arXiv：cs.LG（机器学习，全量分类）· Ayesha Tanveer, Khandakar Ahmed, Assefa Teshome, Oyetunde Gbadeyan, Ziad Nehme·· 14 小时前AI 评分33
维多利亚州急救医疗服务多年数据分析：量化救护车滞留的影响
Quantifying the Impact of Ambulance Ramping: A Multi-Year Analysis of Victorian Emergency Medical Services Cases
AI 导读
一项基于维多利亚州2020年1月至2024年3月2,850,575次救护车出勤记录的分析显示，救护车滞留累计造成1,491,127小时损失，相当于研究期内每天约96个十小时救护车班次被浪费。59家医院中10家贡献了57.8%的损失小时数，年度损失上升57%至2022年峰值，而转运需求反而下降3.7%。交接时长随前一小时到达同一医院的救护车数量单调上升，该效应在一天中每个小时均持续存在。
正文
Abstract:Ambulance ramping, the delay between hospital arrival and patient handover, is a critical operational bottleneck in Emergency Medical Services (EMS), yet its systemic magnitude and dynamics remain inadequately characterised at scale. This paper quantifies the scale, trajectory, and operational correlates of ramping across an entire statewide EMS system, analysing 2,850,575 ambulance attendances in Victoria, Australia from January 2020 to March 2024 using an Exploratory Data Analysis (EDA). After systematic preprocessing, an analytical cohort of 2,026,569 Emergency Department (ED) transports across 59 hospitals with ED and 79 Local Government Areas (LGA) was examined through interval decomposition, Pareto concentration, hourly cross-correlation, hospital arrival concurrency and priority-stratified operational comparisons. Cumulative Ambulance Hours Lost (AHL) totalled 1,491,127 hours, equivalent to approximately 96 ten-hour ambulance shift lost every day of the study window. Ten of 59 hospitals account for 57.8% of lost hours from 50.9% of cases. Annual losses rose 57% to a 2022 peak while transported demand fell 3.7%, indicating deterioration in per-case handover rather than growth in demand. Handover duration varies little with patient acuity, but rises monotonically with the number of ambulances arriving at the same hospital in the preceding hour, an effect persisting within every hour of the day. Hourly demand is moderately associated with ramping two to four hours later (r = 0.365). These findings establish the empirical preconditions for hospital-state aware ambulance routing.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.00818 [cs.LG] |
| (or arXiv:2610.00818v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.00818 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Khandakar Ahmed [view email]
[v1]
Wed, 30 Sep 2026 23:23:03 UTC (8,401 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org