The work, the standard, the record
Background
Seemab Hassan is a healthcare data analyst building reproducible program integrity systems for U.S. federal healthcare programs. The current portfolio covers Medicare Parts A, B, C, and D, with eight completed analytical projects spanning prescriber risk, opioid prescribing, drug waste, durable medical equipment, telehealth, hospital DRG billing, home health and hospice, and county level access equity.
Each project follows the same methodological commitment. Every numerical claim in the final report, dashboard, and academic preprint traces to a persisted result file in a public rebuild repository. Every flagged provider, drug, or county can be reproduced from the same CMS public use files that any independent reviewer can download. All eight methodologies are now posted as preprints on SSRN and released as public code repositories.
He holds an undergraduate degree from Lahore University of Management Sciences and is currently employed at NSK Wholesale Group in the United States.
Methodological commitments
Public data only
Every analysis uses CMS Public Use Files, the HHS OIG List of Excluded Individuals and Entities, HRSA datasets, or other publicly released federal data. No restricted, proprietary, or internal data is used at any stage.
Full traceability
Every numerical claim in every artifact (report, dashboard, preprint) maps to a persisted result file. Independent reviewers can pick any number and trace it to a specific notebook cell, the originating CMS column, and the exact computational basis.
Honest holdouts
Where temporal validation is feasible, training and test data are split on a future event boundary. The Part D prescriber model trains on data through 2022 and tests against LEIE exclusions filed in 2023. Inflated in sample accuracy is never reported as predictive performance.
Field of work and national importance
The field is healthcare data systems and program integrity. The work protects federal healthcare programs from improper billing while improving access for vulnerable populations, using reproducible statistical and machine learning frameworks that federal agencies and contractors can deploy directly against the same public datasets.
Its importance is national by design. Improper payments in the Medicare program totaled roughly fifty-seven billion dollars in fiscal year 2025 according to CMS and GAO reporting. Because each method is documented in a public preprint and released as a public repository, the benefit reaches the entire program integrity field rather than a single organization. The work is presented as anomaly detection for investigative prioritization, not as a determination of fraud.
Education and credentials
Lahore University of Management Sciences, undergraduate degree. U.S. Department of State Global UGRAD program participant. Punjab Educational Endowment Fund recognition. Letter of acknowledgment from the Chief Minister of Punjab.
Current role
Employed at NSK Wholesale Group in the United States. NSK has adopted the Medicare analytics methodology across its operating group. The methodology was developed before joining NSK using public CMS data on personal time, and remains his personal intellectual property. Because the work is self directed and built on public data, it can be advanced independently of any single employer.
National interest
This portfolio is the basis for a national interest case: the endeavor has substantial merit and national importance, the record shows a person well positioned to advance it, and the United States benefits from letting that work continue. The full case is set out on a dedicated page.