Referral paths in the U.S. physician network

• Published in: Applied network science Vol. 3; no. 1; p. 20
• Main Authors: An, Chuankai, O’Malley, A. James, Rockmore, Daniel N.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 31-07-2018; Springer Nature B.V; SpringerOpen
• Subjects: Big data; Complexity; Computer Appl. in Social and Behavioral Sciences; Computer Science; Health care; Health record analysis; Health services; Medical referrals; Myocardial infarction; Network Medicine in the era of Big Data in Science and Healthcare; Network science; Patients; Physicians; Prediction models; Predictive modeling; Simulation and Modeling; Social network analysis; United States > US; Health record analysis; Big data; Predictive modeling; Social network analysis; Network science
• ISSN: 2364-8228; 2364-8228
• DOI: 10.1007/s41109-018-0081-4

In this paper, we analyze the millions of referral paths of patients’ interactions with the healthcare system for each year in the 2006-2011 time period and relate them to U.S. cardiovascular treatment records. For a patient, a “referral path” records the chronological sequence of physicians encountered by a patient (subject to certain constraints on the times between encounters). It provides a basic unit of analysis in a broader
referral network
that encodes the flow of patients and information between physicians in a healthcare system. We consider referral networks defined over a range of interactions as well as the characteristics of referral paths, producing a characterization of the various networks as well as the physicians they comprise. We further relate these metrics and findings to outcomes in the specific area of cardiovascular care. In particular, we match a referral path to occurrences of Acute Myocardial Infarction (AMI) and use the summary measures of the referral path to predict the treatment a patient receives and medical outcomes following treatment. Some referral path features are more significant with respect to their ability to boost a tree-based predictive model, and have stronger correlations with numerical treatment outcome variables. The patterns of referral paths and the derived informative features illustrate the potential for using network science to optimize patient referrals in healthcare systems for improved treatment outcomes and more efficient utilization of medical resources.