Social-Network Analysis for Pain Medications: Influential physicians may not be high-volume prescribers - Choudhury, Abhinav; Kaushik, Shruti; Dutt, Varun (2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2017)
According to the Institute of Medicine of the National Academies, more than 100 million Americans suffer from chronic pain related to diabetes, heart disease, and cancer combined. Adoption of pain medications and safe healthcare practices is a major global policy concern. This adoption process is highly influenced by the interpersonal network of physicians prescribing medications to treat pain. However, existing research into physician networks have been hospital-specific, applied to a smaller number of physicians, and dependent upon physicians' self-reports. In this paper, using big-data and data-mining, we overcome these limitations: By using a case of 30+ hospitals spanning across 2000+ physicians, we create a social network containing physicians' prescription data and adoption behavior of pain medications. The social network assumes that connected physicians work in the same hospital and belong to the same specialty or specialty group. Then, using the centrality measures, degree and eigenvector centrality, we analyze prescription volumes and proportion of adopters of pain medications. We also analyze gender effects. Results revealed that the most influential physicians were not the high-volume prescribers. Male physicians were more influential compared to female physicians; however, females prescribed more volume compared to males. Our results help us identify critical physicians from certain core specialties and specialty groups who may be approached by patients seeking pain relief.