Abstract
Opioid use disorder (OUD) is an epidemic that costs the U.S. healthcare systems $504 billion annually and poses grave mortality risks. Existing studies investigated OUD treatment barriers via surveys as a means to mitigate this opioid crisis. However, the response rate of these surveys is low due to social stigma around opioids. We explore user-generated content in social media as a new data source to study OUD. We design a novel IT system, SImilarity Network-based DEep Learning (SINDEL), to discover OUD treatment barriers from patient narratives and address the challenge of morphs. SINDEL significantly outperforms state-of-the-art NLP models, reaching an F1 score of 76.79 percent. Thirteen types of treatment barriers were identified and verified by domain experts. This work contributes to information systems with a novel deep-learning-based approach for text analytics and generalized design principles for social media analytics methods. We also unveil the hurdles patients endure during the opioid epidemic.
Original language | English (US) |
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Pages (from-to) | 166-195 |
Number of pages | 30 |
Journal | Journal of Management Information Systems |
Volume | 38 |
Issue number | 1 |
DOIs | |
State | Published - 2021 |
Keywords
- Computational design science
- HealthTech
- addiction treatment
- deep learning
- health IT
- opioid addiction
- social media analytics
ASJC Scopus subject areas
- Management Information Systems
- Computer Science Applications
- Management Science and Operations Research
- Information Systems and Management