A drug classification pipeline for Medicaid claims using RxNorm
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913295207759872 |
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| author | Williams, Nicholas Rudolph, Kara E. |
| author_facet | Williams, Nicholas Rudolph, Kara E. |
| contents | Objective: Freely preprocess drug codes recorded in electronic health records and insurance claims to drug classes that may then be used in biomedical research.
Materials and Methods: We developed a drug classification pipeline for linking National Drug Codes to the World Health Organization Anatomical Therapeutic Chemical classification. To implement our solution, we created an R package interface to the National Library of Medicine's RxNorm API.
Results: Using the classification pipeline, 59.4% of all unique NDC were linked to an ATC, resulting in 95.5% of all claims being successfully linked to a drug classification. We identified 12,004 unique NDC codes that were classified as being an opioid or non-opioid prescription for treating pain.
Discussion: Our proposed pipeline performed similarly well to other NDC classification routines using commercial databases. A check of a small, random sample of non-active NDC found the pipeline to be accurate for classifying these codes.
Conclusion: The RxNorm NDC classification pipeline is a practical and reliable tool for categorizing drugs in large-scale administrative claims data. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_01514 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A drug classification pipeline for Medicaid claims using RxNorm Williams, Nicholas Rudolph, Kara E. Quantitative Methods Databases Objective: Freely preprocess drug codes recorded in electronic health records and insurance claims to drug classes that may then be used in biomedical research. Materials and Methods: We developed a drug classification pipeline for linking National Drug Codes to the World Health Organization Anatomical Therapeutic Chemical classification. To implement our solution, we created an R package interface to the National Library of Medicine's RxNorm API. Results: Using the classification pipeline, 59.4% of all unique NDC were linked to an ATC, resulting in 95.5% of all claims being successfully linked to a drug classification. We identified 12,004 unique NDC codes that were classified as being an opioid or non-opioid prescription for treating pain. Discussion: Our proposed pipeline performed similarly well to other NDC classification routines using commercial databases. A check of a small, random sample of non-active NDC found the pipeline to be accurate for classifying these codes. Conclusion: The RxNorm NDC classification pipeline is a practical and reliable tool for categorizing drugs in large-scale administrative claims data. |
| title | A drug classification pipeline for Medicaid claims using RxNorm |
| topic | Quantitative Methods Databases |
| url | https://arxiv.org/abs/2404.01514 |