Gain control over medical loss ratios with agentic AI

<p><b>By embedding agentic AI workflows in the claims value chain, health payers can contain medical spend and improve their bottom line.</b></p>

<p>The healthcare industry has focused heavily on agentic AI workflows that optimize operational workflows. However, agentic AI can do much more than improve intake efficiencies to cut costs.</p> <p>As populations age and utilization rates increase, agentic workflows can help payers significantly improve medical loss ratios and margins, reduce medical spend volatility, and improve outcomes and member and provider experiences.</p> <p>Achieving these goals requires a vision that goes beyond workflow optimization. Payers will need to reimagine how they do business, using AI agents to add intelligent, predictive insights and coordinate their decision making and actions across the payer value chain. By doing so, they can identify and avoid fraud, waste and abuse; address high spend trends; and deliver proactive care.</p> <h4>How agentic AI can improve medical loss ratios</h4> <p>In an agentic AI workflow, intelligent agents can manage outcomes across the entire claims value chain. Instead of simply streamlining intake or deflecting calls, the agents can interpret and apply context, policy and clinical logic in real time.</p> <p>Additionally, they can coordinate with each other across multiple data sources and enterprise systems to trigger actions. Such actions might include validating coverage, sequencing clinical steps and closing care gaps, with the goal of improving outcomes and quality measures.</p> <h4>Examples of agentic AI workflows that reduce medical losses</h4> <p>Here are just a few examples of how healthcare payers can shift from an efficiency mindset with agentic AI, to improving their medical loss ratios—and boosting the member and provider experience along the way.&nbsp;</p> <p><b>Old approach: Automate prior authorization</b></p> <p><b>New approach: Proactively manage episodes of care&nbsp;</b></p> <p>While AI agents can streamline prior authorization processes, automation alone doesn’t address the underlying drivers of medical cost.</p> <p>With agentic AI, however, payers can focus on reducing avoidable spend before a claim is ever made. They can do this by using predictive intelligence to proactively manage episodes of care.</p> <p>Here’s how: In an agentic workflow spanning the claims value chain—from eligibility through adjudication—AI agents can:</p> <ul> <li>Continuously evaluate a member’s longitudinal record.</li> <li>Look at benefits, prior utilization, pharmacy history, and labs and clinical documentation.</li> <li>Apply evidence-based guidelines and risk models to anticipate the care a member is likely to need.</li> </ul> <p>With this information, agents can identify the risk of medication non-adherence, detect gaps in preventive screenings or chronic condition management and predict emerging high-cost trajectories early enough to influence outcomes. The result: more precise and timely interventions.</p> <p>Members, it seems, would welcome these interventions. In our recent <a href="https://www.cognizant.com/en\_us/industries/documents/cognizant-voice-of-the-member-2026.pdf" target="_blank" rel="noopener noreferrer">Voice of the Member survey</a>, plan members said they want payers to take a more active role in helping them manage their health. This agentic approach meets that demand.</p> <p>The same predictive layer can also strengthen affordability and integrity. Agents can monitor utilization and claims behavior to surface patterns associated with waste, abuse or fraud, as well as identify procedures, drug classes or provider behaviors that are driving disproportionate costs. These insights can trigger targeted alerts and interventions in near real time by routing only high‑risk scenarios for human investigation while allowing low‑risk activity to proceed uninterrupted. This shifts payment integrity from reactive recovery to proactive prevention, reducing leakage while focusing scarce clinical and investigative resources where they matter most.</p> <p>This approach fundamentally changes how prior authorization can function. For example, many authorization requests would become “instantly knowable” because the necessary context—coverage, eligibility, prior therapies, diagnoses and clinical criteria—is already available to confirm medical necessity and policy alignment. Payers could shift to an exception‑based model, where automation clears standard, well‑supported requests and human reviewers focus on extenuating circumstances, ambiguous clinical scenarios and truly high‑cost and high‑risk procedures.</p> <p>The result is fewer unnecessary prior authorizations, faster decisions and reduced provider abrasion, while preserving auditability, regulatory safeguards and human oversight where it is most needed. This agentic, proactive approach reduces avoidable medical cost while improving outcomes because it minimizes complications, untreated conditions and unnecessary emergency department visits and readmissions. It also improves patient and provider satisfaction by replacing administrative drag with timely, context‑aware decisions that feel supportive rather than punitive.</p> <p><b>Old approach: Optimize auto-adjudication</b></p> <p><b>New approach: Automate contract enforcement, leakage control and actionable intelligence</b></p> <p>While AI agents can improve auto-adjudication rates and payment accuracy, agentic workflows can unlock far greater value by transforming claims operations from a downstream processing function into an active system of control and intelligence.</p> <p>For example, agentic AI workflows can:</p> <ul> <li><b>More effectively enforce reimbursement logic and help avoid the contract leakage that affects medical loss ratios.</b> A team of AI agents can continuously analyze claims in near-real-time against fee schedules and contract terms. This analysis exposes inconsistencies that are difficult to detect through periodic audits alone, such as misapplied edits, incorrect modifier usage, statistical coding anomalies concentrated among specific providers or inappropriate unbundling of services.<br> <br> By identifying and correcting these patterns early, agentic workflows reduce “silent leakage” caused by both payer and provider processing errors. This improves payment integrity while stabilizing medical cost trends. Just as important, enforcing contracts consistently and transparently reduces downstream appeals and disputes, minimizing provider friction and preserving trust.<br> <br> </li> <li><b>Turn claims adjudication into a powerful source of real-time business and clinical Intelligence.</b> By correlating signals across claims, member benefits, provider contracts and evidence‑based guidelines, agents can reveal how care is actually being delivered. This is because they can highlight variations in pathway adherence, emerging cost drivers or opportunities to shift utilization toward higher‑value care.<br> <br> These insights can inform care management strategies, guide network and benefit design decisions and support more informed contract negotiations and payment models. In this model, claims are no longer just a record of what happened but a living feedback loop that continuously improves cost control, care quality and strategic decision‑making.</li> </ul> <p><b>Old approach: Retroactive remedication</b></p> <p><b>New approach: Continuous quality improvement</b></p> <p>Agentic workflows can streamline the tedious medical code extraction and record reviews required for quality and compliance reporting. But instead of focusing just on these after-the-fact activities, payers can greatly improve quality and compliance when agentic workflows run in near real-time and coordinate with eligibility verification and care management workflows.</p> <p>This broader agentic workflow helps ensure upstream activities, such as care plan creation, align with quality measures and guidelines.</p> <p><b>Here’s how:</b></p> <ul> <li><b>One agent can continuously review inputs</b> from claims, FHIR-based electronic health record systems and member and provider history to identify which members are at risk for care gaps.<br> <br> </li> <li><b>A second AI agent can translate these insights</b>, along with behavioral and utilization signals, into timely, personalized actions.<br> <br> </li> <li><b>The second agent may also autonomously “nudge” the member</b> via a text message, initiate a prescription refill, arrange transportation to a follow-up appointment or notify the member’s primary care provider about care gaps before they escalate into quality or cost issues.</li> </ul> <p>The agentic workflow intervenes with members earlier to improve medical adherence rates, meet preventive screening metrics and reduce uncontrolled chronic condition rates. In turn, these actions should translate into improved member outcomes and an increase in Star ratings and quality bonus payments.</p> <h4>Best practice for improving medical loss ratios with agentic workflows</h4> <p>Data stores and governance guardrails are vital to successfully training and implementing a team of AI agents. Health payers have vast quantities of data, much locked in legacy systems, disparate systems and databases. Technology experts with healthcare expertise can help payers mitigate the complexity and compliance issues associated with preparing data for training AI agents.</p> <p>AI governance should be infused into an organization’s AI strategy or roadmap from the start. Transparency, safety, objectivity and accountability are non-negotiable qualities for each agent-orchestrated process. Automation tools and governance frameworks can accelerate implementation of comprehensive safeguards.</p> <p>The real value in adopting agentic AI can be realized by making a mindset shift from the technical to the conceptual. Payers that treat AI agents as cost‑cutting tools will leave value on the table, while those that deploy them as coordinated systems to manage outcomes can fundamentally improve medical loss ratio performance by lowering medical costs and simultaneously elevating the member and provider experience.</p> <p><i>At Cognizant, we work with leading US health plans to redesign payer operating models using agentic AI, with the goal of reducing administrative costs, accelerating CMS compliance automation and measurably improving member and provider experience.</i></p>