Enhancing research data infrastructure to address the opioid epidemic: the Opioid Overdose Network (O2-Net)

• Published in: JAMIA open 2022-07, Vol.5 (2), p.ooac055
• Main Authors: Lenert, Leslie A, Zhu, Vivienne, Jennings, Lindsey, McCauley, Jenna L, Obeid, Jihad S, Ward, Ralph, Hassanpour, Saeed, Marsch, Lisa A, Hogarth, Michael, Shipman, Perry, Harris, Daniel R, Talbert, Jeffery C
• Format: Article
• Language: English
• Subjects: Case Report; Computational linguistics; Drugs; Language processing; Medical records; Natural language interfaces; Overdose
• ISSN: 2574-2531; 2574-2531
• DOI: 10.1093/jamiaopen/ooac055

Opioid Overdose Network is an effort to generalize and adapt an existing research data network, the Accrual to Clinical Trials (ACT) Network, to support design of trials for survivors of opioid overdoses presenting to emergency departments (ED). Four institutions (Medical University of South Carolina [MUSC], Dartmouth Medical School [DMS], University of Kentucky [UK], and University of California San Diego [UCSD]) worked to adapt the ACT network. The approach that was taken to enhance the ACT network focused on 4 activities: cloning and extending the ACT infrastructure, developing an e-phenotype and corresponding registry, developing portable natural language processing tools to enhance data capture, and developing automated documentation templates to enhance extended data capture. Overall, initial results suggest that tailoring of existing multipurpose federated research networks to specific tasks is feasible; however, substantial efforts are required for coordination of the subnetwork and development of new tools for extension of available data. The initial output of the project was a new approach to decision support for the prescription of naloxone for home use in the ED, which is under further study within the network. Lay Summary Opioid overdose is a very serious problem in the United States. This paper describes the efforts of one research group to adapt existing infrastructure created with National Institutes of Health funding to address this problem using real-world data from hospital emergency departments. The work includes electronic case definitions, use of artificial intelligence methods to unlock data in provider notes, and improved techniques for documentation in electronic medical records with integrated reminders for providers.