Data-driven network diagnostics for optimizing POCUS management: an actionable hospital analytical approach

Point-of-care ultrasound (POCUS) is vital in critical care, yet hospitals struggle with inefficient resource allocation, leading to delays, staff burnout, and system fragility during demand surges. Static scheduling often inadequately addresses dynamic patient needs and hidden workflow bottlenecks.

We developed a three-tier, data-driven analytical approach to diagnose and optimize POCUS management. The study was conducted at Yixing People's Hospital, a 1,700‑bed tertiary teaching hospital in Jiangsu, China. Using data from 2,281 POCUS examinations performed during the one‑year study period (2024), we modeled the POCUS system as a tripartite network (patients, departments, sonographers). Key clinical units involved included the intensive care unit (ICU, 34 beds), the emergency department (ED, 12 resuscitation beds plus a 15‑bed EICU), and the Department of Respiratory and Critical Care Medicine (81 beds). We integrated structural, temporal, and functional analyses to identify bottlenecks, forecast demand surges, and detect latent collaborative communities.

The network exhibited efficient but vulnerable "small‑world" properties, with over‑reliance on critical hubs (e.g., ICU, sonographer U2165). Time‑series analysis revealed periodic extreme surges coinciding with network contraction. Community detection exposed workload imbalances, with one community handling 57.8% of examinations.

We propose an actionable management analytical approach: (1) a demand‑responsive staffing strategy activated by forecast‑based alerts; (2) priority green channels for critical diagnostic pathways; and (3) functionally differentiated allocation based on community workload. This approach may enable hospital administrators to systematically balance efficiency with resilience, moving POCUS management from intuition toward evidence‑based optimization.