Our focus on methodologies complements several results-focused reviews that cover much of the same territory. We set out to assess the motivations, substance, contributions, and needs of network analyses of routinely collected health care datasets (NARCHD): What problems have motivated this work, and what research programs have emerged? What has this research contributed to knowledge and methodology, and what further advances can be made? The paper provides a comprehensive survey of this literature and an evaluation of ongoing projects. 1 In this article, we review the work being done at the interface of these tools. A recent systematic review of data acquisition and analysis in systems medicine identified RCHD such as electronic medical records and public databases as the most common sources of data and NA as the most common modeling paradigm. This has led to an explosion in applications of network analysis (NA), which have proved successful in many domains at extracting conceptual insights and predictive power from large and messy datasets.

The computer science revolution behind this progress has also facilitated the development and implementation of rapid, accurate algorithms to perform calculations on mathematical graphs and sample from distributions. Network analysis, graph theory, secondary use, administrative data, EHR BACKGROUND AND SIGNIFICANCEĪs electronic information systems have increased in capacity, efficiency, and accessibility, digitized clinical recordkeeping has made routinely collected health care data (RCHD) of unprecedented depth, scope, and variability available to researchers.

Applied network analysis definition