Social network analysis and quantification of a prototypical acute pain medicine and regional anesthesia service.

Objective: The objective of this study was to quantify the network complexity, information flow, and effect of critical-node failures on a prototypical regional anesthesia and perioperative pain medicine (RAPPM) service using social network analysis.

Design: Pilot cross-sectional investigation.

Setting: This study was conducted at a prototypical single-center, multi-location academic anesthesiology department with an active RAPPM service.

Interventions: We constructed an empirically derived prototypical social network representative of a large academic RAPPM service.

Outcome Measures: The primary objective was measurement of network complexity via network size, structure, and information flow metrics. The secondary objective identified, via network simulation, those nodes whose deletion via single, two-level, or three-level node failures would result in the greatest network fragmentation. Exploratory analyses measured the impact of nodal failures on the resulting network complexity.

Results: The baseline network consisted of 84 nodes and 208 edges with a low density of 0.03 and high Krackhardt hierarchy of 0.787. Nodes exhibited low average total degree centrality (mean ± standard deviation [SD]) of 0.03 ± 0.034 and mean eigenvector centrality of 0.164 ± 0.182. The RAPPM resident-on-call was identified as the critical node in a single-node failure, with the resulting network fragmentation increasing from 0 to 0.52 upon node failure. A two-level failure involved both the RAPPM resident-on-call as well as the RAPPM attending-on-call, with the resulting fragmentation expanding to 0.772. A three-level node failure included the RAPPM resident-on-call, the main block-room attending, and block room fellow with fragmentation increasing to 0.814.

Conclusions: The RAPPM service entails considerable network complexity and increased hierarchy, but low centrality. The network is at considerable fragmentation risk from even single-node failure.