Key Takeaways From the 340B Health Webinar on Recapturing Referral-based Savings

As part of 340B Health's Updates From the Field series, Plenful SVP of Pharmacy Tim L'Hommedieu, PharmD, presented on referral-based eligibility, one of the most consistently underutilized areas of 340B program optimization. The session covered HRSA eligibility fundamentals, why most programs struggle to operationalize referral capture at scale, and the technology and workflow changes making it more achievable.

We’ve recapped the three key takeaways that resonated with the hundreds of webinar participants.

The eligibility rules are clear. The execution isn't.

Under HRSA guidance, a prescription qualifies for 340B pricing through a referral when three conditions are met: the covered entity (CE) initiates the referral, the CE maintains ongoing responsibility for the patient's care, and the relationship is properly documented.

That's straightforward in principle. In practice, "properly documented" is where programs diverge — and where leakage begins.

Some organizations require a formal referral order in the EHR. Others accept a provider's progress note or free-text summary. Neither approach is wrong, but inconsistencies among clinical staff create gaps that are difficult to track manually. As Tim put it: 

"Savings you can't defend are savings you don't keep."

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Referral leakage: real, measurable, and mostly invisible

The most common response Tim hears when he brings up referral capture is: "We know we're missing savings, but we don't know how much."

That's the problem in a sentence. Most programs recognize the issue but can't quantify it — which makes it nearly impossible to build a case for fixing it.

The breakdowns tend to cluster around the same places:

• Inconsistent use of structured referral workflows in the EHR
• Referral signals buried in unstructured clinical notes and visit summaries
• Manual processes required to connect referrals with downstream prescriptions
• Staffing and resource constraints that limit consistent review

Specialty care is the most impacted area. Specialty and specialty-light prescriptions carry savings of $1,800 to $2,000 or more per fill. So when you're talking about a handful of missed referrals per week — across high-cost oncology, immunology, or infusion medications — the impact on a program is material, not incremental.

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AI changed what's possible here

Until the past few years, connecting a sentence in a clinical note to a specific downstream prescription claim required manual effort that simply wasn't scalable. Then large language models changed that.

AI can now review unstructured documentation—progress notes, visit summaries, free-text fields—and identify referral signals that would otherwise require a human to find one chart at a time. It can match those signals to prescription claims, rank findings by potential value, and generate targeted work lists for your 340B team.

This isn't an area where AI introduces clinical risk. There's no patient harm in the loop. It's an administrative application with your compliance experts as the final validation step—which means you can move fast and stay defensible. As Tim shared:

​"Shift your team from manually hunting for referral evidence to reviewing targeted, high-confidence findings - and keep your 340B experts in the loop for compliance."

Five operational levers that separate strong programs from the rest

1. Strengthen documentation practices. Focus on encouraging clinical staff to document referrals in whatever format they use. Fighting every provider to adopt a formal EHR pathway isn't realistic at scale. Making sure documentation exists somewhere is a far more achievable goal, especially when AI can find it regardless of format.

2. Look beyond discrete fields for referral signals. Referral evidence is often in the note rather than in the structured order. Programs that treat unstructured documentation as a dead end are missing their largest opportunity.

3. Prioritize by value. Not all referral claims are equal. Focus your team on high-cost medications and high-referral specialties first. That's where the savings are, and that's where you'll build the ROI case for expanding the program.

4. Shift from manual search to targeted review. The goal is to stop asking 340B experts to hunt for evidence and start giving them high-confidence findings to validate. The machine does the search. Your team confirms compliance. That's a sustainable workflow.

5. Build for audit readiness from day one. Documentation you can't defend is savings you don't keep. Whatever process you put in place, make sure the referral trail is accessible, accurate, and survives scrutiny.

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Referral data is more than a 340B play

Here's the part that often gets overlooked: when you start systematically capturing referral data, you're building an asset that goes well beyond drug savings.

Referral data tells you where your patients are going—and where care is leaking out of your system entirely. It shows which referring providers are most active in your network. It gives your leadership team visibility into referral alignment that most organizations lack today.

Programs using this data aren't just recovering missed claims. They're informing network strategy, supporting care-coordination conversations, and enabling smarter decisions about specialist relationships across the care continuum. As Tim shared:

"The 340B savings are certainly valuable, but they're the floor, not the ceiling."

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Want to see what referral capture looks like in practice?

With the Medicare Maximum Fair Price program expanding to 15 additional drugs next year, programs that build optimization strategies now will have a meaningful advantage over those that wait.

As Tim put it,

"Referral leakage is real, it's measurable, and it's recoverable. But building the infrastructure to find it is the key."

That's exactly what Plenful's Referral Agent is built to do. Plenful's Referral Agent is the first agent purpose-built for 340B. It surfaces referral signals across both structured and unstructured clinical documentation and automatically generates referral documentation, enabling your team to capture savings that manual processes miss while maintaining an audit-ready, defensible record.

At a leading Mid-Atlantic academic medical center, Referral Agent expanded referral claim review from 130 claims per month to 12,700 – a 98x capacity increase. At more than $1,850 in average 340B savings recaptured per claim, the impact compounds with every future fill.

Request a demo to see how Referral Agent can supercharge your program.

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