Work in the national interest
Reproducible analytics that help federal agencies protect the Medicare trust funds from improper payments while improving access for the beneficiaries who depend on them.
The case for advancing this work rests on three questions: does the endeavor have substantial merit and national importance, is the person well positioned to advance it, and would the United States benefit from letting that person proceed. Each is addressed directly below.
Substantial merit and national importance
Protecting Medicare from improper billing is a recognized federal priority measured in tens of billions of dollars each year. Methods released openly serve the entire field, not a single employer.
Well positioned to advance it
Eight completed projects across the Medicare program, each with a reproducible pipeline, a public preprint, a public code repository, full claims traceability, and real-world adoption.
A national benefit to proceeding
The value is national and method based, realized through public release. Tying it to one job offer or labor certification would constrain a benefit that is already public and broadly deployable.
Prong one: substantial merit and national importance
The endeavor is the design and public release of reproducible analytics that help federal healthcare agencies and their contractors detect anomalous Medicare billing and identify access gaps, built entirely from public CMS data so that any agency can deploy them directly against the same datasets.
The merit is concrete. According to CMS and GAO reporting, improper payments in the Medicare program totaled roughly fifty-seven billion dollars in fiscal year 2025, including about twenty-eight point eight billion dollars in fee-for-service Medicare alone. Improper payments are not all fraud; they include overpayments, underpayments, and payments lacking sufficient documentation. But every dollar paid in error is a dollar that does not reach the beneficiaries the program exists to serve, and reducing that error rate is a standing objective of CMS, the HHS Office of Inspector General, the Government Accountability Office, and federal healthcare fraud enforcement.
This is a declared federal priority, not a niche concern. In March 2026 the Government Accountability Office documented that CMS uses data analytics to detect anomalous Medicare billing as a core program-integrity method, crediting the approach with preventing nearly twelve billion dollars in potentially fraudulent payments from fiscal years 2022 through 2024 (GAO-26-107799). In February 2026 the Department of Health and Human Services and CMS announced a shift to a real-time detect-and-deploy strategy and issued a public Request for Information on the analytics and artificial-intelligence methods most effective at identifying healthcare fraud. This portfolio advances that same nationally declared goal as independent, open, reproducible work, and makes no claim of participation in any federal program.
The national importance follows from the design. These are not bespoke tools tied to one organization. Each method is documented in an academic preprint, released as a public code repository, and validated against public federal data, so the benefit extends to the whole field of program integrity rather than to a single firm. Work that any federal agency or contractor can pick up and run against the national data has importance that is national by construction.
Prong two: well positioned to advance the endeavor
The record is a completed body of work, not a proposal. Eight program integrity projects span Medicare Parts A through D: prescriber risk, opioid prescribing, Part B drug waste, durable medical equipment, telehealth, hospital inpatient DRG billing, home health and hospice, and county level access equity. The combined analytical universe spans more than forty million Medicare beneficiaries, prescribers, providers, and counties, including 1.38 million Part D prescribers, 27.3 million inpatient discharges across five fiscal years, and all 3,198 U.S. counties.
Each project is built to a single standard. Every numerical claim in every report, dashboard, and preprint traces to a persisted result file in a public repository, so an independent reviewer can take any number and follow it to the originating CMS column and computation. Where temporal validation is feasible, the models train and test on a future event boundary, so reported performance is predictive rather than in sample. The Part D prescriber model, for example, trains on data through 2022 and tests against exclusions filed in 2023.
The work is also published and adopted. All eight methodologies are posted as preprints on SSRN and released as public repositories with reproducible pipelines. The methodology has been adopted across the NSK operating group, and is the subject of a memorandum of understanding with Altechra, a letter of intent with Clones, and a certificate of contribution from a hospital partner. A person who has completed, published, validated, and placed this work into use is well positioned to continue it.
Prong three: a national benefit to waiving the job offer requirement
On balance, the United States benefits from letting this work proceed without a job offer and labor certification. The reason is the nature of the work itself. Its value is realized through public release: the preprints, the repositories, and the reproducible pipelines are already available to every federal agency and contractor working on Medicare program integrity. That benefit does not depend on the petitioner holding any particular position.
Requiring a specific employer sponsor and a labor certification would tie a nationally deployable public good to a single job, and would test the U.S. labor market for a role that does not capture what the work delivers. The endeavor is self directed and built on public data, so the petitioner can advance it independently of any one employer. Given the scale of Medicare improper payments and the standing federal interest in reducing them, the national benefit of letting qualified, productive work continue outweighs the protective value of the labor certification process in this case.