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.