Health plans are operating under a level of regulatory scrutiny that few anticipated even a year ago.
Risk Adjustment Data Validation (RADV) audits by the U.S. Centers for Medicare & Medicaid Services (CMS) are projected to expand sharply, with the agency planning to increase audits from roughly 60 Medicare Advantage plans per year to approximately 550 and to grow its medical coder team from 40 to around 2,000. CMS has cited estimates that Medicare Advantage plans overbill the federal government by up to $43 billion annually, raising the stakes for accurate, defensible risk capture.
At the same time, the clinical reality is growing more complex. In 2023, 76.4% of U.S. adults had at least one chronic condition, and for adults over 65, that figure climbs to 93%, according to the CDC.
The challenge for payers is that much of the information needed to reflect this complexity resides in narrative clinical notes rather than structured fields, creating a persistent disconnect among claims data, clinical documentation, and the workflows that translate them into coded risk. Without a unified, AI-driven operating model, these pressures erode quality performance, increase administrative burden, and put revenue at risk.
An end-to-end operating system for risk and revenue
To address this gap, leading plans are rethinking how risk adjustment is operationalized, not as a retrospective coding exercise but as a real-time clinical workflow. Lightbeam Health Solutions helps payers close this gap by unifying data ingestion, analytics, AI-powered workflows, and clinician-facing insights into a single platform that drives both clinical and financial performance.
At the core is a shift from retrospective chart review to prospective risk capture. Lightbeam AI continuously analyzes clinical, claims, utilization, and social determinants of health (SDOH) data to anticipate rising risk before outcomes worsen or costs escalate. It surfaces HCC, Stars, and care gap insights directly within the EHR at the point of care.
Lightbeam supports a shift from retrospective coding to proactive, point-of-care decision support. Even before a high-risk Medicare Advantage member presents for a visit, the platform compiles claims history, lab results, and prior clinical data to surface likely undocumented conditions and open care gaps. These insights are then made available within the provider’s workflow at the time of the encounter, enabling validation and documentation during the visit. At the same time, the member is prioritized for follow-up care management when additional needs are identified, closing the loop among risk capture, quality performance, and ongoing care.
Clinicians can evaluate and document conditions while the patient is still present, improving coding accuracy and enabling more proactive care. Lightbeam also helps coding teams prioritize records most likely to contain undocumented risk, so effort is directed where impact is greatest.
The platform extends well beyond risk adjustment. AI-powered cohorting and automated workflows prioritize members at rising risk and scale care management without adding headcount. Conversational AI, SMS, and IVR automate member outreach at scale while escalating only interactions that require clinical intervention.
A FHIR-native architecture supports interoperability, digital quality measures, and prior authorization readiness, helping payer teams remain compliant while reducing administrative burden.
The results are measurable: Lightbeam has delivered more than $5 billion in gross savings since 2014, driven an 8X increase in care manager capacity, reduced medical PMPM costs by 8%, and lowered readmissions by 30%.
Looking Ahead
Improving risk adjustment ultimately comes down to bridging the gap between claims data, clinical documentation, and provider workflows, and providing care teams with the insight they need at the moment decisions are made.
By unifying these sources and applying AI that works prospectively rather than retroactively, health plans can capture risk more accurately, support providers at the point of care, reduce administrative burden, and direct resources to the members who need them most.
As AI and data integration capabilities continue to advance, payers that adopt this connected, intelligence-driven model will be well positioned to improve risk-adjustment accuracy, strengthen population health, and convert today’s regulatory pressure into a lasting competitive advantage.
