The US healthcare system is amid its most significant payment realignment in a generation. Alternative Payment Models (APMs), once a policy aspiration, have become the structural backbone of how care is reimbursed across Medicare, Medicaid, and increasingly, the commercial sector. According to the 2025 Alternative Payment Model Measurement Effort released by AHIP in collaboration with CMS and the Blue Cross Blue Shield Association, participants in the 2025 survey represent over 271 million people nationwide, or 87.5% of covered lives across all lines of business[i], signaling that APM adoption is no longer a peripheral experiment but a system-wide operating reality.
Yet the velocity of payment reform has outpaced the maturity of the digital infrastructure required to manage it. Most payors and providers still rely on retrospective claims-based reporting cycles, fragmented quality measurement workflows, and manual reconciliation processes that were designed for a fee-for-service world. As risk shifts from payors to providers and as outcome-linked reimbursement expands, the absence of real-time, integrated performance tracking is emerging as the single largest constraint on APM scalability.
APM adoption is accelerating, but execution capacity is lagging. Seventy percent of respondents anticipate more APM activity over the next two years, attributing this to provider readiness and health plan involvement and operational capacity[ii]. The optimism is matched by federal commitment: CMS introduced 10 new transformative payment models in December 2025, including the Advancing Chronic Care with Effective, Scalable Solutions (ACCESS) model, which tests Outcome-Aligned Payments (OAPs) in Original Medicare[iii] and ties full payment to demonstrated clinical improvement relative to each patient’s baseline.
The execution gap remains substantial. Even as payor and provider organizations broadly align on the goals of value-based care, recent public reporting from the CMS ecosystem and related policy organizations shows that fragmented data, limited interoperability, and manual workflow burden continue to slow execution. In practice, value-based care still relies heavily on manual coordination, documentation, and reporting, making it difficult to consistently scale performance improvement across organizations.
While leaders agree on direction, the underlying digital systems for measuring and reconciling performance in risk-bearing contracts remain insufficient. Performance tracking is no longer an IT concern. It is a financial control function, a clinical governance imperative, and a competitive differentiator.
Traditional APM performance measurement is structurally retrospective. Claims data is aggregated quarterly, quality measures are reconciled annually, and provider feedback loops often arrive 6 to 18 months after the care episode. In a fee-for-service world, this lag was tolerable. In a risk-bearing world, it is financially dangerous. Key limitations include:
The cumulative effect is what oncology practices have described to the American Society for Clinical Oncology as fundamental challenges, including a lack of understanding of value-based care terms, limited use of data for improvement, difficulty tracking care costs, limited data sharing and compliance, and a lack of integrated technology.
AI and advanced analytics are rapidly becoming the backbone of APM performance management, shifting it from retrospective scorekeeping to continuous value intelligence. The Office of the National Coordinator for Health Information Technology notes that over 70% of hospitals use predictive analytics or AI tools as of 2024[iv], and the World Health Organization highlights their growing role in early intervention and chronic disease management. The economic opportunity is significant. The National Academy of Medicine identifies avoidable care as a major cost driver, while the Mayo Clinic suggests analytics-driven interventions can reduce hospitalizations by 15%–25%[v]. Together, these findings underscore AI and advanced analytics as foundational capabilities for APM performance management—spanning predictive risk stratification, automated capture of quality measures, real-time contract reconciliation, prior authorization optimization, risk adjustment accuracy, total cost of care attribution, and continuous provider performance benchmarking. As APM contracts grow in complexity and financial exposure, these use cases collectively shift performance management from a periodic compliance exercise to a continuous, data-driven operating discipline. Organizations that integrate these capabilities into a unified analytics layer will be best positioned to manage downside risks, maximize shared savings, and scale value-based arrangements with confidence.
AI-driven models can continuously analyze claims, clinical, and social determinants data to identify patients at risk of acute episodes before they occur. Documented results show meaningful financial returns. In a peer-reviewed study conducted within Kaiser Permanente, one of the largest integrated health systems in the US, an AI-driven alerting system that used hundreds of millions of EHR data points to predict patient deterioration prevented more than 500 deaths a year while reducing high-risk readmissions by 10%[vi]. A PubMed-indexed economic analysis found that using a machine-learning triage strategy for upper gastrointestinal bleeding could generate more than $3.4 billion in cumulative US savings over five years from a payor perspective, by safely identifying patients who do not need admission[vii]. For payors and providers in shared-risk arrangements, the ability to intervene days or weeks earlier directly translates into avoided downside risk and improved shared savings performance.
A significant share of APM operating costs is consumed by prior authorization, claims review, attribution logic, and quality reporting. AI is meaningfully compressing these workflows. Montage Health’s deployment of AI-powered prior authorization and claims adjudication automation achieved a 22% reduction in the authorization work queue volume, and it processed over 5,600 authorization status checks in one year, saving its healthcare organization 300 staff hours per month and decreasing accounts receivable days by 13%[viii]. Agentic AI is increasingly being deployed to group claims into relevant categories, such as attributing them to providers participating in the alternative payment model and filtering out ineligible beneficiaries based on defined algorithms.
Natural language processing and machine learning are enabling continuous extraction of quality-measurement evidence from unstructured clinical notes, eliminating the lag between care delivery and HEDIS or STAR rating impact. Beyond clinical quality capture, advanced analytics platforms are strengthening actuarial precision by ingesting a wider range of data sources, including social determinants of health, pharmacy utilization, and behavioral health indicators, to more accurately forecast healthcare costs and utilization patterns. This expanded analytical capability is essential for setting defensible financial benchmarks under two-sided risk arrangements, where even marginal errors in cost projections can translate into significant shared savings shortfalls or unexpected downside exposure.
Enterprise analytics platforms are unifying claims, clinical, and operational data into role-based dashboards that allow executives to monitor performance across population segments, risk tiers, and contract types in near real time. This represents a fundamental shift from periodic reporting to continuous operational intelligence.
Despite the enthusiasm, AI-enabled performance tracking faces a critical credibility hurdle. In the 2025 survey referenced above and summarized by the NIH/PMC, healthcare organizations continue to face resistance tied to limited AI literacy, liability concerns, implementation cost, and training gaps[ix], while a separate workforce study found that 11% of patients view AI in healthcare as a potential danger and only 50% see it as an important opportunity[x].
The implication for leadership is unambiguous: the technology is ready, but the governance, workforce readiness, and trust frameworks are not. Closing this gap is now the principal determinant of whether AI-enabled performance tracking will scale as a strategic capability or stall as a series of disconnected pilots.
Digital performance tracking should be evaluated as a long-term investment in operational and financial control infrastructure. Leadership at payor and provider organizations should focus on four imperatives:
Performance tracking has historically been treated as a downstream compliance and reporting activity. Under risk-bearing APMs, it becomes the central nervous system of financial and clinical operations. Organizations that invest in unified data platforms, real-time analytics, and AI-enabled decision support will achieve compounding returns across all contract types. Those who continue to treat performance tracking as periodic reporting will face compounding financial and operational risk, as retrospective visibility alone cannot support the real-time intervention and course correction that two-sided risk contracts demand.
Resources should be directed first to workflows with the highest financial and clinical stakes, such as risk adjustment, quality measure capture, prior authorization, total cost of care attribution, and readmission prevention. These domains share a common profile: high transaction volumes, multisource data dependencies, and material downside exposure under two-sided risk. Sequencing investment around measurable financial impact builds the evidentiary foundation for broader transformation.
The greatest near-term risk is not under-investment in AI but under-investment in the governance frameworks that make AI defensible. Leadership should define explicit positions on model validation, human-in-the-loop oversight, bias monitoring, audit trails, and regulatory alignment before scaling deployment. Governance maturity precedes scalability, and in a regulated environment, it determines whether AI deployments survive their first audit.
Without clearly defined performance benchmarks, AI and digital performance tracking initiatives risk becoming exploratory exercises that struggle to secure sustained executive sponsorship. Leaders should establish baseline measurements across target workflows before deployment and define KPIs upfront, including:
Measurement drives sustainability. Quantified outcomes validate continued investment and provide the foundation for expanding adoption into adjacent contracts and lines of business.
Healthcare payment reform is now entering its most consequential phase. With APM arrangements covering the vast majority of insured lives and CMS launching a new generation of outcome-aligned models, the gap between contractual ambition and operational capability will define winners and losers over the next five years. Digital performance tracking powered by AI, predictive analytics, and integrated data infrastructure is the structural enabler that will determine which organizations can absorb and thrive under expanded risk.
For leadership at payor and provider organizations, the strategic message is clear. Digital performance tracking is no longer a back-office function. It is the operating system of value-based care. The next wave of APMs is already taking shape through models such as ACCESS, Transforming Episode Accountability Model (TEAM), Ambulatory Specialty Model (ASM), and Long-term Enhanced ACO Design (LEAD), and this signals a decisive shift toward outcome-aligned payments, mandatory episode-based accountability, and technology-enabled chronic care management over multiyear performance horizons. These models are structurally more complex than their predecessors; they embed real-time clinical outcome thresholds, require tighter specialty care integration, and increasingly expect participants to leverage AI and digital tools as core delivery infrastructure rather than optional enhancements. Organizations that embed AI-enabled performance intelligence within disciplined modernization road maps—anchored in strong governance, workforce readiness, and measurable outcomes—will not merely comply with this next generation of APMs. They will institutionalize the resilient, data-driven operating model required to lead the industry’s transition from volume to value.
[i] https://www.ahip.org/news/articles/new-survey-demonstrates-health-plans-continued-commitment-to-value-based-care-models
[ii] https://www.ahip.org/news/articles/new-survey-demonstrates-health-plans-continued-commitment-to-value-based-care-models
[iii] https://bipartisanpolicy.org/issue-brief/paying-for-ai-in-u-s-health-care/
[iv] https://healthit.gov/data/data-briefs/hospital-trends-use-evaluation-and-governance-predictive-ai-2023-2024/
[v] https://pmc.ncbi.nlm.nih.gov/articles/PMC7467834/
[vi] https://www.ama-assn.org/practice-management/digital-health/kaiser-permanente-s-ai-approach-puts-patients-and-doctors-first
[vii] https://pubmed.ncbi.nlm.nih.gov/37753930/
[viii] https://akasa.com/case-studies/montage-auth-status
[ix] https://pmc.ncbi.nlm.nih.gov/articles/PMC12119536/
[x] https://journals.sagepub.com/doi/abs/10.1177/15563316251340074
By Eratha Poongkuntran, Associate Director, Avasant
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