The opioid epidemic, which is responsible for nearly 400,000 overdose deaths since 1999, has received increased attention from researchers and policymakers as a leading cause of injury-related death in the United States [
1]. Unfortunately, few evidence-based solutions to the epidemic exist due to limited prior attention and investments in research infrastructure for a condition often stigmatized or marginalized [
2]. The passage of the Substance Use-Disorder Prevention that Promotes Opioid Recovery and Treatment (SUPPORT) for Patients and Communities Act, however, has generated marked enthusiasm and support to address gaps in research, surveillance, and care for opioid use disorder (OUD) using increasingly available electronic and digital data sources such as electronic health records (EHRs) [
3]. While the National Institute of Health encourages the use of common data elements (CDEs) “to improve data quality and opportunities for comparison and combination of data from multiple studies and with electronic health records” [
4], numerous challenges still exist in identifying and incorporating OUD-specific CDEs into research initiatives [
5,
6]. Prior work has identified numerous gaps in EHRs or data standards that preclude high-quality OUD research, including single site-specific definitions that cannot be generalized for observational studies or surveillance as well as the use of disparate data EHR data systems between vendors when capturing and storing health data [
7,
8]. Additionally, fragmented CDEs that are not easily translated across settings or data systems that are inherently designed for select types or structured or clinically oriented data prevent the effective development of quality measurement or surveillance systems [
9,
10]. For example a common National Institutes of Health (NIH) CDE is derived from the Timeline Followback Method Assessment, which collects information about opioid use in the past week [
11]. However, this CDE does not map to any existing data standard or system which are inherently designed for more structured data or hierarchies of data terms in ontologies not specific to a single question.
The creation and inclusion of opioid relevant CDEs in clinical data registries and EHRs would both enable and improve the quality of substance use disorder research and the evaluation of interventions to improve outcomes [
6]. For example, improving EHR data infrastructure for OUD data elements could provide the building blocks for future quality measures, performance benchmarking, and answering important research questions, such as “how many providers provide naloxone or administer buprenorphine for OUD?” or “what proportion of emergency department (ED) patients with OUD have low back pain?” [
12], which would improve our understanding of the scope of this issue, as well as evaluate interventions. We therefore aimed to identify and categorize existing CDEs in relation to OUD and assess their alignment with common data standards, which is required prior to infrastructure implementation.