VBC Risk Is Outpacing Provider Readiness, CMS Must Act

CMS is accelerating downside risk in value-based care (VBC), even though many clinicians, practices, and health systems lack the operational and measurement infrastructure needed to succeed. Misalignment between organizational readiness and VBC performance requirements is a significant reason VBC programs have produced mixed results.^1,2 As risks increase, organizations at different levels of maturity must strengthen the executive, operational, and clinical capabilities needed for success.

Transition to New Models

Since July 2025, CMS and the Center for Medicare and Medicaid Innovation have announced several new models. Below are high-level summaries of 4 models chosen to illustrate key shifts in design, especially with respect to quality measures, financial performance expectations, and technology implementation: Advancing Chronic Care with Effective, Scalable Solutions Model (ACCESS),³ Ambulatory Speciality Model (ASM),⁴ Make American Health Again: Enhancing Lifestyle and Evaluating Value-based Approaches Through Evidence (MAHA ELEVATE),⁵ and Long-term Enhanced ACO Design Model (LEAD).

Many earlier VBC models had limited downside risk, allowing organizations to gain experience with care management, attribution, and performance measurement without major financial exposure. These models require or reward more advanced capabilities and different combinations of total-cost-of-care accountability, measurable quality performance, and longitudinal, condition-specific outcomes. Payment adjustments are increasingly tied to performance relative to benchmarks rather than to participation alone.

Here are some key highlights from the 4 models profiled:

1. ACCESS^3,7: This model focuses on clinical improvement or control of a condition based on each person’s baseline. There are 4 clinical tracks: early cardio-kidney-metabolic (CKM) conditions, existing CKM conditions, musculoskeletal (chronic pain), and behavioral health conditions (depression and anxiety). ACCESS introduces the concept of “outcome-aligned payments.” During the first year, organizations can earn full payment if at least 50% of applicable patients meet all required targets. If performance falls below that threshold, payment is reduced proportionately, with the reduction capped at 50% of the gross payment. The model also encourages the use of technology-supported care options.
2. ASM⁴: This model represents CMS’ first mandatory outpatient specialty model focused on chronic conditions in the ambulatory setting. Specialists in select regions who treat specific conditions (eg, heart failure and low back pain) will be required to participate. Measures will be condition-specific and focus on chronic condition management, avoiding unnecessary procedures, and care coordination.
3. MAHA ELEVATE⁵: This is a unique initiative designed to test evidence-based approaches to improve health beyond those currently covered by original Medicare, including functional or lifestyle medicine. Organizations applying for the cooperative agreements must propose a nutrition or physical activity component and provide data showing outcomes from their own program implementation prior to applying.
4. LEAD⁶: LEAD is the successor to ACO REACH⁸, which ends in 2026. LEAD introduces a 10-year performance period, revised benchmarking, and new participation and payment features. The intent is for this program to be more accessible to smaller, more rural, and independent practices, as well as those that serve high-needs patients. There are 2 risk-sharing arrangements:
   • Global risk: eligible to receive up to 100% of their savings and liable for up to 100% of total losses relative to their established performance benchmark
   • Professional risk: eligible to receive up to 50% of total savings and liable for up to 50% of total losses relative to their established performance year benchmark

The LEAD model also introduces a new way to encourage specialty practice participation through CMS-administered risk arrangements. It introduces CMS-supported episode-based risk arrangements between accountable care organizations and their specialists and provider organizations and establishes relationships with these downstream health care providers.

These models all set high expectations for participating organizations to identify individuals needing attention, manage populations, coordinate care across settings, and demonstrate measurable improvements in cost and quality. For many participants, these models will require new data infrastructure, a focus on data quality and completeness, performance analytics, and digital quality reporting capabilities. For specialty practices, some of which will now be required to participate in the ASM, the transition may be even harder due to limited experience with population management, longitudinal quality tracking, and financial risk management.

Organizations Need to Get Ready

The signals from CMS are clear. Health care organizations, population health entities, technology vendors, and investors need to recognize that 4 major transitions are underway:

1. Payment is shifting toward higher risk-based models. CMS is driving this now, but commercial payers and Medicaid agencies will likely follow.
2. Measurement is moving toward digital quality measures. The ability to build more sophisticated logic into measures, use clinical data from multiple sources, and provide timely, actionable insights and guidance within clinician workflows will influence care in ways that our current measurement model cannot.
3. Access to timely, high-quality clinical data to support multiple clinical, operational, and financial use cases is becoming the most critical element of the transition. Without it, the implementation of artificial intelligence (AI) will never achieve its full potential.
4. Member (patient) engagement through technology-supported, personalized channels and data sharing is becoming essential to success VBC models, and CMS is now incorporating these expectations into models such as ACCESS.

Organizations need a cohesive strategy to address these priorities. Since the pace of external change is difficult to influence, effective internal planning must be flexible and anticipate policy changes and technological advancements. For example, if operational changes advance faster than payments evolve, organizations may implement costly technology that erodes margins. If the transition to digital measurement moves faster than operations can accommodate new data flows and insights, clinicians may be overwhelmed with data and reporting requirements. If access to high-quality data does not keep pace with digital quality measurement needs, data gaps could undermine performance on quality measures and lead to adverse financial implications.

What Organizations Should Do

Although there are many operational and structural areas organizations could address, here are 3 that are fundamental:

1. Improve access to high-quality clinical data: accessible, high-quality clinical data are essential for success. Without such data, digital quality measures will produce incomplete results. In addition, AI, predictive modeling, and other clinical, operational, and financial activities (eg, attribution monitoring, population health initiatives, and enterprise management) will be adversely affected. Organizations should map their current internal and external data sources to improve data access and quality and prioritize sources that conform to the United States Core Data for Interoperability (USCDI)⁹ and the HL7 Fast Healthcare Interoperability Resources (FHIR)¹⁰ standards.
2. Translate data into personalized care and population health improvement: success in VBC requires organizations to optimize care at the individual and population levels. These are related, but not the same. Getting better clinical data is necessary but insufficient unless it generates insights at the point of care to support discussions, decisions, and delivery of care. For example, individualizing care plans depends on having information about clinical conditions, patient preferences, social needs, and risks. Without these data, clinical teams will not be able to personalize approaches to clinical and behavioral interventions or foster adequate patient engagement. Organizations need to work toward surfacing context-sensitive, clinically relevant, patient-specific data to support quality gap closure and personalized care plans. At the system level, these same data elements should support robust population health improvement through programs intentionally designed to meet the needs of specific population segments based on their clinical and health-related social needs.
3. Use digital tools to extend care beyond clinical encounters: much of health management occurs between encounters, yet research shows that patients immediately forget 40% to 80% of the information clinicians share.¹¹ How well people manage their health conditions depends heavily on their actions and the choices they make between encounters with health professionals. Digital behavior change interventions that deliver personalized or adaptive guidance can make a difference.¹² Organizations should consider implementing condition-agnostic platforms that deliver customized, situation-sensitive reinforcement of behaviors and education. Ideally, the platform would escalate important and urgent matters to the care team to help avoid unwarranted utilization and morbidity.

What Should CMS Do?

Develop a New Generation of Digital Quality Measures. CMS and VBC payment proponents should acknowledge the limitations of current measures for distinguishing between higher- and lower-performing providers for differential payment. In addition, it is insufficient to digitize the current measurement portfolio. Doing so will perpetuate the limitations of retrospective, accountability-based measures and those created by the historical limitation on access to digital clinical data. CMS should provide additional financial support for measure developers to build the next generation of digital quality measures. These measures should be aligned with the needs of each level of the health care system, address improvement and accountability, and connect more meaningfully with clinical care across primary and specialty medicine. New measures should be based on current national standards and align with expectations of the CMS Interoperability Framework¹³ and existing interoperability policies,¹⁴ including USCDI, FHIR, Trusted Exchange Framework and Common Agreement, Office of the National Coordinator for Health Information Technology certification, and information blocking rules.

Couple Payment Models with Technology Support. Payment models need to acknowledge the operational and financial challenges organizations face regarding readiness for these major changes. Reimbursement models should help participants cover the cost of the structural, operational, and technological improvements necessary to succeed. Yet that may not be sufficient. The Health Information Technology for Economic and Clinical Health Act,¹⁵ part of the American Recovery and Reinvestment Act of 2009, provided support for Regional Extension Centers (RECs),¹⁶ which were designed to support the transition to electronic health records (EHRs) and achieved significant success in engaging small, rural, and underserved practices to adopt EHRs and meaningful use. There hasn’t been a comparable program since, other than the Transforming Clinical Practice Initiative,¹⁷ which created Practice Transformation Networks and Support & Alignment Networks that provided technical assistance to primary and specialty care to develop comprehensive quality improvement strategies. Although Quality Improvement Network/Quality Improvement Organizations¹⁸ also support quality improvement, they are not charged with assisting with payment model transformation.

CMS should consider reintroducing focused technology and operational support for this major transition through a program like the RECs or a similar effort in Maryland, which introduced Care Transformation Organizations¹⁹ as part of the state’s Primary Care Program. The recent LEAD Request for Applications²⁰ describes a new Tech Enabler Initiative (TEI) and group learning forums. According to CMS, the TEI will help participating organizations “identify high-value technology and AI use cases, build out tech application requirements, and establish a channel for vendors to publicly share key features of their technology applications and how they support identified use cases and align with CMS tech application requirements.” The learning forums will be virtual and are described as “peer-to-peer collaboration, networking, and real-time problem solving with participants in LEAD and other CMS Innovation Center models.” The TEI and learning forums will likely be sufficient for practices further along in the technology adoption curve, but may not be as beneficial for those earlier in the transition.

Conclusion

The transition to VBC is not just a new payment model. It is a system redesign that requires changes across quality measurement, clinical operations, digital infrastructure, and patient engagement. Organizations are entering this transition from very different starting points. Some have extensive experience in VBC and have invested in analytics, data exchange, and workflow redesign. Others are early in their journey and are building the needed capabilities.

CMS is clearly intent on moving the health care system toward greater financial accountability, involving specialists in risk arrangements, and adopting technology-supported care. These are laudable goals, but payment models alone will be insufficient to achieve them.

Without accessible, high-quality clinical data and better digital quality measures, CMS will be unable to adequately differentiate performance for payment or generate population-level improvements in outcomes. AI will also fail to achieve its potential, and clinicians will continue to struggle with fragmented workflows and administrative burden.

The next phase of VBC will require more than incremental policy adjustments. Payment, measurement, technology, and operations must evolve together, and CMS must play a more active role. Otherwise, CMS risks accelerating downside accountability faster than many organizations can realistically build the infrastructure and operational capabilities for success.

References

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