Where AI Is Working for 340B Programs: Lessons from Coalition

340B teams are running lean while compliance and regulatory scrutiny only tighten. Manufacturer data demands are expanding, MFP reconciliation has landed on already-stretched teams, and experienced 340B talent is hard to find and harder to keep. At 340B Health's Summer Coalition Conference, Plenful’s SVP of Pharmacy Tim L'Hommedieu made an encouraging case for AI in 340B programs. Instead of piling more manual work onto stretched teams, AI is already safeguarding compliance, surfacing missed savings, and speeding up decisions for 340B programs today.

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Where AI creates the most impact

Across the programs that have adopted AI, the impact tends to concentrate in three places.

1. Safeguarding 340B compliance

AI can monitor for documentation gaps, audit exposure, and compliance risk continuously, so a program stays defensible without a person manually checking every claim. Instead of sampling a fraction of claims and hoping the rest hold up, a program can screen everything and catch exposure before an auditor does. As manufacturers tighten their reporting protocols, that always-on layer of protection matters more, not less.

2. Surfacing missed 340B savings

AI identifies savings that manual processes can't reach at scale, from uncaptured referrals to misclassified claims. It also catches value that quietly slips away, like therapies that were administered but never made it onto a claim. With a rebate model looming, that kind of financial oversight becomes even more critical.

3. Accelerating decision-making

AI absorbs the reasoning load of pattern recognition and data synthesis, so teams reach decisions faster and spend less of their day on tedious, low-value work. The point isn't to make the decision for anyone. It's to hand staff a clear, organized picture so the judgment call is the only part left to make.

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Referral capture: investigating what manual review can't reach

A large share of eligible savings hides in EHR documentation that's challenging to investigate at scale: progress notes, discharge summaries, or long PDFs where a person would have to physically comb through every line to find the evidence that connects a prescription to an eligible encounter. Most health systems don't have the resources to invest here meaningfully, so the savings are simply left on the table.

AI tools like our Referral Agent solution can scan that documentation at roughly 98 times the volume a manual team can manage, then point staff to the highest-value opportunities first, ranked by drug cost and referral specialty. The compliance loop closes on its own: documentation is generated and routed back to referring providers through EHR integrations, building a defensible, audit-ready record automatically.

"Plenful combs through the chart, identifies key words and phrases, and links all the pieces together to show the closed loop referral pathway. What's even better - it goes beyond detecting referrals to generating documentation you can stand up in an audit."‍

Chief Pharmacy Officer
Leading Mid-Atlantic Academic Medical Center

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MFP and rebate reconciliation: closing a 20% gap

Maximum Fair Price reconciliation is manual, and extraordinarily tedious. Plenful customers are reporting that as much as 20% of expected MFP refunds are missing, underpaid, or absent from the Medicare Transaction Facilitator entirely. Recovering them means reconciling across multiple platforms, filing good faith inquiries before deadlines close, and chasing outliers one at a time.

Automation changes the math here. Our MFP Intelligence solution, featuring GFI Agent, organizes and submits the required data elements to manufacturers accurately and completely, eliminating the manual assembly across fragmented sources that delays or forfeits payments. It surfaces missing refunds, duplicate situations, and delayed reimbursements, then runs the good faith inquiry (GFI) and ESP submission workflow to recover them automatically. And finance and compliance leaders get real-time visibility, reconciling claims, refunds, and missing amounts in a single view instead of scattered spreadsheets.

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How to Adopt AI in Your 340B Program: 4 Change-Management Principles

The most common reason AI adoption fails in 340B has nothing to do with the technology. It's skipping the groundwork. Tim's advice for where to begin came down to four principles.

1. Map before you build. Define the current-state workflow before layering automation on top of it, or you'll just automate confusion.
2. Define success before you go live. Set baseline metrics upfront, and make sure your systems can actually report on the outcomes you care about.
3. Start narrow and prove impact fast. Pick one high-visibility use case, show the value, then expand. Momentum matters more than scope.
4. Build champions across functions. Compliance leads, 340B analysts, and CFOs each need a different reason to care. AI adoption is a team sport.

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AI gives scarce, experienced staff their time back for the work that needs their expert judgment, and takes the repetitive, tedious tasks off their plates. For 340B teams, that means faster savings recovery, reduced staff burnout, and more room to serve the patients who depend on the program.