Concentration and Specialty Pair Patterns of Interdepartmental Consultations in Hospitalized Patients Using Real-World Data: Retrospective Cohort Study.
A small number of department pairs are associated with a large proportion of interdepartmental consultations.
Concentration and Specialty Pair Patterns of Interdepartmental Consultations in Hospitalized Patients Using Real-World Data: Retrospective Cohort Study.
Interdepartmental consultations are essential for managing complex inpatient care but are often inefficient.
This study aimed to analyze the distribution and network characteristics of interdepartmental consultations across a large tertiary hospital, focusing on high-frequency collaboration pairs and their disease associations.
This retrospective cohort study included all interdepartmental consultations for inpatients and emergency patients at Peking Union Medical College Hospital (Beijing, China) from January 1 to December 31, 2024.
Consultation activity exhibited marked concentration.
The Emergency Department issued the most consultation requests (19,698/102,858, 19.15%), far exceeding the median departmental request volume of 1899.5 (IQR 1359.5-3759.25).
Meanwhile, the Internal Medicine Consultation Service received the highest number of consultations (10,428/102,858, 10.14%), substantially above the median reception volume of 1886.0 (IQR 521.25-4056.75) across departments.
The per capita request intensity varied widely, with Critical Care Medicine highest value (21.64) vs a hospital-wide average of 0.32.
This study provides a novel, hospital-wide, network-based mapping of interdepartmental consultations using real-world data.
Unlike prior work limited to single departments or diseases, it reveals that collaboration is concentrated, Pareto-like, and disease-driven.
The identification of stable, disease-specific consultation pairs offers a data-driven framework for understanding multidisciplinary collaboration.
These findings offer a data-driven framework for understanding multidisciplinary collaboration as a networked system.