Cardiac AI Reimbursement: Who Wins (and Loses) Billions?

The seismic shift in healthcare reimbursement, moving from a volume-centric fee-for-service model to outcome-based value-based care, is deeply reshaping the field for remote algorithmic monitoring. This transition, particularly as it applies to digital health, creates a bifurcated market where certain stakeholders are poised for significant financial gains while others face substantial barriers to entry and sustainability. This analysis dissects the financial winners and losers under current Centers for Medicare and Medicaid Services (CMS) policies, offering critical insights for healthcare investors and policy advisers working through this evolving domain.

The Shifting Sands of Remote Monitoring Reimbursement

The Centers for Medicare and Medicaid Services (CMS) has been instrumental in driving the adoption of remote patient monitoring (RPM) and remote therapeutic monitoring (RTM) through specific Current Procedural Terminology (CPT) codes. These codes, which the American Medical Association (AMA) manages, have laid the groundwork for reimbursing digital health interventions. However, the nuances within the CMS Physician Fee Schedule and the AMA’s digital medicine code statements reveal a clear bias towards entities capable of strong integration and scale. Initially, the introduction of RPM codes (e.g., CPT 99453, 99454, 99457, 99458) provided a pathway for providers to bill for the collection and interpretation of physiologic data. For 2026, new RPM codes 99445 (for device supply for 2-15 days) and 99470 (for 10-19 minutes of monitoring and management) have been introduced to cover shorter monitoring durations. Subsequently, RTM codes (e.g., CPT 98975, 98976, 98977 for device supply, and 98980, 98981 for monitoring and management) expanded this to include therapeutic interventions, often using Software as a Medical Device (SaMD) platforms. For 2026, new RTM codes 98984, 98985 (for device supply for 2-15 days) and 98979 (for 10-19 minutes of monitoring and management) have been added to enhance flexibility. The critical distinction lies in the administrative and clinical burden associated with these codes. While they open up new revenue streams, they also demand significant infrastructure for data ingestion, analysis, and clinical workflow integration.

CMS Policies and AMA Code Updates: A Financial Dissection

The CMS Physician Fee Schedule final rules consistently underscore the agency’s commitment to value-based care, linking reimbursement to demonstrable patient outcomes. For remote algorithmic monitoring, this translates into a preference for platforms that can not only collect data but also generate actionable insights that prevent adverse events or improve chronic disease management. The Medicare reimbursement rates for RPM and RTM codes, while attractive on paper, come with stipulations regarding patient engagement, data transmission frequency, and clinical oversight. CMS Physician Fee Schedule final rule The American Medical Association’s digital medicine code statements further illuminate the requirements for successful reimbursement. The AMA’s CPT Editorial Panel, responsible for developing and maintaining the CPT code set, has emphasized the need for clinical validation and integration into existing care pathways. For instance, the inclusion of Category III CPT codes for emerging technologies often is a precursor to Category I codes, provided there is sufficient evidence of clinical utility and widespread adoption. Companies that can demonstrate a clear return on investment in terms of improved patient health and reduced healthcare utilization are better positioned to secure favorable reimbursement and, eventually, permanent Category I codes. Consider the example of Biofourmis, a company developing remote monitoring platforms. In August 2025, Biofourmis closed a $463.6 million funding round to advance its AI-driven care solutions. Their business model thrives on providing complete solutions that integrate various data streams and offer AI-driven insights for proactive care. Their ability to demonstrate improved patient outcomes in complex conditions like heart failure or chronic obstructive pulmonary disease directly aligns with the value-based reimbursement model. Such platforms, which can support the administrative and clinical requirements for billing RPM and RTM codes effectively, are clear beneficiaries.

Winners and Losers in the Value-Based Digital Health Reimbursement Arena

The transition to outcome-based reimbursement for remote algorithmic monitoring creates distinct advantages for some stakeholders while presenting significant hurdles for others.

The Winners: Large Health Systems and Integrated Digital Health Platforms

Large health systems, particularly those with existing infrastructure for electronic health record (EHR) integration and strong IT departments, are well-positioned to capitalize on these changes. Their capacity to invest in sophisticated remote monitoring platforms, manage complex data flows, and implement standardized clinical protocols allows them to efficiently bill for RPM and RTM services. Plus, their ability to aggregate patient data across a broad population strengthens their position in demonstrating population-level outcome improvements, which is important for value-based contracts. Integrated digital health platforms, like Biofourmis, that offer end-to-end solutions for remote monitoring, from device provision to AI-powered analytics and clinical support, also stand to benefit immensely. These companies often possess the technical expertise and regulatory acumen (e.g., achieving SaMD clearance and adhering to GMLP principles) to build compliant and effective solutions. Their ability to deliver a “data moat” through proprietary datasets and sophisticated algorithms enhances their competitive advantage, making it difficult for new entrants to replicate their performance. AMA CPT Editorial Panel minutes

The Losers: Smaller Clinics and Unintegrated Point Solutions

Conversely, smaller clinics, independent practices, and those lacking significant capital for technological investment face high implementation barriers. The administrative burden of managing RPM and RTM programs, including patient onboarding, data reconciliation, and continuous monitoring, can be overwhelming without dedicated staff and integrated systems. The upfront costs of purchasing and deploying remote monitoring devices, coupled with the complexities of billing and demonstrating outcomes, can erode the financial viability of such programs for smaller entities. Similarly, unintegrated point solutions that offer only a single component of remote monitoring (e.g., a standalone blood pressure cuff without a complete data platform) will struggle to thrive. The value-based model demands well-rounded solutions that can contribute to a broader care continuum and demonstrate measurable impact. Without the ability to smoothly integrate into existing clinical workflows and provide actionable insights, these solutions become difficult to justify under an outcome-based reimbursement framework.

Methodology and Source Note

Our analysis draws primarily from publicly available information, including the Centers for Medicare and Medicaid Services (CMS) Physician Fee Schedule final rules and the American Medical Association (AMA) CPT Editorial Panel minutes. We also consider market intelligence reports and company disclosures from leading digital health innovators to assess the practical implications of these regulatory and reimbursement policies. Biofourmis investor relations This approach allows us to map the financial and policy impact on various stakeholders, providing a clear picture of the market winners and losers as value-based care continues to redefine healthcare delivery. The shift towards outcome-based reimbursement for remote algorithmic monitoring is not merely a change in billing codes. It represents a fundamental reorientation of incentives within the healthcare system. Investors and policy advisers must recognize that success in this new model hinges on the ability to deliver integrated, clinically validated, and outcome-driven solutions that align with the evolving regulatory field. The future of healthcare AI regulatory compliance will increasingly favor those who can navigate these complexities, demonstrating both technological prowess and a deep understanding of reimbursement mechanisms.