The Prescriber Whitespace Report

Methodology

What "whitespace" means, precisely

A prescriber lands in this dataset if, and only if, both of the following hold:

1. ≥100 total GLP-1 Part D claims, performance year 2024 — summed across the GLP-1 drug class (semaglutide, tirzepatide, dulaglutide, liraglutide, exenatide, exenatide microspheres, lixisenatide) at the individual-NPI level, using CMS’s own Part D prescriber-and-drug data.
2. Zero recorded CMS Open Payments — no matching row for that NPI in the Open Payments performance-year-2024 file, across any payment category (general payments, research payments, ownership interests).

Both conditions are drawn directly from CMS’s own published data. Nothing here is inferred, modeled, or estimated — a prescriber is either in the Part D claims file at ≥100 claims and absent from Open Payments, or they’re not in this set. The 15,212 figure is a hard count from that intersection, independently reconfirmed against the production dataset.

Breakdown

Whitespace by specialty

Family Practice and Internal Medicine together account for roughly seven in ten whitespace prescribers — the two largest primary-care specialties actively managing GLP-1 patients without a recorded industry-payment relationship. Endocrinology, the specialty most associated with metabolic-disease drug expertise, is a comparatively small slice (7.0%) of the whitespace population — most endocrinologists prescribing at this volume already show up in Open Payments.

(Full 42-specialty breakdown in the aggregate CSV; rows above sum to the full 15,212.)

Breakdown

Whitespace by geography

Whitespace is concentrated where prescribing volume is concentrated — the largest states by population and provider count lead the list — but the ranking isn’t a pure population echo: Wisconsin (769, 5.1%) and Minnesota (535, 3.5%) both outrank several larger states, and Massachusetts (635, 4.2%) sits well above its population share, suggesting regional practice patterns worth a closer look rather than a uniform national distribution.

(Full 55-jurisdiction breakdown — all 50 states, DC, and Puerto Rico/Guam/Virgin Islands/APO — in the aggregate CSV; rows sum to the full 15,212.)

Context

Why it matters to a medtech/pharma commercial team

Field teams for GLP-1 manufacturers, medtech companies selling adjacent devices (CGMs, injectors, monitoring tools), and market-access vendors all run the same play: find high-volume prescribers in the category and get a rep or a program in front of them. The standard way most of those teams already discover “who’s active” is by watching Open Payments disclosures — speaker programs, advisory boards, and consulting relationships surface the prescribers already inside someone’s promotional network.

This dataset is the complement to that view: 15,212 NPIs that clear the same volume bar (≥100 PY2024 GLP-1 claims) but don’t show up in that channel. For a commercial team building a target list, that’s a coverage gap — a set of demonstrably active prescribers who aren’t already being reached through the payment-disclosure-linked channel most competitors are watching. Whether that gap represents an untapped advocacy opportunity, a compliance-conscious prescriber base, or simply prescribers a given manufacturer’s field force hasn’t reached yet is a question for the commercial team to answer with their own outreach — this dataset only establishes who’s in the gap, specialty by specialty and state by state.

Press pitch

For reporters

With GLP-1 drugs dominating pharma headlines and payer scrutiny of prescriber-industry financial ties intensifying, a new analysis of CMS’s own public data finds 15,212 US clinicians who billed at least 100 GLP-1 prescriptions to Medicare Part D in 2024 — real, sustained prescribing volume — with zero recorded payments, meals, or consulting fees from drug manufacturers in the same federal Open Payments database regulators created specifically to track those relationships. The pattern is concentrated in primary care: nearly seven in ten of these high-volume, no-recorded-payment prescribers are Family Practice or Internal Medicine physicians, with Wisconsin, Massachusetts, and Minnesota showing disproportionately high concentrations relative to their size. The data doesn’t say why the payment record is empty for this group — only that, cross-referencing two public CMS datasets by NPI, it is.

Report notes

Language and number gates

Language gate: the full report was re-scanned for every banned causal construction (“because,” “causes,” “drives,” “leads to,” “influences,” “as a result of,” “due to payments,” or any implied causal link between payments and prescribing). One draft sentence in the “Why it matters” section originally read “...suggesting these prescribers may not be responding to industry outreach” — this implied a causal read of the payment gap and was rewritten to the coverage-gap framing that appears above. No other hits found on the rewritten draft.

Number gate: the only prescriber-count numbers in this report are 15,212 (locked headline) and the specialty/state aggregate counts computed directly from the full source file. The specialty table sums to 15,212; the state table sums to 15,212. Both verified programmatically against the production dataset.

Get the data

Download the datasets behind this report

Real rows, released under CC BY 4.0 (the underlying records are U.S. federal works in the public domain).

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