An empirical study of drone medical logistics transportation in a multi-campus model of Chinese public hospitals: Real-world data-driven validation of timeliness and application effects

In the real-world application scenario of China's public hospital multi-site operation model, this study systematically evaluates the feasibility, timeliness, and stability of drone-based medical logistics transportation between hospital campuses, providing high-quality empirical evidence for optimizing cross-campus medical supply transfer processes. This retrospective analysis collected complete drone medical logistics transport data between two campuses of Deyang People's Hospital from April 8 to July 30, 2024. The primary outcome measure was drone transport time per unit distance (min/km), compared with road traffic time per unit distance measured by three mainstream navigation apps (Baidu Maps, Amap, and Tencent Map) at different time points. Intergroup differences were analyzed using the Mann-Whitney U nonparametric test, with effect sizes calculated via Cliff's Delta. Statistical significance was set at P < 0.05. A total of 750 valid drone flight records were included, covering a distance of (5.95 ± 0.03) km with a unit time of (1.64 ± 0.14) min/km. The unit time for drone transportation was significantly lower than that measured by Baidu Maps (2.06 ± 0.12 min/km), Amap (2.01 ± 0.12 min/km), and Tencent Map (2.03 ± 0.09 min/km) at the 0-point road traffic unit time (all P < 0.001). At all nine time points monitored by Tencent Map, the unit time per kilometer exceeded that of the UAV. During the 10:00 peak period, Tencent Map recorded a unit time of (4.12 ± 0.09) min/km, with the UAV achieving a time savings rate of 60.2%. Mann-Whitney U tests revealed significant differences across all time points (P < 0.001), with Cliff's Delta absolute values consistently exceeding 0.75, indicating extremely large effect sizes. Drone-based medical logistics demonstrated significant advantages in timeliness and stability under the multi-campus model of urban hospitals, particularly during peak traffic congestion periods. This study provides crucial empirical support for establishing an efficient, intelligent medical logistics system, holding significant implications for enhancing healthcare service efficiency and improving public health emergency response capabilities.