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Quantifying the Impact of Ambulance Ramping: A Multi-Year Analysis of Victorian Emergency Medical Services Cases

Ayesha Tanveer, Khandakar Ahmed, Assefa Teshome, Oyetunde Gbadeyan, Ziad Nehme

arXiv:2610.00818v1cs.LG

TL;DR

Ambulance ramping is recognized as a manifestation of ED crowding, but how delays accumulate across EMS operations remains insufficiently characterized. This study uses structured exploratory analysis of statewide ambulance records to examine ramping over time and across hospitals. Cumulative losses were substantial, and the authors identify patterns that form preconditions for further prediction and modeling.

  • Problem

    How ramping delays accumulate across the broader EMS workflow and vary by patient acuity remains underexamined, despite offload delays’ association with reduced ambulance availability and longer subsequent response times.

    Source: §I · p. 1

  • Method

    The study applies a structured exploratory framework to a large-scale EMS dataset across temporal, spatial, and operational dimensions, decomposing service time and stratifying analyses by acuity and transport priority.

    Source: §I · p. 2

  • Results

    1,491,127 cumulative AHL hours accrued; annual losses rose 57% to a 2022 peak as ED-bound transports fell 3.7%, and 10 hospitals accounted for 57.8% of AHL from 50.9% of cases.

    Source: F. Hospital Level Concentration of AHL · p. 7, §IV · p. 12

  • Takeaways & Limitations

    The observed concentration, timing, and arrival-volume patterns are described as preconditions for prediction and as delineating a framework for further analysis.

    Source: §IV · p. 12

    Without a pre-pandemic baseline, longer-term ramping direction cannot be established, and the reported analyses are descriptive and correlational rather than causal.

    Source: §III · p. 4

Abstract

from arXiv · show

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.

I. INTRODUCTION

Ambulance ramping links hospital congestion with prehospital performance, but how delays accumulate across the EMS workflow and vary by acuity remains underexamined. The study frames exploratory data analysis as a structured way to examine ramping across temporal, spatial, and operational dimensions.

  • Offload delays reduce ambulance availability and are associated with prolonged response times for subsequent calls, connecting hospital congestion with prehospital performance.Source: §I · p. 1
  • Existing work leaves limited evidence on how bottlenecks redistribute delays across the EMS workflow and whether that pattern varies by patient acuity.Studies also use heterogeneous delay definitions and measurement points, complicating comparisons across the wider workflow.Source: §I · p. 1
  • The authors argue that EDA can guide problem formulation, model selection, and validation, yet its use as a primary framework for examining EMS system behaviour remains limited.Prior EMS analyses have used exploratory techniques, commonly as preliminary inspection before regression or predictive modelling.Source: §I · p. 2
  • The study applies a structured exploratory framework to a large-scale EMS dataset across temporal, spatial, and operational dimensions, deconstructing service time into five phases and stratifying by acuity and transport priority.Source: §I · p. 2

II. METHODOLOGY

The study retrospectively examines Ambulance Victoria attendances in Victoria from January 2020 to March 2024. Its statewide data span metropolitan, regional, and rural services and include timestamped ambulance cases.

II. METHODOLOGY

  • The methodology section outlines the study design, data source, operational definitions, and analytical framework used to explore ramping in Victoria.Source: §II · p. 2

A. Study Design & Setting

  • Ambulance Victoria is Victoria’s sole statutory ambulance provider, delivering prehospital care and emergency and non-emergency transport across metropolitan, regional, and rural areas.The retrospective cohort covers 01/01/2020 to 31/03/2024.Source: A. Study Design & Setting · p. 2

B. Data Source

  • 2,850,575 emergency ambulance cases attended by Ambulance Victoria from January 2020 to March 2024 were examined for offload delay.Source: B. Data Source · p. 2
  • AV services are divided into seven regions: Metro East and West, plus five regional and rural regions.Source: Figure 1 · p. 3

C. Study Cohort

The study defines its cohort using ambulance dispatch records and restricts ramping analyses to ED-capable facilities. It operationalizes ramping and related service intervals from recorded timestamps and derives a measure of recent hospital arrivals.

C. Study Cohort

  • 2,850,575 dispatches were recorded across 283 facilities and 100 LGAs; 59 facilities were ED-capable, and 79 Victorian LGAs were mapped to six AV regions.Source: C. Study Cohort · p. 3

D. Operational Definitions

  • Ambulance ramping is measured in minutes from arrival at the destination hospital to patient offload from the stretcher; Ambulance Hours Lost aggregates ramping time to quantify operational burden.Source: D. Operational Definitions · p. 3
  • A 10-hour period is used as the study’s consistent operational definition of an ambulance shift.Source: D. Operational Definitions · p. 3
  • The study defines dispatch-to-scene, on-scene, transport, and clearing intervals from recorded timestamps, and treats total service time as dispatch through ambulance availability.The workflow figure maps seven timestamped events to operational intervals, including arrival at the medical facility, patient transfer, and return to service.Source: D. Operational Definitions · p. 3, Figure 2 · p. 3
  • Hospital Arrival Concurrency counts other ambulance arrivals at the same hospital during the 60 minutes before a case’s arrival; higher counts indicate more congested arrival environments.Source: D. Operational Definitions · p. 4

E. Software and Analytical Tools

The analysis uses a separate cohort for each measure, excludes implausible handover intervals, and summarizes typical ramping with the median because the distribution is right-skewed. The results are descriptive and correlational, with pandemic conditions and the absence of a pre-pandemic baseline limiting interpretation over time.

E. Software and Analytical Tools

  • Python, DuckDB, pandas, matplotlib, and Seaborn supported data processing, querying, manipulation, and visualization; custom Python functions handled concurrency and cross-correlation analyses.Source: E. Software and Analytical Tools · p. 4

F. Data Cleaning & Assessments

  • 2,727,454 retained dispatches support demand analyses, while ramping analyses use 2,026,597 ED-capable transports with plausible handover intervals.Non-public-emergency-call cases were excluded, and analysis-specific cohorts were used rather than requiring complete plausible intervals for every case; the cohort figure reports sample sizes by results subsection.Source: F. Data Cleaning & Assessments · p. 4, Figure 3 · p. 4
  • 44.15 minutes is the mean ramping duration, compared with a 30.98-minute median; the median was used as typical experience because the distribution is strongly right-skewed.The 90th percentile is 91.3 minutes, almost three times the median.Source: F. Data Cleaning & Assessments · p. 4

III. RESULTS AND DISCUSSION

  • The results are descriptive and correlational, and without a pre-pandemic baseline they cannot establish the longer-term direction of ramping performance.Pandemic operating conditions may partly explain the 2021–22 peaks; later reductions should not be read as evidence of system improvement or a specific intervention’s effect.Source: §III · p. 4

A. Ambulance Demand Distribution

Ambulance demand is concentrated in a small number of LGAs, while ramping varies relatively little with patient acuity beyond some expediting of the most critical cases.

A. Ambulance Demand Distribution

  • Greater Geelong averages 2,557 cases per month, compared with 728.9 for the 30th-ranked LGA and fewer than one in the lowest-ranked LGAs.The highest-volume LGA is regional, while metropolitan councils occupy 13 of the 15 highest-demand positions.Source: A. Ambulance Demand Distribution · pp. 4–5, Figure 4 · p. 5

B. Decomposition of Service Time by Dispatch Code and Transport Priority

  • Ramping shows limited, non-monotonic variation across transport priorities: priority 1 has a 21.9-minute median, while priority 4 has the longest 90th-percentile delay at 116.2 minutes.Only the most critical group appears relatively insulated from handover delays; transport time varies little across priority groups.Source: B. Decomposition of Service Time by… · p. 5, Table I · p. 5
  • Call-to-dispatch medians range from 2.0 minutes for Priority 0–2 cases to 29.5 minutes for Code 3, whereas transport time ranges only from 18.7 to 21.4 minutes.Intervals before on-scene assessment are grouped by dispatch code; later intervals are grouped by transport priority, and the table reports medians and 90th percentiles.Source: B. Decomposition of Service Time by… · p. 5, Table I · p. 5

C. Ambulance Hours Lost: System and Regional Burden

Cumulative ambulance time lost was substantial and concentrated in metropolitan Melbourne; annual losses rose to a 2022 peak despite broadly stable transported demand.

C. Ambulance Hours Lost: System and Regional Burden

  • 1,491,127 cumulative AHL hours equal roughly 96 ten-hour ambulance-shift equivalents lost per day over the 4.25-year study window.Metropolitan Melbourne accounted for 76.7% of total AHL, compared with 23.3% collectively across the five regional zones.Source: C. Ambulance Hours Lost: System and… · pp. 5–6

D. Yearly Trajectory of Ambulance Hours Lost

C. Ambulance Hours Lost: System and Regional Burden

E. Yearly Composition and Ramping Trajectory by Transport Priority

Transport-priority composition shifted while total volume stayed broadly stable, and ramping worsened across priorities through 2022 before partly easing in 2023.

E. Yearly Composition and Ramping Trajectory by Transport Priority

  • Priority 1 cases rose from 47,957 in 2020 to 62,745 in 2023, increasing from 10.0% to 13.3% of annual volume as lower-priority counts declined.Annual transports ranged from 461,418 in 2022 to 509,647 in 2021 across the four full calendar years.Source: E. Yearly Composition and Ramping… · p. 7
  • For priority 1, 90th-percentile ramping rose from 41 minutes in 2020 to 72 minutes in 2022, then remained elevated at 67 minutes in 2023.The deterioration-to-peak-then-partial-easing pattern also held across priorities 2, 3, and 4.Source: E. Yearly Composition and Ramping… · p. 7
  • Lower-acuity arrivals waited longer at handover than time-critical arrivals in every year, and the absolute gap widened as the system deteriorated.Priority 4 had the heaviest tail, with p90 ramping of 76, 121, 140, and 132 minutes across 2020–23.Source: E. Yearly Composition and Ramping… · p. 7

F. Hospital Level Concentration of AHL

Ambulance hours lost (AHL) were concentrated in a bounded group of hospitals, whose share of lost hours exceeded their share of transport cases. Similar total burdens could reflect different operational profiles.

F. Hospital Level Concentration of AHL

  • 1,491,127 cumulative AHL hours were distributed across the 59 hospitals with EDs in the analytical cohort.The largest contributor accounted for approximately 109,530 hours, or 7.03% of total AHL.Source: F. Hospital Level Concentration of AHL · p. 7
  • 57.8% of total AHL came from the 10 highest-contributing hospitals, which received 50.9% of cases, a 6.9 percentage-point excess.The excess declined to 5.7 percentage points at rank 20 and was negligible by rank 50.Source: Table V · p. 7, F. Hospital Level Concentration of AHL · p. 7
  • 75.0% of AHL came from the top 15 hospitals, which received 68.6% of cases; 13 of these hospitals were metropolitan.The cumulative excess was bounded mainly to the first 25 to 30 hospitals.Source: F. Hospital Level Concentration of AHL · p. 7, Figure 8 · p. 8
  • AHL burdens could reflect different operational profiles: H14’s AHL-to-transport ratio was 1.36, compared with 0.85 for H06.Their total AHL contributions were similar, but H14 had higher handover time per case while H06’s burden was driven by case volume.Source: F. Hospital Level Concentration of AHL · pp. 7–8

G. Temporal Cascade of Demand, Arrivals, and Ramping

Hourly demand, ED arrivals, and ramping show a daily progression, while raw hourly correlations associate demand and arrivals with ramping at later lags. The analysis cautions that averaged daily patterns do not establish a complete causal delay.

G. Temporal Cascade of Demand, Arrivals, and Ramping

  • r = 0.365 was the maximum raw hourly association between demand and ramping at a three-hour lag; arrivals peaked at r = 0.364 at a two-hour lag.Across lags one to four, correlations were broad rather than concentrated at a single lag.Source: Table VI · p. 8, G. Temporal Cascade of Demand,… · p. 8
  • Demand peaked at 12:00, ED arrivals at 13:00, and mean ramping at 50.26–50.65 minutes from 18:00 to 20:00.The ramping peak lagged the arrival peak by seven hours and the demand peak by eight.Source: G. Temporal Cascade of Demand,… · p. 8, Figure 10 · p. 9
  • The averaged daily profiles reached r = 0.899 for both demand at a three-hour lag and arrivals at a two-hour lag, but these patterns reflect shared time-of-day structure.The hourly averages smooth short-term variation, and ambulance activity alone explains only a limited proportion of raw hourly ramping variation.Source: G. Temporal Cascade of Demand,… · p. 9
  • Ramping was calculated for ED arrivals with a recorded handover delay, and hospital-side factors such as capacity, staffing, and patient flow were not captured by ambulance arrivals alone.The hourly series tracked demand by dispatch hour, arrivals by hospital-arrival hour, and mean ramping by arrival hour.Source: G. Temporal Cascade of Demand,… · pp. 8–9

H. Hospital Level Temporal Heterogeneity

Hospitals differ in the timing and severity of ramping, and recent arrivals at a hospital are associated with longer ramping even within hour-of-day strata. That concurrency pattern is observed across hospitals, but the observational analysis does not quantify the effect of redistributing arrivals.

H. Hospital Level Temporal Heterogeneity

  • Hospitals differed in ramping magnitude and timing: most had lower p90 delays around 05:00–06:00, while many reached their highest levels between 12:00 and 20:00.H03, H09, and H14 frequently exceeded 130 minutes, whereas several high-volume hospitals remained below or near 100 minutes.Source: H. Hospital Level Temporal Heterogeneity · p. 9, Figure 11 · p. 10, Figure 12 · p. 10
  • High ambulance case load did not necessarily correspond to the most severe ramping; H14 and H15 had comparatively fewer cases but substantial delays throughout the day.The authors note that capacity, bed availability, discharge processes, staffing, and operational practices also contribute to offload delays.Source: H. Hospital Level Temporal Heterogeneity · pp. 9–10

I. Hospital Arrival Concurrency and Its Association with Ramping

  • 45.2 minutes was the median ramping for cases arriving after 10 or more prior arrivals, compared with 23.6 minutes after zero prior arrivals.The corresponding p90 values were 127.9 and 66.1 minutes, respectively.Source: §I · pp. 10–11, Table VII · p. 11
  • Ramping generally increased with concurrency across hours of the day, while ramping also varied by hour within concurrency bins.Higher values generally occurred during 12:00–20:00 except in the zero-concurrency bin.Source: Figure 13 · p. 11, §I · p. 11
  • Median ramping rose progressively with preceding-hour arrivals across hospitals, while p90 patterns varied: H14 remained between 127 and 138 minutes across concurrency levels.The nine-hospital figure displays median ramping as blue circles and p90 as red triangles.Source: §I · pp. 11–12, Figure 14 · p. 12
  • The analysis supports concurrency and hour of day as independent correlates, but does not quantify how actively redistributing arrivals would change ramping.The authors state that causal claims require intervention studies or natural experiments.Source: §I · pp. 11–12

IV. CONCLUSION

The conclusion characterizes ramping as concentrated, temporally predictable, and state dependent, with rising capacity loss despite little change in aggregate case loads. These properties delineate a modelling framework for further analysis.

IV. CONCLUSION

  • 1,491,127 cumulative AHL hours accumulated as annual losses rose 57% to their 2022 peak while ED-bound transports fell 3.7%.The conclusion traces concentration to 25–30 hospitals, demand-to-handover propagation to two to four hours, and concurrency effects within hour of day.Source: §IV · p. 12
  • The observed concentration, temporal predictability, and state dependence together delineate the modelling framework underpinning further analysis.Source: §IV · p. 12

A. Identified systemic challenges

Ramping burden is concentrated and worsened during the study window, with demand timing and hospital arrival concurrency associated with handover delays. Interpretation is bounded by ambulance-only, observational data and the study’s pandemic-era, single-jurisdiction scope.

A. Identified systemic challenges

B. Limitations of this study

  • Ambulance-only records omit self-presentations and ED measures, leaving hospital-side mechanisms unresolved; LGA statistics average over heterogeneous remoteness, density, and hospital distance.Source: B. Limitations of this study · p. 12
  • Observational design cannot establish intervention effects; ramping above 600 minutes was excluded, and multi-unit responses contributed one dispatch-level record per responding unit.Source: B. Limitations of this study · p. 12
  • Without a pre-pandemic baseline, the study cannot distinguish later improvement from post-pandemic normalisation; lower 2023–24 delays do not establish better performance.Source: B. Limitations of this study · p. 13
  • The findings come from one statewide jurisdiction; the framework is transferable, but its specific empirical patterns may not generalise to other EMS systems.Source: B. Limitations of this study · p. 13
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