient outcomes using interoperable data.
Why is CMS modernizing quality measurement?
The Centers for Medicare & Medicaid Services (CMS) is modernizing quality measurement to support its transition from fee-for-service reimbursement to prospective, value-based payment models. As Medicare increasingly rewards organizations for improving patient outcomes, managing population health, and coordinating care across settings, quality measurement must evolve beyond encounter-based reporting to evaluate performance across the entire patient journey.
Why are traditional quality measures no longer sufficient?
Many traditional clinical quality measures were designed for a healthcare system built around individual encounters, retrospective reporting, and manual data collection. While many remain clinically important, they often do not fully capture longitudinal care management, prevention, patient engagement, or care coordination. As payment models shift toward population-based accountability, quality measurement must better reflect how organizations improve health outcomes over time.
What is longitudinal outcomes measurement?
Longitudinal outcomes measurement evaluates patient care across time rather than during a single point in time. Instead of measuring whether a required action occurred during an office visit, longitudinal measurement assesses whether healthcare organizations identify care gaps, coordinate services, engage patients, prevent disease progression, and improve health outcomes throughout the patient’s care journey.
How do MIPS Value Pathways (MVPs) support value-based care?
Merit-based Incentive Payment System (MIPS) Value Pathways (MVPs) organize quality measures, improvement activities, and cost measures around specific specialties, conditions, or episodes of care. This approach makes reporting more clinically relevant than traditional MIPS reporting. However, achieving meaningful value-based care also requires measures within MVPs to evolve beyond encounter-based logic and better reflect longitudinal accountability and patient outcomes.
What role does interoperability play in quality measurement?
Interoperability enables healthcare organizations to securely exchange clinical information across providers, health plans, public health agencies, and patients. Standards such as FHIR APIs, Qualified Health Information Networks (QHINs), electronic health records (EHRs), and health information exchanges support more complete patient information, improve care coordination, and provide the data needed for digital quality measurement and population health management.
How are digital quality measures different from electronic clinical quality measures (eCQMs)?
Electronic clinical quality measures (eCQMs) digitized many traditional quality measures by using electronic health record data instead of manual chart abstraction. Digital quality measures (dQMs) build on this foundation by using modern interoperability standards, including FHIR and CQL, to improve data exchange, automation, and scalability. However, simply expressing a measure digitally does not modernize its underlying clinical logic.
Why is prospective payment changing quality measurement?
Prospective payment models reward healthcare organizations for managing the health of patient populations rather than billing for individual services. Because providers are increasingly accountable for outcomes, prevention, care coordination, and total cost of care, quality measurement must evaluate these longitudinal activities instead of focusing primarily on documentation associated with individual encounters.
What capabilities do healthcare organizations need to succeed in value-based care?
Success in value-based care requires more than meeting reporting requirements. Organizations need the ability to integrate longitudinal clinical and claims data, understand quality measure logic, identify and close care gaps proactively, exchange data through interoperable systems, support coordinated care teams, monitor patient outcomes continuously, and use analytics to improve quality, cost, and operational performance.
How can healthcare organizations prepare for the future of CMS quality measurement?
Healthcare organizations can prepare by investing in interoperability, data governance, digital quality measurement capabilities, analytics, and clinical workflows that support proactive care management. Organizations that align policy, technology, quality measurement, and operational transformation will be better positioned to succeed as CMS expands prospective payment, accountable care, and outcomes-based reimbursement.
[1] Centers for Medicare & Medicaid Services. CMS National Quality Strategy. Available at: https://www.cms.gov/medicare/quality/meaningful-measures-initiative/cms-quality-strategy.
[2] Quality Payment Program. MIPS Value Pathways (MVPs). Available at: https://qpp.cms.gov/reporting-requirements/ways-to-report/mvp.
[3] Quality Payment Program. Promoting Interoperability: APP Requirements. Available at: https://qpp.cms.gov/reporting-requirements/ways-to-report/app/promoting-interoperability.
[4] Centers for Medicare & Medicaid Services. Optimal health for All Within Nation’s Health and Long-Term Care Systems: CCSQ FY2025–2028 Strategic Roadmap. March 11, 2026. Available at: https://www.cms.gov/newsroom/blog/optimal-health-all-within-nations-health-long-term-care-systems-ccsq-fy2025-2028-strategic-roadmap.
[5] Centers for Medicare & Medicaid Services. Certified EHR Technology. Available at: https://www.cms.gov/medicare/regulations-guidance/promoting-interoperability-programs/certified-ehr-technology.
[6] Ibid