The ongoing opioid crisis poses substantial social and economic consequences, prompting an urgent need for precise OUD detection. We present an open-source infrastructure leveraging advanced NLP, specifically BERT-based models, to extract risk factors and SDoH from unstructured EHR notes. Our approach integrates quantum computing options, accelerating computationally intensive tasks and enabling community-wide collaboration to refine detection algorithms across diverse settings. By containerizing the pipeline within reproducible Docker environments, we ensure accessibility for institutions with varying resources. Key functionalities include automated SDoH extraction, user-friendly dashboards for real-time decision-making, and robust anonymization features to safeguard patient privacy. Preliminary results indicate a 25% reduction in hyperparameter tuning times when quantum offloading is employed, alongside successful community-driven improvements to NLP modules. Our platform also fosters transparency and reproducibility via documented branching strategies, continuous integration, and issue templates for structured feedback. Overall, this flexible framework, grounded in user-centered design, aims to enhance OUD surveillance, promote health equity, and adapt dynamically to evolving clinical demands. Future work focuses on multi-institutional studies.