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A drug classification pipeline for Medicaid claims using RxNorm

Nicholas Williams, Kara E. Rudolph

TL;DR

The paper addresses the need to convert US National Drug Codes (NDCs), which denote products rather than drug classes, into actionable drug-class labels for research. It presents a free, open-source pipeline that leverages the NLM RxNorm API (and RxNav-in-a-Box) to map NDCs to the WHO Anatomical Therapeutic Chemical (ATC) classification, using an R interface and handling various RxCUI statuses and remapping via semantic clinical drug concepts. In a Medicaid claims dataset (2016–2019, 26 expansion states), the pipeline linked 59.4% of unique NDC to ATC, and these classified codes accounted for 95.5% of all claims, with 12,004 NDC identified as opioids or non-opioids for pain; a small audit supported accuracy. The approach achieves performance comparable to commercial databases, offers reproducibility, and can be extended to additional classification schemes or non-US contexts via RxNorm extensions, enhancing large-scale pharmacological research capabilities.

Abstract

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.

A drug classification pipeline for Medicaid claims using RxNorm

TL;DR

The paper addresses the need to convert US National Drug Codes (NDCs), which denote products rather than drug classes, into actionable drug-class labels for research. It presents a free, open-source pipeline that leverages the NLM RxNorm API (and RxNav-in-a-Box) to map NDCs to the WHO Anatomical Therapeutic Chemical (ATC) classification, using an R interface and handling various RxCUI statuses and remapping via semantic clinical drug concepts. In a Medicaid claims dataset (2016–2019, 26 expansion states), the pipeline linked 59.4% of unique NDC to ATC, and these classified codes accounted for 95.5% of all claims, with 12,004 NDC identified as opioids or non-opioids for pain; a small audit supported accuracy. The approach achieves performance comparable to commercial databases, offers reproducibility, and can be extended to additional classification schemes or non-US contexts via RxNorm extensions, enhancing large-scale pharmacological research capabilities.

Abstract

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.
Paper Structure (8 sections, 3 tables)