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Health Serv Outcomes Res Methodol. 2016 Sep;16(3):132-153. Epub 2016 Jun 27.

An analysis of patient-sharing physician networks and implantable cardioverter defibrillator therapy.

Author information

1
The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, One Medical Center Dr., Lebanon, NH 03756.
2
The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, One Medical Center Dr., Lebanon, NH 03756; The Department of Medicine, Geisel School of Medicine at Dartmouth, One Medical Center Dr., Lebanon, NH 03756.
3
The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, One Medical Center Dr., Lebanon, NH 03756; Department of Economics, Dartmouth College, Hanover NH 03755.
4
The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, One Medical Center Dr., Lebanon, NH 03756; The Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, One Medical Center Dr., Lebanon, NH 03756.

Abstract

The application of social network analysis to the organization of healthcare delivery is a relatively new area of research that may not be familiar to health services statisticians and other methodologists. We present a methodological introduction to social network analysis with a case study of physicians' adherence to clinical guidelines regarding use of implantable cardioverter defibrillators (ICDs) for the prevention of sudden cardiac death. We focus on two hospital referral regions (HRRs) in Indiana, Gary and South Bend, characterized by different rates of evidence-based ICD use (86% and 66%, respectively). Using Medicare Part B claims, we construct a network of physicians who care for cardiovascular disease patients based on patient-sharing relationships. Approaches for weighting physician dyads and aggregating physician dyads by hospital are discussed. Then, we obtain a set of weighted network statistics for the positions of hospitals in their referral region, global statistics for the physician network within each hospital, and of the network positions of individual physicians within hospitals, providing the mathematical specification and sociological intuition underlying each measure. We find that adjusting for network measures can reduce the observed differences between referral regions for evidence-based ICD therapy. This study supports previous reports on how variation in physician network structure relates to utilization of care, and motivates future work using physician network measures to examine variation in evidence-based medicine.

KEYWORDS:

Social network analysis; centrality; degree distribution; evidence-based medicine; exponential random graph model; implantable cardioverter defibrillators; structural equivalence

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