Concentration and Specialty Pair Patterns of Interdepartmental Consultations in Hospitalized Patients Using Real-World Data: Retrospective Cohort Study.

Background: Interdepartmental consultations are essential for managing complex inpatient care but are often inefficient. Hospital-wide, data-driven analyses are needed to guide process improvements; yet, most existing studies have focused on single departments or specific diseases, leaving a gap in understanding hospital-level collaboration networks. Understanding these patterns is crucial for optimizing clinical workflows, reducing delays, and improving patient outcomes in large tertiary hospitals. Objective: 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. Methods: 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. Secondary data were extracted from the Hospital Information System. In total, 102,858 valid consultations involving 42 clinical departments were analyzed. Outcome measures included consultation requests and receptions per department, per capita request intensity, and pairwise collaboration volume. High-frequency collaboration pairs were defined as those with an annual consultation volume of 300 or more. Descriptive statistics (medians and IQRs) and proportions with 95% CIs were used. Results: 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. Collaboration demonstrated a strong Pareto distribution: the top 5.37% (65/1210) of department pairs (65 pairs) accounted for 42.02% (43,221/102,858) of all consultations. These high-volume pairs were predominantly disease-specific. Examples include: Endocrinology-Ophthalmology primarily for diabetic and thyroid eye disease (1287/102,858, 1.25%); General Surgery-Otolaryngology mainly for preoperative thyroid airway assessment (1225/102,858, 1.19%); General Surgery-Clinical Nutrition for perioperative support (1032/102,858, 1.00%); Endocrinology-Clinical Nutrition for metabolic disease management (703/102,858, 0.68%); Orthopedics-Rehabilitation Medicine for postoperative rehabilitation (686/102,858, 0.67%); Oncology Medical Center-Clinical Nutrition for nutrition support in patients with cancer (681/102,858, 0.66%). Recurring clinical scenarios generated these stable, predictable consultation pathways. Conclusions: 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. In practice, administrators and clinicians can use this evidence to prioritize resources, design standardized multidisciplinary team pathways, and implement spatial or digital interventions to reduce delays and improve patient flow and outcomes.