ArticleResearch square2026
Algorithm for Improving Beneficiary Identifier Quality in Medicaid Administrative Data.
Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately linking Medicaid beneficiaries over time and across administrative files is essential for health policy research, yet the federally assigned Chronic Conditions Warehouse (CCW) identifier and state-assigned identifier are subject to systematic data quality problems that can lead to inaccurate linkages and biased analyses. Drawing on MAX Person Summary files (1999-2015) and T-MSIS Analytic Files (2014-2022), this paper documents four key identifier problems: identifier reassignment beginning in 2020, missing identifiers, discordant identifiers, and identifier clustering. It provides a structured algorithm for correcting each. Our corrections are applied to enrollment, utilization, and mortality files, and result in a composite identifier that allows beneficiaries to be uniquely and reliably tracked over time and across files.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.