Payer Use Cases
Four use cases across two GTM tiers: immediate opportunities in member experience and provider credentialing, with prior authorization and claims as expansion plays.
Several straightforward conversational contexts. Advantage: simple SOPs, scripts, and backend system content - integration may still be complex, but the AI layer is clean. Starting in 2024, CMS is introducing a Health Equity Index (HEI) to reward plans for good experiences for vulnerable populations.
- Plan selection and coverage explanation: "Does my plan cover acupuncture?" Answers on services, procedures, medications, and treatments, including coverage limits and pre-approval requirements.
- Cost estimators: "What's my out-of-pocket max and have I met it?"
- Provider directory: "Is this hospital in-network for my plan?" Also: Provider
- Explanation of Benefits (EOB): Post-claim artifact explaining how a claim was processed, what the plan paid, what the provider billed, and any adjustments or denials.
Advantage: insurance information is notoriously complex - AI can patiently explain deductibles, copays, covered services, and exceptions. Especially valuable for Medicare plans, where the complexity of Original Medicare, MA, Part D, and supplemental plans drives high call volume. Handling spikes during Nov-Dec open enrollment is a fast pilot opportunity.
- "What is the status of my claim for my ER visit?" or "Why did I get charged $50 for my lab test?"
- Especially disputes and denials. Members call the payer even when the billing error originates with the provider - the payer call is the first diagnostic step a patient takes.
- Coordination of benefits (COB) disputes: If a member has two insurance plans and primary/secondary coordination is wrong, the resulting member bill is incorrect through no fault of the provider.
- Balance billing disputes: Member sees an in-network provider but gets a surprise bill from an out-of-network provider (eg. the anesthesiologist).
- Care gap identification and reminders: Proactively flag missing preventive care and nudge patients to complete them. Schedule preventive screenings or promote chronic disease management programs.
- Can be aligned specifically to metrics like cancer screenings or diabetes checks that directly lift CMS Star Ratings and NCQA scores.
- AI can hyper-personalize outreach by language, zip code, SDOH factors, and Social Deprivation Index (SDI). Also: Provider
Providers in value-based care arrangements are also measured on preventive care metrics - higher screening or immunization rates can improve HEDIS scores and generate financial bonuses, making this a dual-sided opportunity.
- ID Cards: High-volume handling of "I lost my insurance ID card" or portal login issues. Involves authentication, education ("Download your digital ID card here"), and/or action (mailing a new card), plus real-time status tracking.
- Administrative and onboarding help: Account help ("I forgot my password"), completing forms (eg. Health Risk Assessment), onboarding steps for new members.
Formal verification and approval of a provider to participate in a payer network and receive reimbursement. Involves confirming licenses, board certifications, malpractice history, DEA registrations, NPI numbers, and other documentation. Payers re-credential every 2-3 years.
- Application status: Stuck in queue, haven't heard back in weeks or months.
- Missing documentation: The payer flagged something (expired license, missing attestation form, gap in work history) and the provider either never got the notification or disagrees with the finding.
- Claims denied due to credentialing: Provider is already seeing patients but reimbursement is blocked. They call to understand the timeline and sometimes request retroactive credentialing.
- Profile issues: Credentialed but the effective date in the system is wrong, delegated credentials confusion, recredentialing failure, etc.
The underlying data is structured and status-oriented, which makes credentialing inquiries well suited to AI agents handling status checks, document checklists, and timeline updates without clinical complexity.
Prior auth requires integrating with EHR systems to extract clinical data, format it to payer-specific requirements, and submit requests. The workflow involves high volumes of phone calls, fax, email, and scheduling, and is time-sensitive for both member experience and clinical outcomes.
However, the clinical context involved makes this more complex as an initial AI deployment. Prior auth is better positioned as a second-wave use case after the platform has established trust with simpler workflows.
Inquiries about specific claims come from both providers and members, including denial explanations. AI agents can deliver detailed, patient explanations of denial reasons and next steps without the emotional friction of a frustrated human agent.
The challenge: effective claims status handling requires access to clinical information, which raises integration complexity for initial deployments.