Social-Network Analysis for Pain Medications Influential physicians may not be high-volume prescribers
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 hi...
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| Veröffentlicht in: | 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) S. 881 - 885 |
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| Format: | Tagungsbericht |
| Sprache: | Englisch |
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New York, NY, USA
ACM
31.07.2017
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| ISBN: | 1450349935, 9781450349932 |
| ISSN: | 2473-991X |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Kaushik, Shruti Choudhury, Abhinav Dutt, Varun |
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| Editor | Diesner, Jana Ferrari, Elena Xu, Guandong |
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| Keywords | Social network analysis pain medications gender eigenvector centrality |
| Language | English |
| License | 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only. |
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| Snippet | According to the Institute of Medicine of the National Academies, more than 100 million Americans suffer from chronic pain related to diabetes, heart disease,... |
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| SubjectTerms | Applied computing Applied computing -- Law, social and behavioral sciences Applied computing -- Law, social and behavioral sciences -- Sociology Applied computing -- Life and medical sciences Applied computing -- Life and medical sciences -- Consumer health Applied computing -- Life and medical sciences -- Health care information systems Applied computing -- Life and medical sciences -- Health informatics eigenvector centrality gender Human-centered computing Human-centered computing -- Collaborative and social computing Human-centered computing -- Collaborative and social computing -- Collaborative and social computing design and evaluation methods Human-centered computing -- Collaborative and social computing -- Collaborative and social computing design and evaluation methods -- Social network analysis Information systems Information systems -- Information systems applications Information systems -- Information systems applications -- Data mining Networks Networks -- Network types Networks -- Network types -- Overlay and other logical network structures Networks -- Network types -- Overlay and other logical network structures -- Online social networks pain medications Social network analysis |
| Subtitle | Influential physicians may not be high-volume prescribers |
| Title | Social-Network Analysis for Pain Medications |
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