Adverse Drug Reactions (ADRs) are a leading cause of hospital admissions and healthcare costs. Traditional methods of ADR reporting often rely on post-marketing surveillance, and manual reporting of ADRs to the local or national pharmacovigilance agencies for causality assessment and final reporting to the WHO. High-income countries have their own national (i.e., USFDA) and regional (i.e., European Medicines Agency / EMA) pharmacovigilance agencies. However, this process is slow and inefficient. This article proposes a novel framework for integrating ADR detection into clinical workflows using Electronic Medical Record (EMR) systems, crowdsourced reporting from patients and healthcare professionals, and graph theory for generating automated ADR signals and reports to the local or national pharmacovigilance agencies. The system leverages automated data collection from EMRs (drug prescriptions, clinical notes) by EMR data scraping, integrating ADR dictionaries and drug databases to automate the generation of ranked ADR signals. By applying graph theory, the system filters and upranks connections between drugs and ADRs, considering the temporal relationship between drug administration and ADR occurrence. This automated approach offers a significant improvement in ADR reporting, enabling faster detection and more accurate predictions. Methodologies, framework visualizations and python code snippets are included to aid implementation.