Ambulance demand: random events or predicable patterns?

Cantwell, Kate; Dietze, Paul; Morgans, Amee; Smith, Karen

doi:10.1136/emermed-2012-201852

Cited by 20 publications

(13 citation statements)

References 51 publications

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“…Time of day patterns show a typical bimodal distribution with peaks in the morning and evening and less activity at night [23, 24], which was also present in our data and did not vary by the size of a municipality or year. A higher demand on Fridays [25] and weekends has been found by other authors, especially for alcohol-related and trauma cases [24]. We also observed a slightly higher proportion of emergencies on weekends.…”

Section: Discussionsupporting

confidence: 73%

Use of pre-hospital emergency medical services in urban and rural municipalities over a 10 year period: an observational study based on routinely collected dispatch data

Hegenberg

Trentzsch

Größ

et al. 2019

Scand J Trauma Resusc Emerg Med

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Background Pre-hospital emergency medical services (EMS) are an integral part of emergency medical care. EMS planning can be achieved by analyzing patterns of use. However, long-term time trends of EMS use have rarely been studied. The objective of this retrospective study was to investigate utilization patterns over a ten year period, and to compare utilization trends between urban and rural municipalities and between events with and without prehospital emergency physician (PEP) dispatch. Methods Routine data collected by 26 dispatch centers in the federal state of Bavaria, Germany, from 2007 to 2016 was analyzed. Emergency locations were classified into five levels of rurality. Negative binomial mixed effects regression models were fitted to predict emergency rates and to investigate differences in rates and utilization trends. Graphical representation methods were used to compare distribution of transport rates and distribution across daytime and weekday. Results Twelve million two hundred thousand one hundred fifty-five dispatches assigned to 7,725,636 single emergencies were included. The mean number of emergencies per year and 1000 population (emergency rate) was 42.8 (±16.0) in rural municipalities and 80.7 (±9.3) in large cities. Compared to rural municipalities, cities had higher emergency rates without (IRR = 3.0, CI 2.2–4.0) and with pre-hospital physicians (IRR = 1.5, CI 1.2–2.0). Between 2007 and 2016, the absolute number of emergencies increased by 49.1%. Estimated annual percent change of emergency rates without physician activation ranged from 5.7% (CI 4.3–7.1) in cities to 7.8% (CI 7.6–7.9) in rural areas. Changes in emergency rates with physician attendance were lower, with estimated increases between 1.3 and 2.4%. The average proportion of patients transported to a hospital was lower in cities and remained unchanged. There were no considerable differences or changes in the distribution across daytime and weekdays. Conclusion Differences between cities and other areas suggest that the planning of EMS should be targeted to regional characteristics. A substantial increase in emergency rates was observed across all areas of Bavaria, but did not impact transport rates or temporal distributions. Further research is needed to better understand the urgency of emergency events and reasons behind increasing EMS utilization.

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Section: Discussionsupporting

confidence: 73%

Use of pre-hospital emergency medical services in urban and rural municipalities over a 10 year period: an observational study based on routinely collected dispatch data

Hegenberg

Trentzsch

Größ

et al. 2019

Scand J Trauma Resusc Emerg Med

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show abstract

“…1 The most common methods were either using a telephone triagederived dispatch determinant (most commonly the Medical Priority Dispatch System (MPDS)) [2][3][4][5] or 'a system devised by the authors'. [6][7][8][9][10][11] Categorisation using MPDS occurs at the point-of-call receipt, before ambulance arrival.…”

Section: Introductionmentioning

confidence: 99%

Improving the coding and classification of ambulance data through the application of International Classification of Disease 10th revision

Cantwell¹,

Morgans²,

Smith³

et al. 2014

Aust. Health Review

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Objectives. This paper aims to examine whether an adaptation of the International Classification of Disease (ICD) coding system can be applied retrospectively to final paramedic assessment data in an ambulance dataset with a view to developing more fine-grained, clinically relevant case definitions than are available through point-of-call data.Methods. Over 1.2 million case records were extracted from the Ambulance Victoria data warehouse. Data fields included dispatch code, cause (CN) and final primary assessment (FPA). Each FPA was converted to an ICD-10-AM code using word matching or best fit. ICD-10-AM codes were then converted into Major Diagnostic Categories (MDC). CN was aligned with the ICD-10-AM codes for external cause of morbidity and mortality.Results. The most accurate results were obtained when ICD-10-AM codes were assigned using information from both FPA and CN. Comparison of cases coded as unconscious at point-of-call with the associated paramedic assessment highlighted the extra clinical detail obtained when paramedic assessment data are used.Conclusions. Ambulance paramedic assessment data can be aligned with ICD-10-AM and MDC with relative ease, allowing retrospective coding of large datasets. Coding of ambulance data using ICD-10-AM allows for comparison of not only ambulance service users but also with other population groups.What is known about the topic? There is no reliable and standard coding and categorising system for paramedic assessment data contained in ambulance service databases. What does this paper add? This study demonstrates that ambulance paramedic assessment data can be aligned with ICD-10-AM and MDC with relative ease, allowing retrospective coding of large datasets. Representation of ambulance case types using ICD-10-AM-coded information obtained after paramedic assessment is more fine grained and clinically relevant than point-of-call data, which uses caller information before ambulance attendance. What are the implications for practitioners? This paper describes a model of coding using an internationally recognised standard coding and categorising system to support analysis of paramedic assessment. Ambulance data coded HEALTH SERVICES RESEARCH using ICD-10-AM allows for reliable reporting and comparison within the prehospital setting and across the healthcare industry.

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“…A small number of hourly weather measurements were not available. While a broad concordance between temporal events (time of day and weekend/weekday) and transports has been observed across multiple regions (14), the generaliz-ability of weather findings to different geographic regions cannot be fully established. Additionally, the findings from this study may not be easily extrapolated to extreme weather events, such as severe blizzards, as these occurred infrequently during the studied time period.…”

Section: Limitationsmentioning

confidence: 99%

“…Such data would be of value in resource optimization for EMS agencies, where having sufficient staffing and resources is paramount. Previous investigations have identified that day of the week, time of day, and location are associated with utilization of emergency services (14). Additional information on factors impacting EMS demand could inform EMS administrators in ways to better optimize staffing levels to meet the peaks and troughs of EMS demand (15).…”

Section: Introductionmentioning

confidence: 99%

Weather and Temporal Factors Associated with Use of Emergency Medical Services

Ramgopal¹,

Dunnick²,

Owusu‐Ansah³

et al. 2019

Prehospital Emergency Care

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Background: Fluctuations in emergency medical services (EMS) responses can have a substantial impact on the ability of agencies to meet resource needs within an EMS system. We aimed to identify weather characteristics as potentially predictable factors associated with EMS responses. Methods: We reviewed hourly counts of scene responses documented by 24 EMS agencies in Western Pennsylvania from January 1, 2014 to December 31, 2017 and compared rates of responses to weather characteristics. Responses to counties nonadjacent to the studied weather reporting station and interfacility/scheduled transports were excluded. We identified the mean temperature, meters visibility, dew point, wind speed, total millimeters of precipitation, and presence of rain or snow in 6-hour windows prior to dispatch, in addition to temporal factors of time of day and weekend vs. weekday. Analysis was performed using multivariable linear regression of a negative binomial distribution, reporting incidence rate ratios (IRR) with 95% confidence intervals (CI). Secondary analyses were performed for transports to the hospital and cases involving transports for traumatic complaints and pediatric patients (age <18 years). Results: We included 529,058 responses (54.8% female, mean age 57.2 ± SD 24.7 years). In our multivariable model, responses were associated with (IRR, 95% CI) rain (1.10, 1.08-1.11) snow (1.07, 1.05-1.09), and both rain and snow (1.15, 1.11-1.19). A lower incidence of responses occurred on weekends (0.84, 0.83-0.85) and at night (0.62,0.61-0.62). Increasing temperature in 5 °C increments was associated with an increase in responses across seasons with an effect that varied between 1.16 (1.15-1.17) in winter to 1.31 (1.28-1.33) in summer. Windy weather was associated with increased responses from light breeze (1.10, 1.09-1.11) to fresh breeze or greater (1.23, 1.16-1.30). Transports occurred in a similar pattern to responses. Trauma transports

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Ambulance demand: random events or predicable patterns?

Cited by 20 publications

References 51 publications

Use of pre-hospital emergency medical services in urban and rural municipalities over a 10 year period: an observational study based on routinely collected dispatch data

Use of pre-hospital emergency medical services in urban and rural municipalities over a 10 year period: an observational study based on routinely collected dispatch data

Improving the coding and classification of ambulance data through the application of International Classification of Disease 10th revision

Weather and Temporal Factors Associated with Use of Emergency Medical Services

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