Provider Use Cases
Eight use cases across two GTM tiers: scheduling, referrals, provider directory, and Rx info as the initial set; care coordination, eligibility verification, pharmacy formulary, and claims status as expansion plays.
Scheduling is the most natural entry point - it touches nearly every patient interaction and has abundant, measurable metrics: no-show rate, cancellations, deflection rate.
- Easy start: Appointment reminders (one-way feed). Minimal integration. Immediate no-show impact.
- Predictable evolution: Rescheduling, cancellations with waitlist management, no-show handling, walk-in management.
- Shallow adjacencies (straightforward SOP or data):
- Logistical inquiries - office hours, directions, parking, visitation policies.
- Visit prep instructions - fasting requirements, what to bring, NPO instructions.
- Deeper adjacencies (needs more integration and orchestration):
- Insurance verification at time of scheduling + re-verify 48 hours prior.
- Pre-registration intake - demographics, insurance, consent forms collected before arrival.
- Financial expectation-setting - copay and deductible breakdown.
- Proactive payment collection - deductible/copay pre-collection improves front-end cash flow.
- Patient reactivation - re-engaging patients who haven't scheduled in 6-24 months. More cost-effective than new patient acquisition.
Specialists rely on referrals from primary care, but patients frequently delay or forget to schedule. An AI agent can nudge patients with referral reminders, offer available appointment slots, and answer logistics questions. The primary ROI metric is referral completion rate: appointments scheduled from referred patients versus the baseline leakage rate.
"Who's a Spanish-speaking in-network specialist near me?" or "Is Dr. X in my network?" - members and patients call to confirm availability or plan compatibility even when directories exist online.
- Queries are straightforward, standardized, and happen frequently.
- The underlying metadata is highly transferable across clients - reducing per-customer build cost.
- Strong improvement over status quo - real-time checks on licensure, plan acceptance, and office location are better than static directories.
- Very pilot-friendly: low integration cost, clear ROI metrics (X% calls deflected), deployable quickly, and the agent improves with learning across diverse clients.
Typical inquiries: "When will my Rx be ready?" or "Can it be sent to a different pharmacy?" An AI agent can handle routine refill requests, verify prescription details, and coordinate with pharmacies. This frees clinical staff from high-volume, low-complexity interruptions that currently consume significant phone time.
- Confirms plan coverage at the time of scheduling, then re-verifies 48 hours prior - eliminating denials from coverage changes.
- Non-clinical (still PHI). Relies on structured data. Ambiguous or complex cases can always fall back to a human.
- Advantage: Non-clinical completely, structured data, clean escalation path.
- Disadvantage: Volume of truly agent-viable conversations is lower than expected - routine checks are already automated via EDI. The residual manual volume is often the complex edge cases where human judgment shines.
Source: CAQH Index - about 90% of eligibility checks are already electronic. Manual exceptions take over 12 minutes and cost several dollars in staff labor.
- Continuity of care communications: follow-up on test results, consultations, medication reminders.
- Patients contacted within 24 hours of discharge - scheduled for follow-ups, given lab results.
- Especially valuable in oncology and pediatrics where continuity of care is critical and AI can scale the personal touch.
- Patient feedback and satisfaction surveys.
- Payment reminder texts with convenient links to reduce accounts receivable.
- Prep orchestration:
- Fasting checklists, arrival time, what to bring, NPO texts - reduces no-go appointments.
- Logistics: map links, parking instructions, FAQs.
- "What happens next" expectation-setting.
- Deeper intake: Gather medical history, current symptoms, and necessary forms before the visit - potentially reducing wait time and improving clinical prep.
- Note: Causation is harder to prove here - savings tend to be soft and indirect. Not the primary pilot metric.
- Verifying whether a prescribed drug is covered under a member's plan and what tier or prior authorization it requires.
- AI agent can instantly query the formulary, determine coverage, and provide actionable responses - suggesting alternatives or advising on next steps.
- Disadvantage: Bulk formulary checks are already automated via e-prescribing tools and PBMs. Edge cases involving specialty drugs, coverage disputes, or tiering ambiguities are where human judgment is still needed.
- Advantage: Formulary rules are published internally - this is a rules-based interaction. Strong co-pilot candidate for staff rather than patient-facing agent.
- Providers checking progress of submitted claims - pending, approved, or denied. No clinical judgment needed in theory.
- Disadvantage: ~90% of routine claims status checks are already handled electronically via clearinghouses and EDI. The residual ~10% are the complex cases - denials, missing information, discrepancies - exactly where AI is weakest on first deployment.
- Disadvantage: Learnings from one provider may not translate well to another, limiting cross-client reuse.
- Advantage: When done right, huge labor savings. Complex denial reasons can likely become structured AOPs. Strong co-pilot candidate for staff rather than member-facing agent.
Providers frequently inquire about claims in bulk, and payers field these queries - making this a natural dual-sided opportunity. Market data suggests claims status inquiries are among the most common provider-payer interactions.