Social network methods offer a novel way to investigate complex patterns of healthcare delivery. By explicitly modeling relationships between patients, providers, and organizations, social network methods enable researchers to study healthcare as a complex system. Increasingly, these approaches have been applied to insurance claims and electronic medical records data to study care coordination, healthcare costs, and quality. However, there are tremendous amount of research and application opportunities with network analytics in healthcare that are yet to be explored, including how insurance networks, hospital-based networks, and provider networks impact a range of outcomes.
This instructional workshop aims to provide researchers and professionals with an introduction to the conceptual issues and network analytic tools needed to analyze healthcare data, including both didactic and interactive components. The first part of the workshop will be devoted to an overview of the network analytics applied in healthcare research, presenting the landscape of state-of-the-art research papers, their methodologies and findings. The second section will focus on basic concepts of network
analytics, including the concepts of bipartite networks and network projection, as well as fundamental network properties.
In the third and final section, attendees will have the opportunity to acquire hands-on experience (using python and/or R) on how to process electronic health data to network representation, how to conduct basic analysis on networks, and how to visualize networks. This workshop is intended to cover the needs and interests of individuals with novice to intermediate levels of experience in the field.
Learning Objective: Participants in this workshop will
● Obtain an overview of the rationale for applying social network methods to administrative health data
● Understand how to use administrative claims data to construct and analyze physician networks
● Gain knowledge of the key concepts and basic methods in network analytics o Learn how to utilize widely available data and tools to analyze network data
Craig Pollack (Presenter)
Johns Hopkins Bloomberg School of Public Health
Eva DuGoff (Presenter)
University of Maryland School of Public Health
Yoonyoung Park (Presenter)
Panagiotis Karampourniotis (Presenter)