The U.S. healthcare system is plagued by rising costs and declining quality of care, with patients often prescribed painkillers as a first line of defense without addressing the root cause of their pain. The opioid crisis has claimed over 47,000 lives in 2017 alone, largely due to overprescription of opioid-class drugs by pharmaceutical companies. To tackle this issue, healthcare data analysis techniques must be adapted to handle the vast and complex datasets involved. Traditional statistical Business Intelligence (BI) approaches often struggle with volume and complexity, but recent innovations in machine learning (ML) and graph algorithms enable more efficient processing of large, connected datasets. By analyzing CMS Open Payments data combined with other complementary datasets, researchers were able to identify high-paying physicians who prescribed opioid-class drugs, including Dr. Chun, whose practices may warrant further investigation due to suspicious payments from pharmaceutical companies and Medicare fraud allegations. The use of Neo4j Graph Platform allows for efficient analysis of the entire dataset without needing to employ sampling or summarize data, revealing insights into the relationships between doctors, pharmacies, and pharmaceutical companies that can inform policy and improve healthcare outcomes.